Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 462 appraisals
Sleep medicine reviews
Machine and deep learning in REM sleep behavior disorder: a scoping review and analysis of reporting quality
Rapid eye movement (REM) sleep behavior disorder (RBD) is a parasomnia, and its isolated form is of particular interest, as it is an early phase alpha-synucleinopathy. Machine learning (ML) and deep learning (DL) models offer potential for automated detection, prediction of phenoconversion, and phenotyping. This scoping review identified 75 studies applying ML/DL in RBD and evaluated their methodological and reporting quality using the APPRAISE-AI tool. Most studies (73.3%) focused on RBD detection and mainly used polysomnographic data for this, while 16% addressed prediction of phenoconversion, with imaging data being the most employed modality. Sample sizes were generally small (most studies including only 20-100 individuals). According to APPRAISE-AI scores, 80% of studies had moderate overall methodological and reporting quality. Common deficiencies included lack of transparency in data and code sharing (23.3%), and poor reporting of hyperparameter tuning (17.1%), bias assessment (26.9%), and error analysis (0.66%). Data leakage was observed in 32% of studies. These issues hinder clinical translation and prevent incremental progress between research groups. Without transparent reporting and shared resources, replication and model comparisons become nearly impossible. Future work should adopt open science principles and rigorous validation to advance AI-based tools in sleep medicine.
3 Aug 2026
Read appraisal →Neurological research
Federated deep learning model for epilepsy seizure detection using electroencephalogram (EEG) signal
OBJECTIVES: Epilepsy is a chronic neurological disorder characterized by recurrent seizures due to abnormal brain activity, which affects individuals' health and quality of life. Traditional seizure detection methods face challenges related to data privacy and security as well as difficulty in fully capturing both temporal and spatial relationships within the Electroencephalography signal. To address these limitations, a Federated Learning Enabled Unified Transformer model is proposed. METHODS: The Federated Learning with Paillier Homomorphic Encryption is deployed for preserving data privacy and enabling collaborative model training. Adaptive noise filtering and Independent Component Analysis are deployed to remove the noise and artifacts. The Multi-Scale Wavelet Coefficient is applied for signal decomposition that effectively extracts seizure-related features by decomposing Electroencephalography signals into multiple sub-bands. The Hybrid Graph-Based Attention Framework integrated Edge-Enhanced Graph Convolutional Networks for spatial feature extraction and Spectral Graph Attention for frequency-based feature selection, and refining feature representation for improving the classification accuracy. Further, the proposed technique utilizes a Unified Transformer model for seizure classification that efficiently captures temporal and spatial dependencies in Electroencephalography signals. RESULTS: The proposed model is validated on three datasets and attains 98.91% accuracy, 98.93% security, and 98.82% precision. The simulation outcomes indicate that the Federated Learning Enabled Unified Transformer model achieved outstanding performance when compared to existing models. DISCUSSION: The Federated Learning Enabled Unified Transformer model provided a superior performance by integrating Federated learning and hybrid Deep Learning models. It ensures that the model is more suitable for healthcare users.
2 Aug 2026
Read appraisal →Journal of psychiatric research
Systematic review of machine learning and deep learning models for EEG-based detection of depression.
OBJECTIVE: Depression is a leading cause of global disability, motivating the development of objective and scalable diagnostic approaches. Quantitative electroencephalography (QEEG) combined with machine learning (ML) and deep learning (DL) techniques has gained increasing attention for depression detection. This systematic review aimed to critically examine and descriptively compare the methodologies, performance metrics, and limitations of ML- and DL-based models applied to EEG data for depression detection. METHODS: A systematic review was conducted in accordance with PRISMA 2020 guidelines. Seven electronic databases (PubMed, Scopus, IEEE Xplore, ScienceDirect, Web of Science, SAGE Journals, and MDPI) were searched for peer-reviewed studies published between 2020 and 2024. Eligible studies included human participants, used EEG signals for depression detection, and applied ML or DL algorithms. Extracted information comprised algorithm type, sample size, EEG acquisition parameters, validation strategies, and reported performance metrics, which were synthesized descriptively across studies. Risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool. RESULTS: A total of 42 studies met the inclusion criteria, including 23 ML-based and 19 DL-based investigations. Reported classification accuracy ranged from approximately 76% to 100%. DL studies showed a higher mean reported accuracy than ML studies (93.92% vs. 90.78%); however, this difference was not statistically significant in the exploratory non-parametric comparison. Near-perfect performance values were frequently observed in studies with small sample sizes and subject-dependent or exclusively internal validation strategies, raising concerns regarding overfitting and limited generalizability. Studies relying on publicly available datasets tended to report more stable performance. QUADAS-2 assessment revealed recurrent risk-of-bias concerns, particularly in the domains of patient selection and index test conduct. CONCLUSIONS: Both ML and DL approaches demonstrate potential for EEG-based depression detection, but reported performance differences between them should be interpreted cautiously. Although DL studies tended to report higher accuracy values, this pattern was not statistically significant in exploratory analyses and was strongly influenced by sample size, validation strategy, and methodological design. Future research should prioritize larger and more diverse samples, subject-independent or external validation strategies, and standardized reporting frameworks to enhance methodological rigor and clinical applicability.
2 Aug 2026
Read appraisal →Sleep medicine reviews
Artificial intelligence in sleep medicine I: Diagnosis, treatment, care, and research
Artificial intelligence (AI) is transforming sleep medicine (SM) through improved diagnostics, therapeutics, and research. AI enhances diagnostic accuracy, treatment personalization, workflow efficiency. However, implementation requires careful validation and oversight. This review explores AI applications across the sleep disorders spectrum. AI in diagnostic SM covers applications including polysomnography and home sleep testing. We examine how AI detects early neurodegenerative changes in REM sleep behavior disorder, identify novel biomarkers, and optimize chronotherapeutic timing. Wearables complement these advances by continuously monitoring sleep patterns, movement and physiological signals in natural settings, generating rich datasets ideal for AI analysis. In therapeutics, machine learning models enhance sleep apnea phenotyping, enabling precise treatment selection, and improve CPAP adherence prediction. AI helps identify biomarkers to optimize the timing of chronotherapeutic interventions. In medical care, natural language processing facilitates unstructured clinical chart data extraction. Implementation challenges include data standardization, algorithmic bias, and the "generalization gap." We provide a structured framework for clinical implementation, emphasizing validation requirements and ethical considerations. Large datasets using routine clinical care, cohort studies, and wearable derived data create vast amounts of data. In return, AI tools and methodologies enhance and expand development of phenotypes and endotypes with the final goal of personalized and precision SM.
2 Aug 2026
Read appraisal →Brain and behavior
Machine Learning-Based Prediction of Poor Outcomes in Intracerebral Hemorrhage: A Systematic Review and Meta-Analysis
BACKGROUND: Spontaneous intracerebral hemorrhage (ICH) is associated with high risks of mortality and disability, yet early and accurate outcome prediction remains challenging. This study systematically evaluated the performance of machine learning (ML) models in predicting key adverse outcomes (hematoma expansion [HE], poor functional outcome, mortality) in ICH, aiming to provide consolidated evidence for future research and clinical translation. METHODS: We systematically searched PubMed, Embase, Web of Science, and Cochrane Library up to September 2025. Studies developing and validating ML models for predicting HE, poor functional outcome (modified Rankin Scale 3-6), or mortality in adults with spontaneous ICH were included. Pooled concordance index (C-index), sensitivity, and specificity were calculated using a random-effects or bivariate model. RESULTS: Eighty-three studies (involving at least 136,840 patients) were included. Meta-analysis of model performance, derived predominantly from internal validation set, demonstrated that models integrating both clinical and radiomics features achieved the highest discriminative performance across key prognostic prediction tasks: predicting HE (pooled C-index 0.822, 95% confidence interval [CI] 0.789-0.855), poor functional outcome (C-index 0.850, 95% CI 0.830-0.869), and mortality (C-index 0.860, 95% CI 0.809-0.911). These findings should be interpreted with caution, as true external validation remains sparse. Logistic regression exhibited performance comparable to more complex ML algorithms. CONCLUSIONS: ML models, particularly integrated clinical-radiomics models, demonstrate strong performance for the prediction of outcomes in ICH. They hold significant potential to enhance risk stratification and guide personalized management, pending further validation in diverse cohorts.
1 Aug 2026
Read appraisal →International journal of medical informatics
Machine learning models for predicting readmission after stroke: A systematic review and meta-analysis
BACKGROUND: Hospital readmission following stroke poses a significant challenge for healthcare systems. Machine learning (ML) offers the potential to improve prediction models for readmission risk, surpassing traditional statistical methods. However, the performance of ML models in such context has not been systematically evaluated. We aim to evaluate the performance of ML models in predicting post-stroke hospital readmission, as well as identifying the most important readmission predictors. METHODS: A comprehensive systematic literature review and meta-analysis was conducted.Studies included were those utilizing ML models for stroke readmission prediction. The primary outcome was the predictive performance of ML models for all time stroke readmission as reported by the Area Under the Receiver Operating Characteristic Curve (AUROC). Pooled AUROC values were calculated using a random-effects model. RESULTS: A total of eleven studies involving 380,254 patients were analyzed. Mean age of the patients ranged between 64.5 and 79.7 years. In total, 49 ML models were reported with Logistic regression (LR) and Random Forest being the most used. The number of input predictive variables used for model training ranged from 6 to 10,047. The overall pooled AUROC for predicting hospital readmission was 0.74 (95% CI: 0.69 to 0.78). For 30-day readmission, the pooled AUROC was 0.73 (95% CI: 0.67 to 0.79) while for 90-day readmission, it was 0.75 (95% CI: 0.69 to 0.80). The most frequently reported variables as being top predictors of readmission were Length of stay (LOS) (3 studies), Age (2 studies), National Institutes of Health Stroke Score (NIHSS) (2 studies), HbA1c (2 studies), Homocysteine blood level (2 studies). CONCLUSION: Machine learning models demonstrate moderate predictive performance in stroke readmission risk prediction. Future research should focus on validating and refining existing models and adopting unified methodological approaches to aid in drawing more accurate conclusions.
1 Aug 2026
Read appraisal →Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology
Efficacy and safety of fecal microbiota transplantation in Parkinson's disease: a systematic review and meta-analysis of randomized controlled trials with GRADE assessment
BACKGROUND: Parkinson's disease (PD) is a common neurodegenerative disease that is a growing public health challenge. Recent evidence showed that gut microbiota dysbiosis is involved in the pathogenesis of PD, prompting interest in fecal microbiota transplantation (FMT) as a potential disease-modifying intervention. This systematic review and meta-analysis aim to evaluate the efficacy and safety of FMT in patients with PD. METHODS: We conducted a systematic literature review using PubMed, Scopus, Web of Science, and the Cochrane Library till June 4, 2026, to identify RCTs comparing FMT with placebo or control interventions in PD. After removal of duplicates alongside title and abstract screening, followed by full-text screening, seven randomized controlled trials were included in this systematic review and meta-analysis. RESULTS: A total of seven randomized controlled trials (RCTs) with 318 subjects were analyzed. FMT was delivered via oral capsules, nasojejunal tubes, or colonoscopy, with follow-up periods ranging from 12 to 52 weeks. The meta-analysis indicated a notable short-term enhancement in daily motor activities, as assessed by MDSUPDRS Part II, after one month (mean difference - 2.19, 95% confidence interval - 4.32 to -0.06; P = 0.044). Nevertheless, no significant estimates were noted for MDS-UPDRS Part III, aggregate MDS-UPDRS scores, motor complications, non-motor symptoms, or overall life quality at any assessed time frame. Some individual studies reported gains in cognitive ability, anxious feelings, quality of life related to constipation, and certain PDQ-39 categories, although these improvements were not uniformly observed. Overall, FMT was well tolerated by participants, with adverse reactions mostly comprising mild, self-resolving gastrointestinal issues and no major safety risks. CONCLUSIONS: FMT is a safe treatment for individuals with PD and may offer modest short-term advantages in performing daily motor tasks. However, current evidence does not indicate lasting enhancements in overall motor capabilities, non-motor symptoms, or life quality. More extensive and sufficiently powered trials utilizing standardized FMT procedures and longer observation periods are necessary to elucidate the therapeutic benefits of FMT in Parkinson's disease.
1 Aug 2026
Read appraisal →Scientific reports
Manual federated simulation for multiple sclerosis integrating XGBoost algorithm with SHAP explanation
Multiple sclerosis (MS) is a chronic autoimmune disorder of the central nervous system, underscoring the importance of early and accurate diagnosis. In this study investigates the predictive modelling of MS progression in patients with Clinically Isolated Syndrome (CIS), privacy-preserving for a federated and explainable Machine Learning (ML) framework. To address missing data while preserving inter-feature dependencies, Multivariate Imputation by Chained Equations (MICE) with iterative imputers was employed. Classification was performed using the Extreme Gradient Boosting (XGBoost) algorithm. Model interpretability was developed through Explainable Artificial Intelligence (XAI) techniques, specifically Shapley Additive Explanations (SHAP). To ensure data confidentiality and simulate decentralized clinical environments, an in silico federated learning framework was applied. Experimental results demonstrated strong predictive performance, achieving 96.7% accuracy and 99% ROC-AUC during training, 92.5% accuracy in validation, and 81.8% accuracy with an AUC of 88% on the test set. For the Federated Learning (FL) simulation, the model maintained competitive performance, yielding an accuracy of 76.3% and an AUC of 83.9%. The proposed approach supports early diagnosis, enhances clinical trust through interpretability, and promotes secure data collaboration, thereby contributing to more informed and transparent clinical decision-making and improved patient care.
1 Aug 2026
Read appraisal →Science advances
Multispectral infrared-to-full-color upconversion expanding human vision
The human visual system is inherently blind to infrared radiation due to the insufficient energy of infrared photons to trigger the photoisomerization of the retinal chromophore, resulting in the loss of over half of the solar spectrum. Here, we report a colloidal quantum dot (CQD)-based infrared-to-visible upconverter that enables ultrasensitive upconversion of multispectral infrared light radiation as full-color visible vision. The photon-energy-selective excitonic transitions in quantum-confined states and the photon flux, respectively, enable infrared wavelength- and intensity-dependent number of photogenerated carriers. A dual-emissive-layer organic architecture with the strategically engineered hole-injection barrier could route these carriers into distinct color emission channels, resulting in correlated wavelength/intensity-to-color/luminance mapping. This paradigm shift yields a discrimination sensitivity for subtle infrared variations exceeding more than two orders of magnitude higher than conventional single-color modes, capitalizing on the human eye's intrinsic superiority in chromatic differentiation over only brightness contrast. The resulting upconverter exhibits broadband detection extending beyond 2 μm, luminance exceeding 700 cd m-2, and a photon-to-photon conversion efficiency of 3.85%. The upconverter could be applied as a lightweight, semi-transparent wearable eyeglass that projects multispectral infrared as full-color vision directly onto the retina. Besides, upconverters bound to light-sensitive proteins, potentially as an implantable retinal photoreceptor, confer innate infrared vision. By surpassing the evolutionary constraints of natural vision, this work establishes a versatile foundation for next-generation visual prosthetics and human-integrated sensory expansion.
1 Aug 2026
Read appraisal →Neurosurgical review
Development and external validation of a machine learning model for predicting postoperative hydrocephalus in 1,073 posterior fossa tumor patients
Postoperative hydrocephalus is a common complication following posterior fossa tumor resection, affecting 7-40% of patients. Although preoperative cerebrospinal fluid (CSF) diversion may be required in selected patients with hydrocephalus, decisions remain individualized in routine neurosurgical practice. We therefore developed and externally validated a model to provide supplementary preoperative risk stratification using routinely available variables. We retrospectively analyzed 1,073 patients following resection of posterior fossa tumors (PFTs) treated at five tertiary centers between 2013 and 2024, dividing them into a development cohort (n = 854) and an external validation cohort (n = 219). We initially screened 30 perioperative variables from the multicenter dataset and then selected a clinically implementable model restricted to variables available before tumor resection. Feature importance was ranked using Shapley additive explanations (SHAP), and seven distinct machine learning (ML) algorithms were evaluated. Model performance was comprehensively measured using the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, precision-recall curves, and decision curve analysis. The final clinically implementable model included three preoperative variables: Evans index, tumor-fourth ventricle relationship, and preoperative cerebrospinal fluid diversion status. In the external validation cohort, the support vector machine model showed good external discrimination, with an area under the receiver operating characteristic curve of 0.877, accuracy of 81.3%, sensitivity of 80.8%, and specificity of 81.7%. Logistic regression achieved comparable discrimination. Calibration assessment suggested dataset shift between the development and external validation cohorts, indicating that absolute predicted probabilities should be interpreted cautiously in populations with different baseline risks. A concise three-variable preoperative model showed good external discrimination for clinically relevant postoperative hydrocephalus after posterior fossa tumor resection. The model may support preoperative risk communication and postoperative surveillance planning, but should not be used as a stand-alone indication for cerebrospinal fluid diversion. Prospective validation and local recalibration are warranted before routine clinical implementation.
31 July 2026
Read appraisal →BMC ophthalmology
The effect of lithium on the structure and function of the human retina: a systematic review
BACKGROUND: Bipolar disorder and depression are associated with structural and functional changes in the retina, including a thinner retinal nerve fibre layer (RNFL). Lithium is widely considered the most effective treatment for bipolar disorder, but its mechanism of action is not fully understood. We assessed research looking at the effect of lithium on structural or functional retinal outcomes in humans. METHODS: Searches using the terms 'Lithium' AND 'retina' were carried out to identify peer reviewed studies assessing the impact of lithium on retinal structure or function. These included those with or without a control group comparison, pre- and post- lithium comparisons and observational studies. There were no exclusions based on the quantity or preparation of lithium administered, or the length of administration. Risk of bias was assessed using the Joanna Briggs Institute (JBI) critical appraisal tool for Analytical Cross Sectional Studies, and a narrative synthesis and tabulated summary of the included studies was completed. RESULTS: Seven studies assessing structural outcomes and 10 reporting functional ones were identified, all highly heterogenous and with multiple limitations. Structural outcomes were derived exclusively from optical coherence tomography (OCT) with retinal nerve fibre layer (RNFL) being the most common measurement. There was no evidence of differences in the RNFL between participants with bipolar disorder taking lithium and healthy controls in two larger studies. In six studies looking at differences in those with bipolar disorder taking lithium and those taking valproate, two showed no signs of difference and four showed evidence of thicker RNFL in the lithium group. Studies reporting functional outcomes reported a statistically significant effect of lithium on at least one functional measure, derived from electrooculography, electroretinography, and dark adaptation thresholds. CONCLUSIONS: Current evidence suggests that lithium is likely to have an effect on the retina but limitations in all studies mean better designed and adequately powered prospective studies are required. REGISTRATION: PROSPERO database (Number-CRD42024516635).
31 July 2026
Read appraisal →Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology
Handwritten signatures in neurological and psychiatric disorders: a systematic review with clinical and forensic implications.
BACKGROUND: Signature production represents a complex motor and cognitive behavior, highly automatized and repeated countless times throughout life. It ranks among the most durable graphic forms, often preserved even when writing processes are impaired. Studies examining signature characteristics in clinical cohorts are limited. The available evidence raises questions about its potential as a behavioral marker of neurological and psychiatric disorders. METHODS: We conducted a PRISMA-compliant systematic review up to 4th December 2025 to summarize evidence on how neurological and psychiatric conditions may affect signature features. Eligible articles on this topic were searched in Scopus, Web of Science, and PubMed databases. Sixteen papers were included, and their results were systematically described and qualitatively analyzed. RESULTS: The available literature suggests that signature production appears relatively independent of global cognitive decline, although it is sensitive to specific pathological conditions (e.g., micrographia in Parkinson's disease, increased tortuosity in cerebral small vessel disease, and alterations observed in mania, delirium, obsessive-compulsive disorder, and major depressive disorder). Alterations commonly affect kinematic parameters, as well as visuospatial and constructional features. However, no disorder-specific signature pattern emerges from current evidence, largely due to the methodological heterogeneity across studies. CONCLUSIONS: The handwritten signature holds promise as a behavioral marker of brain disorders, serving as a potentially useful, complementary - rather than a stand-alone - indicator of neurological and psychiatric conditions, which could be potentially useful in clinical settings. The implications for forensic applications remain largely theoretical and have not yet been standardized.
30 July 2026
Read appraisal →Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology
Suicide in neurodegenerative diseases: a systematic review.
BACKGROUND AND OBJECTIVE: Suicide is a public health issue, which differs from suicidality, the continuum from suicidal ideation to the suicidal act, including suicide attempts and completed suicide. The main goal of the present study is to determine the relationship between Alzheimer's disease (AD), Parkinson's disease (PD), amyotrophic lateral sclerosis (ALS), and multiple sclerosis (MS) with suicidality. METHODS: This is a systematic review aiming to determine the relationship between AD, PD, ALS, and MS with suicidality following PRISMA 2020 guidelines by collecting cross-sectional, case-control, and cohort studies; case series; case reports; and retrospective and prospective studies from Google Scholar, PubMed, and Cochrane Library. The protocol of this systematic review was registered on PROSPERO; the registration number is CRD420261422354. RESULTS: From 2247 records identified from electronic databases, only 24 articles were included: three studies focusing on AD, nine on PD, and six studies focusing on ALS and MS, respectively. These studies exhibited moderate to low risk of bias. Despite the broad differences regarding the neurochemistry, pathophysiology, diagnosis, symptoms, and treatments of the selected diseases, patients are at a higher risk of suicidality. Depression and low social connectivity are the most prevalent risk factors. Suicidality is mainly detected during the first years post-diagnosis in PD, ALS, and MS patients, while the results in AD are confusing. CONCLUSIONS: Data about this topic is scarce and largely varying. Further research is required to elucidate this paradigmatic realm, fostering awareness, enhancing therapies, and providing explanations and interpretations of the mechanisms involved.
30 July 2026
Read appraisal →Bioinspiration & biomimetics
Length control of pneumatic artificial muscles inspired by intrafusal-extrafusal muscle interactions
Pneumatic artificial muscles (PAMs) are widely used in pneumatic musculoskeletal robots due to their lightweight nature and high power output. On the other hand, their control typically relies on electro-pneumatic valves, electronic sensors, and peripheral circuits such as A/D converters. This increases the number of distributed electronic components and electrical interconnections, potentially leading to greater system mass as well as higher maintenance requirements, particularly in systems containing many PAMs. To overcome these challenges, inspired by the intrafusal-extrafusal muscle interactions in biological systems, this paper proposes a length control method for PAMs in which the pneumatic feedback loop is implemented mechanically, while the target length is commanded by a minimal electronic actuator. We first examined flow path conditions to suppress oscillations caused by pneumatic delays. Second, Experiments demonstrated a tracking error of2.01% for input frequencies0.20 Hz.
29 July 2026
Read appraisal →Proceedings of the National Academy of Sciences of the United States of America
Active linking through narratives facilitates associative inference
In daily life, we often draw inferences about novel associations from prior experiences. This ability, associative inference, is thought to be a key process by which memory guides future behavior. Prior studies have typically instructed participants to use narratives or mental imagery to aid memory of object associations. However, these studies have not explicitly tested the role of narratives, leaving an important question: how crucial are narratives in associative inference, and what role might they play? We used a modified associative inference paradigm to answer these questions. Our design factorially combined two factors during associative encoding: use of narratives, and explicitly linking the two objects. Participants separately learned AB and BC object pairs within ABC triads using four sets of instructions: i) generate narratives to link objects together, ii) generate narratives for objects separately, iii) compare perceptual properties of two objects, and iv) describe perceptual properties of objects individually. We tested participants' ability to infer AC associations as a function of encoding strategy. Across two experiments, using narratives to actively link objects produced a robust associative memory advantage that persisted across 1-day delay and remained substantial under more stringent retrieval conditions. This benefit was not attributable to a simple linking effect or to surface-level linguistic similarity between stories. A follow-up experiment revealed that this benefit did not generalize to perceptual memory for individual objects; instead, perceptual encoding facilitated perceptual object memory. These results reveal that deep, extended encoding emphasizing either narrativity or perceptual processing can benefit different aspects of retrieval.
28 July 2026
Read appraisal →Journal of neural engineering
A roadmap to navigate the future of neural engineering
A group of leaders in neural engineering collaborated to develop a roadmap to navigate the future of neural engineering. We covered a range of themes, including brain machine interfaces, neural modeling, artificial intelligence and machine learning, neural interfaces, neural imaging, augmented rehabilitation, and neuromaterials. For each topic we reviewed the current status, identified current and future challenges, and speculated on the emerging and necessary advances in science and technology to meet these challenges. Neural engineering will continue to yield the approaches and insights that advance the diagnosis and treatment of nervous system disorders, as well as provide new understanding of neural function.
28 July 2026
Read appraisal →Cerebellum (London, England)
Advancing Cerebro-cerebellar Network Imaging with Ultra-high Field (7T) MRI: Progress and Future Challenges
Ultra-high field (UHF) MRI (≥ 7T) is increasingly used to study the cerebro-cerebellar networks, offering substantial gains in signal-to-noise ratio and blood oxygen level dependent (BOLD) sensitivity that enable submillimeter functional and structural imaging. These advantages are particularly relevant for the cerebellum, whose tightly folded cortex and small deep nuclei have historically been difficult to resolve in vivo. In this targeted review, we synthesize recent progress in UHF-MRI applied to cerebro-cerebellar network mapping and critically examine the methodological challenges that accompany these developments. We highlight how UHF-MRI has enabled more precise delineation of cerebellar functional territories, improved visualization of the dentate nucleus and its connectivity, and facilitated integration of cerebellar nodes into whole-brain network models. We also discuss the technical challenges, including RF-field inhomogeneity, susceptibility-induced distortions, and the trade-off between spatial resolution and signal strength. These issues are compounded in the cerebellum due to its anatomical location and fine-scale organization, increasing the risk of spatial biases and misinterpretation if not carefully addressed. Beyond acquisition, we identify key gaps in analysis methodologies, including the need for cerebellum-specific preprocessing pipelines, segmentation tools that preserve mesoscale anatomy, and frameworks that treat the cerebellum and cerebrum as a unified system. We also discuss emerging opportunities, such as neurostimulation, integration with postmortem imaging and deep learning-based methods that may help bridge microstructural and in vivo findings. Overall, we argue that while UHF-MRI holds transformative potential for cerebellar systems neuroscience, translational progress will depend on rigorous methodological standardization and awareness of its limitations.
27 July 2026
Read appraisal →Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology
The past and future of neuropsychological assessment: Back to the origins to face digital technological changes.
BACKGROUND: The procedures used for the neuropsychological assessment of patients with dementia have been undergoing significant transformations relating to technological developments and the diffusion of artificial intelligence tools. OBJECTIVES: The integration of a theoretical position and a practical approach is needed in clinical contexts so that newly available tools can enhance the fundamental principles underlying neuropsychological assessment. METHODS: Following a historical review of the various approaches to neuropsychological assessment, an analysis of the risks associated with the introduction of digital techniques, when not supervised by expert clinicians, is presented. The need to integrate new technologies in a comprehensive assessment involving clinical observation, ad-hoc tests, and other sources of information such as neuroimaging and neurophysiological data is discussed. RESULTS: The integration of new technologies with a clinical, personalised approach to the patient represents a challenge for neuropsychology that deserves ad-hoc research and discussion.
26 July 2026
Read appraisal →Journal of neural engineering
Cortical-SSM: a deep state space model for motor imagery decoding from EEG signals
Objective.Classification of electroencephalogram (EEG) signals obtained during motor imagery (MI) has substantial application potential, including for communication assistance and rehabilitation support for patients with motor impairments. These signals remain inherently susceptible to physiological artifacts (e.g. eye blinking, swallowing), which pose persistent challenges. Although Transformer-based approaches for classifying EEG signals have been widely adopted, they often struggle to capture fine-grained dependencies within them.Approach.To overcome these limitations, we propose Cortical-SSM, a novel architecture that extends deep state space models to capture integrated dependencies of EEG signals across temporal, spatial, and frequency domains. We validated our method across two large-scale public MI EEG datasets containing more than 50 subjects.Main results.Our method outperformed baseline methods on the two benchmarks. Furthermore, visual explanations derived from our model indicate that it effectively captures neurophysiologically relevant regions of EEG signals.Significance.These results indicate that Cortical-SSM provides a robust and interpretable alternative to attention-based architectures for MI EEG decoding. By enabling physiologically grounded feature learning, our method advances the reliability of subject-independent EEG classification and supports the development of practical and clinically deployable brain-computer interface systems.
26 July 2026
Read appraisal →Nanoscale
Advances in printable flexible and stretchable thin-film electrodes: materials, interfaces, technologies and bioelectronic applications
The mechanical mismatch between rigid clinical electrodes and soft biological tissues remains a primary bottleneck restricting the stability of long-term electrophysiological monitoring. Printable flexible thin-film electrodes offer a compelling solution by enabling additive, high-throughput patterning on flexible and stretchable substrates, thereby circumventing the reliance on vacuum environments and high-temperature processing typical of conventional microfabrication. This review synthesizes recent advances in functional inks, ranging from metals and carbon nanomaterials to conductive polymers and ionogels, together with high-resolution printing techniques. Addressing the critical challenge of interfacial failure in flexible devices, we explore engineering strategies to enhance adhesion at both electrode-substrate and electrode-tissue interfaces. Specifically, we analyze the pivotal roles of physical interlocking and chemical anchoring mechanisms in suppressing dynamic delamination and maintaining device integrity. Finally, the review highlights representative applications in wearable electronics, implantable systems, and emerging organoid interfaces, and outlines key translational challenges, including long-term stability and manufacturing reproducibility.
25 July 2026
Read appraisal →Scandinavian journal of pain
Exercise prescription and monitoring of heart rate parameters in people with chronic pain: a systematic review
INTRODUCTION: Chronic pain affects over 50 million adults in the United States and is associated with autonomic nervous system dysfunction assessed through measurements of heart rate variability (HRV). While exercise has been shown to improve HRV in healthy individuals, the effects of different exercise protocols in chronic pain populations on HRV remain underexplored. CONTENT: The purpose of this systematic review investigated how exercise interventions influence HRV and pain in individuals with chronic pain. A literature search was conducted in accordance with PRISMA guidelines across four electronic databases. Six randomized controlled trials met the inclusion criteria and were critically appraised for methodological quality. Studies were included if they involved an exercise intervention and reported HRV. If pain was assessed, it was included as an outcome measure. SUMMARY AND OUTLOOK: Six randomized controlled trials were identified with 283 participants. All studies measured HRV, while four reported pain outcomes. Five studies reported significant improvements in HRV following exercise interventions (p <0.05), while findings related to pain outcomes were mixed. Methodological reporting of HRV procedures was generally insufficient. Due to heterogeneity across studies, it remains unclear whether exercise improves pain or HRV sufficiently. While pain outcomes are commonly reported, HRV is the only measure specific to \autonomic function, and its reliability as a clinical metric in individuals with chronic pain remains uncertain. Limited research across diverse chronic conditions, sexes, and methodological approaches restricts generalizability. This highlights the need for greater methodological rigor and broader inclusion to strengthen the use of HRV metrics in chronic pain populations.
25 July 2026
Read appraisal →Journal of medical Internet research
Digital Peer Support Intervention for Family Caregivers of Individuals With Neuromuscular Disease: Randomized Controlled Trial
BACKGROUND: Neuromuscular diseases (NMD) affect nerves and muscles, resulting in weakness and often profound disability. Family caregivers of individuals with NMD experience significant psychological burden, stress, and reduced well-being. Digital peer support interventions may help to ameliorate these negative impacts. OBJECTIVE: This study evaluated the effect of a 12-week digital peer support intervention compared to usual care on caregiver mastery, competence, stress, burden, anxiety, and depression among family caregivers of individuals with NMD. METHODS: We conducted a parallel-group multicenter randomized controlled superiority trial in Ontario, Canada. Family caregivers of children or adults with NMD were recruited between August 2022 and September 2023 through 7 sites, social media, and national organizations. Participants were randomized 1:1 to a 12-week digital peer support intervention or usual care. The 12-week intervention comprised access to a trained peer mentor, private app-based communication via aTouchAway (Aetonix, Canada), and weekly moderated digital group discussion forums. The primary outcome was caregiver mastery measured using the Pearlin Mastery Scale, adjusted for baseline score. Secondary outcomes included caregiver stress, competence, burden, anxiety, and depression. We calculated adjusted (for baseline score) mean differences using analysis of covariance and generated multivariable linear regression models exploring associations with the intervention and caregiver age, years of caregiving, care recipient medical diagnosis, care recipient ventilation type, adjusting for baseline outcome scores. Intervention fidelity was evaluated through participant engagement metrics. RESULTS: A total of 100 participants were randomized (n=50 intervention and n=50 control). Participants had a mean age of 46.8 (SD 11.6) years, 70% (n=70) were mothers, with a mean length of caregiving of 11.8 (SD 7.6) years. We found no difference in 12-week Pearlin Mastery Scale scores (adjusted mean difference 0.67, 95% CI -1.7 to 3.1). We also found no difference in any of our secondary outcomes. Mentors and participants sent a mean of 21.3 (SD 33.3) and 17.7 (SD 33.0) messages, respectively. Overall, 62% (n=31) of participants and 92% (n=11) of mentors engaged in at least 1 program element for ≥8 of the 12 weeks. CONCLUSIONS: Our 12-week digital peer support program had no effect on caregiver mastery or other caregiving or psychological outcomes among family caregivers of individuals with NMD. This might be partly due to moderate fidelity and variability in participant engagement. Unlike prior caregiver interventions that incorporated structured psychoeducation or self-management training, this intervention evaluated primarily peer support delivered through a digital platform. This study contributes important evidence regarding the feasibility and limitations of scalable digital peer support programs for caregivers of individuals with NMD. These findings highlight the importance of intervention tailoring, participant matching, and sustained engagement. Future research should evaluate longer-duration and more individualized peer support models targeting caregivers earlier in the caregiving trajectory to improve intervention fidelity and ultimately caregiver well-being.
24 July 2026
Read appraisal →Journal of medicinal chemistry
SMDNet: A Self-Training-Aware and Multi-Modal-Adaptive Deep Learning Network for Low-Data Aβ42 Probe Design and Optimization
Amyloid-β (Aβ) plaque accumulation is a crucial hallmark of Alzheimer's disease, and fluorescence imaging can support disease diagnosis and monitoring. However, Aβ42 probe development is often hindered by trial-and-error experiments due to subtle structure-property effects. Here, we developed SMDNet, a self-training-aware and multimodal-adaptive deep learning (DL) framework for low-data Aβ42 probe design and optimization. SMDNet combines iterative self-training with confidence-aware and distribution-aware sampling to improve data quality, while integrating molecular graphs, fingerprints and protein descriptors through cross-attention and protein-conditional adaptive layer normalization. Ablation studies, external validation and generalization analyses confirmed the strong predictive ability of SMDNet. Interpretability analyses further highlighted chemically meaningful substructures associated with model predictions. As a proof of concept, SMDNet guided the rational design of five ThT-derived probe candidates, with TA3 showing favorable binding affinity and high-contrast imaging performance. Additional validation on coumarin- and naphthalimide-based candidates further supported useful predictive discrimination across distinct fluorescent scaffold classes.
24 July 2026
Read appraisal →BMJ open
Potential benefits of music-based therapies on rehabilitation outcomes in people with multiple sclerosis: a systematic review
OBJECTIVES: To assess the evidence for a benefit of music-based therapies (MBTs) on rehabilitation outcomes in adults with multiple sclerosis (MS). SETTING: Scholarly literature (published in English) from any country. This systematic review received no specific funding and was registered on PROSPERO as CRD420251083656. DATA SOURCES: Six bibliographic databases: AMED (Ebsco), APA PsycINFO (Ovid), CINAHL (Ebsco), Cochrane CENTRAL, EMBASE (Ovid) and MEDLINE (Ovid), plus supplementary searches and citation chasing (search date July 2025). PRIMARY AND SECONDARY OUTCOME MEASURES: Eligible studies used a clinical trial or controlled quantitative observational study design to assess the benefit of MBTs on rehabilitation outcomes in adults with MS. Risk of bias was assessed using Critical Appraisal Skills Programme checklists. Data were synthesised using narrative synthesis. RESULTS: 23 studies were included (955 participants, of whom 880 people had MS and 691 received MBTs). The most frequently assessed outcome was motor function (16 studies), followed by quality of life (12 studies). Evidence suggests potential benefits for MBTs, particularly rhythmic auditory stimulation and music-cued motor imagery, on gait and mobility outcomes, including walking speed, walking distance, cadence, stride length and gait symmetry. Findings for other outcomes were less clear. CONCLUSIONS: MBTs may benefit people with MS, with the most consistent evidence observed for gait and mobility outcomes. However, heterogeneity in intervention type, study design and outcome measurement limits the strength of conclusions. Higher-quality, adequately powered trials with standardised outcomes and direct comparisons between MBTs are needed to clarify effectiveness and inform clinical implementation. PROSPERO REGISTRATION NUMBER: CRD420251083656.
24 July 2026
Read appraisal →Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology
Differential effects of hormone therapy formulations on Parkinson's disease risk: a systematic review and meta-analysis
BACKGROUND: The relationship between menopausal hormone therapy (HT) and Parkinson's disease (PD) risk remains controversial, with inconsistent findings potentially driven by differences in hormonal formulations. METHODS: We conducted a systematic review and meta-analysis following PRISMA 2020 guidelines. MEDLINE/PubMed and EMBASE were searched between January 8 and March 14, 2026. Observational studies evaluating the association between menopausal HT and PD risk were included. Effect estimates were pooled using random-effects models. Subgroup analyses were performed according to HT formulation (estrogen-only vs. combined estrogen-progestin therapy). A multilevel meta-analysis was conducted to account for within-study dependence. RESULTS: Fourteen studies including 753,749 participants (4,433 PD cases) were analyzed. Overall, HT was not significantly associated with PD risk (RR 1.08; 95% CI 0.94-1.23; I² = 49.4%). In subgroup analyses, combined therapy was associated with an increased PD risk (RR 1.40; 95% CI 1.07-1.82), whereas estrogen-only therapy showed no significant association (RR 1.01; 95% CI 0.81-1.27). Multilevel analysis yielded consistent results (combined: RR 1.33; 95% CI 1.00-1.77; estrogen-only: RR 1.03; 95% CI 0.83-1.27), with no statistically significant interaction between formulations (p = 0.12). CONCLUSIONS: Combined menopausal HT was associated with an increased risk of PD, while estrogen-only therapy showed no significant association. Although differences between formulations were not statistically significant, these findings suggest that hormone composition may influence neurological outcomes and warrant further investigation into individualized HT strategies.
23 July 2026
Read appraisal →Journal of neural engineering
Multimodal quantification of cognitive load using a printed wearable facial bio-potential system
Objective.Cognitive load refers to the amount of mental effort required to process information and perform tasks. It has a strong impact on both learning and task performance and, therefore, plays a central role in cognitive neuroscience, psychology, neurology, human-machine interaction, and education research. Despite its significance, measuring cognitive load is challenging due to its complex and dynamic nature, influenced by task complexity, individual differences, and emotional states. While standard methods are based on subjective self-reports, numerous objective methods have been explored to assess cognitive load, such as electroencephalography (EEG), heart rate variability, and performance-based metrics. Despite these efforts, it remains challenging to objectively measure cognitive load in stationary and even more so in freely moving individuals.Approach.In this study, a wireless wearable system was employed to collect facial bio-potential markers and systematically compare their sensitivity to cognitive load. A dedicated task paradigm was developed and validated to induce graded cognitive load, allowing precise synchronization of physiological signals with task events. Facial muscle activity, eye movements, and brain activity were acquired simultaneously using a single facial electrode array.Main results.Across nine participants, entropy-based features tracked cognitive load more closely than conventional band-power measures. Facial EMG was the most informative modality: a cross-subject (leave-one-subject-out) model predicted the NASA-TLX weighted workload score with a mean Spearman correlation= 0.792 (95% CI [0.747, 0.836]; mean absolute error4.3, on a NASA-TLX 0-100 scale), and a single forehead-EMG entropy feature (the EMG-band entropy of forehead channel Ch14, above the right eyebrow) was selected in every cross-subject fold, generalizing across all participants without subject-specific calibration. An EEG-only model carried weaker but genuine cross-subject information (= 0.739), whereas EOG features did not pass the feature-selection threshold in this seated paradigm.Significance.Facial bio-potentials offer a practical, wearable system for objective cognitive load assessment. An outdoor pilot experiment further demonstrated electrophysiological measures under non-laboratory conditions. These findings offer insights into the suitability of different physiological indicators for assessing cognitive load, particularly the informativeness of facial EMG for seated assessment and the robustness of blink-based measures under ambulatory motion, contributing to the future development of practical measurement techniques in dynamic everyday settings.
23 July 2026
Read appraisal →Current pain and headache reports
Chronic Pain and the Risk of Dementia: A Systematic Review and Meta-Analysis
BACKGROUND: The relationship between chronic pain and dementia risk has been extensively studied, but findings remain inconclusive. We conducted an updated meta-analysis to synthesize evidence from recent large-scale cohort studies. METHODS: Two authors independently and systematically searched PubMed, Web of Science, Embase, Cochrane Library, and Chinese National Knowledge Infrastructure for cohort studies published through April 2025, with a minimum follow-up of one year. Hazard ratios (HRs) were considered equivalent to risk ratios (RRs) assuming low event rates. Random-effect models pooled risk ratios (RR) with 95% confidence intervals (CI). Subgroup analyses and meta-regression explored heterogeneity. RESULTS: Of 3,823 publications, 30 studies met the inclusion criteria. After excluding five studies with overlapping populations, 25 studies involving 2,091,835 participants were pooled in the primary analysis. Individuals with chronic pain had a 28% higher dementia risk (RR = 1.28, 95% CI = 1.18-1.38). Heterogeneity was high (I²=91%). Subgroup analyses showed migraine, headaches, arthritis-related pain, and widespread pain were associated with increased risk. The association was significant in Asia but not in America or Europe, and when using ICD (but not DSM) criteria. Chronic pain increased the risk of Alzheimer's disease and non-vascular dementia, but not vascular dementia. Study quality, region, measurement methods, and pain types influenced the relationship. Pain type was a source of heterogeneity. CONCLUSIONS: In this meta-analysis of cohort studies, chronic pain was associated with an increased risk of dementia. Due to the observational nature of the included studies and high heterogeneity, this association should not be interpreted as causal. Further research is needed to determine whether pain management can mitigate dementia risk.
23 July 2026
Read appraisal →JMIR mental health
Optimizing Treatment Strategies in the Bipolar Disorder Spectrum With Classical AI Approaches: Systematic Review of Performance, Bias, and Clinical Applicability
BACKGROUND: Bipolar disorder (BD) is a complex and heterogeneous psychiatric condition, characterized by fluctuating clinical courses that affect approximately 1%-2% of the global population in their lifetime. Despite pharmacological advances, treatment response varies significantly among patients, making the identification of individualized treatment strategies a major challenge. Artificial Intelligence (AI), through its classical approaches, has emerged as a powerful tool in precision psychiatry to identify subtle patterns in complex data and inform personalized clinical decisions. OBJECTIVE: The present systematic review aimed to examine the current evidence on classical AI-supported treatment optimization in the BD spectrum. METHODS: The review was conducted in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 guidelines. Four databases (PubMed, Web of Science, Scopus, and Embase) were searched for original studies published after 2015 on the application of classical AI in the treatment of BD in adult patients. Publication bias was evaluated by visual inspection of a funnel plot. The methodological quality, risk of bias, and clinical applicability of the predictive models were assessed using the Prediction Model Risk Of Bias Assessment Tool for prediction models using regression or AI methods (PROBAST+AI; PROBAST+AI Working Group) tool. RESULTS: A total of 35 studies were included and classified into 5 outcome-based categories, including acute symptomatic response, long-term maintenance response, relapse and readmission risk, safety and dose optimization, and brain aging and phenotyping. Acute symptomatic response models performed modestly (pooled area under the curve [AUC] 0.68), while imaging improved accuracy (74%-77%). Long-term maintenance response models showed moderate-to-high performance (pooled AUC 0.80), with biomarker- and cellular-based models reaching 96%-99% accuracy. Relapse and readmission prediction achieved a pooled AUC of 0.71, with digital phenotyping and rule-based methods performing best (AUC 0.85-0.88). Safety and dose optimization models achieved 85%-97% accuracy. Brain aging and phenotyping studies highlighted accelerated brain aging in BD, partially mitigated by lithium, and revealed novel data-driven subgroups. However, 3 studies were considered at high risk of bias due to small sample sizes associated with disproportionately high-performance estimates. An additional study was identified as potentially biased because it lay markedly distant from the funnel plot's confidence line. Finally, the PROBAST+AI assessment revealed a high risk of bias in most studies, primarily due to data analysis limitations, small sample sizes, and lack of external validation. CONCLUSIONS: The adoption of classical AI tools in BD serves as a driver for therapeutic optimization, although current AI tools in BD should still be considered exploratory rather than ready for clinical use. Effective implementation in real-world clinical scenarios requires more robust, transparent, and externally validated models to ensure reliability and generalizability.
22 July 2026
Read appraisal →Current biology : CB
Looking to the brain to improve energy efficiency of AI
Modern artificial intelligence (AI) systems have achieved remarkable capabilities, but at an extraordinary energy cost. Training and running large-scale models can consume vast resources, posing environmental, economic, and societal challenges. In contrast, biological brains perform lifelong learning, adaptive control, and flexible reasoning using orders of magnitude less energy for learning and adaptation over a lifetime. What accounts for this difference - and how can it guide future AI development? In this review, we identify key biological principles that support energy-efficient capacities in biological brains, and consider how they might inform the design of more sustainable artificial systems. We organize our analysis around three domains: architectural constraints, signaling strategies, and learning algorithms. In each domain, we discuss concrete observations from biology, from cell to circuit to cognitive level, and describe how current and emerging AI systems mirror or diverge from these motifs. One striking feature of biological energy optimization is often overlooked: that brains are remarkably stable in their energy usage across heterogeneous modes, suggesting they may minimize energy needs during active environmental processing through maximizing the utility of 'rest-like' background processes. Overall, rather than advocating for biomimicry for its own sake, we argue for biologically informed engineering. Understanding how natural systems minimize energetic cost while maximizing flexibility may help us build AI that is not only powerful, but also efficient, equitable, and environmentally responsible.
21 July 2026
Read appraisal →Neurosurgical review
Prognostic value of automated pupillometry in traumatic brain injury: a systematic review and meta-analysis
Traumatic brain injury (TBI) remains a major global cause of mortality and long-term disability. Early prognostication is critical for guiding management decisions. Quantitative pupillometry (QP), using automated devices such as the NeurOptics NPi-200/300, enables bedside measurement of pupillary reactivity through indices like the Neurological Pupil Index (NPi), constriction velocity (CV), and dilation velocity (DV). We conducted a PRISMA-compliant systematic review and meta-analysis to evaluate the prognostic value of QP in adults with TBI. Observational studies of quantitative pupillometry within 72 h of traumatic brain injury in adult ICU patients were included. Studies limited to mild TBI were excluded, although mixed-severity cohorts were eligible. Primary outcomes were mortality and poor functional outcome at 3-6 months (GOS ≤ 3 or mRS ≥ 3). Secondary analyses assessed differences in pupillometry metrics according to intracranial hypertension defined by direct ICP measurements (> 20-25 mmHg) or proxy markers reflecting ICP-directed interventions. Odds ratios were pooled for dichotomous outcomes and mean differences for continuous outcomes using random-effects models, with heterogeneity quantified by I². Four studies (n = 354) reported mortality data. Abnormal quantitative pupillary metrics (most commonly NPi < 3 or absent reflexes) were associated with increased odds of in-hospital death (OR 1.84, 95% CI 1.01-3.35; I² = 84%). Five studies (n = 384) assessed functional outcomes, showing significantly greater odds of poor outcome among patients with abnormal pupillary metrics (OR 3.64, 95% CI 1.63-8.10; I² = 39%). Three studies (n = 190) evaluated intracranial pressure, demonstrating lower NPi values in patients with intracranial hypertension compared with those without elevated ICP, though this difference did not reach statistical significance (MD - 1.05, 95% CI - 2.65 to 0.54). Early quantitative pupillometry is associated with increased mortality and worse functional outcomes in TBI in observational studies. Lower quantitative pupillary metrics were also observed in the presence of intracranial hypertension. Despite substantial heterogeneity, these findings support its potential as a prognostic tool. Further standardization, validation and controlled studies are needed to refine its clinical application.
21 July 2026
Read appraisal →Journal of neural engineering
Dynamic functional graph-Laplacian priors integrated with optimization for EEG source localization
Objective. Electroencephalography (EEG) source localization is an ill-posed inverse problem in which conventional methods often rely on static anatomical or smoothness assumptions and may neglect task-related dynamic functional interactions. This study aims to develop a dynamic graph-regularized EEG source localization framework that incorporates time-varying functional connectivity directly into inverse reconstruction and improves source-space motor imagery (MI) decoding.Approach. We propose DynaGraph-alternating direction method of multipliers (DG-ADMM), a source localization framework that combines linearly constrained minimum variance (LCMV) beamforming, region-of-interest-level dimensionality reduction, dynamic phase synchronization analysis, graph-Laplacian regularization, and efficient optimization. Initial source estimates are obtained using a LCMV beamformer. Region-level source signals are then extracted using principal component analysis and sliding-window phase-locking values with surrogate-based statistical testing are used to construct reliable dynamic functional graphs. The resulting graph Laplacian is mapped back to source space and embedded as a structured prior in an ADMMs optimization problem.Main results. Experiments on the MNE sample dataset showed that DG-ADMM produced spatially concentrated and physiologically plausible source patterns. On the PhysioNet MI dataset, the proposed framework achieved a binary left-versus-right MI classification accuracy of 93.52%, outperforming representative deep learning baselines. In a 320-dataset synthetic benchmark covering single-source, double-source, correlated-source, and dynamic-source conditions at two signal-to-noise ratios, DG-ADMM achieved the lowest mean center-of-mass localization error in five of eight conditions and showed its clearest advantage for dynamic sources.Significance. The results demonstrate that dynamic functional connectivity can serve as an informative graph-structured prior for EEG inverse reconstruction. DG-ADMM provides an interpretable and computationally feasible strategy for improving spatial focus, temporal consistency, and source-space decoding performance in EEG-based brain-computer interfaces.
21 July 2026
Read appraisal →Neuroinformatics
NeuroFusion: A Unified Framework for Generalized Visual Stimulus Decoding from fMRI Across Datasets and Subjects
Recent advancements in neural decoding have shown promising results in reconstructing visual experiences from brain activity. However, existing approaches focus primarily on decoding within a single dataset or subject, which limits generalization across various sources of neuroimaging. In this work, we propose a novel framework for the decoding of visual stimuli between subjects and between data sets, integrating neural recordings from multiple publicly available fMRI datasets. To address inherent intersubject and interdataset variability, we introduce a contrastive learning-based alignment strategy using image embeddings from a pre-trained IP-Adapter model. Our approach learns a shared latent space by aligning subject-specific neural representations with image features, enabling generalized decoding across both subjects and datasets. In addition, we propose a simple yet effective data augmentation method using ridge regression. This method synthesizes realistic fMRI-like signals from novel images by predicting voxel activity and injecting learned noise distributions, thus enhancing training diversity and model robustness. To the best of our knowledge, while several recent studies have explored cross-subject decoding, we extend recent cross-subject decoding efforts by training a single unified framework jointly across multiple public fMRI datasets and subjects, enabling cross-dataset transfer in addition to cross-subject generalization. We distinguish this multi-dataset unified training setting, where each dataset contributes training data, from a stricter leave-one-dataset-out transfer setting in which the target dataset is excluded from source pretraining and used only for lightweight alignment-layer adaptation. Empirically, our unified model achieves strong semantic reconstruction across datasets (e.g., up to 94.8% CLIP similarity on NSD (AUG) and 0.403 SSIM on BOLD5000 after lightweight finetuning), demonstrating robust cross-subject and cross-dataset transfer.
20 July 2026
Read appraisal →Nature communications
Enhancing sit-to-stand transitions and walking efficiency in older adults with a soft robotic suit
Age-related declines in muscle strength and neuromuscular control make sit-to-stand transitions and walking progressively more difficult, compromising mobility and independence. Although wearable assistive technologies have been proposed to alleviate these challenges, few have demonstrated clear benefits in facilitating sit-to-stand movements for older adults who retain a degree of independent mobility. Here, we introduce a soft hip exosuit designed to assist both sit-to-stand transitions and walking activities. In a feasibility study involving ten older adults, the exosuit increased 1-minute sit-to-stand repetitions by an average of 1.8 and reduced the metabolic cost of walking by 13.6% compared with the unassisted condition. These improvements were achieved while preserving natural kinematics, lower-limb stability, and maintaining a strong sense of agency. Our findings demonstrate that soft exosuits can enhance sit-to-stand and walking performance in older adults while preserving biomechanical naturalness and user autonomy, highlighting their potential for practical home-integrated mobility assistance.
20 July 2026
Read appraisal →Scientific reports
Walking stability prediction for pedestrians using gait energy images and hybrid deep and few-shot learning models
The prediction and recognition of unstable human walking patterns are of high importance for active video surveillance, smart environments, and assistive healthcare, particularly for fall detection in the elderly. This research investigates the utility of Gait Energy Images (GEIs) combined with deep learning, vision transformers, and few-shot learning architectures to enhance the classification of stable and unstable pedestrian walking patterns. We evaluate and compare twelve methodologies: six classical or feature-based machine learning models (Linear SVM, HOG + SVM, LBP + RBF-SVM, Random Forest, XGBoost, and an adapted GaitSet baseline), three deep learning models (MobileNet, Vision Transformer (ViT), and YOLOv8-cls), and three episodic few-shot learning techniques (Prototypical, Matching, and Relation Networks) under data-scarcity regimes. To facilitate this evaluation, we introduce the Unstable and Stable Walking Pedestrian (USWP) dataset, constructed by fusing and harmonizing sequences from seven public action recognition databases, containing 3250 unique GEIs with a subject-independent evaluation protocol to prevent identity-based domain leakage. Our experiments demonstrate that the YOLOv8-cls model achieves an overall accuracy of 96.92% (97.14% on Loss-of-Balance anomalies and 94.67% on Active Motion anomalies), significantly outperforming the conventional Linear SVM baseline (75.38%) and MobileNet (91.08%). Conversely, Relation Networks exhibit lower few-shot performance (71.08%) due to optimization complexities in learning similarity metrics from sparse data. Leave-One-Dataset-Out (LODO) cross-validation reveals an average generalization accuracy of 83.28%, indicating significant domain bias across source databases and underscoring that within-dataset evaluations overestimate real-world generalization. Computational complexity analysis shows that MobileNet provides an optimal trade-off for real-time edge deployment (4.2 ms latency), while preprocessing ablation studies demonstrate that integrating the Segment Anything Model (SAM) with MediaPipe-derived Regions of Interest (ROIs) yields a 12.50% absolute improvement in accuracy by eliminating background noise.
19 July 2026
Read appraisal →Science advances
Reduction of appearance artifacts in wearable on-skin electronics
Facial electrophysiological signals are crucial to human-machine interfaces and health care monitoring. Soft and skin-conformable electrodes enabled long-term and comfortable signal monitoring. However, the appearance of the electrodes affects the wearer's social interactions and self-identity, making daily usage difficult and leaving appearance artifacts. Here, we developed fully invisible and unperceivable on-skin electrodes free from appearance artifacts. Neither the wearer nor observers can detect the visual and tactile presence of the electrodes on the skin. The unperceivable property was confirmed with sensory experiments and physical characterizations of the film on skin. Furthermore, our invisible electrodes did not affect the psychological conditions of the wearers, which confirms the feasibility of artifact-free monitoring in daily lives. Last, we demonstrated the functionality of our electrode with successful monitoring of various facial electrophysiological signals, including electrooculogram (EOG), electromyogram (EMG), and electroencephalogram (EEG). Our fully invisible electrodes provide a promising direction in developing on-skin bioelectronics, seamlessly integrating health monitoring and human-computer interaction technologies into people's daily lives.
19 July 2026
Read appraisal →Neurosurgical review
Stereotactic radiosurgery for treatment-refractory obsessive-compulsive disorder: a systematic review of targets, targeting methods, lesion geometry, and clinical outcomes.
BACKGROUND: Stereotactic radiosurgery (SRS) is a noninvasive option for carefully selected patients with treatment-refractory obsessive-compulsive disorder (OCD), but the literature is heterogeneous in targets, targeting methods, lesion geometry, and outcomes. METHODS: PRISMA-guided systematic review of PubMed, Scopus, and Web of Science. Full-text human studies of radiosurgical capsulotomy for treatment-refractory OCD were included. Narrative synthesis was performed across technical and clinical domains. When ≥3 comparable studies reported the same endpoint, exploratory random-effects single-arm meta-analyses were performed. RESULTS: Twenty-one OCD-specific studies were included (1999-2024). The anterior limb of the internal capsule (ALIC), particularly the ventral ALIC, was the dominant target. Technique and dose reporting varied across cohorts, with commonly reported maximum doses spanning approximately 120-180 Gy, often using 4-mm collimation and single- or double-shot paradigms. Earlier reports primarily used anatomical MRI/coordinate-based targeting, whereas newer studies increasingly incorporated lesion mapping, tractography, and network-based analyses. Eleven cohorts were eligible for quantitative pooling. The pooled clinical response rate was 57.1% (95% CI, 49.1%-64.9%; I²=21.7%), and pooled remission was 36.8% (95% CI, 26.9%-47.9%; I²=0%). Severe delayed radiation-related injury was uncommon but present (6.4%, 95% CI, 3.1%-12.8%; I²=0%). Mechanistic studies suggested that lesion topography and pathway engagement may be more important than lesion size alone. CONCLUSIONS: SRS is a meaningful option for treatment-refractory OCD. The ventral ALIC remains the principal target, but outcomes likely depend on precise target identification and lesion placement. Standardized reporting and prospective evaluation of pathway-informed targeting are needed.
19 July 2026
Read appraisal →Philosophical transactions. Series A, Mathematical, physical, and engineering sciences
On the next generation for neuromorphic computing and neuromorphic AI
As we learn more about the inner cognitive workings of the brain's information processing and decision-making behaviours, this naturally leads us to consider alternative and additional ways to process information, from chips and architectures through the generation of insights and decisions. In turn, this suggests some options that might respond to challenges that are not addressable by the present state-of-the-art. By their nature, however, they may develop some common features and idiosyncrasies that are associated with human cognition, such as individual expertise and blind spots, illusions, systematic errors and transient mindsets, as well as evolutionarily advantageous fast-thinking facilities, which are applicable within novel and data-poor circumstances. We discuss some of the lessons learned from the reverse engineering of very large-scale neuron-to-neuron simulations (1B neurons) within cortex-like, network-of-networks, architectures. We identify some elements of the dynamical behaviour of the inner sub-networks (neural columns) that are not exhibited by present-day neuromorphic chips, owing to conceptual and design limitations. We describe a novel mathematical framework that might encompass human cognitive processing alongside various future neuromorphic processing concepts. We also identify certain elements of human cognition, reasoning and performance that present-day chips and present-day artificial intelligence (AI) simply cannot fully emulate (match to a high standard, in some artificial way), or simulate (achieve in the same way). We discuss how these aspects might catalyse some new fields of development for both processors and AI methodologies. In short, we discuss how future research and development will respond in radical ways that are precluded by most present-day technologies. This article is part of the theme issue 'Safe, secure and robust AI for safety-critical systems'.
18 July 2026
Read appraisal →Inflammation research : official journal of the European Histamine Research Society ... [et al.]
Lipopolysaccharide-mediated macrophage polarization, conserved pathogenesis, and implications for peripheral neuropathy: a systematic review.
OBJECTIVE AND DESIGN: This systematic review synthesized evidence for a conserved lipopolysaccharide (LPS)-mediated pathogenic mechanism across diverse tissues and evaluated its potential relevance to peripheral neuropathy. METHODS: Studies were identified in which LPS was the independent exposure and pro-inflammatory, M1-like macrophage activation/polarization was an outcome. Structured evidence mapping was used to code in-vivo studies for direct measurement of prespecified steps along a proposed pathway: gut perturbation→barrier disruption→circulating LPS→systemic inflammation→tissue interface disruption→innate immune activation→M1-like macrophage skew→tissue dysfunction. Conditional concordance and downstream chain completeness scores were calculated. RESULTS: Mechanistic patterns were conserved between pulmonary, cardiac, renal, lymphatic, gastrointestinal, central nervous, adipose, osseous, urologic, dental, hepatic, uterine, and pancreatic tissues. Conditional concordance with the proposed pathway was high (mean 0.984 ± 0.053). Eleven studies assessed all downstream steps from LPS exposure to tissue dysfunction, each demonstrating full chain completeness. M1 macrophage skew (87%), innate immune activation (87%), and circulating LPS (82.6%) were the most frequently reported steps. CONCLUSIONS: These findings demonstrate conservation of LPS-driven M1-like macrophage polarization and tissue injury across systems, supporting the need to further investigate the biological plausibility of a gut-immune-nerve axis contributing to peripheral neuropathy.
18 July 2026
Read appraisal →Nature communications
Sensory-guided human-machine joint learning accelerates the acquisition of motor imagery brain computer interface control
Brain-computer interfaces (BCIs) offer the potential to restore function and augment human capabilities. However, non-invasive electroencephalography (EEG)-based BCIs still face challenges in learning efficiency and control precision, particularly for naïve users performing complex tasks. Here, we present a sensory-guided joint learning framework that integrates human motor learning with adaptive machine learning to improve BCI training and performance. In 31 BCI-naïve participants, the framework enabled rapid skill acquisition, achieving average online discrete accuracies of 86.0% for one-dimensional (1D) and 77.5% for two-dimensional (2D) motor imagery tasks, along with continuous control accuracies of 77.5% (1D) and 66.9% (2D). Mechanistically, tactile guidance reduced user exploration and accelerated neural adaptation, while sample reweighting aligned decoder updates with human learning trajectories. By coupling reinforcement-driven neural plasticity with adaptive algorithmic optimization, this framework advances BCI training from passive calibration to active human-machine joint learning, enabling practical and scalable neural interfaces for communication and rehabilitation.
17 July 2026
Read appraisal →Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology
Creative thinking in Parkinson's disease: A systematic review and meta-analysis
INTRODUCTION: Creativity in Parkinson's disease (PD) has aroused research interest due to its neurological underpinnings, which involve brain regions crucial for creative and divergent thinking (a core-process of creative thinking), and because of some patients who displayed an increased artistic drive following dopaminergic treatment. From a cognitive point of view, creative thinking underlies several cognitive abilities, such as executive functions and memory. Therefore, a better understanding of whether it is increased (or, at least, preserved) in PD patients can provide useful insights for sustaining cognitive functioning. The aim of the present study was to investigate whether PD patients regularly assuming dopaminergic medications present higher divergent thinking skills than healthy controls. METHODS: A meta-analysis was conducted according to the PRISMA guidelines to provide a statistical synthesis of the studies. Study quality was assessed using the QUADAS -2 tool. RESULTS: Ten studies were included in the meta-analysis, which indicated the absence of significant differences between PD patients and healthy controls in tasks assessing divergent thinking (Cohen's d = -0,095 (95% CI: -0,308, 0,118)). CONCLUSIONS: Such findings support the notion that divergent thinking can be spared by the disease, maybe constituting a possible resource for patients' cognitive functioning, as it involves those cognitive abilities that can compensate impairments in PD. Clinical implications and guidance to further studies and interventions to support PD patients' cognition were discussed.
17 July 2026
Read appraisal →Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology
Association between circadian rhythm disturbances and cognitive decline in the elderly: a systematic review
BACKGROUND: Circadian rhythm dysregulation may contribute to sleep-wake disorders and cognitive impairment. In the elderly, circadian abnormalities have been observed in Mild Cognitive Impairment (MCI), suggesting a possible association between neurocognitive deficits and circadian dysfunctions. OBJECTIVE: To analyze the association between circadian rhythm disturbances and cognitive decline in the population aged ≥ 60 years. METHODS: This systematic review included studies evaluating the association between circadian rhythm disturbances and cognitive decline in elderly individuals aged 60 years or older, with self-reported or objectively measured sleep data and cognitive assessment via questionnaires. Studies tracking progression from MCI to Alzheimer's disease (AD) were retained, as they capture circadian changes across the MCI-to-dementia continuum. Studies that evaluated other sleep disorders and/or other conditions of cognitive impairment exclusively or that did not specify the mean age of the participants were excluded. RESULTS: Ten studies were included, totaling 5,731 participants (5,707 eligible for analysis). Circadian exposures were organized into three domains: (1) actigraphic rest-activity rhythm metrics (amplitude, robustness, interdaily stability, intradaily variability, and acrophase); (2) sleep quality and architecture parameters (efficiency, fragmentation, latency, WASO, and melatonin timing); and (3) circadian-disrupting lifestyle exposures (rotating night shift work). Across these domains, circadian dysregulation was consistently associated with a higher risk of development or progression of MCI and dementia. CONCLUSION: Circadian and sleep disturbances negatively impact cognitive health in the elderly, reinforcing the need for further research on this association and its public health implications.
17 July 2026
Read appraisal →NeuroImage
Rapid multi-parametric quantitative MRI via deep learning-based synthetic-to-real reconstruction and 3D SSFP-MOLED imaging
Multi-parametric quantitative magnetic resonance imaging (mqMRI) holds significant clinical potential through multi-parametric tissue characterization, yet its adoption is hindered by prolonged scan time and sensitivity to non-ideal signal conditions, especially in high-resolution whole-brain protocols. To address these challenges, we propose a novel signal encoding method integrating phase-modulated three dimensional steady-state free precession with multiple overlapping-echo detachment (3D SSFP-MOLED). This method simultaneously encodes six physiological parameters (M0, T1, T2, T2*, B1+, ΔB0) into k-space by controlling overlapping echo detachment in signal acquisition. A physics-constrained synthetic data pipeline was developed to simulate MR signal evolutions with realistic field variations (ΔB0, B1+ inhomogeneities), enabling robust training of network for real-time parameter mapping. Whole-brain parametric maps (1×1×2 mm³ resolution) can be delivered within 3 minutes with only 2x parallel acquisition acceleration. Validation was performed on phantom, healthy volunteers, and clinical cases with tumors/hemorrhage. Experimental results show that our method can achieve rapid multi-parametric quantitation with high accuracy and reproducibility. By synergizing adaptive signal encoding, physics-informed synthetic training, and reproducible deep learning reconstruction, this work establishes a new paradigm for efficient and reliable mqMRI in clinical signal processing applications.
17 July 2026
Read appraisal →Analytical methods : advancing methods and applications
Emerging trends in amino acid detection: wearable devices and machine learning-assisted signal processing
As critical metabolic biomarkers, amino acids exert essential physiological functions, and their abnormal levels are closely associated with a range of diseases such as cancer, neurodegenerative disorders, and cardiovascular conditions. In recent years, amino acid analysis technologies have achieved remarkable advancements in the field of personalized healthcare. This review navigates the latest progress in amino acid analysis, with a focus on two prominent emerging trends. The first trend encompasses the development of flexible and wearable sensing devices for non-invasive, continuous amino acid monitoring in diverse biofluids, such as blood, sweat, and interstitial fluid. Particular attention is given to their design principles, operational mechanisms, practical applications, and key performance metrics. The second trend involves the application of machine learning (ML) for processing and interpreting complex response signals. Specifically, this review discusses how various ML approaches, including classical chemometric linear regression models, deep learning models, support vector machines (SVMs), ensemble learning, and tree-based models, address common challenges in complex environments, such as signal interference and nonlinear drift. Furthermore, this review outlines the current challenges and proposes future research directions, aiming to advance amino acid analysis toward more intelligent, integrated, and personalized health monitoring systems.
17 July 2026
Read appraisal →Journal of neurology
Effects of respiratory muscle training on respiratory function in patients with amyotrophic lateral sclerosis: a systematic review and meta-analysis
BACKGROUND: Respiratory decline is prognostically important in amyotrophic lateral sclerosis (ALS), but the efficacy of respiratory muscle training (RMT) remains uncertain. We synthesized randomized evidence on respiratory and related outcomes. METHODS: We searched databases, specialized registers, and trial registries through June 19, 2025, for randomized RMT trials in ALS. Risk of bias was assessed with the Cochrane tool. Random-effects meta-analyses reported standardized mean differences (SMDs) with 95% confidence intervals (CIs), and certainty was rated with GRADE. RESULTS: Six studies were included; five concurrently randomized trials contributed quantitative data, whereas Pinto 2013 was retained for qualitative context. RMT improved maximal expiratory pressure (MEP; SMD 0.387, 95% CI 0.192-0.581; P = 0.008) and showed small favorable effects on maximal inspiratory pressure (MIP; SMD 0.156, 95% CI 0.089-0.224; P = 0.005), sniff nasal inspiratory pressure (SNIP; SMD 0.216, 95% CI 0.039-0.392; P = 0.034), peak expiratory flow (PEF; SMD 0.205, 95% CI 0.088-0.323; P = 0.017), and ALSFRS-R (SMD 0.214, 95% CI 0.039-0.388; P = 0.030). Forced vital capacity showed a borderline favorable trend (SMD 0.129, 95% CI - 0.003 to 0.262; P = 0.053). Low heterogeneity estimates were imprecise because most endpoints included only three to four small studies. Certainty was moderate for MIP, low for MEP, FVC, and PEF, and very low for SNIP and ALSFRS-R. Exploratory analyses identified no reliable effect modifiers. CONCLUSIONS: RMT was associated with small, directionally consistent improvements mainly in pressure- and flow-based outcomes, whereas effects on FVC and longer-term clinical outcomes remain uncertain. RMT may be considered an individualized adjunct to ALS respiratory care, not a disease-modifying therapy. Larger, longer, standardized trials with patient-important endpoints are needed.
16 July 2026
Read appraisal →Journal of ethnopharmacology
Efficacy of Wendan Decoction combined with conventional medicine for depression: a systematic review and meta-analysis
ETHNOPHARMACOLOGICAL RELEVANCE: Wendan Decoction is well-known traditional Chinese medicine formula commonly used in China for the treatment of gastrointestinal disorders and mood disorders related to phlegm-dampness and spleen deficiency. In clinical practice, depending on different symptoms, additional herbs may be included, resulting in a modified formula known as modified Wendan Decoction. AIM OF THE STUDY: To evaluate the efficacy of modified Wendan Decoction plus conventional medicine in the treatment of depression. MATERIALS AND METHODS: We independently conducted a meta-analysis of studies on the efficacy of modified Wendan Decoction for depression from inception to February 28, 2025, using electronic databases, including PubMed, Embase, Cochrane, Web of Science, Wanfang, VIP and SinoMed. Data from randomized controlled trials were extracted to evaluate treatment outcomes based on depression scales. RESULTS: A total of 402 articles were identified through database searches, and 13 studies, encompassing 1117 patients, were included. The initial pooled analysis revealed a significant treatment effect of the experimental group, yielding a standardized mean difference (SMD) of -4.84 (95% CI: -7.54 to -2.14), with high heterogeneity (I2 = 99%). Sensitivity analysis, excluding studies with high risk of bias, demonstrated an SMD of -4.18 (95% CI: -5.26 to -3.10) with moderate heterogeneity (I2 = 83%). Notable regional differences were found, with the study in Southern region exhibiting greater efficacy (SMD = -4.81, 95% CI: -6.38 to -3.23) compared to the study in Northern region (SMD = -3.03, 95% CI: -3.73 to -2.32). CONCLUSIONS: Our meta-analysis supports the efficacy of combining modified Wendan Decoction with conventional medicine for treating depression. Exploratory subgroup results suggest slightly greater improvements in Southern China, though this finding requires cautious interpretation and confirmation in future high-quality trials. PROSPERO Registration ID: CRD420251015690.
15 July 2026
Read appraisal →Journal of affective disorders
Transcranial focused ultrasound for emotion regulation: A systematic review and quantitative summary of human studies.
BACKGROUND: Emotion regulation is a core transdiagnostic process in mood, anxiety, and stress-related disorders. While existing non-invasive brain stimulation approaches, such as transcranial magnetic stimulation (TMS) and transcranial direct current stimulation (tDCS), can modulate affective networks, their clinical use is limited by restricted spatial precision and depth penetration. Transcranial focused ultrasound stimulation (tFUS) offers submillimeter focality and access to both cortical and deep subcortical structures, making it a promising tool for affective neuromodulation. METHODS: We systematically searched PubMed, Embase, and PsycINFO for human studies using tFUS to modulate emotion regulation, affective processing, or related symptoms. Eligible studies included randomized controlled trials (RCTs), open-label, and within-subject designs. Data on stimulation parameters, target regions, outcomes, and safety were extracted. Effect sizes were calculated and pooled using a random-effects model, with subgroup analyses by clinical domain. RESULTS: Eleven studies met inclusion criteria, targeting the amygdala (n = 5), prefrontal cortex (n = 5), or subcallosal cingulate cortex (n = 1), with protocols varying in frequency (250-650 kHz), duty cycle (0.5-70%), and number of sessions (1-25). Across six studies reporting behavioral symptom outcomes, the pooled effect was moderate-to-large (Hedges' g = 0.88, 95% CI [0.47, 1.29]), with larger effects in depression-related measures (g = 1.31) than in anxiety-related measures (g = 0.67). Imaging outcomes were reported in a smaller subset of studies and were not included in the pooled estimates. No serious adverse events were reported. CONCLUSIONS: tFUS is a safe and well-tolerated intervention capable of engaging deep affective circuits; however, quantitative estimates remain preliminary given the small and heterogeneous evidence base. Future large-scale, harmonized, and mechanistically informed trials are warranted to refine protocols, establish durability, and optimize translation into clinical practice.
15 July 2026
Read appraisal →NeuroImage
Editorial for the Special Issue on Harmonization Techniques for MRI
This editorial introduces the special issue on neuroimaging harmonization and situates its contributions within the broader methodological landscape of multi-site MRI analysis. As large-scale neuroimaging studies continue to aggregate data across scanners, protocols, and institutions, harmonization has become essential for reducing non-biological variability while preserving meaningful biological signals. We review the major classes of harmonization approaches, including statistical methods based on the ComBat family and deep learning methods that operate at the voxel level. We also review domain generalization strategies designed for previously unseen sites, and network-aware harmonization techniques that go beyond the voxel for connectivity and connectome data. Across these developments, several cross-cutting challenges emerge, including modality-specific performance, preservation of biological information, validation using traveling-subjects and large observational datasets, and the need for scalable, standardized, and privacy-preserving frameworks. Collectively, the articles in this special issue illustrate the rapid progress of the field and highlight that robust harmonization will be critical for enabling reproducible and generalizable discoveries in multi-site neuroimaging.
15 July 2026
Read appraisal →Sleep
Consensus roadmap on periodic leg movements during sleep: outcomes of the international PLMS taskforce
Periodic leg movements during sleep (PLMS) are a frequently observed motor phenomenon in polysomnography, yet their biological meaning and clinical relevance are debated. Further, how these should be assessed and tabulated for optimal evaluation is uncertain. To address these gaps, an international, multidisciplinary taskforce convened a series of online meetings followed by an in person taskforce workshop held in Troina, Italy, on November 14-15, 2025, where agreement was reached through iterative discussion (qualitative; no Delphi or predefined voting thresholds). This taskforce roadmap summarizes the taskforce conclusions across five priority domains: (1) clinical significance and impact, (2) measurement and characterization, (3) treatment, (4) basic and translational research, and (5) PLMS across the life span and epidemiology. For each domain, we delineate where the field currently stands, identify critical gaps in knowledge, and propose concrete strategies, including key milestones, study designs, and infrastructure needs, to advance the field in a coordinated and standardized manner. A central unifying theme is that PLMS should no longer be viewed solely as a countable polysomnographic index, but rather as a spectrum of sleep-related motor activity with heterogeneous neurophysiological substrates and variable clinical impact. Achieving progress will require harmonized scoring standards, validated automated and wearable technologies, large longitudinal and outcome-oriented studies, and sustained international collaboration. This roadmap provides a framework to guide research, clinical innovation, and future guideline development, but it is not a clinical practice guideline or standards statement.
14 July 2026
Read appraisal →Biomedical physics & engineering express
EEG-EMG spatiotemporal cross-attention fusion network for functional upper-limb movement classification
Objective. Electroencephalography (EEG) and electromyography (EMG) are widely used for decoding motor intentions, yet unimodal approaches often suffer from low robustness and limited representational information. EEG-EMG hybrid brain-computer interfaces (BCIs) can bridge cortical intention and muscular execution, but challenges remain in signal alignment, fusion modeling, and clinical generalization.Approach. To address these issues, we propose spatio-temporal cross-attention fusion (STCAFusion), a spatiotemporal cross-attention (CA) framework that integrates EEG and EMG through multi-band based dual-branch convolutional encoders and parallel temporal and spatial CA modules. This design enables detailed modeling of inter-modal correlations across both time and space. We evaluate STCAFusion on a newly collected dataset of synchronous EEG-EMG recordings from 12 subjects, where the data were acquired under two paradigms (Reaching and Lifting) designed from daily functional upper-limb activities to emphasize directional and strength control.Main results. With leave-one-run-out cross-validation, STCAFusion achieves average accuracies of 84.15% and 95.22% in the two paradigms, outperforming the strongest competing EEG-EMG fusion baselines by 3.4% in the Reaching paradigm and 1.8% in the Lifting paradigm. Visualization of learned attention weights further reveals meaningful spatiotemporal EEG-EMG coupling patterns, offering insights into neural-muscular coordination patterns relevant to rehabilitation-oriented BCI design.Significance. These results highlight the potential of CA-based multimodal physiological signal fusion in building reliable hybrid BCI and wearable devices for upper-limb control and rehabilitation.
14 July 2026
Read appraisal →ACS biomaterials science & engineering
AI-Driven Design and Clinical Translation of Nucleotide-Peptide and Peptide-Drug Conjugates
While nucleotide-peptide conjugates and peptide-drug conjugates demonstrate a considerable evolution as next-generation therapeutics, the successful translation of these complex agents relies on advances in bioconjugation strategies. By anchoring nucleotides or pharmaceutical payloads to multifaceted peptide scaffolds, researchers have engineered molecular platforms with dual mechanisms of action. However, to fully realize the potential of these hybrid systems, further refinements are required to ensure both in vivo stability and retention of the desired biological activities over time. This comprehensive review examines ongoing progress toward optimizing conjugation chemistry for the development of stable and efficacious NPC and PDC candidates. Through evaluation of fundamental design principles and their clinical relevance, it aims to accelerate the journey of these multilayered biomolecular constructs from theory to practice. It details various strategies to mitigate challenges such as enzymatic degradation and rapid systemic clearance, including covalent linkages (e.g., aminoacylation and thiol oxidation) and noncovalent interactions (e.g., ligand-receptor binding). Central to this emerging field is the integration of artificial intelligence and deep learning, which are rapidly transforming the design and development of NPCs and PDCs. AI-driven tools enable predictive modeling of peptide-drug interactions, intelligent linker optimization, and in silico screening of bioactive scaffolds-thus redefining the pipeline for precision therapeutics with improved selectivity and translatability. The potential of hybrid nanostructures formed by peptide-DNA conjugates for advanced drug delivery and diagnostic applications is also discussed. Furthermore, the clinical translation of PDCs demonstrates their promise in treating conditions such as cancer, neurodegenerative diseases, and inflammatory disorders, alongside the ongoing challenges regarding peptide availability, linker optimization, and scalable synthesis. The review concludes by discussing future directions of NPCs and PDCs, highlighting the role of interdisciplinary approaches in advancing precision medicine and addressing complex therapeutic needs.
14 July 2026
Read appraisal →Journal of molecular neuroscience : MN
Scalp Acupuncture Combined with Mouse Nerve Growth Factor Acupoint Injection for Post-stroke Cognitive Impairment
Post-stroke cognitive impairment (PSCI) markedly impairs rehabilitation efficacy and quality of life. Safe and effective interventions are urgently needed. Eighty-four patients with PSCI were randomly allocated into three groups (n = 28 per group). Group A received conventional cognitive training. Group B received conventional training plus scalp acupuncture. Group C received the treatment of Group B combined with mouse nerve growth factor (mNGF) acupoint injection. All interventions lasted 4 weeks. All groups exhibited significant within-group improvements in MoCA, ADL, MBI, and FMA scores after intervention. Group C achieved the optimal cognitive efficacy: the post-treatment MoCA score was 26.7 ± 2.9 (95% CI, 25.53-27.87), significantly higher than that in Group A (21.5 ± 3.5 (95% CI, 20.22-22.78), P < 0.001) and Group B (23.8 ± 3.2)(95% CI, 22.48-25.12), P = 0.003). The ADL score in Group C was the lowest among the three groups (F = 28.963, P < 0.001), suggesting better independence. Moreover, motor function (FMA) and basic self-care ability (MBI) were also significantly improved in all groups after intervention. There were significant inter-group differences in post-treatment FMA and MBI scores (F-FMA = 29.745, P < 0.001; F-MBI = 35.872, P < 0.001). The scores increased gradually from Group A to Group C, with Group C achieving the best improvement. Conventional cognitive training improves cognitive function and daily living ability in patients with PSCI. Scalp acupuncture combined with conventional training is more effective than training alone. The triple therapy of conventional training, scalp acupuncture, and mNGF acupoint injection further enhances rehabilitation outcomes, with prominent clinical application value.
13 July 2026
Read appraisal →Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology
Effects of transcranial direct current stimulation on motor function in patients with multiple sclerosis: a systematic review and meta-analysis
OBJECTIVE: To assess the effectiveness of transcranial direct current stimulation (tDCS) in enhancing motor function recovery in the upper and lower extremities of individuals with multiple sclerosis. DATA SOURCES: PubMed, Embase, Web of Science, Cochrane Library, and China Knowledge Network (CNKI) databases from inception until December 28, 2025. STUDY SELECTION: Randomized controlled trials that investigated transcranial direct current stimulation and tDCS combined with physical training on motor function in the upper and lower limbs of individuals with MS. DATA EXTRACTION: Two reviewers extracted the data and assessed the risk of bias. Standardized mean difference (SMD) and 95% confidence intervals (CI) were calculated using a random-effects model based on heterogeneity. The analysis included 10 articles with a total of 299 subjects. DATA SYNTHESIS: The Meta-analysis shows tDCS significantly improves lower limb motor function in MS patients (SMD = 0.31, 95% CI: 0.16 - 0.46, p < 0.0001). Subgroup analyses found significant increases in walking distance (p = 0.0005) and static balance (Berg Balance Score, p = 0.0002), but no significant improvements were observed in walking speed and dynamic balance. For upper limb motor function, no statistically significant difference was found in hand dexterity (9-HPT) (p = 0.11). Notably, subgroup analyses of stimulation parameters showed that high intensity (2.0-2.5 mA, p = 0.007) and a 20-minute duration (p = 0.03) yielded significant effects, whereas low intensity (1.0-1.5 mA) and 15-minute duration showed no significant benefits. CONCLUSIONS: Transcranial direct current stimulation is effective in enhancing lower limb motor function in patients with MS. However, the efficacy of tDCS in improving upper limb fine motor skills remains unclear. Evidence suggests that stimulation at 2.0-2.5 mA for 20 min may be the optimal protocol for clinical intervention.
13 July 2026
Read appraisal →Science advances
Transforming static interfaces into tactile channels with steerable transdermal foci
Grasping objects such as handles, mice, and phones plays a central role in human-machine interaction, yet the contact surfaces of the hand, despite their exquisite tactile sensitivity, are rarely exploited as channels for information transfer. Here, we introduce transdermal all-directional targeting (TADT), a static-contact interface design strategy that converts passive grip surfaces into active tactile displays, allowing users to grasp stably while receiving spatially and temporally evolving information on the fingertips. TADT enhances sub-actuator-pitch energy delivery by integrating a microtextured skin-coupling interface with phase-controlled vibrotactile actuation, thereby generating steerable three-dimensional tactile foci beneath the skin, enabling >200-hertz vibration and sub-actuator-pitch spatial super-resolution. Psychophysical experiments show that TADT lowers detection thresholds by 30% and reduces power consumption by 80% compared with designs without TADT. Using static grip alone, users reliably identified six directional cues and completed nonvisual indoor navigation and virtual reality interaction tasks, demonstrating a compact, energy-efficient modality for expressive tactile communication.
12 July 2026
Read appraisal →Science advances
Motor-free hip exosuit via high-output fibrous dielectric elastomer actuators
Exosuits can assist gait and reduce fatigue for both healthy and pathological populations, yet their bulky, rigid actuators (usually motors or pneumatic actuators) hinder natural, comfortable movement. Dielectric elastomer actuators (DEAs) provide a lightweight, compliant alternative, but are constrained by insufficient force, energy output, and integration challenges. Herein, we propose a motor-free hip exosuit driven by high-output fibrous DEAs, offering a previously unexplored paradigm for lower-limb assistance. We develop high-aspect-ratio fibrous DEAs that deliver high blocked stress (381.6 mN·mm-2), energy density (260 J/kg), and power density (1,664 W/kg), enabled by a dual-polar molecular design of the elastomer to overcome the intrinsic trade-offs between dielectric and mechanical properties. A Lego-like integration strategy is established to efficiently bundle fibers for force amplification. The resulting exosuit reduces the walking metabolic cost by 13.9% compared to no assistance, surpassing most hip exoskeletons. These findings advance DEAs toward practical wearable robotics for real-world human assistance.
12 July 2026
Read appraisal →Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology
Combined transcranial direct current stimulation and mirror therapy for upper limb rehabilitation in patients with stroke: a systematic review and meta-analysis
Upper limb impairment is among the common consequences of stroke. Combining transcranial direct current stimulation (tDCS) and mirror therapy (MT) have recently been used to improve neural plasticity and functional recovery. This review aims to evaluate the effects of combined tDCS and MT on upper limb function in patients with stroke. "PubMed, Scopus, Web of Science, CINAHL, PEDro, and EMBASE" were searched from their inception to March 2026. Randomized controlled trials investigating the combination of tDCS and MT in patients with stroke and assessing upper limb function were included. The quality and the certainty of evidence were evaluated. Seven studies (454 participants) met the eligibility criteria. Significant improvements were demonstrated in upper limb motor function (SMD = 0.73, 95% CI 0.34 to 1.12, p = 0.0002), activities of daily living (SMD = 0.84, 95% CI 0.46 to 1.22, p < 0.0001), and neurophysiological outcomes reflecting corticospinal conduction efficiency, assessed using central motor conduction time (CMCT) (SMD = -1.26, 95% CI -1.60 to -0.92, p < 0.0001). Subgroup analysis demonstrated greater effects in subacute stroke (SMD = 1.03, 95% CI 0.73 to 1.33). Other outcomes reported improvements in hand function and grip strength. Preliminary evidence suggests that combining tDCS and MT may improve upper limb motor and functional outcomes after stroke; however, these findings should be interpreted with caution due to heterogeneity and the limited data. Further studies are strongly needed.
11 July 2026
Read appraisal →Philosophical transactions of the Royal Society of London. Series B, Biological sciences
The hippocampus as a generative model
A generative model can be defined as a model of the latent causes of sensory input that can be used to generate new data samples. By examining empirical evidence and computational theory, we propose that the hippocampus can be characterized as a generative model. The hippocampus is a brain region important for memory. Recordings of neural activity from the hippocampus have led to the view that the hippocampus represents a cognitive map by abstracting a low-dimensional representation of the external world. We extend this view to suggest the hippocampus represents the latent, unobserved causes of sensory data by virtue of the position of the hippocampus within the deep cortical hierarchy. These representations of unobserved latent causes endow the hippocampus with capacity to generate new data samples that allow exploration of future hypotheticals and provide an internally generated training signal back to the generative model. We explore how perturbations to the hippocampal generative model may explain core symptoms of neuropsychiatric disorders such as those observed in psychosis. Together, this perspective provides a unified account of hippocampal function that explains how computations performed by the hippocampus support higher-order cognition and adaptive behaviour. This article is part of the theme issue 'The role of hippocampal predictions in cognition: bridging perception and memory'.
11 July 2026
Read appraisal →Brain imaging and behavior
Associations between olfactory dysfunction and structural MRI findings in Parkinson's disease: a systematic review
Olfactory dysfunction is a common and early non-motor symptom of Parkinson's disease (PD). Structural Magnetic Resonance Imaging (MRI) studies have reported changes in olfactory-related brain regions, but findings remain inconsistent. This review synthesizes MRI evidence to clarify neuroanatomical correlates of olfactory dysfunction in PD. We performed a systematic review to find the associations between olfactory dysfunction and structural MRI findings in people with PD. A systematic search in PubMed, Scopus, and Web of Science yielded 20 eligible studies. The eligible studies involved 1,092 patients with PD. Although there were inconsistent regions of interest and types of analysis, we identified structural changes in the olfactory sulcus and bulb, cortical gray matter, hippocampus, parahippocampus, and basal ganglia that are associated with olfactory dysfunction. Olfactory dysfunction in PD is linked to structural changes in primary olfactory, limbic, and cortical regions. These MRI findings may serve as early biomarkers, but standardized, longitudinal studies are needed to confirm their diagnostic or prognostic value.
10 July 2026
Read appraisal →Nature communications
Empowering clinical trial design with agentic intelligence and real-world data
Clinical trial design (CTD) is a time-consuming process that requires substantial domain expertise. Large-scale real-world data (RWD), such as electronic health records (EHR), encodes practice-based evidence that is of tremendous value to CTD. In recent years, many machine learning methods have been developed to extract such real-world evidence (RWE) from the RWD to inform CTD, but they still need to be communicated with the domain experts extensively in an iterative manner to be further refined and ultimately useful. In this paper, we introduce EmulatRx, an agentic framework that derives RWE for helping with CTD. Through the iterative conversation and analysis across agents with different roles, EmulatRx can autonomously refine trial protocols and finally generate a robust report containing insights that inform better CTD. We applied EmulatRx on the CTD process for both acute diseases (e.g., septic shock, acute heart failure, acute pulmonary edema, and acute kidney injury) using the MIMIC-IV data and chronic diseases (e.g., Alzheimer's disease and Parkinson's disease) using the INSIGHT Network across five New York City health systems. The results demonstrate EmulatRx's capabilities in facilitating and accelerating the CTD process.
9 July 2026
Read appraisal →Translational stroke research
Quantitative Cerebrovascular Analysis for Improved Prediction of Post-Stroke Complications
Endovascular thrombectomy (EVT) has transformed the treatment of acute ischemic stroke (AIS). However, a substantial proportion of AIS patients experience poor outcomes despite successful recanalization, often due to severe neurological deterioration or life-threatening complications. Early identification of these high-risk patients remains a major unmet need. In this study, we developed and validated machine-learning (ML) models that integrate automated quantitative brain arterial morphology and collateral grading with demographic, clinical, laboratory, and imaging variables to predict major post-EVT complications and early neurological outcomes. Using a prospectively collected database of 727 AIS patients that underwent EVT, we developed ML models to incorporate patient-specific vascular morphometry with conventional clinical, laboratory, and imaging data to predict emergence of early neurological deterioration (END), symptomatic intracranial hemorrhage (sICH), malignant brain edema (MBE) requiring surgical decompression, and neurogenic respiratory failure and dysphagia requiring tracheostomy/gastrostomy (TC/PEG). Our analysis of morphological features, including increased tortuosity and reduced vessel diameter, showed strong associations with complications. Morphology-informed (MI) models consistently outperformed baseline-clinical (BC) models for patients with END (AUROC 0.81 for MI model vs. 0.73 for BC), sICH (AUROC 0.68 MI vs. 0.56 BC model), MBE (AUROC 0.67 MI model vs. 0.56 BC), or patients who underwent TC/PEG (AUROC 0.66MI vs. 0.58 BC model). Statistical testing confirmed significant AUROC improvements for END, sICH and mRS (p < 0.05), Finally, patient-specific calibrated probability profiles enabled individualized, multidimensional risk stratification, revealing distinct complication-specific risk patterns across patients. These findings demonstrate that cerebrovascular structure-an often overlooked yet physiologically fundamental determinant of ischemic injury and reperfusion dynamics-provides significant predictive information that is not captured by standard clinical or visual imaging assessments. Automated vascular segmentation and collateral grading techniques enable rapid and objective integration of cerebrovascular metrics into prognostic models, offering a scalable tool for precision risk stratification, supporting earlier intervention, targeted monitoring, and improved post-EVT management.
9 July 2026
Read appraisal →Journal of neuro-oncology
Feasibility evaluation of tumor treating fields for brainstem gliomas
PURPOSE: Tumor treating fields (TTFields) are FDA-approved for supratentorial glioblastoma, but feasibility in infratentorial tumors remains poorly defined. This simulation study evaluated TTFields dose in brainstem gliomas using patient-specific modeling with scalp-only transducer arrays. METHODS: MRI and CT imaging from seven patients with brainstem gliomas were used for TTFields planning with MAXPOINT® (Novocure, Switzerland). Clinical target volume (CTV) was defined as enhancing tumor on T1 post-contrast MRI (Gross tumor volume, GTV) plus a 3 mm peritumoral expansion. The platform optimized scalp-only array layouts, and finite element calculations generated maps of local minimum field intensity (LMiFI, V/cm) and local minimum power density (LMiPD, mW/cm³). Values were compared against a standard, unplanned layout using one-sided paired t-tests, with LMiFI ≥ 1.0 V/cm as a therapeutic reference. RESULTS: MAXPOINT-optimized layouts achieved significantly higher LMiFI than the standard layout across all regions (all p ≤ 0.019). GTV median LMiFI was 1.1 vs. 1.0 V/cm (p = 0.019). CTV median LMiFI was 1.1 vs. 1.0 V/cm (p = 0.002). Brainstem median LMiFI was 1.3 vs. 1.1 V/cm (p = 0.002). Posterior fossa showed the largest difference: median LMiFI 1.5 vs. 1.2 V/cm (p < 0.001). All optimized LMiFI values met or exceeded the therapeutic reference. LMiPD was also significantly higher with MAXPOINT® across all regions (all p ≤ 0.006). CONCLUSION: This simulation study demonstrates that patient-specific modeling can achieve favorable TTFields intensities in brainstem gliomas using scalp-only arrays, supporting prospective clinical evaluation for infratentorial tumors.
8 July 2026
Read appraisal →PLoS computational biology
Predictive coding explains asymmetric connectivity in the brain: A neural network study
Seminal frameworks of predictive coding propose a hierarchy of generative modules, each attempting to infer the neural representation of the module one level below; the predictions are carried by top-down feedback projections, while the predictive error is propagated by reciprocal forward pathways. Such symmetric feedback connections support visual processing of noisy stimuli in computational models. However, neurophysiological studies have yielded evidence of asymmetric cortical feedback connections. We investigated the contribution of neural feedback in visual processing for computing grasp parameters, by utilizing convolutional neural network models that had been augmented with predictive feedback and were trained to compute grasp positions for real-world objects. After establishing an ameliorative effect of symmetric feedback on grasp detection performance when evaluated on noisy stimuli, we characterized the performance effects of asymmetric feedback, similar to that observed in the cortex. Specifically, we tested model variants extended with short-, medium-, long- and longer-range feedback connections (i) originating at the same source layer or (ii) terminating at the same target layer. We found that the performance-enhancing effect of predictive coding under adverse conditions was optimal for medium-range asymmetric feedback. Moreover, this effect was most prominent when medium-range feedback originated at a level of representational abstraction that was proximal to the input layer, in contrast to more distal layers. To conclude, our simulations show that introducing biologically realistic asymmetric predictive feedback improves model robustness to noisy visual stimuli in a neural network model optimized for grasp detection.
8 July 2026
Read appraisal →PloS one
Effects of immersive virtual reality on limb motor function, balance, gait and quality of life after stroke: A systematic review and meta-analysis
Immersive virtual reality (IVR) has been increasingly integrated into stroke rehabilitation albeit the effectiveness across different outcome domains remains unclear. This study aimed to review evidence from randomized controlled trials evaluating the effects of IVR, used alone or in combination with conventional rehabilitation, on motor-, functional-, and participation-related outcomes after stroke. Ovid MEDLINE, Embase, Cochrane Library and CINAHL were screened up to April 2025 to identify eligible randomized controlled trials (RCTs). A systematic literature search was conducted. Randomized controlled trials including adults with stroke and evaluating immersive virtual reality interventions were eligible. Studies using non-immersive VR, non-randomized designs, or not reporting motor, functional, or participation outcomes were excluded. Meta-analyses were performed where data were sufficiently homogeneous; otherwise, outcomes were summarized descriptively. Risk of bias was assessed using standardized appraisal tools, and certainty of evidence was evaluated using the GRADE approach. Out of 3,574, we included 34 RCTs involving post-acute, subacute and chronic stroke survivors, with a total of1200 participants. Meta-analyses showed IVR, most commonly delivered in combination with conventional rehabilitation, to be associated with improvements in upper extremity motor impairment, particularly measured by the Fugl-Meyer Assessment Upper Extremity (FMA-UE). Small but significant effects were also found for selected gait parameters. In contrast, effects on functional performance, balance, and quality of life were inconsistent and could not be robustly quantified. The overall certainty of evidence ranged from low to very low, primarily due to heterogeneity, methodological limitations, and small sample sizes. In conclusion, IVR appears to be a promising and safe supplement to conventional stroke rehabilitation, particularly for improving motor impairment. However, evidence remains limited and inconsistent across functional outcomes. More well-designed, adequately powered trials with standardized interventions and clinically meaningful outcome measures are needed to clarify the role of IVR in stroke rehabilitation. PROSPERO (Registration number: CRD420250639792).
7 July 2026
Read appraisal →Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology
Topography and functional connectivity analysis of heart surgery-associated brain lesions: a literature review
BACKGROUND: Cardiac surgery is a major therapeutic advancement but remains associated with neurological complications, including ischemic stroke and postoperative cognitive decline. Compared with stroke of other etiologies, ischemic brain injury following cardiac surgery may involve distinct vascular territories and functional networks, potentially influencing its characteristic cognitive profile. METHODS: We performed a systematic review of case reports describing ischemic brain lesions occurring after cardiac surgery in adults with available neuroimaging data. Lesions were manually traced onto a standard brain atlas and compared with ischemic stroke lesions from the ATLAS database, matched for hemispheric involvement. Lesion topography was analyzed at the arterial territory and voxel levels. Lesion network mapping was conducted using normative resting-state functional connectivity data to identify patterns of functional disconnection. Permutation-based statistical analyses were applied, with correction for multiple comparisons and lesion volume included as a covariate. RESULTS: Nineteen articles met the inclusion criteria, yielding 20 cases of post-cardiac surgery ischemic lesions. Compared with stroke controls, these lesions more frequently involved posterior arterial territories, particularly the occipital branches of the posterior cerebral arteries and posterior thalamic regions. Lesion network mapping revealed a distinct pattern of functional disconnection in the occipital lobes, posterior thalamus (including the pulvinar), and medial frontal cortex. CONCLUSIONS: Our results suggest that ischemia associated with cardiac surgery may preferentially involve posterior arterial territories and be associated with distinct patterns of functional disconnection involving the occipital lobe, the pulvinar, and the medial frontal cortex.
6 July 2026
Read appraisal →Journal of medical Internet research
Are Traditional Registries Becoming Obsolete in the Modern Digital Health Ecosystem?
Registries have long been a cornerstone of medical research and public health, providing systematically collected data on diseases, treatments, and health outcomes. However, in the era of digital health, we argue that the traditional model of stand-alone registries needs reconsideration, given the context of increasingly digitized and interoperable health data ecosystems. Unless registries evolve to embrace embedded, standards-based data services, operating across interoperable infrastructure, they will become obsolete while digitalization is reshaping how data can be collected, shared, and used. In this viewpoint, we recount how the present health data ecosystem came to be and what role registries have come to play therein. Following that, we show how recent regulatory initiatives such as the Trusted Exchange Framework and Common Agreement in the United States or the European Health Data Space Regulation signal a shift toward cross-network health information exchange, promoting patient-centric data integration within electronic health record systems. We further illustrate how electronic health records are consequently set to evolve into information hubs, acting as the primary gateway for individuals through which they may access and control their personal health data spread throughout increasingly connected health data ecosystems. This, in turn, might stimulate the creation of digital twins and continuous learning health systems in practice. Following this line of thought, we discuss the opportunities and challenges of interconnected health data ecosystems. Ultimately, we propose that next-generation registries need to be designed as dynamic, service-oriented software stacks for research, leveraging the common data infrastructures that are currently being established around the world. Given the points raised in this viewpoint, we invite health care professionals and researchers alike to equally rethink the role that registries should play within the globally emerging interconnected health data ecosystems and contribute their findings. References included in this viewpoint were identified through searches of PubMed and Google Scholar with various search terms and combinations thereof pertinent to the topics touched on, for example, "patient registry," "clinical registry," "digital twin," "healthcare," "clinical research," "virtual twin," "TEFCA," or "EHDS." Only papers in English were reviewed. The final reference list was generated on the basis of originality and relevance to the broad scope of topics covered in this viewpoint, aiming to present a balanced overview of topic-related findings and arguments.
5 July 2026
Read appraisal →Journal of neurology
The dual role of mTOR in multiple sclerosis pathophysiology: a systematic review
Multiple sclerosis (MS) is a chronic autoimmune disease characterized by demyelination, neuroinflammation, and progressive neurodegeneration. The mechanistic target of rapamycin (mTOR) pathway plays a key role in regulating immune responses, cell metabolism, autophagy, and repair processes. Although the role of mTOR in neurodegeneration has been explored in previous reviews, a systematic assessment of its function in MS across different models is still lacking. This systematic review aimed to examine the role of mTOR signaling in the pathophysiology of MS. Following Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines, we screened preclinical and clinical studies using two databases and assessed the risk of bias specific to the study types. A total of 189 records were identified, of which 90 met the inclusion criteria for qualitative analysis. Studies using in vitro and in vivo (mainly rodent models, both sexes) models of MS, as well as MS patient tissue or data, consistently demonstrated that mTOR is involved in the MS-related processes neuroinflammation, myelination, autophagy, gliosis, mitochondrial dysfunction, and oxidative stress. mTOR inhibition reduces pro-inflammatory signaling and may enhance autophagy, offering neuroprotection. In contrast, activation of mTOR promotes remyelination by enhancing oligodendrocyte differentiation and maturation. These remyelinating effects may be masked in inflammatory environments, because activation of mTOR supports immune cell expansion and glial reactivity, inducing inflammation and oxidative stress. Overall, our findings underscore a dual role of mTOR in MS pathology, with important implications for disease stage and timing of intervention. Although mTOR is mechanistically important in MS, its therapeutic modulation is unlikely to be readily clinically translatable without a substantial risk of unintended and context-dependent effects.
5 July 2026
Read appraisal →Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology
Telemedicine for headache disorders in real-world practice: from patient experience to health system impact.
BACKGROUND: Telemedicine has expanded rapidly in neurological care and is increasingly applied to headache management to improve access and continuity of follow-up. However, patient experience and healthcare system impact in real-world practice remain insufficiently explored. OBJECTIVE: The objective of this narrative review is to evaluate clinical effectiveness, patient experience, and healthcare system impact in the management of headache disorders. METHODS: A narrative review of the literature was conducted, including randomized controlled trials, observational studies, real-world evidence contributions, and reviews addressing telemedicine in headache care. Evidence was synthesized thematically, focusing on clinical outcomes, patient acceptance, and implementation aspects. RESULTS: Available evidence supports the clinical effectiveness and safety of telemedicine in selected headache patients, particularly in non-acute settings and structured follow-up pathways. Outcomes assessed using validated instruments (HIT-6, MIDAS, VAS) are generally comparable to in-person visits. High patient satisfaction and willingness to continue telemedicine are consistently reported, mainly due to convenience and time and cost savings. Real-world studies suggest telemedicine may improve access to specialist care and optimize healthcare resource utilization, although challenges related to digital literacy, organizational requirements, and equity remain. CONCLUSIONS: Telemedicine is a valuable component of contemporary headache management when integrated into structured clinical pathways. Beyond clinical effectiveness, patient experience and organizational context are critical for successful implementation. Future research should focus on standardized care models, larger populations, and long-term real-world outcomes.
5 July 2026
Read appraisal →Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology
Limb apraxia in Parkinson's disease and atypical parkinsonian syndromes: a systematic review
BACKGROUND: Evidence regarding the clinical manifestations, disease-specific profiles and diagnostic significance of limb apraxia in Parkinson's disease and Atypical Parkinsonian Syndromes (APS) is limited. OBJECTIVES: The present systematic review aims to consolidate current knowledge on limb apraxia across neurodegenerative disorders, including Parkinson's disease (PD), corticobasal syndrome (CBS), Progressive Supranuclear Palsy (PSP) and Multiple System Atrophy (MSA). METHODS: A systematic literature review was conducted in accordance with PRISMA guidelines. Studies were included if they enrolled ≥ 10 patients in at least one of the patient groups and ≥ 10 control subjects with quantitative data on apraxic deficits. Risk of bias assessment was assessed. RESULTS: Twenty-two studies met inclusion criteria (PD n = 11; CBS n = 10; PSP n = 7; MSA n = 3). Across PD and APS, praxis assessment primarily involved gesture imitation, pantomime, action sequencing, actual tool use and measures of fine motor coordination. CBS demonstrated the most severe/widespread apraxic impairment, affecting both meaningless and meaningful (transitive and intransitive) gestures, as well as action sequencing and fine motor control. In PD, apraxic deficits were generally milder but shared overlapping features with CBS. Direct comparative studies between PD and CBS remain scarce. PSP was characterized by less frequent and predominantly sequence-related impairments, whereas findings in MSA were heterogeneous and less pronounced. CONCLUSIONS: Limb apraxia phenotypes differ across PD, CBS, PSP and MSA and may contribute to their differential diagnosis. Future research should adopt standardized, multimodal praxis assessment protocols in larger cohorts including all major neurodegenerative parkinsonian disorders, to facilitate the direct comparison of limb apraxia across these diseases.
4 July 2026
Read appraisal →Neuroscience and biobehavioral reviews
Anxiety detection using neural and physiological signals and artificial intelligence: A comprehensive review.
Anxiety disorders are a significant challenge to global health, yet current diagnostic methods rely mainly on subjective and episodic assessment. The application of artificial intelligence (AI) in combination with neural and physiological signals is a promising pathway toward objective and continuous monitoring. This review provides a comprehensive evaluation of machine learning (ML) and deep learning (DL) techniques applied to electroencephalography (EEG), electrocardiography (ECG), Photoplethysmography (PPG), and electrodermal activity (EDA) for anxiety detection. A systematic review of the literature covering the period 2015-2025 indicates that the field has evolved from traditional ML systems based on hand-crafted features to modern end-to-end deep learning schemes. Our review indicates that while classical models remain effective, hybrid models such as CNN-LSTM and more sophisticated architectures, like Transformers, consistently deliver state-of-the-art results, particularly in multimodal data integration scenarios. Despite the impressive accuracies reported, a critical examination identifies several key challenges including reliance on narrow, laboratory-trained datasets, a lack of standardized validation procedures, and limited transparency in complex models, collectively impeding clinical translation. Progress in this field requires the creation of large-scale, clinically-validated datasets; the development of fault-tolerant, generalizable, and interpretable schemes; and a transition from basic state classification towards longitudinal, personalized monitoring in support of just-in-time intervention. As a comprehensive and up-to-date narrative synthesis of AI-driven anxiety detection using neural and physiological signals, this review consolidates a fragmented literature, articulates the translational gaps limiting real-world deployment, and establishes a clear research agenda for the development of clinically impactful digital mental health technologies.
3 July 2026
Read appraisal →Physiology (Bethesda, Md.)
Wearable Sensing for Clinical Physiology Monitoring: Emerging Paradigms
Advances in wearable sensing technology are driving a new era of personalized health monitoring. In contrast to the hard, rigid form factors of conventional wearable sensors, these emerging skin-interfaced systems support high-quality physiological measurements across biophysical, biochemical, and kinematic signals of interest. These platforms enable continuous monitoring of complex physiological processes with unprecedented detail as a result of a seamless, conformal skin interface and advanced wireless communications capabilities. These platforms integrate flexible materials, miniaturized electronics, and wireless communication to provide detailed physiological data during daily activities. This review examines how skin-interfaced wearables are advancing patient care, remote monitoring, and large-scale health studies. We highlight critical barriers to clinical adoption including interpreting data, validating devices, and integration into health care systems. Key opportunities include sustainable manufacturing, point-of-care fabrication, and development of disease-specific digital biomarkers. By addressing these challenges through collaboration among engineers, clinicians, and data scientists, wearable sensors can expand patient access to advanced physiological monitoring and transform personalized medicine.
2 July 2026
Read appraisal →The Behavioral and brain sciences
Large language models are not about natural language.
Large Language Models are useless for linguistics, as they are probabilistic models that require a vast amount of data to analyze externalized strings of words. In contrast, human language is underpinned by a mind-internal computational system that recursively generates hierarchical thought structures. The language system grows with minimal external input and can readily distinguish between real language and impossible languages.
2 July 2026
Read appraisal →Journal of Alzheimer's disease : JAD
Early diagnosis of Alzheimer's disease through handwriting analysis and deep learning: A review.
Alzheimer's disease (AD) is one of the most prevalent neurodegenerative disorders worldwide, requiring early identification for timely intervention and to slow disease progression. However, existing diagnostic approaches, while effective at later stages, remain limited in detecting early-stage AD. Handwriting analysis has recently emerged as a non-invasive, cost-effective, and ecologically valid digital behavioral biomarker that reflects neurocognitive impairment. This review examines the role of handwriting as a neurocognitive marker for AD, focusing on integrating deep learning methodologies to enhance early diagnostic accuracy. It also elucidates the neurocognitive mechanisms linking handwriting behavior and AD, addressing current methodological and translational challenges. We performed a PRISMA-informed structured literature search and narrative synthesis of handwriting- and drawing-based studies for detecting AD/mild cognitive impairment (MCI), including offline handwriting images and online pen-stroke kinematics captured by digital devices. Task paradigms, data dimensions, preprocessing pipelines, modeling strategies (traditional machine learning and deep learning), evaluation practices, and translational considerations were summarized, and studies were organized by detection purpose and analytic approach. Our findings show that handwriting-based models generally discriminate AD/MCI from healthy controls with accuracy exceeding 80%, while deep learning models (e.g., convolutional neural network and multimodal Transformer fusion) approach 90% in structured tasks like clock drawing and figure copying. Online kinematic markers (e.g., reduced velocity, prolonged in-air time, increased pausing, and pressure instability) recur across studies, and multimodal integration with speech, gait, or facial signals can further improve sensitivity and ecological validity, although most studies are small and single-center.
2 July 2026
Read appraisal →IEEE transactions on bio-medical engineering
EMI Cancellation for Shielding-Free Ultra-Low-Field MRI
OBJECTIVE: Ultra-Low-Field Magnetic Resonance Imaging (ULF MRI) offers low cost and portability but suffers from electromagnetic interference (EMI) in unshielded environments. This study developed a deep learning-based active EMI suppression method to overcome these limitations. METHODS: Using a 68mT ULF MRI system, human body coupling was identified as a primary EMI pathway. We proposed EMIC-Net, a U-Net architecture incorporating Transformer and hybrid attention mechanisms, to learn the data-driven nonlinear mapping from sensing coil signals to radio-frequency (RF) receiver coil interference. Acquired data underwent phase and gain compensation prior to model training. The model's efficacy was validated through in vivo human brain imaging, comparing its performance with EDITER and standard CNN methods, and by assessing the impact of varying EMI coil numbers and training data volumes. RESULTS: EMIC-Net effectively suppressed complex dynamic EMI. It restored image SNR from 2.35 dB to 17.63 dB, with significant PSNR and SSIM improvements. Image quality neared shielded acquisitions and surpassed comparative methods. Three EMI coils provided optimal balance, and the model showed data efficiency, requiring a small dataset for effective training. CONCLUSION: The EMIC-Net method accurately predicts and efficiently removes EMI in unshielded ULF MRI, offering superior performance and practicality. SIGNIFICANCE: This research promotes portable, low-cost ULF MRI for primary healthcare and bedside diagnosis. It also offers insights for mitigating complex EMI issues in other RF sensing domains.
2 July 2026
Read appraisal →Medical image analysis
A review of deep learning-based Unsupervised Anomaly Detection in brain MRI
The manual assessment of brain Magnetic Resonance Imaging (MRI) scans can be labor-intensive and time-consuming for radiologists. Deep Learning methods have demonstrated the potential to aid this process. However, their effectiveness relies on the availability of large, annotated data sets. Unsupervised Anomaly Detection (UAD) presents a promising alternative, offering the potential to identify and localize anomalies without per-pixel annotations. Instead, a normative distribution is learned using healthy data, enabling the identification of abnormalities as deviations. This allows UAD methods to detect abnormalities that were unseen during training. This appealing feature has led to numerous studies proposing innovations and novel approaches. In this work, we provide a review of the literature and systematically collect and compare the proposed approaches. We observe that UAD has made significant advancements in brain MRI analysis. However, individual approaches are often evaluated in different contexts, i.e., changes in acquisition parameters, pre- and post-processing, and anomaly scoring. This variability makes it challenging to assess which models perform best, underscoring the need for comprehensive comparative studies concerning the specific context of MRI scans. Our collection, featuring public data sets, research studies, and open implementations, is available at our GitHub repository https://github.com/FinnBehrendt/Unsupervised-Anomaly-Detection-in-Brain-MRI.
1 July 2026
Read appraisal →Journal of bodywork and movement therapies
Wearable technologies in neurorehabilitation and cardiac rehabilitation: A narrative review
BACKGROUND/OBJECTIVE: Wearable devices are sensor-based technologies capable of continuous physiological and movement monitoring, improving health tracking and clinical care. They can play a crucial role in clinical practice including cardiac and neurological rehabilitation, aiding in conditions as stroke, Parkinson and heart failure. These tools enhance treatment adherence and outcomes through remote, continuous, and personalized monitoring. By overcoming barriers of traditional rehabilitation, wearable devices offer cost-effective, accessible, and patient-centered solutions for chronic care. The purpose of this study is to evaluate the state of the art of the use of wearable devices in cardiology and neurology clinical rehabilitation practice. METHODS: A structured literature search was performed in PubMed, Embase, Web of Science, Scopus and Cochrane Library databases, for original studies published between 2014 and 2024 involving adult patients and clinical data. Only original studies with patient intervention or observation were included. RESULTS: Of the 25 studies included, 19 focused on neurology and 6 on cardiology, all exploring wearable devices in adult rehabilitation from 2014 to 2024. Neurological studies targeted conditions like stroke and Parkinson's disease, using robotic aids and virtual reality, while cardiac studies focused on activity monitoring and recovery support. Devices ranged from IMUs and sensor-equipped gloves to wrist-worn ECG monitors, with outcomes assessing motor function, heart rate, and rehabilitation adherence. Patient acceptance was generally positive, particularly in tele-rehabilitation contexts, although usability and long-term adherence were inconsistently reported. CONCLUSIONS: Wearable devices effectively improve neurological and cardiac rehabilitation by supporting personalized, data-driven therapies and remote monitoring, helping to monitor activity and therapeutic adherence. Overall, they promote better outcomes, patient autonomy, and access to care, although there is less evidence. Future studies should further investigate the utility of these devices in different rehabilitation settings.
1 July 2026
Read appraisal →The British journal of radiology
Artificial intelligence in nuclear medicine
Artificial intelligence (AI) holds great promise for advancing diagnostics and treatment in nuclear medicine. The rapid growth of AI over the past decade has been largely driven by advances in hardware components such as graphics processing units (GPUs) and the introduction of deep learning (DL) and convolutional neural networks (CNN). The integration of AI and medical imaging has the potential to revolutionize nuclear medicine by, for example, accelerating image acquisition, enhancing image quality, enabling advanced image generation, assisting image interpretation, and aiding treatment planning. Clinical applications have been demonstrated for most medical specialties, including oncology, neurology, and radionuclide therapy. The utilization of AI to provide automated, standardized procedures can help bring advanced imaging from major university centres to smaller local clinics, thus benefiting a broader range of patients. Additionally, AI has vast potential for predicting optimal treatment strategies, assessing risk, optimizing patient flow and outcomes, and even improving productivity, but these capabilities have yet to be fully utilized. The fraction of clinical AI applications in general healthcare reaching beyond the prototyping phase is reported to be as low as 2%. Indeed, in nuclear medicine, very few AI developments have reached commercial maturity. Currently, most AI applications in nuclear medicine follow the imaging flow from image acquisition and reconstruction, post-processing and image preparation, image analysis, and decision support for clinical interpretation. Below, we will briefly review selected areas and comment on challenges and opportunities for AI in nuclear medicine, with a special focus on the transition from development to clinical implementation.
1 July 2026
Read appraisal →Langmuir : the ACS journal of surfaces and colloids
Wearable Sleep Detection System Based on Piezoelectric Signals and Convolutional Neural Network Analysis
Long-term and comfortable sleep monitoring is a core requirement for daily health management. However, existing sleep monitoring technologies fail to simultaneously achieve accurate sleep stage classification, wearing comfort, and direct monitoring of characteristic physiological signals. Here, we report a wearable sleep monitoring system (SMS) based on piezoelectric sensing and convolutional neural network (CNN) analysis. The system assesses sleep structure and quality by capturing characteristic eye movement signals and provides actionable recommendations for long-term sleep health management. The sensor employs a highly stereoregular polyacrylonitrile (PAN) composite fiber film modified with PDA@ZnO as the sensing unit, providing high sensitivity, excellent wearing comfort, and long-term durability. It conforms closely to the upper eyelid, enabling effective acquisition of direct physiological signals associated with sleep. Furthermore, we developed a CNN model for five-state sleep classification and validated it using original-recording-level 5-fold validation and a 1000-sample processed held-out test set, achieving an accuracy of 95.90% with a Cohen's Kappa coefficient of 0.949. The system efficiently presents sleep quality information through a simple and user-friendly human-computer interaction interface. The proposed system introduces a novel approach to convenient health monitoring and provides a feasible technical solution for accurate and long-term sleep health assessment in daily life.
1 July 2026
Read appraisal →Langmuir : the ACS journal of surfaces and colloids
An Ultrasoft Initiator-Free Eutectogel for Strain Sensing and Gesture Recognition Assisted by Machine Learning
With the rapid development of Internet of Things and artificial intelligence technologies, flexible wearable sensors have shown great potential in human-machine interaction and health monitoring fields. However, traditional hydrogel sensors face challenges such as water loss, freezing, and the use of toxic initiators during the preparation process, which lead to biological safety issues. To address these challenges, this paper proposes a green, initiator-free polymerization strategy based on the deep eutectic solvent system composed of choline chloride (ChCl) and D-sorbitol. By utilizing the property that the nitrogen-containing quaternary ammonium group in the ChCl molecule can generate free radicals upon ultraviolet irradiation, this study achieves the rapid polymerization of acrylamide in an initiator-free manner. The prepared eutectogel exhibits high transparency (≈96%), skin-fitting elastic modulus, good antifreezing performance, suitable breathability, and broad-spectrum adhesion. The flexible strain sensor constructed based on the eutectogel has high sensitivity, wide detection range, and good fatigue resistance. Combined with machine learning algorithms, this sensor system achieves a high accuracy (98.5%) of recognizing Curwen gestures. This research provides an innovative approach for developing safe, reliable, and environmentally adaptable intelligent wearable devices and has broad application prospects in intelligent music education and modern human-machine interaction.
1 July 2026
Read appraisal →Diving and hyperbaric medicine
Bipolar spectrum disorders in divers: risks, recognition, and recommendations.
Bipolar disorder is a recurrent psychiatric condition characterised by episodic mood disturbances, residual functional impairment, and high rates of psychiatric and medical comorbidity. While many individuals achieve symptomatic remission, persistent deficits in cognition, emotional regulation, and insight may remain, raising concerns for participation in safety-critical activities such as scuba diving. This systematic review synthesised evidence from psychiatric, occupational, aviation, and diving medicine literature to examine the clinical course of bipolar disorder, treatment considerations, functional outcomes, and safety-relevant factors pertinent to fitness-to-dive assessments. Bipolar disorder exhibits marked heterogeneity in syndromal and functional outcomes. Even during euthymia, subtle impairments in attention, executive functioning, and decision-making may persist. Pharmacological stability is essential for diving, but treatment regimens, particularly lithium use, polypharmacy, and antidepressant therapy, introduce additional considerations. Comorbidity, circadian disruption, sleep deprivation, and reduced insight during early relapse further complicate risk assessment. Empirical data on diving outcomes in individuals with bipolar disorder are scarce, necessitating reliance on expert opinion and extrapolation from related safety-critical domains. Fitness-to-dive assessments in bipolar disorder should prioritise sustained functional stability, reliable treatment adherence, and illness insight over symptom absence alone. A cautious, individualised approach is warranted, incorporating medication effects, comorbidity, operational context, and relapse-prevention planning, supported by collaboration between mental health professionals and diving medical examiners.
30 June 2026
Read appraisal →Brain topography
Local Field Potential Recordings Using Deep Brain Stimulation: A Practical Workflow and Open-Source Signal Processing Pipeline
Deep brain stimulation (DBS) systems with electrophysiological recording capabilities offer a unique opportunity to chronically record local field potentials (LFP) from implanted electrodes, enabling real-time monitoring of subcortical neural dynamics and the development of adaptive neuromodulation strategies. However, translating raw LFP into clinically relevant biomarkers remains challenging due to numerous technical considerations. To address this, we share both a practical guide for conducting LFP experiments and BrainWave-DBS, an open-source pipeline that implements the workflow and supports analysis of patient-derived LFP data. The pipeline covers data import, preprocessing, spectral analysis, and visualization, offering a reproducible framework for LFP analysis. Furthermore, we demonstrate the functionality of the pipeline using a representative Parkinson's disease dataset and highlight key technical insights gained through this work. By improving access to neural signal acquisition and analysis, our goal is to foster broader adoption of this technology, which could advance closed-loop neuromodulation of deep brain structures.
29 June 2026
Read appraisal →Biomedical physics & engineering express
Challenges and strategies in fusion of probes and imaging equipment for neural imaging
Recent years have seen significant progress in imaging technology, which has deepened our understanding of the brain. However, challenges remain in detecting nanoscale structures like the extracellular space and mapping complex neural networks. Various chemical probes have been developed to improve the imaging precision of microstructures and functional imaging. Yet, the integration of imaging technologies and probes stays underdeveloped. Differences in disciplinary paradigms, unclear quantitative and qualitative relationships, and a lack of optimized cross-functional orchestration are the main causes of this problem. In this paper, we propose potential strategies to achieve deep integration of probes and equipment for high-quality imaging. Specifically, we advocate for three main approaches: (1) designing imaging sequences driven by probe properties, (2) constructing quantitative dose-effect models, and (3) leveraging artificial intelligence for system optimization. Importantly, these strategies establish an adaptable framework that will facilitate the seamless integration of emerging probe and equipment technologies in future brain science research. Ultimately, this enhanced integration promises to advance our knowledge of brain function and structure.
29 June 2026
Read appraisal →Journal of neuro-oncology
Endovascular treatment of tumor-related dural sinus stenosis, venous outflow obstruction, and secondary intracranial hypertension: a systematic review of the literature.
INTRODUCTION: Tumors involving dural venous sinuses can lead to venous outflow obstruction and secondary intracranial hypertension. While surgical resection can decompress the sinus, such procedures carry significant risks. With the success of endovascular interventions for idiopathic intracranial hypertension (IIH), venous sinus stenting (VSS) and angioplasty have emerged as potential treatments for tumor-related sinus stenosis. However, evidence remains limited and dispersed. This systematic review aimed to synthesize the available literature on the safety and efficacy of endovascular treatment for tumor-related venous sinus obstruction. METHODS: A systematic search of PubMed, Embase, and Web of Science was conducted from database inception to November 2025. Eligible studies included any report describing endovascular treatment (VSS and/or angioplasty) for tumor-related venous sinus obstruction, whether due to tumor invasion or compression, or even stenosis following surgery or radiotherapy. Data on patient demographics, tumor characteristics, procedural details, and outcomes were extracted and summarized descriptively. RESULTS: Twenty studies comprising 57 patients were included. Most patients were female (65%) with a mean age of 48 years. Meningioma represented the most common underlying tumor (75%). The majority of obstructions resulted from tumor compression or invasion (86%), and most patients underwent VSS alone (74%), while the rest underwent VSS and angioplasty (23%). No periprocedural complications were reported. Mean trans-stenotic pressure gradient decreased by 10 ± 9 mmHg post-treatment and mean lumbar opening pressure decreased by 11 ± 12 cmH2O. Clinical improvement was reported in 63% of patients, including resolution or improvement of papilledema in 94% and vision changes in 94%. Repeat intervention was required in 28% of cases, and only about half of patients achieved further improvement after reintervention. CONCLUSION: Endovascular therapy for tumor-related venous sinus stenosis appears safe and effective, leading to meaningful pressure reduction and symptomatic improvement in two-thirds of patients. These findings support VSS as a viable adjunct or alternative to surgery in selected patients. Nonetheless, the certainty of evidence remains very low, and current data cannot establish definitive conclusions.
28 June 2026
Read appraisal →PloS one
Hybrid deep learning for mental workload classification using EEG with enhanced preprocessing and interpretability
Mental workload classification is critical in safety-sensitive fields such as healthcare and aviation. However, electroencephalography-based approaches still face challenges with generalizability, noise robustness, and interpretability. In this study, we propose an integrated hybrid deep learning framework to address these limitations and enable robust, interpretable electroencephalography-based mental workload classification. The proposed approach uses a Variational Autoencoder to enhance noise reduction and feature extraction from band-wise topographical videos, a Convolutional Block Attention Module to adaptively focus on important spatial-channel Electroencephalogram features, and a Bidirectional Long Short-Term Memory network to capture complex temporal dependencies under leave-one-subject-out cross-validation. We conducted ablation studies to identify each architecture component's contribution and sensitivity analyses to determine the optimal parameters. The model achieved the highest overall accuracy among the baselines and reached an average accuracy of 83.9% across subjects for classifying four mental workload levels. Ablation studies confirmed the added value of all three architecture components for improving performance. Sensitivity analyses identified optimal parameters, including a 10-second window length that balances temporal context and specificity. For better interpretability and neurophysiological insight, we used Gradient-weighted Class Activation Mapping to visualize key frontal-parietal brain regions and frequency bands associated with workload dynamics. Future research could explore adaptive windowing strategies, multimodal data integration, cross-dataset benchmarking, and evaluation against transformer-based and graph neural network architectures under consistent subject-independent evaluation settings to further enhance model generalizability.
28 June 2026
Read appraisal →Brain impairment : a multidisciplinary journal of the Australian Society for the Study of Brain Impairment
Rehabilitation technologies perceived positively by patients and facilitated intensive therapy within sessions.
BACKGROUND: Australian inpatient rehabilitation facilities are increasingly integrating rehabilitation technologies to support patient recovery. However, little is known about patient perceptions of rehabilitation technologies and the intensity of therapy able to be facilitated. This study aimed to quantify the amount of practice being achieved by patients in an Australian inpatient rehabilitation setting and explore their perceptions of upper limb rehabilitation technologies. METHODS: A multimethod study design was used to investigate the perceptions and use of rehabilitation technologies (i.e. InMotion, Fourier ArmMotus, SaeboReJoyce and Touch Accessible Platform for Interactive Technology) by adult inpatients. Semi-structured interviews and the System Usability Scale were completed. Observations of routine therapy sessions were undertaken to quantify the amount and intensity of technology-based practice. RESULTS: Ten participants were recruited (50% male, mean age 62 years). A total of 15 sessions were observed across all participants using robot-assisted therapy and Touch Accessible Platform for Interactive Technology at an average duration of 35 min/session; of which 29 min was active therapy. Most participants described their experiences with rehabilitation technologies positively, highlighting benefits for recovery, enjoyability and easy usability. Participants rated the technologies highly on the System Usability Scale, with total scores ranging between 65 and 100 for usability, and a minimum rating of 'good' for user-friendliness. CONCLUSION: Rehabilitation technologies were well received by adults undergoing inpatient rehabilitation and with application for a range of impairments. The technologies facilitated enjoyment of daily therapy, and participants were comfortable with limited therapist input when using the devices. Additionally, participants were able to engage in highly intensive practice using the technologies.
27 June 2026
Read appraisal →Science advances
AI-driven tripartite classification for optimizing wearable bioelectronics in depression management
Current disease-sensing devices primarily focus on distinguishing between healthy and diseased states, effective for diagnosis but limited in guiding optimal intervention timing for prevention. We developed a tripartite framework identifying pre-disease state in depression, a reversible phase preceding irreversible onset. Using complex systems theory, we analyzed early-warning signals emerging as biological systems approach critical transitions. Continuous monitoring of nine multimodal biomarkers-spanning electrophysiological, behavioral, and biological-enabled classification into normal, pre-disease, and disease states by quantitatively defining critical points. An artificial intelligence agent classified disease states with 95.2% accuracy using multimodal data, enabled by ultrasoft neural probes for stable, low-damage recordings. Therapeutic validation with a skin-attachable wireless vagus nerve stimulator integrating soft three-dimensional electrodes demonstrated superior efficacy during pre-disease states. Subjects treated during pre-disease showed faster recovery and greater therapeutic responses, while those treated after disease onset failed to achieve full recovery. This framework provides evidence-based rationale for early intervention.
27 June 2026
Read appraisal →Brain impairment : a multidisciplinary journal of the Australian Society for the Study of Brain Impairment
Validating telehealth assessment of physical concussion symptoms
BACKGROUND: Telehealth could expand access to specialised concussion care; however, physiotherapists report low confidence in assessing physical symptoms remotely. This study adapted measures of vestibular, oculomotor, cervical and autonomic function for telehealth delivery, and evaluated their feasibility and comparability with in-person assessment. METHODS: Using a repeated-measures design, 16 participants with concussion and 20 controls completed assessment modalities in random order (3 × 4 block randomisation). Feasibility was evaluated through completion rates, technical issues, adverse events and clinician feedback. Concurrent validity was examined using percentage agreement, Cohen's kappa, and sensitivity and specificity analyses. RESULTS: Telehealth assessment was feasible; most concussion participants and all controls completed the protocols, there were minimal technical issues, no adverse events and shorter administration times. Clinicians reported high usability and efficiency. High agreement and sensitivity were observed across vestibular, ocular convergence, autonomic and cervical range of motion measures. Nuanced assessment of ocular smooth pursuits showed lower agreement, and a potential ceiling effect on autonomic assessment was identified. CONCLUSION: This study preliminarily supports the feasibility and comparability of telehealth compared with in-person physical concussion assessments. Assessment of smooth pursuits and autonomic function require protocol refinement to enhance accuracy. Replication in larger samples is required to establish comparability.
27 June 2026
Read appraisal →Biomedical physics & engineering express
Intelligent integrated system for dysphagia rehabilitation and assessment
Current dysphagia assessment suffers from subjectivity and reliance on complex clinical tools, while rehabilitation lacks objective feedback. The critical mechanism of respiration-swallowing coordination (RSC) for preventing aspiration is often overlooked in existing monitoring systems. This study aimed to develop and validate an intelligent integrated system for dysphagia rehabilitation and that provides a closed-loop solution for quantitative assessment and personalized rehabilitation. The system's core innovation is the tight integration of a wireless wearable hardware subsystem for synchronous swallowing sound and respiratory waveform acquisition with an intelligent software subsystem. The software incorporates novel algorithms for automated swallowing identification, respiratory cycle analysis, and real-time classification of RSC patterns into aspiration risk levels. This enables an interactive platform that closes the loop between assessment, visual feedback, and personalized audiovisual guidance for rehabilitation training. Validation included hardware performance tests and a clinical trial with 16 participants (8 healthy participants and 8 participants with dysphagia). The hardware demonstrated reliable performance, achieving a robust swallowing sound SNR (14.07 dB in a noisy environment) and showing acceptable temporal consistency in the respiratory waveform. On a 0-100 scale, the system significantly differentiated swallowing function between healthy (82.72) and dysphagic (57.25) subjects (p< 0.01). We also conducted an exploratory experiment in two dysphagic participants using audiovisual guided training. A preliminary upward trend in swallowing-coordination scores was observed after training, suggesting a potential short-term training effect of the audiovisual guidance module. The integrated system represents an innovation by seamlessly merging wearable sensing with intelligent, coordination-focused algorithms into a unified closed-loop platform. It offers a practical and effective solution for transforming dysphagia management towards objective, personalized, and intelligent rehabilitation.
27 June 2026
Read appraisal →Scientific reports
Multimodal emotion recognition using hybrid deep feature fusion under speaker-independent evaluation
Emotion recognition is one of the most important and complex challenges for machines to understand, as most robots and AI agents struggle with human-centric perception and interpretation. Therefore, this paper introduces a novel multimodal emotion recognition system that analyzes emotions through two complementary channels: voice and facial expressions. The proposed approach is evaluated on the RAVDESS and CREMA-D datasets, which consist of acted emotional expressions across multiple discrete emotion categories. Utilizing an advanced multimodal deep feature fusion technique, the system combines handcrafted audio features (e.g., Mel-Frequency Cepstral Coefficients (MFCCs)) with deep visual features extracted from an attention-based VGGFace model. These features are integrated into a unified representation through a hybrid fusion strategy that jointly employs concatenation, cross-attention, gated fusion, and multiplicative fusion mechanisms to capture complementary cross-modal interactions. To ensure a comprehensive and realistic assessment, the model is evaluated under both random-split and strict speaker-independent protocols. On the RAVDESS dataset, the proposed system achieves an accuracy of 95.83% under random-split evaluation and 48.06% ± 9.76% accuracy under speaker-independent Leave-One-Speaker-Out (LOSO) testing, while on the CREMA-D dataset it attains 73.54% accuracy using random splits and 53.12% ± 2.65% accuracy under subject-exclusive speaker-independent 5-fold cross-validation.
27 June 2026
Read appraisal →Genome biology
MLMarker: a machine learning framework for tissue inference and biomarker discovery
MLMarker is a machine learning tool that computes continuous tissue similarity scores for proteomics data, addressing the challenge of interpreting complex or sparse datasets. Trained on 34 healthy tissues, its Random Forest model generates probabilistic predictions with SHAP-based protein-level explanations. A penalty factor corrects for missing proteins, improving robustness for low-coverage samples. Across three public datasets, MLMarker revealed brain-like signatures in cerebral melanoma metastases, achieved high accuracy in a pan-cancer cohort, and identified brain and pituitary origins in biofluids. MLMarker provides an interpretable framework for tissue inference and hypothesis generation, available as a Python package and Streamlit app.
26 June 2026
Read appraisal →Journal of medical Internet research
Explainable and Interpretable AI for Voice and Speech Analysis in Clinical Care: Systematic Review
BACKGROUND: Driven by recent advances in artificial intelligence (AI), particularly in medicine, audio-based voice and speech biomarkers are increasingly investigated for various medical applications as a complementary or even alternative modality to traditional medical devices. The adoption of deep learning techniques in recent literature is motivated by their superior performance compared to classical machine learning methods. However, ethical and regulatory concerns regarding the black-box nature of these models have limited their integration into clinical workflows. Consequently, explainable artificial intelligence (XAI) has recently been used to address this issue by generating explanations for opaque model outputs. Ideally, medical XAI systems aim to provide human-understandable, clinically grounded explanations essential for enhanced AI trustworthiness and, thereby, facilitate adoption into real-world clinical settings. OBJECTIVE: We conduct a systematic literature review of XAI methods applied for explaining deep learning techniques in audio-based voice and speech clinical applications. We aim to identify what XAI methods have been used to explain the decisions of deep learning voice and speech AI systems in health care, as well as XAI-informed insights. Additionally, we aim to contextualize these findings with respect to clinical applicability and stakeholder relevance. Lastly, we identify opportunities and recommendations for future clinical audio XAI design. METHODS: We used PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). Six electronic databases (IEEE Xplore, ACM Digital Library, Scopus, PubMed, Web of Science, and Nature) were searched for papers published between January 2015 and February 2025. Eligible studies applied explainability or interpretability methods to deep learning models for voice or speech audio in health care contexts. Risk of bias was assessed using PROBAST+AI (Prediction Model Risk of Bias Assessment Tool). The results were thematically synthesized across explainability categories, input representations, clinical domains, validation strategies, and stakeholder considerations. RESULTS: A total of 30 studies met the inclusion criteria. These studies used a range of explainability approaches, including gradient-based methods, perturbation-based techniques, surrogate model-based methods, model-internal representation analyses, concept-based detectors, and attention-based explanations. Applications spanned diverse clinical domains, including voice disorders, neurodegenerative diseases, psychiatric conditions, and traumatic brain injury. Overall, results indicate that most studies relied primarily on qualitative interpretation of explainability outputs, with limited quantitative validation of explanation consistency across external datasets. Furthermore, none of the included studies explicitly conducted human-in-the-loop evaluations with relevant stakeholders, highlighting a substantial gap in stakeholder alignment. CONCLUSIONS: Current XAI practices in clinical voice and speech analysis are limited by insufficient validation, lack of domain-specific design, and misalignment with clinical stakeholder needs. This review highlights opportunities for developing validated, audio-aware, and stakeholder-centered XAI approaches to support trustworthy clinical deployment. Interpretation of these findings should consider limitations related to single-reviewer study selection, potential high-risk of bias, and the repeated use of benchmark datasets.
25 June 2026
Read appraisal →Journal of medical Internet research
Vision-Based Artificial Intelligence Technologies for Epilepsy Monitoring: Scoping Review and Taxonomy Development Study
BACKGROUND: Artificial intelligence (AI) technologies for vision-based epilepsy monitoring are advancing rapidly in health care. Despite growing research using various video data sources and analytical approaches, no comprehensive framework exists to classify these technologies. OBJECTIVE: This scoping review aimed to develop and validate a taxonomy for AI technologies in vision-based epilepsy monitoring and to characterize visual AI approaches in epilepsy care. METHODS: Using an extended taxonomy development framework, we developed the taxonomy in 5 iterative cycles, drawing on theory and practice. We conducted a scoping review, market analysis, and applicability evaluation with market-ready solutions. We searched Scopus, Web of Science, and PubMed, including MeSH (Medical Subject Headings) terms; the final search was completed on January 16, 2026. We included primary studies from 2013 onward on AI-based or machine learning-based monitoring or prediction of epileptic seizures in humans using visual data. We excluded reviews, non-English publications, nonepilepsy studies, studies focused only on electroencephalography or wearables, animal studies, and pre-2013 publications. Evidence was charted through narrative and tabular synthesis and descriptive frequency analysis. In line with scoping review guidance, we did not conduct a meta-analysis or critical appraisal. To assess validity and practical relevance, 9 domain experts evaluated the taxonomy using a Delphi technique. RESULTS: We included 40 original studies. Study analysis yielded 16 dimensions, including data acquisition source, tracking target, image processing, classifier type, performance metrics, environment, seizure classification, data privacy, and user interface. Expert feedback added 4 further dimensions, including communication mode and information purpose. The final taxonomy comprises 23 dimensions with 102 characteristics. The review identified structural evidence gaps across settings, evaluation maturity, and reporting practices. Detection and classification in stationary settings predominated, whereas predictive approaches and real-time feedback were limited. Deep learning detection methods were common, but performance reporting was inconsistent, and patient-facing functionalities were limited. Privacy safeguards and standardized metrics were often incompletely reported, reducing comparability and maturity assessment. The taxonomy translates these patterns into guidance for benchmarking, procurement evaluation, user interface, and explainable AI design. We synthesized 5 main findings and 10 implications for research and practice. Key challenges concern standardization, seizure prediction, and real-time applicability. CONCLUSIONS: Vision-based AI technologies for epilepsy monitoring are still dominated by proof-of-concept and pilot evaluations, indicating a gap between technical feasibility and deployment-ready systems. This scoping review presents an implementation-oriented taxonomy integrating application context, system architecture, visual analysis, AI models, performance reporting, and feedback design into a single classification framework. Unlike prior work that mainly maps methods or data sources, the taxonomy provides a shared structure for consistent system-level characterization and comparison across studies and emerging solutions. It may support benchmarking, implementation-focused evaluation, procurement, and translation into clinical and home settings.
25 June 2026
Read appraisal →Reviews in the neurosciences
Probiotics and seizure susceptibility in preclinical epilepsy models: mechanistic insights from a systematic review and meta-analysis
The gut microbiota can affect neuronal excitability, inflammation, and oxidative balance via the gut-brain axis, shaping seizure susceptibility. To translate these findings into clinical approaches, a synthesis of preclinical microbiome-based evidence is needed. This systematic review and meta-analysis examined the putative anticonvulsant, anti-inflammatory, antioxidant, and neuroprotective effects of probiotics in rodent models. An extensive systematic search up to July 2025 identified eligible animal studies in which probiotics were administered in seizure models. Reported outcomes included seizure latency, duration, severity, and frequency, as well as inflammation, oxidative stress, and behavioral measures. Of the 24 studies that met the inclusion criteria, 19 provided sufficient data to be included in the meta-analysis. The most frequently used strains belonged to the Lactobacillus (e.g., acidophilus, casei, fermentum) and Bifidobacterium genera (e.g., bifidum, longum), with occasional synbiotic combinations. Probiotics significantly increased seizure latency (MD = 22.09; 95 % CI: 10.52 to 33.67) and reduced seizure severity (MD = -1.08; 95 % CI: -1.39 to -0.76) and duration (MD = -23.19; 95 % CI: -35.56 to -10.82). Probiotics significantly reduced IL-1β, IL-6, and TNF-α levels while MDA showed a nonsignificant trend toward reduction (p = 0.076). Behaviorally, improvements in spatial learning (p < 0.05) and reduced anxiety-like behavior (p < 0.001) were observed. Probiotic supplementation appears to exert anticonvulsant, anti-inflammatory, antioxidant, and behavioral benefits in preclinical epilepsy models, although the evidence is heterogeneous and limited to animal studies. Mechanistic evidence indicates modulation of the gut-brain axis, enhanced GABAergic signaling, and improved mitochondrial function. These findings support further investigation of specific probiotic formulations as promising adjunct candidates in well-designed, mechanism-driven clinical trials.
25 June 2026
Read appraisal →ACS applied materials & interfaces
Biodegradable Electrohydraulic Actuators for Wearable Haptic Interfaces
Biodegradable soft actuators have gained significant attention due to their inherent conformability, adaptability, and ability to safely interact with humans, while substantially reducing long-term environmental waste. Although various biodegradable materials have been explored for fabricating soft devices and their electrical interfaces, many still suffer from limited durability and operational stability under ambient conditions. In particular, biodegradable electrohydraulic actuators often exhibit a performance degradation over time. In this work, we systematically evaluate the influence of various biodegradable materials, including compliant electrodes, dielectric films, and dielectric fluids, on actuator performance. The optimal material combination was identified and used to fabricate a biodegradable actuator that demonstrated stable operation over 10,000 cycles with no noticeable variation in actuation strain. Under industrial composting conditions, the device underwent degradation within 50 days, supporting its environmental sustainability. Furthermore, a wrist-worn biodegradable haptic device is demonstrated that is capable of generating diverse and salient tactile sensations, highlighting its potential applications in assistive technology and virtual/augmented reality.
25 June 2026
Read appraisal →Journal of medical economics
From options to decisions: an innovative model for treatment sequencing in relapsing-remitting multiple sclerosis
AIMS: With the expanding range of disease-modifying therapies (DMTs) for relapsing-remitting multiple sclerosis (RRMS), clinicians face increasing complexity in defining optimal treatment sequences and timing of therapy switches. In this study, we adapted an earlier computer-assisted model to optimize therapeutic decisions and identify preferred treatment pathways, based on expert opinion and observations from clinical practice in Spain. MATERIALS AND METHODS: The original model was updated to integrate magnetic resonance imaging (MRI) activity data and define reaching an Expanded Disability Status Scale (EDSS) score of 3 - indicating moderate disability without ambulation impairment - as a criterion for switching treatment. Matrices were designed to model options when switching DMTs, triggered by either a lack of effectiveness or safety concerns. Change in DMT was based on a set of composite criteria (encompassing contributions from relapses, disability worsening, MRI activity, costs, and quality of life) according to the disease activity level. A maximum of three DMTs could be administered within the 8-year time horizon evaluated. RESULTS: The revised model identified high efficacy treatment, in particular cladribine tablets as the preferred initial DMT for patients with RRMS with mild or moderate disease activity. Of these patients with mild and moderate disease activity, most were switched to ofatumumab (79.4%) and ocrelizumab (75.9%), respectively, when disease progression occurred. Patients with high disease activity mostly received natalizumab if they were John Cunningham virus (JCV)-negative, or ocrelizumab if they were JCV-positive. LIMITATIONS: The model is informed by a Spain-based healthcare expert panel and has been evaluated using a simulated cohort of 10,000 patients with RRMS. CONCLUSION: This computational model helps to inform clinicians towards making optimal treatment decisions for RRMS, and identified high-efficacy DMTs as the preferred model-based option for all levels of disease activity, supporting early and effective control of disease activity in real-world clinical practice.
25 June 2026
Read appraisal →Biomedizinische Technik. Biomedical engineering
Arm support as hidden assistance during Sit-To-Stand movement: a systematic review
Sit-To-Stand (SitTS) training is often performed by patients who may require arm assistance to perform it successfully. This study examines the role of arms in performing the SitTS maneuver to better understand their contribution and clinical significance. This review synthesizes the reported outcome measures of arm-assisted SitTS exercise programs in people with physical impairments. Web of Science (WoS) and Scopus™ databases were searched for relevant studies up to May 2025. Inclusion and exclusion criteria were used to screen the titles and abstracts of articles identified using key search terms related to SitTS activity and arm support. Only articles that met the key terms were extracted and analyzed in this study. A total of 47 studies reported arm support measures during SitTS, either in clinical or biomechanical contexts. These studies highlighted various methods and tools used to assess the body's state during the rising movement. Joint angle and SitTS duration are clinically applicable and more easily measured compared to seat, foot, and arm forces which require specialized instrumentation. Balancing clinical measures with detailed biomechanical assessments is important in enabling practitioners and researchers to evaluate and enhance SitTS interventions that involve arm assistance, ultimately improving rehabilitation outcomes.
25 June 2026
Read appraisal →Journal of neurology
Exercise- and dietary pattern-based lifestyle interventions and neurocognitive function in adults: a Bayesian multilevel network and dose-response meta-analysis
OBJECTIVE: To systematically review the effects of lifestyle interventions focused on exercise and dietary patterns on neurocognitive function in adults, and to examine their relative effects and dose-response relationships. METHODS: PubMed, Embase, Web of Science, and the Cochrane Library were searched from database inception to March 20, 2026. A total of 23 randomized controlled trials (RCTs) involving 12,286 participants were included. Bayesian multilevel pairwise, network, and dose-response meta-analyses were conducted to evaluate overall cognition, memory, and executive function. RoB 2 and CINeMA were used to assess the credibility of the evidence. RESULTS: Combined interventions yielded small, but statistically significant cognitive benefits (overall cognition: SMD = 0.18; memory: SMD = 0.10; executive function: SMD = 0.09). These benefits were driven primarily by differences between the combined intervention and control or usual care, as well as exercise alone; compared with diet alone, the combined intervention still showed a small advantage for executive function. Network meta-analysis suggested that AE_MIND and AE_RT_MED were more beneficial for overall cognition, AE_RT_MIND and AE_RT_MED for memory, and AE_RT_CR and AE_MIND for executive function. Exploratory dose-response analyses suggested possible curvature in some spline-based models; however, comparison with simpler monotonic specifications did not provide robust evidence for a clinically interpretable non-linear or monotonic dose-response relationship. CONCLUSION: Combined lifestyle interventions may produce statistically significant but modest improvements in cognitive function in adults, with benefits varying across cognitive domains. However, current evidence remains insufficient to support a standardized precision prescription, and high-quality RCTs with long-term follow-up are needed to determine the optimal combinations and dosages.
25 June 2026
Read appraisal →JMIR medical informatics
Computational Insights Into Smart Bioelectronics in Digital Health Care (2020-2024): Topic Modeling Study
BACKGROUND: Smart bioelectronics are electronic medical devices that combine hardware and artificial intelligence (AI)-based software. These convergent medical devices analyze bio-signals measured through hardware using AI algorithms and deliver physical stimulation to enhance therapeutic effects. OBJECTIVE: This study aimed to systematically analyze recent research trends in smart bioelectronics to understand their evolving role in digital health care and to provide evidence-based insights for shaping future research and development strategies. METHODS: A total of 92 publications indexed in PubMed between 2020 and 2024 were analyzed. Latent Dirichlet allocation-based topic modeling, optimized using coherence scores, was applied to identify latent research themes. RESULTS: The results indicate a steady increase in related research over the past 5 years, along with a clear shift in research focus from bio-signal sensing and bioelectronic device materials toward AI-driven analysis and disease-oriented applications, ultimately evolving into intelligent and adaptive bioelectronic therapeutic systems. Three major research topics were identified: bio-signal-based neuromodulation (n=23, 25%), AI-driven neurological disease analysis (n=32, 34.7%), and implantable bioelectronics and biomaterials (n=37, 40.2%). CONCLUSIONS: By mapping the evolving landscape of smart bioelectronics, this study provides valuable insights into their multidisciplinary development and highlights their potential applications in clinical decision support, personalized rehabilitation, and next-generation medical device innovation.
24 June 2026
Read appraisal →Lasers in medical science
Effect of photobiomodulation on pain and quality of life in fibromyalgia syndrome: a systematic review
Fibromyalgia Syndrome (FMS) is a chronic pain disorder characterized by widespread pain and central sensitization that significantly impacts the quality of life (QoL). For management to be effective, a multidisciplinary approach to care is typically required. Photobiomodulation therapy (PBMT), a non-pharmacological treatment, has garnered attention lately, though its clinical relevance and applications are not well defined. The objective of this review was to assess the effectiveness of PBMT in reducing FMS symptoms. This systematic review was registered at PROSPERO (CRD420251084730) and conducted following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) reporting guidelines. A total of seven randomized controlled trials were identified following an extensive literature search across various databases, including PubMed, Scopus, Web of Science, the Cochrane Library, Embase, Ovid and ProQuest. To evaluate the methodological quality of these studies, the Cochrane Risk of Bias (RoB 2.0) tool was applied. PBMT demonstrated consistent short-term reductions in pain intensity and improvements in QoL. Additional positive effects on sleep quality and psychological well-being were observed, indicating that PBMT may provide additional therapeutic benefits beyond pain reduction, including improvements in sleep quality and psychological well-being. PBMT has shown promise as a safe, non-pharmacological adjunct therapy that may provide short-term improvements in pain levels and QoL, but substantial heterogeneity limits generalizability. Clinical trials with large samples and standardized methodologies should be conducted to better clarify the role of PBMT in multidisciplinary therapy for FMS.
24 June 2026
Read appraisal →BMJ global health
Lifestyle interventions for dementia prevention in low- and middle-income countries: a systematic review.
INTRODUCTION: By 2050, two-thirds of people with dementia will live in low-and-middle-income countries (LMICs). However, multimodal prevention lifestyle interventions for people at risk are being developed predominantly in higher-income countries. METHODS: We systematically reviewed randomised controlled trials (RCTs) evaluating non-pharmacological interventions in individuals with mild cognitive impairment and subjective cognitive decline in LMICs. We assessed quality using the Mixed Methods Assessment Tool, meta-analysed and synthesised evidence. RESULTS: We included 25 RCTs from six countries (most in China, n=17), involving 1304 participants. In the 15 studies with sufficient data to meta-analyse, we found significant positive effects on cognition favouring interventions (1.49 (standardised mean difference, 95% CI 1.06 to 1.93)). There was significant publication bias. We classified interventions into exercise, multidomain and arts/creative expression. Exercise (1.67, 95% CI 1.24 to 2.11, n=8) and multidomain (1.22, 95% CI 0.22 to 2.21, n=5) had replicated evidence of effectiveness. There was insufficient data to meta-analyse the arts/creative category. CONCLUSION: We propose greater consideration and investment in the development of interventions accounting for specific LMIC contexts from the outset, so they are acceptable and used by local services. PROSPERO REGISTRATION NUMBER: CRD42023403908.
24 June 2026
Read appraisal →Journal of neural engineering
Recalibration of implantable brain-computer interfaces to enable long-term independent use - a systematic review
Background.Implantable brain-computer interfaces (iBCIs) decode neural signals to generate command signals for effector devices to restore lost functions, such as movement or speech. However, maintaining device performance over time requires recalibration of decoding algorithms due to inherent instability in neural signals.Objective.To systematically review recalibration procedures in iBCIs for patients with motor impairments, focusing on the clinical implications of recalibration requirements and strategies which can enable long-term, independent use.Approach.A systematic search was conducted across EMBASE, MEDLINE, and CINAHL databases to identify studies involving recalibration of iBCIs. Data on recalibration frequency, duration, staff requirements, and location were extracted and analyzed.Main results.Recalibration practices varied widely amongst studies and were typically performed according to predetermined study protocols, rather than practical need following deteriorating device performance. Common practices include manual recalibration requiring a specialist research team, semi-automatic recalibration which could be performed by a non-specialist caregiver, and automatic recalibration methods whereby patients did not require assistance. Devices utilizing electrocorticography (ECoG) recording arrays generally required less frequent recalibration compared to those using microelectrode arrays (MEAs). Extended independent use was more frequently reported with ECoG-based iBCIs.Significance.Reducing recalibration frequency or complexity can improve patient autonomy, which is crucial for enhancing long-term independent iBCI use in home and clinical settings. ECoG iBCIs typically have a low recalibration burden due to inherent signal stability. Conversely, MEA iBCIs typically involve a higher recalibration burden, though recent studies have reduced this by incorporating spectral data and continuously updating models. Despite this progress, recalibration procedures are often not fully defined in iBCI studies, and where they are, they usually relate to the study protocol rather than the clinically meaningful recalibration requirement due to worsening device performance. Future studies should continue to develop user-friendly recalibration procedures and outline the clinically relevant recalibration requirements where possible.
24 June 2026
Read appraisal →PloS one
Applications of electromyography in Amyotrophic Lateral Sclerosis: A systematic review
This systematic review examined the use of surface electromyography (sEMG) for the neuromuscular assessment of individuals with Amyotrophic Lateral Sclerosis (ALS), focusing on clinical parameters, the muscle groups evaluated, acquisition protocols, technical properties of the recording systems, integration with other technologies, and signal processing strategies. We included observational studies that applied sEMG to individuals diagnosed with ALS, with or without comparison to healthy controls, and without restrictions on publication year. The analyses included signals recorded at rest and during voluntary contractions, with or without the use of biofeedback. Most studies employed conventional or high-density surface electrodes, with sampling frequencies ranging from 500 Hz to 3000 Hz. The results showed that the primary parameters assessed were muscle fatigue, fasciculation patterns, the number of motor units (MUNE/MUNIX), motor unit firing rates, and signal complexity. These parameters demonstrated sensitivity to disease progression and may contribute to early diagnosis, phenotypic stratification, and functional monitoring of ALS. Additionally, the studies highlighted the increasing use of advanced computational approaches, such as machine learning, for feature extraction and automated classification. In conclusion, sEMG is a promising tool for functional assessment in ALS, with the potential to improve diagnostic accuracy and support new therapeutic strategies based on electrophysiological biomarkers. However, despite technological advances, the included studies displayed substantial methodological heterogeneity and limited protocol standardization. Integration with other neurophysiological modalities also remains underexplored, despite its significant clinical potential.
23 June 2026
Read appraisal →Journal of robotic surgery
Robot-Assisted versus non-robotic frame-based deep brain stimulation: a systematic review and meta-analysis of targeting accuracy and clinical outcomes
Deep brain stimulation (DBS) outcomes depend on accurate lead placement. Robot-assisted DBS (RA-DBS) has been increasingly adopted, yet its advantages over conventional non-robotic frame-based DBS (NRA-DBS) regarding targeting accuracy, operative efficiency, and clinical outcomes remain debated.To compare RA-DBS versus NRA-DBS for targeting accuracy and clinical outcomes in patients undergoing DBS for movement disorders.This meta-analysis and systematic review was registered in PROSPERO (CRD420261336001) and adhered to PRISMA. Patients with mobility disorders treated with RA-DBS versus frame-based NRA-DBS were included in comparative studies (RCTs and prospective/retrospective cohorts). Targeting accuracy measures (such as target point error, vector error, and radial error) and extreme targeting outliers were the main results. Operative time measurements, perioperative problems (such as cerebral bleeding), and motor improvement (UPDRS-III) were secondary outcomes. Fixed-effects or random-effects models based on heterogeneity were used to compute pooled odds ratios (OR) and weighted mean differences (MD) with 95% confidence intervals (CI). Nine high-quality comparative studies encompassing 495 patients (227 RA-DBS vs. 268 NRA-DBS) were synthesized. RA-DBS significantly minimized target point error (MD = -0.31 mm, 95% CI: -0.61 to -0.01; P = 0.04) and significantly reduced the incidence of clinically significant targeting outliers (OR = 0.15, 95% CI: 0.04 to 0.51; P = 0.002). Comprehensive three-dimensional vector error was marginally lower in the RA-DBS cohort (MD = -0.70 mm; P = 0.05), whereas two-dimensional radial error and total operating room time, surgical duration, anesthesia time, perioperative complications, and 12-month UPDRS-III motor improvements were statistically equivalent between groups. Subgroup analyses revealed that the spatial precision advantage of RA-DBS was geographically robust across East Asian and Western cohorts, whereas chronological efficiency was heavily dependent on the specific robotic platform used.Compared with conventional frame-based stereotaxy, robot-assisted DBS significantly enhances target point precision and reduces electrode deviations. However, these technical accuracy advantages do not translate into overall clinical superiority, as both approaches yield comparable operative efficiency, perioperative safety, and mid-term motor symptom relief.
23 June 2026
Read appraisal →Scientific reports
Classifying mental stress from eye tracking data: deep learning approaches for out-of-the-lab conditions
Eye-tracking signals such as pupil diameter and gaze behavior have been widely used for stress detection, yet most approaches rely on task-specific features, controlled laboratory settings, or multimodal sensor combinations, limiting scalability in less controlled environments. This work investigates whether unimodal eye-tracking time-series data can support task-agnostic stress detection beyond static laboratory tasks. We analyze stress classification across two complementary datasets: a virtual reality goalkeeper task with moderate visuomotor activity and stable recording conditions, and a virtual job interview dataset reflecting less controlled settings with uncalibrated signals. The results show that these signals alone contain informative patterns related to stress-associated autonomic and oculomotor responses. Under favorable conditions, performance reaches up to [Formula: see text] macro-averaged F1-score. At the same time, performance varies substantially across datasets, indicating that effective learning depends strongly on data quality, calibration, signal characteristics, and task design. Overall, the findings demonstrate the potential of unimodal eye tracking as a lower-burden alternative to more complex multimodal systems, while highlighting that reliable stress detection is fundamentally conditioned by the interplay of data, signal representation, and modeling approach.
23 June 2026
Read appraisal →Bioinspiration & biomimetics
A bioinspired hybrid robotic joint for safer physical human-robot interaction
In many robotic applications, such as humanoids, wearable robots or supernumerary limbs, there is a growing shift from rigid, traditional mechanisms toward softer, more compliant systems. This trend is driven by the need for safer physical human-robot interaction and the ability to operate in unstructured environments. In this work, we present a hybrid approach to controlling a single-degree-of-freedom robotic joint that combines a rigid frame with soft pneumatic actuators to enable both precise and versatile interaction. The joint is bioinspired, as it is powered by a couple of actuators mounted in an antagonist configuration, such as the human elbow. A key innovation is the design of pneumatic artificial muscles using thermoplastic polyurethane, which achieve high isometric force (400 N at 240 kPa), significant stretchability (80 mm), and low density(0.3gcm-3), making them competitive with state-of-the-art alternatives. Two model-free controllers were developed to independently regulate joint position and stiffness. Angle control achieved high precision (<2∘RMSE) with minimal overshoot (<1%) and fast response (rise time<1.3s). Stiffness control modulated joint compliance across a range of 0.054-0.076 Nm deg-1, with the expected trade-off of reduced workspace. A final proof-of-concept demonstrated the concurrent use of both controllers to modulate the joint's dynamic behavior in response to external disturbances. While future work will address multi-DOF coordination and wearable integration, this study represents a foundational step toward safe, adaptable robotic actuation through the combination of rigid structures and soft actuation.
21 June 2026
Read appraisal →Science advances
Bioinspired heliconical auxetic biofibers for intelligent biomechanical surveillance
Wearable bio-based fibers are emerging as platforms for biomechanical sensing and physiological interaction. However, achieving high sensitivity to subtle mechanical cues while maintaining flexibility, self-powered output, and distributed perception remains challenging. Inspired by the helical twining mechanics of climbing plant stems, we design a mechano-adaptive auxetic electronic bio-based fiber as an embodied intelligence sensing unit. The dual-modulus helical confinement, achieved by wrapping rigid aramid fiber/polydimethylsiloxane helices around a flexible collagen aggregate/waterborne polyurethane core, enables programmable deformation and a stable negative Poisson's ratio (ν = -0.47). Nonlinear intercomponent coupling facilitates synergistic axial-radial dynamics, amplifying interfacial contact variation under strain. The optimized fiber exhibits ultrahigh sensitivity (strain factor: 11.75), strong electrical output (8.1 volts), and remarkable power density (8.48 milliwatts per square meter) with excellent cyclic stability. Integrated into fiber-sensing arrays, it decodes microstrain patterns linked to lower-limb function, offering a scalable strategy for self-powered, adaptive, and durable biomechanical monitoring.
21 June 2026
Read appraisal →The journal of headache and pain
The emerging role of the meningeal lymphatic and glymphatic systems in migraine pathophysiology: a systematic review
BACKGROUND/OBJECTIVE: Migraine is a common, debilitating neurological disorder of unclear pathophysiology. Recent evidence has implicated impaired meningeal lymphatic function and its interaction with the glymphatic system in the development of neuroinflammation and pain sensitization. This systematic review aimed to summarize existing evidence on the role of the meningeal lymphatic and glymphatic systems in migraine pathophysiology. METHODS: Following the PRISMA 2020 recommendations, we searched PubMed, Web of Science, and Scopus from inception to December 15, 2025. Inclusion criteria comprised human or animal studies examining meningeal lymphatic or glymphatic function in migraine or validated migraine animal models. SYRCLE's risk of bias tool for animal studies and the Joanna Briggs Institute (JBI) critical appraisal tools were used for quality assessment. RESULTS: The search yielded a total number of 457 records, out of which 10 articles fulfilled the eligibility criteria. These comprised six imaging studies conducted on human populations and four preclinical studies performed on animal models. Studies using dynamic contrast-enhanced (DCE)-MRI have shown changes in lymphatic enhancement characteristics in both episodic and chronic migraines. Diffusion tensor imaging along perivascular spaces (DTI-ALPS) yielded heterogeneous findings, with abnormalities more consistently observed in chronic and high-frequency migraine. Preclinical models demonstrated that cortical spreading depression, nitroglycerin exposure, and familial hemiplegic migraine mutations impaired glymphatic influx and reduced cerebrospinal fluid efflux through meningeal lymphatic vessels. DISCUSSION: Initial research has shown that altered lymphatic and glymphatic systems may be related to migraine. Nonetheless, there is no existing literature on whether these conditions can be considered causative agents for migraines. There is a need for longitudinal imaging studies in a larger population. REGISTRATION: PROSPERO ID: CRD420251266296.
20 June 2026
Read appraisal →Journal of neural engineering
Uncertainty-aware multi-path framework with dynamic arbitration for Alzheimer's disease MRI classification
Objective.As a progressive neurodegenerative disorder, Alzheimer's disease (AD) requires early and accurate diagnosis to delay pathological progression and improve clinical outcomes. Recently, Deep-learning methods based on structural MRI (sMRI) have been widely used for computer-aided AD diagnosis. However, reliable deployment remains challenging due to its sample-specific heterogeneity and the lack of explicit mechanisms for handling prediction uncertainty. In addition, common test-time perturbations and augmentation-based inference strategies could introduce anatomical inconsistency in medical images, leading to unstable decisions for hard cases.Approach.Accordingly, TriPathNet with Arbiter, as an uncertainty-aware multi-path framework, is proposed in the study. The framework learns spatially complementary representations via multi-view parallel encoding with metric-guided coordination. In addition, an augmentation-aware arbitration mechanism is introduced to adaptively correct predictions for high-uncertainty samples during inference.Main results.On the ADNI dataset for the AD vs. cognitively normal task, the proposed method achieves 98.85% accuracy, 99.18% sensitivity, 97.06% specificity, and 99.61% AUC, yielding satisfactory performance.Significance.The results indicate that TriPathNet with Arbiter provides an accurate and robust solution for sMRI-based computer-aided AD diagnosis, maintaining stable performance under test-time perturbations.
20 June 2026
Read appraisal →Progress in biomedical engineering (Bristol, England)
Beyond the lab: a review on neurophysiological mental states assessment in real-world settings.
Recent advancements in wearable, non-invasive neurophysiological sensors have increased interest in applying Human factors (HF) research beyond controlled laboratory settings. HF research aims to objectively quantify individuals' (alone or working in team) mental and emotional states to ensure safety, maintain good performances and prevent risks. These devices enable real time, unobtrusive monitoring of individuals in real-world environments, overcoming the limitations of traditional subjective assessments. Unlike self-reports, neurophysiological signals provide objective, real-time data, allowing for a more accurate and continuous understanding of mental states. This review examines the latest advancements over the past ten years in real-time monitoring of the most studied and operationally relevant mental states, including mental workload (MW), stress, attention, fatigue, drowsiness, and teamwork, by analyzing studies that contribute to research advancing toward real-world application direction. While wearable biosensors were used as one criterion for selecting studies, this review extends beyond device usage to explore crucial methodological aspects relevant to real-world applications, such as data quality, customized processing steps, and the robustness of results compared to traditional laboratory-grade devices. Thus, this review analyzes 123 articles exploring current advancements in real-time monitoring of MW, stress, attention, mental fatigue, drowsiness, and teamwork using wearable devices. The findings provide a comprehensive overview of methodology advancement toward real-time, objective individuals monitoring in real world settings.
20 June 2026
Read appraisal →Progress in biomedical engineering (Bristol, England)
Assessing the relevance of biosignal-controlled robotic rehabilitation technologies: a systematic review.
Technology is transforming rehabilitative medicine by enhancing accessibility and personalisation. Robot-assisted rehabilitation uses robotic systems for recovery from physical and neurological impairments, enabling intensive, repetitive training with real-time feedback. These systems aim to restore motor function and mobility and to increase independence in daily life, needs that are growing in light of population ageing. Rather than revisiting control algorithms or mechanical design, this review aims to investigate the combined use of bio-signals and robotic rehabilitation systems, and to discuss their potential in rehabilitation settings outside disease-specific clinical frameworks. We reviewed studies published from 2020 through early 2025 to capture recent advances in this rapidly evolving field. Our objective was to assess the relevance of these technologies to rehabilitation outcomes, such as improvements in motor function or other clinical metrics. We considered robot-assisted systems driven by biosignals such as electromyography (EMG), electroencephalography, or electrocardiography and included only studies reporting measurable rehabilitative outcomes. Following PRISMA guidelines, we searched Scopus, PubMed, and WoS databases. Of 207 records screened, 15 met the inclusion criteria. Overall, the included studies suggest that contemporary biosignal-controlled robotic rehabilitation, particularly EMG-driven approaches, may support more intensive, personalised, and engaging therapy than conventional care alone, although the current evidence remains preliminary. Preliminary findings indicate that, by closing the loop among patient, device, and therapist, these systems may reduce muscle activity and support improvements in motor performance; however, the evidence base remains heterogeneous and generally underpowered. To support future clinical adoption, the field needs adequately sized, head-to-head trials with standardised outcome measures, longer follow-up, and transparent reporting of adherence, adverse events, user experience, and implementation barriers, including training and cost. These steps will be important to further assess efficacy and evaluate usability in real-world settings.
20 June 2026
Read appraisal →ACS nano
Learning Human-Environment Interactions via Wearable AI Interfaces
Wearable artificial intelligence (AI) interfaces are reshaping the boundaries between humans and the environment. While prior works often focus on narrow human-machine interactions, this review proposes an intact interaction information flow. It introduces a comprehensive interaction blueprint spanning local interaction and global interaction to the interaction entity, showing how humans interact with the environment. This review first examines advances in wearable form factors, sensing performance improvement strategies, and data analysis. Special emphasis is onspot on how AI interprets heterogeneous data from tactile signatures for local interaction, wearable vision for global interaction with human motion, and electrophysiological signals for the interaction entity. We then discuss the essential applications of this interaction framework, such as human-machine interaction and smart healthcare. By discussing potential barriers in device reliability, algorithm generalization, and scalable applications of wearable AI interfaces, this review provides an outlook on data-driven inverse sensor design, general intelligence strategies, and building a standard ecosystem for scalable applications. The wearable AI interfaces are toward on-body intelligence, actively perceiving, understanding, and assisting in the complex dynamic human-environment interactions.
19 June 2026
Read appraisal →Brain research bulletin
Neural substrates of apathy in Parkinson's disease: A systematic review of structural and functional neuroimaging studies
BACKGROUND: Apathy is a frequent and disabling non-motor symptom in Parkinson's disease (PD), characterized by diminished motivation, reduced goal-directed behavior, and emotional indifference. Growing evidence suggests that apathy in PD represents a distinct neuropsychiatric syndrome that may coexist with depression and global cognitive impairment, while also showing partially dissociable clinical and neural correlates. OBJECTIVE: This systematic review aimed to investigate the neural substrates of apathy in PD by synthesizing evidence from structural and functional neuroimaging studies. METHODS: Following PRISMA guidelines, we searched PubMed, Embase, Web of Science, and Scopus for studies assessing apathy in PD through neuroimaging techniques, including structural MRI, resting-state functional MRI, diffusion tensor imaging, PET/SPECT molecular imaging, and connectivity analyses. Apathy severity was evaluated using validated clinical scales, and findings were compared between PD population with and without apathy, as well as healthy controls. RESULTS: Across 16 heterogeneous studies, recurrent findings implicated structural, functional, and molecular alterations within fronto-striatal-limbic networks. However, differences in apathy measures and neuroimaging modalities limit direct comparability across studies and do not support a single unified imaging signature of apathy in PD. Apathetic PD subjects consistently exhibited reduced gray matter volume and altered spontaneous activity in the dorsolateral, inferior frontal gyri, anterior cingulate cortex (ACC), and nucleus accumbens, alongside disrupted functional and structural connectivity in fronto-limbic tracts. These alterations were significantly correlated with apathy severity and, in several studies, remained detectable after accounting for depressive symptoms or global cognitive status. Furthermore, the available findings suggest that apathy in PD may involve heterogeneous neurobiological mechanisms, including variable involvement of frontal, striatal, and limbic circuits; however, current evidence remains insufficient to support firm transmitter-based subtyping or treatment stratification. CONCLUSIONS: Current evidence suggests that apathy in PD involves partially dissociable neural correlates across prefrontal, striatal, and limbic systems. However, methodological heterogeneity across studies limits strong generalization, and future harmonized longitudinal studies are needed to clarify which findings are robust and clinically meaningful.
16 June 2026
Read appraisal →Journal of affective disorders
Assessing symptom severity in patients with markedly treatment-resistant depression: Insights from the RECOVER trial
BACKGROUND: The performances of the Montgomery-Åsberg Depression Rating Scale (MADRS) and the Quick Inventory of Depressive Symptomatology-Clinician (QIDS-C) and Self-Report (QIDS-SR) ratings have never been evaluated in markedly treatment-resistant depression (TRD). RECOVER, a 12-month randomized, controlled trial, compared adjunctive active to adjunctive sham vagus nerve stimulation, provided data to address this knowledge gap, and to examine the basis for the differential sensitivities of these scales in detecting overall and between-treatment-group symptom changes. METHODS: We employed classical test and item response theory methods to characterize each scale. Effect sizes identified items that were most sensitive to change over time across treatments, best discriminated between the two treatments, and best detected at least a partial symptom response in clinician global ratings. RESULTS: All three inventories were unidimensional, with high retest reliability and strong internal consistency. MADRS total scores were less sensitive in detecting overall and between-treatment-group symptom differences. On each scale, five symptom domains-mood, energy/lassitude, interest/inability to feel, negative self-view/pessimism, and concentration-covaried most strongly with overall depression severity and had the largest effect sizes in detecting overall therapeutic change and between-group difference in efficacy. LIMITATIONS: These findings may not generalize to less treatment-resistant populations or to different treatments. The relative insensitivity of suicidal ideation, sleep, and appetite items may be due to study design or concomitant medications. CONCLUSION: The MADRS, QIDS-C, and QIDS-SR are suitable for use in markedly TRD. Whether sadness, energy, interest, guilt, and concentration are "core" symptoms that are especially sensitive to change deserves further study.
16 June 2026
Read appraisal →Brain research
BDNF relates to prefrontal cortex activity in the context of physical exercise
Exercise has been shown to support brain health, cognitive function, and increase levels of brain-derived neurotrophic factor (BDNF). While BDNF is known to support the central nervous system through improved brain metabolism, vasculature, neurotransmission and synaptic plasticity, the association between exercise-induced changes in BDNF concentrations and exercise-related cognitive improvements is still unclear. This study investigated the relationship between exercise-induced changes in plasma BDNF (pBDNF) and serum BDNF (sBDNF), and haemodynamic indicators of prefrontal cortex function in sedentary adults. Participants (n = 23, female = 7) were randomized into intervention (12-week cycling programme) and control groups (no intervention). Participants completed V̇O2max tests to assess changes in fitness. pBDNF and sBDNF were measured pre- and post-V̇O2max testing, and a battery of executive function and memory tasks were also conducted, alongside functional near-infrared spectroscopy (fNIRS) to assess haemodynamic changes in the prefrontal cortex activity. Changes were assessed using the correlation-based signal improvement (CBSI) method. Results indicated that participants in the exercise intervention group did not show increased levels of resting-state s/pBDNF levels at the end of the intervention; however, they did exhibit a significant exercise-induced increase in sBDNF at week 12. This increase was correlated with changes in V̇O2max. Higher pBDNF levels and exercise-induced sBDNF were associated with a decrease in CBSI values in the frontopolar, dorsolateral and orbitofrontal prefrontal cortex during attention and inhibition tasks, but not during the memory task. These results suggest that increasing physical fitness can enhance BDNF transcription in response to acute bouts of exercise. This might, in turn, play a part in the modulation of neural function during executive tasks after acute exercise.
15 June 2026
Read appraisal →Journal of affective disorders
Brain aging in bipolar disorder using a neuroimaging and machine learning-derived metric: Findings from the ENIGMA BD Working Group.
BACKGROUND: Bipolar disorder (BD) is associated with clinical and biological markers of premature aging. In this largest study of brain age in BD to date, with 2919 participants, we compared brain-predicted age difference (brain-PAD) in individuals with BD and healthy comparison (HC) participants. Brain-PAD is a machine learning-estimated metric that quantifies the difference between an individual's predicted brain age and their chronological age, a potential clinical bio-signature of premature brain aging. Within individuals with BD, we also examined how medication and clinical characteristics were related to brain-PAD. METHODS: Age was predicted from 77 MRI measures of regional subcortical and lateral ventricle volumes, cortical thickness, and surface area for 1342 BD and 1577 HC adult participants, aged 18-75 yrs. old (μ = 37.2; SD = 12.3), from the curated ENIGMA Bipolar Disorder working group (ENIGMA-BD) and leveraging an ENIGMA machine learning model previously trained and validated using independent samples. Chronological age was subtracted from predicted age to produce an individual-level estimate known as brain-PAD. Linear mixed models (adjusting for sex and age as fixed effects and site as a random effect) were used to examine group differences and clinical associations. RESULTS: BD was associated with higher brain-PAD, compared to HC, primarily among older patients, as demonstrated by a significant age by diagnosis interaction (+0.05 [SE: 0.02] years). Individuals with BD on antiepileptic (AED) medications only (+3.20 [SE: 0.78] years) or on both AED and second-generation antipsychotics (SGA) (+3.74 [SE: 0.89] years) demonstrated greater brain-PAD compared to individuals who were not on any of the examined medications. Those taking lithium, whether alone or with AED and SGA independently, showed no difference in brain-PAD compared to individuals not taking any of the examined medications. However, individuals who were taking lithium showed lower brain-PAD compared to those on AED (-4.48 [SE: 0.84] years) or AED and SGA (-5.01 [SE:0.92] years). Individuals with a BD I subtype diagnosis had a higher brain-PAD (+1.50 [SE:0.55] years) compared to those with BDII or subtypes that are not otherwise specified (NOS). CONCLUSIONS: Results from this study suggest compounding effects of BD diagnosis and older age on brain-PAD, an ML-derived summary metric of structural alterations. Within BD, brain-PAD was differentially related to medication use, consistent with prior findings from ENIGMA-BD. Notably, AED use was generally related to more advanced brain age. Lithium use, alone or in combination with other medications, was not associated with advanced brain age, suggesting a possible neuroprotective effect of lithium. Brain-PAD as an ML-derived summary metric of structural alterations of the brain may provide clinical utility in assessing long-term holistic brain health to monitor the effectiveness of lifestyle modifications or treatments over time. LIMITATIONS: The cross-sectional nature of the study design and the limited granularity of the clinical data limit interpretation. Longitudinal studies with detailed chronicity data, medications and clinical measures overtime will improve brain-PAD modeling in BD.
15 June 2026
Read appraisal →British journal of sports medicine
Multimodal symptom-targeted treatment for young people with persisting post-concussion symptoms: a randomised clinical trial
OBJECTIVE: To evaluate the effectiveness of Concussion Essentials (CE), a symptom-targeted, multidisciplinary treatment for persisting post-concussion symptoms (PPCS). METHODS: This single-site, open-label, assessor-blinded, parallel-group randomised clinical trial recruited 8-18-year-olds within 17 days of concussion from the emergency department of a tertiary paediatric hospital and from community referrals. Participants who endorsed ongoing PPCS on review at 14 (±3) days post-injury were invited to a baseline assessment at 3 weeks post-injury, where symptoms were re-assessed. Symptomatic participants were randomised to CE or usual care (UC). CE participants received individualised, multidisciplinary treatment modules (education, psychological, physiotherapy) for up to 8 weeks, while UC participants received weekly monitoring. Primary outcome was full recovery at intervention completion, defined as <1 symptom of increased severity compared with pre-injury on the Post-Concussion Symptom Inventory, parent version (PCSI-P). RESULTS: Participants were recruited between August 2019 and July 2024. Outcomes were analysed for 158 participants (CE=78; UC=80, mean age 13±2.5 years, 57.6% male). Pre-treatment PCSI-P total mean scores were: CE 36.7 (95% CI 31.0 to 42.4) and UC 33.5 (95% CI 28.4 to 38.7). Proportions with full resolution were CE 62.5% and UC 37.0% (adjusted risk difference=25.5%, 95% CI -10.0 to 41.0%). Post-treatment mean scores were CE 8.5 (5.9-11.2), UC 21.8 (95% CI 15.6 to 27.9) (p<0.0001). Seven adverse events, unrelated to trial factors, were recorded. CONCLUSION: Individualised, multidisciplinary treatment is associated with accelerated recovery in young people with PPCS. Implementation of such approaches should be considered in routine clinical care of concussion. TRIAL REGISTRATION NUMBER: Australian New Zealand Clinical Trials Registry (number: ACTRN12617000418370).
14 June 2026
Read appraisal →Journal of neurology, neurosurgery, and psychiatry
Clinically reported covert cerebrovascular disease and risk of neurological disease: a whole-population cohort of 367 988 people using natural language processing
BACKGROUND: The relevance of covert cerebrovascular disease (CCD) in practice is uncertain, partly because estimation of risk in whole clinical populations is difficult. Studies have had success extracting CCD from clinical text using natural language processing (NLP), though they have been limited to specific CCD phenotypes. Here, we used NLP to measure multiple clinically-reported CCD phenotypes in a large clinical cohort and estimated subsequent disease risk in health record data. METHODS: From all people with brain imaging in Scotland (2010-2018), we selected people with no prior hospitalisation for neurological disease (n=367 988). NLP of imaging reports identified: white matter hypoattenuation or hyperintensities (WMH), lacunes, cortical infarcts and cerebral atrophy. Adjusted HRs (aHRs) were estimated between each phenotype and stroke, dementia and Parkinson's disease (conditions previously associated with CCD), epilepsy and colorectal cancer (control conditions). RESULTS: For each phenotype, the aHR of stroke was WMH 1.4 (95% CI 1.3-1.4), lacunes 1.6 (1.5-1.6), cortical infarct 1.8 (1.7-1.9) and cerebral atrophy 1.1 (1.0-1.1). The aHR of dementia was WMH 1.3 (1.3-1.3), lacunes 1.0 (0.9-1.0), cortical infarct 1.1 (1.1-1.2) and cerebral atrophy 1.7 (1.7-1.8). The aHR of Parkinson's disease was WMH 1.1 (1.0-1.2), lacunes 1.1 (0.9-1.2), cortical infarct 0.7 (0.6-0.9) and cerebral atrophy 1.4 (1.3-1.5). The aHRs between CCD phenotypes and epilepsy and colorectal cancer were around the null. CONCLUSION: CCD and atrophy have implications for future disease risk and can be identified at scale using NLP of clinical reports. Prevention of neurological disease in people with CCD should be a priority for healthcare policy makers.
14 June 2026
Read appraisal →PloS one
Internet-based vestibular rehabilitation versus written instructions after acute vertigo: A randomised controlled trial
BACKGROUND: Acute vestibular syndrome (AVS) causes sudden and continuous vertigo, dizziness, and postural instability and is a common reason for emergency department visits, most commonly due to vestibular neuritis and, in rare cases, stroke. Vestibular rehabilitation (VR) is an evidence-based exercise therapy that facilitates vestibular compensation and is effective in chronic vestibular disorders. However, evidence for VR in acute vertigo remains limited and, despite guideline support, it remains underused in practice. Internet-based VR has demonstrated benefit in chronic dizziness but has not been evaluated in acute vertigo. This study aimed to evaluate the efficacy of an internet-based VR tool compared with standard care (written instructions) in reducing vestibular symptoms after acute onset vertigo. METHODS AND FINDINGS: A multicentre, randomised, evaluator-blinded superiority trial was conducted across nine hospitals in Sweden. Adults with ongoing AVS were recruited within 1-7 days after symptom onset and randomised (1:1) to six weeks of internet-based VR with personalised, progressively adjusted home exercises or to written instructions for home-based VR exercises. The primary outcome was the between-group difference in vestibular symptoms at six weeks measured using the Vertigo Symptom Scale Short Form (VSS-SF; range 0-60), with a difference of ≥3 points prespecified as clinically significant. Secondary outcomes included dizziness-related disability, walking speed, and balance. Of 184 randomised participants, 183 were included in the analyses (94 online VR; 89 written instructions; median age 56 years). In the intention-to-treat analysis, both groups improved at six weeks, with no significant between-group difference (adjusted mean difference -2.0 points, 95% CI -4.9 to 0.9; p = 0.18). Per-protocol analyses were consistent (-1.7 points, 95% CI -4.7 to 1.3; p = 0.27). Over 12 weeks, both groups showed reduced vestibular symptoms and improved disability, balance, and walking speed, without significant between-group differences. No serious adverse events were attributed to the interventions, and compliance was high in both groups. CONCLUSIONS: Internet-based VR was not superior to written instructions in reducing vestibular symptoms six weeks after acute onset vertigo. Both groups demonstrated improvement from baseline, with no statistically significant between-group differences. These findings suggest that ensuring access to vestibular rehabilitation exercises may be more important than the specific mode of delivery after acute onset vertigo, and internet-based tools represent viable alternatives for patients who prefer or may benefit from a digital format. TRIAL REGISTRATION: Clinicaltrials.gov NCT05056324. https://clinicaltrials.gov/study/NCT05056324. Registered on September 24, 2021.
14 June 2026
Read appraisal →Journal of mathematical biology
Spiking neural models for decision-making tasks with learning.
In cognition, response times and choices in decision-making tasks are commonly modeled using Drift Diffusion Models (DDMs), which describe the accumulation of evidence for a decision as a stochastic process, specifically a Brownian motion, with the drift rate reflecting the strength of the evidence. In the same vein, the Poisson counter model describes the accumulation of evidence as discrete events whose counts over time are modeled as Poisson processes. This model has a spiking neurons interpretation as these processes are used to model neuronal activities. However, these models lack a learning mechanism and are limited to tasks where participants have prior knowledge of the categories. To bridge the gap between cognitive and biological models, we propose a biologically plausible Spiking Neural Network (SNN) model for decision-making that incorporates a learning mechanism and whose neurons activities are modeled by a multivariate Hawkes process. First, we show a coupling result between the DDM and the Poisson counter model, establishing that these two models provide similar categorizations and reaction times and that the DDM can be approximated by spiking Poisson neurons. To go further, we show that a particular DDM with correlated noise can be derived from a Hawkes network of spiking neurons governed by a local learning rule. In addition, we designed an online categorization task to evaluate the model predictions. This work provides a significant step toward integrating biologically relevant neural mechanisms into cognitive models, fostering a deeper understanding of the relationship between neural activity and behavior.
14 June 2026
Read appraisal →PloS one
Motherhood choice in multiple sclerosis (MoMS) - Pilot trial of web-based decision support
BACKGROUD: Uncertainty concerning motherhood is common among women with multiple sclerosis (wwMS). Therefore, we developed and pre-tested a patient decision aid (PtDA) and a nurse-led decision coaching intervention (DC) to support motherhood choice. The DC includes the PtDA, a decision guide on motherhood choice, and decision coaching. METHODS: We conducted a randomised pilot trial across Germany to test feasibility, with decisional conflict (Decisional Conflict Scale, DCS) as an exploratory endpoint. Initially, we planned a 3:1 randomisation ratio (planned; PtDA = 48, DC = 16). Due to recruitment difficulties, we switched to a 1:1 randomisation ratio to ensure an appropriate sample size (PtDA > 20, DC > 10). Women between 18 and 45 years old with relapsing-remitting MS or clinically isolated syndrome, and who had not yet decided about motherhood, were eligible. We recruited two nurses for our decision coaching training course. We used questionnaires to measure decisional conflict, programme feasibility, knowledge, and worries regarding pregnancy in MS. Interviews were conducted to gain in-depth information on the potential feasibility of the programmes. Interviews were analysed thematically and questionnaires descriptively. We conducted explorative group comparisons and merged findings using joint display analysis. RESULTS: We trained two decision coaches in the DC group and recruited 35 wwMS (PtDA = 22; DC = 13) over five months. Median DCS scores in the DC group were 49 at baseline and 15 at follow-up (range 0-100; higher scores indicate greater decisional conflict). In the PtDA group, scores were 54 (baseline) and 31 (follow-up). Explorative group comparison indicated lower decisional conflict at follow-up in the DC group than in the PtDA group (p = 0.035). Interviewees (PtDA = 5; DC = 6; nurses = 2) described both interventions as helpful for decision-making. Qualitative findings indicate greater satisfaction levels in the DC group. CONCLUSION: Both interventions appear useful for wwMS in making motherhood choices. The DC programme seems more promising in supporting decision-making. TRIAL REGISTRATION: German Clinical Trials Register (DRKS); DRKS00038534.
14 June 2026
Read appraisal →Journal of neurology, neurosurgery, and psychiatry
On the relationships between apathy, depression and anhedonia
BACKGROUND: Apathy, depression and anhedonia are clinically overlapping constructs, which hinders diagnostic clarity and treatment development. This study aimed to comprehensively characterise these syndromes to identify a core set of non-redundant symptoms that maximally dissociate them and to investigate the psychological nature of key distinguishing features. METHODS: Data from seven datasets (N=4578) of healthy individuals and patients with major depressive disorder were analysed using the Apathy Motivation Index, Beck Depression Inventory and Snaith-Hamilton Pleasure Scale. A machine-learning algorithm identified the most informative, non-redundant items for dissociating 'pure' apathy, depression and anhedonia. The nature of emotional apathy was further investigated with follow-up studies. RESULTS: Although substantial symptom overlap existed, 'pure' syndromes were present. Factor analysis revealed a robust five-factor structure, separating depression, anhedonia and three distinct apathy domains (behavioural, social and emotional). Machine learning identified 10 core symptoms that differentiated the pure syndromes with high accuracy (area under the curve >0.90) and could also identify well the presence of each syndrome in individuals suffering from two or more syndromes. Emotional apathy negatively correlated with depression and was specifically associated with reduced affective empathy and a diminished sensitivity to the intensity of negative facial emotions, rather than with alexithymia or antidepressant-induced emotional blunting. CONCLUSIONS: Apathy, depression and anhedonia are dissociable constructs with distinct symptom signatures. Emotional apathy is a unique dimension which provides a novel target for research. A 10-item Apathy-Depression-Anhedonia Measure developed here provides a pragmatic tool for rapid, precise phenotyping to guide more personalised therapeutic strategies.
14 June 2026
Read appraisal →Journal of neurology, neurosurgery, and psychiatry
Impact of an artificial intelligence-driven triage system on workflow and transfer efficiency: stratified analysis of 4548 admissions to four thrombectomy hubs receiving transfers from sixty spokes.
BACKGROUND: We aimed to evaluate the impact of implementing an artificial intelligence (AI)-enabled acute ischaemic stroke triage system on workflow efficiency and transfer optimisation in a large academic healthcare network. METHODS: A prospectively maintained database was reviewed comparing equivalent time periods before and after AI-enabled triage platform implementation (January 2021-December 2022). The primary analysis compared workflow metrics between AI-enabled and non-AI spokes during the same calendar period (2022) to control for temporal confounding. Benjamini-Hochberg correction was applied for multiple comparisons, and analyses were adjusted for age and baseline National Institutes of Health Stroke Scale. Evaluated outcomes included door-in-door-out (DIDO) times, door-to-puncture (DTP) times, endovascular therapy (EVT) utilisation rates, cost analysis and clinical outcomes at discharge. RESULTS: The study included 4548 admissions with 844 EVT patients (394 pre-implementation, 450 post-implementation) across four hub centres. In the primary same-period analysis (2022), AI-enabled spokes demonstrated significantly shorter DIDO times compared with non-AI spokes (median 103 (92-118) vs 134 (103-162) min; adjusted difference -41.6 min (95% CI -60.9 to -24.1); p<0.001, Q<0.001) and shorter DTP times (21 (14-43) vs 40 (18-65) min; adjusted difference -10.9 min (95% CI -17.9 to -3.7); p=0.003, Q=0.009). A difference-in-differences analysis demonstrated that DIDO improvements were specific to AI-enabled spokes (-27 min; 95% CI -62 to -4; p=0.029). EVT utilisation was also significantly higher in AI-enabled versus non-AI spokes where AI-enabled spokes had increased EVT rates by +17.8% (39.3% to 57.1%) compared with +1.1% in non-AI spokes (41.3% to 42.4%, Pinteraction=0.006). DTP improvements were more pronounced at community hubs (86 (48-108) to 51 (22-77) min; adjusted difference -24.9 min; p=0.021, Q=0.041) compared with academic hubs (60 (23-87) to 55 (22-73) min; adjusted difference -15.5 min; p<0.001, Q=0.002). Subgroup analyses demonstrated consistent DIDO benefits across age, stroke severity and sex strata with no significant treatment effect heterogeneity (all P-interaction >0.05). Probabilistic cost analysis estimated savings of $3.6 million (95% CI $1.5M to $6.1M) per 1000 AI-enabled spoke transfers. Clinical outcomes, including functional status and mortality at discharge, were similar between groups (all Q>0.05). CONCLUSION: Implementation of an AI-enabled triage platform was associated with significant reductions in workflow times and increased EVT utilisation, with effects specific to AI-enabled spokes rather than secular trends alone. The proportion of transfers who did not proceed to EVT decreased in AI-enabled spokes, though counterfactual outcomes for non-transferred patients remain unknown. Clinical outcomes at discharge were unchanged.
14 June 2026
Read appraisal →Journal of neuro-oncology
Symptomatic radiation necrosis following intracranial brachytherapy: a systematic review and meta-analysis
PURPOSE: To estimate the pooled incidence of symptomatic radiation necrosis (RN) following intracranial brachytherapy and explore associations between clinical and technical variables and RN risk. METHODS: PubMed, Embase, Web of Science, and the Cochrane Library were searched from 1954 - 2024 in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Eligible human studies reported symptomatic RN after intracranial brachytherapy. RN ascertainment was secondarily classified as clear, partial, or unclear based on reproducibility of diagnostic criteria. A random-effects generalized linear mixed model was used to pool incidence rates, with subgroup analyses by tumor type, implant technique, isotope, dose rate, and prior radiation. Study quality was assessed using National Institutes of Health tools. RESULTS: Eighty-three studies encompassing 3,666 patients were included. The pooled incidence of symptomatic RN was 5.67% (95% CI: 3.84%-8.29%; I2 = 75.7%). RN ascertainment was clear in 25 studies, partial in 43, and unclear in 15. In sensitivity analyses, pooled symptomatic RN incidence was 8.71% among studies with clear RN definitions and 7.48% among studies with clear or partial definitions. Subgroup RN rates were 3.72% for low-grade gliomas, 8.44% for high-grade gliomas, 2.07% for brain metastases, and 8.98% for meningiomas. Isotope-specific rates were 5.83% for Iodine-125, 7.61% for Iridium-192, and 2.07% for Cesium-131. No statistically significant subgroup differences were observed. CONCLUSIONS: Symptomatic RN occurs in approximately 5.7% of patients following intracranial brachytherapy, within the range reported for other focal radiation therapies. Subgroup findings were not statistically significant and should be interpreted in the context of clinical and methodological heterogeneity.
13 June 2026
Read appraisal →Current pain and headache reports
Artificial Intelligence in Neuropathic Pain: From Mechanisms to Neuromodulation and Regenerative Strategies
PURPOSE OF REVIEW: Neuropathic pain remains challenging due to its heterogeneous mechanisms and variable treatment response. This narrative review evaluates recent advances in artificial intelligence (AI) and machine learning (ML) for patient phenotyping, treatment selection, outcome prediction, neuromodulation, and regenerative therapies. RECENT FINDINGS: ML-based phenotyping integrates genomic, neuroimaging, sensory, and behavioral data. Multimodal automatic pain assessment is approaching clinical deployment, supported by recent expert consensus. Large language models have been benchmarked against multidisciplinary teams for spinal cord stimulation (SCS) candidate selection. Real-world cohort data confirm sustained 24-month SCS benefit. AI is increasingly applied to regenerative interventions, including pulsed radiofrequency, which is now linked to epigenetic remodeling of dorsal root ganglion pathways. Most studies remain single-center and retrospective. AI may enable mechanism-based stratification, individualized neuromodulation programming, and rational integration of regenerative therapies in the treatment of neuropathic pain. Robust prospective validation, transparent reporting, and ethical safeguards are required before approaching translational maturity in selected settings.
13 June 2026
Read appraisal →Journal of medicinal chemistry
Integrating AI in Medicinal Chemistry for Accelerated Drug Discovery: A Comprehensive SAR (CSAR) Optimization Strategy and Discovery of Potent ALDH3A1 Inhibitors
Developing potent, selective small-molecule inhibitors remains a major challenge in drug discovery. ALDH3A1, a detoxifying aldehyde dehydrogenase isoform implicated in cancer and neurodegeneration, is a promising yet underexplored therapeutic target. To accelerate inhibitor optimization, we developed an AI-guided, reaction-based hit-to-lead workflow combining sequential reaction enumeration, pharmacophore-informed docking, and predictive modeling to support scalable SAR expansion. Applied to ALDH3A1, two rounds of enumeration using Enamine building blocks generated about 250,000 virtual analogues. Combined deep learning and docking-based triage prioritized 150 compounds for synthesis, leading to a roughly 1,000-fold improvement in biochemical potency from 1.41 μM to 1 nM for NCATS-SM0707, together with 4 nM cellular activity for NCATS-SM0708. Crucially, this methodology can be expanded by applying various chemical reactions at different positions. This study highlights CSAR as a scalable, generalizable complementary strategy to accelerate hit optimization through reaction-based enumeration and AI-guided prioritization of larger synthetically accessible chemical space.
13 June 2026
Read appraisal →PloS one
Feasibility of mechanomyography-based fatigue classification for passive lower-limb exoskeleton evaluation: A pilot study
Mechanomyography (MMG) enables non-invasive monitoring of muscle mechanical activity, while its utility in time-resolved fatigue detection during dynamic human-exoskeleton interaction remains underexplored. This pilot study explored the feasibility of combining MMG with machine learning to characterize neuromuscular fatigue and evaluate passive lower-limb exoskeleton assistance during repetitive 10 kg squat-lifting tasks. MMG signals from five lower-limb muscles were extracted for time-domain, frequency-domain and nonlinear features, and fatigue identification was implemented via a spectral-based criterion and multi-muscle voting optimization. A radial basis function-enhanced random forest (RBF-RF) model integrated with data augmentation was validated through leave-one-subject-out cross-validation. The results demonstrated that the 1/5 voting rule achieved optimal performance, with mean accuracy of 0.913 ± 0.057, AUC of 0.792 ± 0.073, and a low fatigue detection error of 1.4 ± 0.8 s. Data augmentation steadily improved model robustness, and predicted fatigue levels were significantly correlated with subjective perceived exertion (ρ = 0.756, p < 0.001). This pilot study demonstrates the feasibility of MMG-based fatigue monitoring for wearable assistive systems. The proposed framework supports objective, high-temporal-resolution fatigue monitoring, and may serve as a viable tool for assessing wearable assistive systems. Further large-cohort studies are required to validate its generalizability for practical applications.
13 June 2026
Read appraisal →Biomedical physics & engineering express
Enhancing brain tumor classification with a simplified CNN through hyperparameter optimization
Brain tumors remain a major public health challenge because of their high mortality rate and the need for timely and accurate diagnosis. Magnetic resonance imaging (MRI) is widely used for brain tumor assessment, yet conventional diagnostic workflows still depend largely on the subjective interpretation of radiologists, which may lead to inter-observer variability. In this context, deep learning approaches-particularly convolutional neural networks (CNNs)-have gained increasing attention for automated medical image analysis, as they are capable of learning discriminative features directly from imaging data and improving classification accuracy. Nevertheless, the performance of CNN-based models is strongly influenced by the choice of hyperparameters, whose optimization remains a non-trivial task due to complex and non-linear interactions within the network.This study investigates the impact of different hyperparameter optimization strategies on the performance of a simplified CNN model for brain tumor classification from MRI images. Three widely used optimization methods, Genetic Algorithms (GA), Bayesian Optimization (BO), and HyperBand-are considered and systematically compared. To emphasize efficiency and practical applicability, the CNN architecture is deliberately kept lightweight, aiming to reduce computational complexity while preserving sufficient modeling capacity. Experiments are carried out on a publicly available brain MRI dataset that includes glioma, meningioma, pituitary tumor, and healthy subjects.To account for performance variability and ensure statistical reliability, each optimized model is evaluated over ten independent runs using different random initializations. The reported results therefore include both the mean performance values and the corresponding standard deviations. The experimental analysis shows that the GA-based approach achieves a mean accuracy of 96.41%±0.36%, while BO improves this performance to 97.37%±0.30%. Among the evaluated strategies, HyperBand yields the highest average accuracy of 97.73%±0.48%, indicating its ability to consistently identify effective hyperparameter configurations for the proposed CNN architecture.
13 June 2026
Read appraisal →Translational stroke research
AI-Guided Patient-Derived Endothelial Modeling Identifies a Reversible Moyamoya-Specific miRNA Deficiency Corrected by Stem Cell-Extracellular Vesicles
Moyamoya disease (MMD) is a progressive cerebrovascular disorder characterized by abnormal vascular remodeling and impaired angiogenesis. We profiled plasma extracellular vesicle (EV)-derived microRNAs (miRNAs) from MMD patients and healthy controls and generated patient-specific induced pluripotent stem cell-derived endothelial cells (iPSC-ECs) to investigate disease-associated molecular alterations and therapeutic reversibility. Both plasma EVs and iPSC-ECs from MMD patients showed significant depletion of angiogenesis-related miRNAs. Small RNA sequencing data revealed mesenchymal stem cell-derived EVs (MSC-EVs) contain a miRNA repertoire that substantially overlaps with miRNAs deficient in MMD. Treatment of MMD iPSC-ECs with MSC-EVs restored the majority of the depleted miRNAs to normal levels, normalized predicted target mRNA expression, and rescued angiogenic function in a dose-dependent manner, as demonstrated by tube formation assays. To improve identification of biologically relevant miRNA-mRNA interactions, we developed an artificial intelligence-based framework integrating experimentally validated databases, endothelial-focused literature mining, and large language model (LLM)-assisted mechanistic scoring. Incorporation of LLM-derived contextual features modestly improved prediction performance compared with conventional sequence-based models (baseline AUROC ~ 0.51; LLM-enhanced AUROC ~ 0.72), suggesting that literature-informed biological context provides meaningful signal beyond structural target features alone. Furthermore, our findings demonstrate that MSC-EV therapy can substantially reverse molecular and functional endothelial deficits. Integrated profiling and AI-driven interaction mapping together establish a hypothesis-generating framework ("Exo-Courier") for biologically informed prioritization of EV-based therapeutic targets in vascular disorders.
12 June 2026
Read appraisal →Physics in medicine and biology
Unsupervised multimodal deformable medical image registration based on feature perceptual contrast learning
Objective.Multimodal medical image registration has extensive applications in clinical diagnosis and is fundamental for a series of medical analysis tasks. However, the presence of modality differences makes the registration process challenging. Existing methods often employ modality-independent feature descriptors that are sensitive to noise, or attempt to bridge differences within networks, which typically results in translation inaccuracies and misaligned anatomical information.Approach.In this paper, we propose a novel unsupervised approach utilizing a Feature Perceptual Contrast Learning Network (FP-net) to learn descriptors that bridge modality differences while accurately capturing common details. We unify the feature representation of anatomical information under homogeneous and heterogeneous intensity distributions through local sampling-based feature perceptual contrast learning and image reconstruction learning. The trained FP-net is subsequently employed to drive an unsupervised registration framework without requiring ground-truth deformation fields.Main results.We extensively evaluated our method on two public benchmarks: the BraTS 2021 dataset for brain T2-T1 and T1-T1ce registration, and the Learn2Reg 2021 dataset for challenging abdominal CT-MR registration. By passing multimodal image pairs with shape differences through the fixed FP-net, we generate optimization gradients that successfully update the registration network. Quantitative evaluations demonstrate our method's superiority over state-of-the-art baselines. Specifically, our model achieved a dice similarity coefficient (DSC) of 76.3% and 77.7% in tumor-bearing T2-T1 and T1-T1ce tasks, respectively. Furthermore, in the complex abdominal CT-MR task, it reached a DSC of 50.1%, significantly improving structural alignment.Significance.Our method effectively shifts the burden of bridging the modality gap away from the registration network, enabling standard U-Net architectures to achieve state-of-the-art deformable registration. This provides a robust, accurate, and easily deployable unsupervised solution for complex clinical multimodal image analysis.
12 June 2026
Read appraisal →Developmental cell
A machine learning-based megakaryocyte identification system uncovers resident organs, markers, and functional diversity
The organ- and stage-specific diversity of megakaryocytes (MKs) has prompted a reassessment of their distribution and functions. By integrating single-cell transcriptomic data across multiple organs and developmental stages, we identified previously unreported MK and platelet markers, including Tnik, a key regulator of MK function and platelet production. Using these markers alongside established ones, we developed a machine learning-based MK identification system (MKIDS) that enables MK detection in the brain, heart, and placenta in mice and humans. Functional studies demonstrated that brain-resident MKs are essential for neural development, underscoring organ-specific roles of MKs in regulating tissue development and function. Transcriptomic integration of MKs across organs and stages, with functional validation, revealed a developmental shift in platelet production-from a mitochondria-low to a mitochondria-enriched subpopulation. Our findings offer a transformative perspective on the MK system, highlighting its cellular diversity, functional complexity, and developmental dynamics.
12 June 2026
Read appraisal →Child's nervous system : ChNS : official journal of the International Society for Pediatric Neurosurgery
Primary Intracranial High Grade Congenital Glial Lesions (PIHGCL): A systematic review of case reports from 1985 to 2025
BACKGROUND: Among congenital intracranial space-occupying lesions, teratomas, gliomas, and neuroblastomas are more frequently reported. However, primary congenital glioblastoma (cGBM) and congenital anaplastic astrocytoma (cAA) are underrecognized entities. Their detection remains challenging due to nonspecific prenatal findings and limited awareness among pediatricians and obstetric care providers, contributing to delayed diagnosis and worsened outcomes. OBJECTIVE: To summarize the clinical presentations, diagnostic imaging characteristics, reported cases, histopathological features, and neuro-oncological management strategies of primary intracranial high grade congenital glial lesions (PIHGCL). MATERIALS AND METHODS: A comprehensive literature search following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines was conducted in 2025 over PubMed and Google Scholar using keywords and Boolean operators, retrieving 35 studies (44 patients) with PIHGCL published 1985 to 2025. Quality assessment was done using Joanna Briggs Institute (JBI) tool. The data was analyzed descriptively as well as analytically to determine survival outcomes in treatment groups. RESULTS: A total of 28 studies (37 patients) discussed cGBM, and 7 studies (7 patients) described the occurrence of cAA. Most of the patients were infants i.e., 65.9% (29), presenting with macrocephaly in 27.7% (10) patients. Most of the lesions were intracerebral, 72.7% (32), lateralized equally to both sides, i.e., 46.5% (20). Surgical excision alone was done in 29.5% (13) patients, surgical excision with adjuvant chemotherapy was given to 43.2% (19) patients, chemotherapy alone in 6.8% (3) patients whereas 20.5% (9) underwent only conservative treatment. Kaplan Meir-analysis revealed a significant difference in survival in patients who received adjuvant treatment along with surgical excision in contrast to other treatment groups (χ2 = 7.83, df = 3, p = 0.05). CONCLUSION: PIHGCL presents during fetal or neonatal period with clinicopathological features distinct from adult and pediatric glioblastomas with distinct genetic etiology. Extent of resection is not a reliable marker of treatment outcomes in PIHGCL due to anatomical and physiological variations at the age of presentations. Adjuvant chemotherapy along with surgical resection results in improved patient survival.
11 June 2026
Read appraisal →Translational stroke research
Transcarotid Artery Revascularization for Asymptomatic Carotid Stenosis: A Systematic Review and Meta-Analysis
Management of asymptomatic carotid stenosis remains controversial despite advances in contemporary best medical therapy. Transcarotid artery revascularization (TCAR) has been introduced as an alternative revascularization option alongside carotid endarterectomy (CEA) and transfemoral carotid artery stenting (TF-CAS). Evidence focused specifically on asymptomatic carotid disease, however, remains limited and is derived mainly from observational analyses of clinical databases, with only a small amount of single-center retrospective evidence. The present study therefore examined the available comparative evidence on TCAR for asymptomatic carotid stenosis. A systematic review and meta-analysis was performed of studies reporting outcomes of TCAR in asymptomatic carotid stenosis. Eligible studies included asymptomatic cohorts or mixed populations with extractable asymptomatic subgroup data and reported outcomes after TCAR with comparison against CEA and/or TF-CAS. The primary pooled outcomes were stroke, death, stroke or death, and myocardial infarction at 30 days, together with the corresponding stroke, death, and stroke or death outcomes at approximately 1 year. For TCAR versus CEA, no significant between-group differences were identified in the pooled 30-day risks of stroke (risk ratio (RR), 1.25; 95% CI, 0.98-1.59; I²=82%), death (RR, 0.94; 95% CI, 0.64-1.38; I²=57%), or stroke or death (RR, 1.10; 95% CI, 0.95-1.26; I²=75%). In contrast, myocardial infarction within 30 days occurred less frequently after TCAR (RR, 0.68; 95% CI, 0.48-0.96; I²=71%). At approximately 1 year, pooled estimates likewise showed no significant difference in stroke (RR, 1.27; 95% CI, 0.70-2.29; I²=0%) or death (RR, 1.14; 95% CI, 0.97-1.35; I²=0%), whereas the pooled estimate for stroke or death favored CEA (RR, 1.25; 95% CI, 1.15-1.36; I²=31%). A different pattern emerged in the TCAR versus TF-CAS comparison. TCAR was associated with significantly lower pooled 30-day risks of stroke (RR, 0.60; 95% CI, 0.48-0.73; I²=0%), death (RR, 0.55; 95% CI, 0.38-0.81; I²=20%), and stroke or death (RR, 0.60; 95% CI, 0.52-0.70; I²=6%), while no significant difference was observed for 30-day myocardial infarction (RR, 0.91; 95% CI, 0.65-1.29; I²=44%). At approximately 1 year, stroke remained comparable between the 2 groups (RR, 0.75; 95% CI, 0.46-1.23; I²=26%), whereas death (RR, 1.09; 95% CI, 1.00-1.20; I²=66%) and stroke or death (RR, 0.79; 95% CI, 0.71-0.87; I²=45%) differed, with the pooled stroke-or-death estimate favoring TCAR. Current observational evidence suggests that TCAR compares favorably with TF-CAS in asymptomatic carotid stenosis. When compared with CEA, TCAR does not appear to provide a clear benefit in pooled perioperative neurologic outcomes, although myocardial infarction may occur less frequently after TCAR.
11 June 2026
Read appraisal →Journal of neural engineering
Computational neuroelectrophysiology and artificial intelligence for drug-resistant epilepsy: recent advances, current challenges, and future directions
Drug-resistant epilepsy (DRE) affects approximately 30% of epilepsy patients, with surgical cure rates below 70%. This challenge drives a fundamental paradigm shift from localizing a discrete epileptogenic zone toward characterizing and modulating the dysfunctional brain networks that initiate and propagate seizures. This review critically synthesizes how computational neuroelectrophysiology and artificial intelligence (AI) converge to propel this shift. We chart the trajectory from quantifying local biomarkers, including high-frequency oscillations and excitation-inhibition balance metrics, to mapping the topological properties of epileptic networks through functional and effective connectivity. The integration of these network features with AI techniques, particularly spatiotemporal deep learning architectures, has demonstrated significant potential for enhancing both the localization of the epileptogenic network and the prediction of postoperative outcomes. However, the translation of this network-centric paradigm into clinical practice remains constrained by several challenges, including spatial sampling bias, the inherent label instability of surgical outcomes, and the 'black-box' interpretability crisis. Future directions must emphasize EEG foundation models, causal AI, and the development of interactive multimodal AI agents. The convergence of mechanism-driven network models and data-driven AI frameworks promises a new era of personalized, network-guided therapy for DRE.
11 June 2026
Read appraisal →Neurology
Hematopoietic Stem Cell Transplant and Brain Volume Changes in Adults With Sickle Cell Disease
BACKGROUND AND OBJECTIVES: Adults with sickle cell disease (SCD) are at risk of decline in brain health and cognition, even without clinical stroke. SCD-related changes on MRI include brain atrophy and T2 fluid attenuated inversion recovery (FLAIR) white matter hyperintensity (WMH) growth. Hematopoietic stem cell transplant (HSCT) can be curative, but conditioning regimens risk new complications, particularly for adults with accumulated organ damage. We tested whether successful HSCT for adults with SCD was associated with reduced brain atrophy or WMH growth. METHODS: In this cohort study, adults with severe SCD underwent HSCT as part of 5 research protocols at the National Institutes of Health Clinical Center, including autologous gene therapy, HLA-matched donor HSCs, and haploidentical HSCs. Serial brain MRI scans acquired before and after transplant for up to 12 years included 3D T1-weighted and axial FLAIR sequences. Longitudinal brain volumes, segmented using FreeSurfer and corrected for total intracranial volume (TICV), were used to measure brain parenchymal fraction (BPF). A deep learning algorithm segmented WMH volumes from FLAIR sequences. Volumes were log2-transformed as a percentage of TICV. We tested whether HSCT success (persistent engraftment vs graft failure) or change in hemoglobin (Hb) (post-pre-HSCT) was associated with the rate of brain atrophy or WMH growth using mixed-effects linear regression. Random effects included scanner, participant intercept, and slope, while fixed effects included age and sex. RESULTS: Seventy-seven participants (mean age 32 ± 9 years, 43% female) who underwent HSCT were followed for a median 5.3 years (interquartile range [IQR] = 4.6). For 65 participants with successful HSCT, BPF declined nonsignificantly (-0.055%/year; 95% CI -0.12, 0.006; p = 0.08). Twelve participants with graft failure had a 7-fold greater BPF decline (-0.42%/year; 95% CI -0.57, -0.26; p < 0.001; group difference p < 0.001). A smaller change in Hb after HSCT was associated with steeper BPF decline (0.035%/year per unit Hb; 95% CI 0.017, 0.053; interaction p < 0.001). WMH growth was similar in both groups (0.038% vs 0.030% per year; interaction p = 0.50) and was associated with older age and baseline WMHs, but not treatment success. DISCUSSION: Successful HSCT was associated with preserved brain volume, but not WMH growth, another brain health biomarker. Whether these HSCT-related brain structural changes translate to cognitive preservation remains to be determined. TRIAL REGISTRATION INFORMATION: ClinicalTrials.gov NCT02140554 registered 2014-05-14, first enrollment February 2015; ClinicalTrials.gov NCT00061568; registered 2004-07-16, first enrollment July 2004; ClinicalTrials.gov NCT02105766; registered 2014-04-01, first enrollment April 2014; ClinicalTrials.gov NCT03077542; registered 2017-03-10, first enrollment April 2017; ClinicalTrials.gov NCT00977691; registered 2009-12-14, first enrollment December 2009. CLASSIFICATION OF EVIDENCE: This study provides Class III evidence that in adults with sickle cell disease, successful hematopoietic stem cell transplant is associated with slower declines in BPFs over a median period of 5 years, compared with patients with transplant failure.
9 June 2026
Read appraisal →Neurology
Acute Kidney Injury and Risk of Adverse Neurocognitive Outcomes: A Systematic Review and Meta-Analysis
BACKGROUND AND OBJECTIVES: Chronic kidney disease is a recognized risk factor for adverse neurocognitive outcomes, but the effect of acute kidney injury (AKI) on brain health remains less well defined. We conducted a systematic review and meta-analysis to evaluate associations between AKI and subsequent risk of stroke, delirium, and dementia. METHODS: Eligible studies were identified by searching Ovid MEDLINE and Embase from inception (Ovid: January 1946; Embase: January 1970) until April 2025. Studies were included if they reported quantitative estimates with measures of precision for the association between AKI and delirium, stroke, or dementia in adult populations. Two reviewers independently screened and extracted data, and study quality was assessed using standardized criteria. Study characteristics, participant demographics, and adjusted effect estimates (hazard ratios [HRs] or odds ratios [ORs]) with 95% CIs were extracted. Pooled HRs and ORs with 95% CIs were calculated using random-effects models. Heterogeneity was evaluated with the χ2 test and I2 statistic, and sources of heterogeneity were explored through prespecified subgroup analyses and meta-regression. RESULTS: We identified 49 studies comprising 11,253,825 participants with 1,279,145 events. Individuals with AKI were at increased risk of stroke (pooled adjusted HR 1.35, 95% CI 1.20-1.52), delirium (pooled adjusted OR 1.76; 1.42-2.17), and dementia (pooled adjusted HR 1.64, 1.41-1.89). A gradient of risk across increasing AKI stages was demonstrated for stroke (stage 1: HR 1.11; 1.00-1.23; combined stages 2 and 3: HR 1.57; 1.35-1.81). AKI was also associated with higher in-hospital and 90-day mortality poststroke (pooled HR 2.13, 1.56-2.90, and 4.81, 2.55-9.08, respectively) and with 90-day disability (pooled adjusted OR 1.47, 1.22-1.76). Associations between AKI and all outcomes were directionally consistent across sensitivity analyses and pooled propensity score-matched studies. DISCUSSION: In this systematic review and meta-analysis, AKI was consistently associated with increased short-term and long-term neurocognitive risk, including stroke, delirium, and dementia. These findings suggest that AKI may identify individuals vulnerable to both acute and chronic brain injury. Further studies are needed to clarify mechanisms linking AKI to brain injury and to identify strategies to mitigate neurocognitive risk in this high-risk population.
9 June 2026
Read appraisal →Physics in medicine and biology
Scan-wise generalized PET denoising with contrastive adversarial learning
Objective.Deep learning has significantly advanced low-count positron emission tomography (PET) denoising. However, models trained on specific distributions often yield biased outputs when applied to scans with different activity distributions caused by anatomical and physiological variations (distribution shifts). Existing methods fail to generalize well across these scan-wise variations. Our goal is to formulate PET denoising as a scan-wise domain generalization (DG) problem to mitigate these variations and achieve robust, unbiased denoising for unseen scans.Approach.We propose a contrastive adversarial DG (CADG) framework to learn scan-invariant features efficiently. We leverage the property that multiple low-count noise realizations can be generated from a single raw list-mode PET scan to form a scan-wise domain distribution. Unlike conventional adversarial training with cross-entropy (CE) loss, we propose a contrastive adversarial framework that minimizes the mutual information between feature and scan-wise domains. Considering that each subject can have multiple scans in longitudinal studies, we further propose an ordered and memory-queued contrastive adversarial framework. This method efficiently includes realizations from the same scan as positive pairs, different scans of the same subject as pseudo-positive pairs, and different subjects as negative pairs in a memory batch. We explicitly exploit their ordered relationship as prior knowledge using a novel noisy-robust multi-positive ordinal contrastive loss.Main results.We systematically validated the effectiveness of our methods using 1920 noise realizations derived from 80 subjects with 192 longitudinal scans of MK-6240 tau PET data. The proposed CADG approach demonstrated superior denoising performance compared to CE-based adversarial methods and standard baselines. The ordered contrastive loss successfully improved the peak signal-to-noise ratio and structural similarity index consistently while reducing bias and standard deviation in Alzheimer-related regions and the whole brain.Significance.To our knowledge, this is the first attempt to alleviate the performance degradation in cross-scan denoising from the perspective of DG. Our study is also a pioneering work in utilizing longitudinal scans as pseudo-positives within an ordered contrastive learning scheme to exploit fine-grained relationships for robust clinical PET imaging applications.
9 June 2026
Read appraisal →Neurosurgical review
Rho-associated kinase inhibitors in cerebral cavernous malformations: a preclinical systematic review
Cerebral cavernous malformations (CCMs) are vascular abnormalities characterized by clusters of dilated capillaries. They are most associated with loss-of-function mutations in three genes (Ccm1, Ccm2, and Pdcd10/Ccm3). In capillary endothelial cells, mutations activate the Rho-associated coiled-coil-containing protein kinase (ROCK), leading to non-heme iron deposition and lesion formation, thereby contributing to CCM pathophysiology. To address this, ROCK inhibitors are being explored as potential stabilizing therapies in CCM. By reviewing the existing literature, this study aims to provide a descriptive evaluation of their effects on non-heme iron deposition and lesion formation in murine models. This systematic review followed the PRISMA 2020 guideline and was registered in PROSPERO (CRD420251048073). PubMed, Embase, Web of Science, and Scopus were searched from inception to 2 February 2026. Eligible studies included in vivo murine CCM models with mutations in the Ccm1, Ccm2, and Pdcd10/Ccm3 genes. Studies had to evaluate direct ROCK inhibitors, such as fasudil and BA-1049, or indirect modulators of the RhoA/ROCK pathway, such as statins, regardless of dosage, route, or duration, and provide molecular evidence of RhoA/ROCK pathway modulation. The primary outcome was lesion burden, and the secondary outcomes included non-heme iron deposition and ROCK activity. Systematic searches identified 389 records, of which 4 studies were included, demonstrating that fasudil, a ROCK inhibitor, consistently reduced non-heme iron deposition and lesion burden in preclinical CCM models in mice. In Ccm1+/-Msh2-/- mice, fasudil reduced non-heme iron deposition and stage 2 lesions; in Ccm2+/-Msh2-/- mice, fasudil reduced non-heme iron deposition and lesion burden. One study demonstrated that BA-1049 caused a dose-dependent reduction in non-heme iron deposition and lesion burden. Studies have shown that ROCK pathway modulation reduces non-heme iron deposition and lesion burden. Further clinical investigations involving patients with CCM are essential to verify whether these experimental benefits can be reproduced in clinical settings.
9 June 2026
Read appraisal →Gene
Utilization of next generation sequencing from glioma patient liquid biopsy to determine survival prognostics: A systematic review
Glioma is a malignant cancer that affects the central nervous system. Early detection of glioma is still difficult due to the hard-to-reach location of the cancer. The use of next generation sequencing is one of the new developments that can be used for diagnosis, however the use of this modality for prognosis is still rarely discussed. This systematic review aims to determine the benefits of using next generation sequencing (NGS) on the assessment of ctDNA or cfDNA concentration, progression free survival (PFS), and overall survival (OS). This systematic review was made using data from six databases, namely PubMed, PMC, ProQuest, Google Schoolar, ScienceDirect, and SCOPUS, and was compiled based on guidelines from PRISMA. Fourteen studies were included in this systematic review with a total of 663 glioma patients. The results of ctDNA measurements generated through the NGS method with liquid biopsy samples show that the higher the grading and sampling when the tumor progresses can increase the ctDNA value. When assessing ctDNA concentrations at baseline when samples were taken, most studies showed a worse prognosis for PFS and OS survival time. However, the grouping of patients in the current studies is still highly variable so further studies with standardized ctDNA cut off values are needed for more precise determination of prognosis.
7 June 2026
Read appraisal →Behavioural brain research
Function-specific repetitive transcranial magnetic stimulation enhances motor strategy adaptation when combined with motor practice.
Effective motor strategy adaptation is essential for both high-performance sports and neurorehabilitation. Repetitive transcranial magnetic stimulation (rTMS) can modulate brain function; however, its efficacy may depend on the specificity of the stimulation targets. This study investigated whether function-specific rTMS, guided by fMRI-defined activation during motor imagery, enhances motor strategy adaptation. Forty-six healthy adults were randomized into three groups: function-specific rTMS, hotspot rTMS, and a no-stimulation control. All participants received identical motor imagery and physical practice in the backward glide shot put. Kinematic and resting-state functional magnetic resonance imaging (rs-fMRI) data were collected before and after the 7-day intervention, and functional connectivity (FC) was analyzed. Results showed that function-specific rTMS led to significantly greater improvements in trunk angle during the glide phase, a critical biomechanical parameter. FC between the right inferior frontal gyrus (IFG) and primary sensorimotor cortex increased significantly in this group and was positively correlated with kinematic improvements. These findings demonstrate that rTMS targeting task-relevant brain regions enhances motor strategy adaptation via mechanisms involving FC associated with motor performance, highlighting its potential for optimizing motor learning in both athletic and clinical populations.
7 June 2026
Read appraisal →Behavioural brain research
Effects of anodal transcranial direct current stimulation on athletic performance among elite athletes: A systematic review and meta-analysis
BACKGROUND: Non-invasive brain stimulation techniques, such as transcranial direct current stimulation (tDCS), have been proposed as promising approaches for enhancing motor performance. However, evidence regarding their effects on different domains of athletic performance in elite athletes remains inconclusive. OBJECTIVE: This study aims to quantify the effects of anodal tDCS on athletic performance in elite athletes, compared with a sham condition. METHODS: A comprehensive search of six electronic databases (PubMed, Web of Science, SportDiscus, ECSCO, Embase, and the Cochrane Library) was conducted to identify randomized clinical trials (RCTs). Standardized mean differences (SMDs) based on post-intervention scores were pooled using random-effects models to estimate pooled effect sizes. Subgroup analyses were performed to examine potential moderating variables. Study quality and risk of bias were evaluated using the Cochrane Risk of Bias Tool. RESULTS: A total of 18 studies involving 259 elite athletes were included in the analysis. The results indicated that anodal tDCS had a moderate but significant overall effect on athletic performance in elite athletes (SMD = 0.57, 95% CI [0.39, 0.75], p < 0.01), although moderate heterogeneity was observed ( I²= 51.38%). Subgroup analyses revealed significant domain-specific variability: the effects were strongest in sport-specific performance (SMD = 0.79, 95% CI [0.51, 1.08], p < 0.01) and physical fitness (SMD = 0.58, p < 0.01), whereas the effects on recovery (SMD = 0.17, p = 0.65) and psychological variables (SMD = 0.38, p = 0.09) were negligible and non-significant. Additionally, the M1 area was found to be the most effective cortical target. CONCLUSIONS: Our findings provide evidence that anodal tDCS is a promising tool for enhancing performance in elite athletes, but its effectiveness is highly domain-dependent. It is particularly potent for improving sport-specific skills and physical fitness, while its role in accelerating recovery remains unsupported. Coaches and practitioners should consider these domain-specific variations and the ceiling effect of elite populations when designing tDCS interventions.
7 June 2026
Read appraisal →Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation
Association of intensive blood pressure lowering with health-related quality of life after endovascular thrombectomy: a post-hoc analysis of the ENCHANTED2/MT trial
BACKGROUND AND PURPOSE: Intensive blood pressure (BP) targets of less than 120 mm Hg have been shown to worsen functional outcomes after endovascular thrombectomy (EVT) for acute ischemic stroke. However, their effects on self-reported health-related quality of life (HRQoL) remain uncertain. METHODS: This post-hoc exploratory analysis used data from ENCHANTED2/MT, an open-label, blinded-endpoint randomized trial conducted in 44 Chinese hospitals. Adults with elevated BP within 3 h after successful reperfusion were randomized to intensive (< 120 mmHg) or less intensive (140-180 mmHg) systolic BP targets. HRQoL at 90 days was assessed using the EQ-5D-3 L and EQ visual analogue scale (EQ VAS). Functional outcome was measured by the modified Rankin Scale (mRS). Treatment effects were estimated using adjusted regression models, and causal mediation analysis was performed to explore indirect and direct pathways. RESULTS: Of 816 randomized patients, EQ-5D data were available in 685. At 90 days, both EQ-5D index (0.66 vs. 0.72) and EQ VAS (73.01 vs. 76.84) were lower in the more intensive BP group. Adjusted mean differences favored the less intensive treatment group: 0.06 (95% CI, 0.01 ~ 0.11; P = 0.017) for EQ-5D index and 3.49 (95% CI, 0.38 ~ 6.60; P = 0.028) for EQ VAS. Mediation analysis showed that the association of intensive BP lowering with worse HRQoL was mainly indirect via worse mRS outcomes. CONCLUSIONS: Intensive BP lowering after EVT was associated with modestly worse HRQoL at 90 days. The observed differences were small in magnitude, particularly for EQ VAS relative to commonly reported thresholds for clinical importance. These findings are consistent with less intensive BP targets after EVT and highlight the importance of incorporating patient-reported outcomes in acute stroke trials. CLINICAL TRIAL REGISTRATION: ClinicalTrials.gov number, NCT04140110 the Registration Date, 22/10/2019.
7 June 2026
Read appraisal →Behavioural brain research
Neural correlates of rumination in psychiatric disorders: A systematic review of fMRI evidence
BACKGROUND: Rumination is a transdiagnostic, persistent factor across many psychiatric conditions. Mapping its neural mechanisms may help differentiate ruminative profiles. OBJECTIVE: To systematically review fMRI studies that evaluated rumination in psychiatric populations METHODS: We searched four databases (PubMed, EMBASE, Web of Science, Scopus) and included studies that integrated rumination measures with fMRI data. RESULTS: Thirty-two articles met criteria; most were non-randomized (n = 31, 97%) and focused on depression (n = 24, 75%). Across disorders, clinical groups showed higher rumination than healthy controls, including in euthymic participants. In major depressive disorder, neural alterations implicated the default mode network, limbic and striatal regions, dorsolateral prefrontal cortex, and anterior cingulate cortex consistent with maladaptive ongoing processing. Neural signatures often persisted in remitted patients, suggesting possible trait markers of vulnerability. Intervention studies (n = 7) support using neuroimaging to track ruminative profiles and to inform more personalized interventions. CONCLUSION: This review highlights the importance of examining rumination, here measured via trait questionnaires, state-induction paradigms, cognitive tasks, or resting-state fMRI, using more consistent conceptual and methodological approaches. Although rumination is transdiagnostic, implicated brain regions and networks may show disorder-specific patterns. Given heterogeneity in the literature, future work should prioritize controlled and interventional studies to clarify neural mechanisms and guide circuit-based, targeted treatments. PROSPERO REGISTRATION: #CRD532512.
7 June 2026
Read appraisal →PLoS computational biology
A prototype-augmented graph representation learning framework for identifying brain disorder-associated genes and facilitating drug repurposing
Many genetic loci were identified as associated with neuropsychiatric disorders and neurodegenerative disorders by Genome-wide association studies (GWAS). How these loci impact these diseases is unclear. Advances in deep-learning approaches and multi-omics data have the potential to link GWAS findings with disease mechanisms. Here, we proposed the Multi-omics Graph Transformer Network (MOGT), a semi-supervised graph neural network that leverages graph representation learning to model biological networks derived from multi-omics data to predict disease-associated genes. MOGT outperforms the current approaches in disease gene prediction for two psychiatric disorders and three neurodegenerative/neurological diseases. High-risk genes (HRGs) for Parkinson's disease (PD) predicted by MOGT were used to drug discovery by integrating with the CMAP database. Finally, 10 drugs were identified as potential candidates. Among them, the effect of drug UK-356618 was experimentally verified in a primary neuron model, showing that UK-356618 reversed the abnormal expression of PD-associated genes and improved the cell-level phenotypes of PD. Together, these results indicate that MOGT can be used to identify HRGs for brain disorders, and these predicted HRGs provide high-level insights into the mechanisms and treatments of brain disorders.
1 June 2026
Read appraisal →Lancet (London, England)
Efficacy and safety of a bodyweight-adjusted higher dose of ocrelizumab in relapsing (MUSETTE) and primary progressive (GAVOTTE) multiple sclerosis: two multicentre, randomised, double-blind, parallel-group phase 3b trials.
BACKGROUND: Ocrelizumab is a humanised anti-CD20 monoclonal antibody approved for people with relapsing (RMS) or primary progressive multiple sclerosis (PPMS). In a post-hoc analysis of phase 3 trials in RMS and PPMS using a 600 mg dose, higher exposure to ocrelizumab was associated with greater B-cell depletion and lower risk of confirmed disability progression. Here, we prospectively assessed the efficacy and safety of a high dose of ocrelizumab in patients with RMS or PPMS. METHODS: Two multicentre, double-blind, phase 3 controlled trials were conducted to compare high-dose ocrelizumab with the approved 600 mg dose of the drug in patients with RMS (MUSETTE) and PPMS (GAVOTTE) aged 18-56 years. MUSETTE involved 122 centres in 21 countries and GAVOTTE involved 149 centres in 22 countries. Participants were randomly assigned 2:1, with a permuted-block randomisation method, to high-dose ocrelizumab (1200 mg or 1800 mg for baseline body weight <75 kg or ≥75 kg, respectively) or 600 mg ocrelizumab. Patients, investigators, and the sponsor were blinded to treatment allocation. Patients received ocrelizumab infusions every 24 weeks for a minimum 120 weeks and until a prespecified minimum number of confirmed disability events (MUSETTE, 205; GAVOTTE, 357) had occurred. In both trials, the primary endpoint was time to onset of 12-week composite confirmed disability progression (cCDP), assessed by prespecified increases in Expanded Disability Status Scale, Timed 25-Foot Walk Test, or 9-Hole Peg Test scores. Efficacy endpoints were evaluated in all randomised participants and safety endpoints were evaluated in participants who received at least one ocrelizumab infusion. These studies are registered with ClinicalTrials.gov: MUSETTE, NCT04544436; GAVOTTE, NCT04548999. FINDINGS: Participants in MUSETTE were enrolled between Nov 26, 2020, and Aug 30, 2022; participants in GAVOTTE were enrolled between Dec 3, 2020, and May 15, 2023. In MUSETTE, 860 patients were randomly assigned (high-dose ocrelizumab, n=577; 600 mg ocrelizumab, n=283) and had median overall treatment duration of 184·4 weeks. In GAVOTTE, 753 patients were randomly assigned (high-dose ocrelizumab, n=500; 600 mg ocrelizumab, n=253) and had median overall treatment duration of 174·1 weeks. In MUSETTE, the percentage of patients with 12-week cCDP was 34% (198 of 577) with high-dose ocrelizumab versus 37% (104 of 283) with 600 mg ocrelizumab (hazard ratio [HR] 0·93 [95% CI 0·73-1·18]; p=0·53). In GAVOTTE, the percentage of patients with 12-week cCDP was 47% (235 of 500) with high-dose ocrelizumab versus 49% (124 of 253) with 600 mg ocrelizumab (HR 0·95 [95% CI 0·76-1·18]; p=0·64). Safety profiles were similar for the high-dose ocrelizumab and 600 mg ocrelizumab; in MUSETTE, rates of adverse events (552 [96%] of 577 and 267 [94%] of 283), serious adverse events (77 [13%] of 577 and 34 [12%] of 283), and fatalities (four [1%] of 577 and one [<1%] of 283) were comparable, as were rates of adverse events (447 [90%] of 499 and 230 [91%] of 254), serious adverse events (61 [12%] of 499 and 29 [11%] of 254), and fatalities (two [<1%] of 499 and three [1%] of 254) in GAVOTTE. INTERPRETATION: In both studies, high-dose ocrelizumab did not further improve control of disability progression in either RMS or PPMS, and no new safety concerns were identified. FUNDING: F Hoffmann-La Roche.
1 June 2026
Read appraisal →Neurology
Associations of Baseline Clinical Phenotypes With White Matter Hyperintensity Volume Change: A Study of 4,329 UK Biobank Participants
BACKGROUND AND OBJECTIVES: White matter hyperintensities (WMHs) relate to cognitive and physical impairment. Although WMHs typically progress over time, regression has also been observed. Our aim was to explore baseline clinical phenotypes associated with subsequent WMH volume change in the UK Biobank (UKB). METHODS: We included participants with total volume of WMHs at first and follow-up brain MRI. We assessed 107 preselected clinical phenotypes measured at baseline and preprocessed using the Phenome Scan Analysis Tool (PHESANT). We derived a measure of WMH change as the residual from a linear regression of follow-up WMH volumes on baseline WMH volumes to account for baseline WMH burden. We ran (1) linear regressions for continuous WMH change as the outcome and (2) multinomial logistic regressions comparing progression, regression, and stable groups after categorizing WMH volume change using an established percentile-based approach. Models were unadjusted, partially adjusted (age, sex, total brain tissue volume, follow-up time), or fully adjusted (further adding blood pressure; BP). We corrected p values using false discovery rate (FDR) correction. RESULTS: We included 4,329 participants (median age 52; interquartile range 12), 54.6% female, with a median follow-up of 2.3 years (range 1-7 years). Across follow-up, total brain volume decreased (mean decrease 16,058 mm3) and WMH volume increased (median increase 293 mm3; both p < 0.001). WMHs progressed in 53.9% of participants, regressed in 26.01%, and remained stable in 20%. In fully adjusted linear models, higher diastolic BP was the only baseline phenotype associated with greater WMH progression (0.10 SD higher WMH change per 1 SD higher inverse-rank normal transformed diastolic BP; 95% CI 0.05-0.15, FDR p < 0.05). In multinomial models, hip pain lasting more than 3 months (odds ratio [OR] 0.25, 95% CI 0.11-0.58) and faster walking pace (OR 0.79, 95% CI 0.66-0.95) were inversely associated with WMH progression, whereas hip pain was also inversely associated with WMH regression vs stability (OR 0.31, 95% CI 0.12-0.79; all FDR p < 0.05). DISCUSSION: We established vascular risk factors associated with subsequent WMH volume change in the studied cohort. Distinct clinical and demographic profiles characterized WMH progression, regression, and stable groups. Results suggest that vascular factors relate to WMH change but are sensitive to covariate control. Further studies should establish factors differentially and causally related to WMH progression, regression, and stability.
1 June 2026
Read appraisal →Lancet (London, England)
Efficacy and safety of intravenous prasinezumab in individuals with early-stage Parkinson's disease on stable symptomatic monotherapy (PADOVA): a phase 2b, multicentre, randomised, double-blind, placebo-controlled study
BACKGROUND: Prasinezumab has previously shown potential for reducing the progression of motor signs (Movement Disorder Society-sponsored Revision of the Unified Parkinson's Disease Rating Scale [MDS-UPDRS] Part III) in patients with early-stage Parkinson's disease who were treatment-naive or receiving monoamine oxidase type B (MAO-B) inhibitors. The aim of the PADOVA trial was to evaluate the efficacy and safety of prasinezumab in a broader population of patients receiving stable symptomatic medication. METHODS: This phase 2b, multicentre, double-blind, parallel-group, placebo-controlled, randomised, superiority trial recruited participants with early-stage Parkinson's disease (age 50-85 years, 3 months to 3 years from diagnosis, Hoehn and Yahr stage 1 or 2) on stable symptomatic medication from 110 centres in nine countries in Europe and North America. Participants were individually randomly assigned (1:1) via permuted blocks (stratified by symptomatic medication [levodopa or MAO-B inhibitor]) to intravenous prasinezumab (1500 mg) or placebo every 4 weeks for at least 76 weeks and until the target number of motor progression events was reached. Participants, investigators, and clinical assessors were masked to group assignment. The primary endpoint was time to a confirmed motor progression event (≥5-point increase in MDS-UPDRS Part III off-medication score), assessed in the full analysis set (all randomly assigned participants according to the treatment to which they were assigned). Safety and tolerability were assessed in all randomly assigned participants who received at least one dose of study drug, with participants grouped according to treatment received. The trial is registered with ClinicalTrials.gov (NCT04777331) and EudraCT (2020-004997-23), and is active, not recruiting. FINDINGS: Between May 5, 2021, and March 22, 2023, 787 individuals were screened. 586 were enrolled and randomly assigned (n=293 per group; mean age 64·2 years [SD 7·3]; 214 [37%] female and 372 [63%] male); 550 completed double-blind treatment (prasinezumab, n=277; placebo, n=273). The primary endpoint was not met; the primary analysis showed a non-significant delay in motor progression with prasinezumab versus placebo (hazard ratio 0·84 [95% CI 0·69-1·01]; p=0·066); median time to confirmed motor progression in the prasinezumab group was 61·1 weeks (95% CI 52·3-71·9), compared with 49·7 weeks (40·1-58·1) in the placebo group. The incidence of one or more serious adverse events was similar between groups (prasinezumab, 34 [12%] of 292; placebo, 34 [12%] of 290) and three recorded deaths were unrelated to the study drug (prasinezumab, n=1 [<1%]; placebo, n=2 [1%]). INTERPRETATION: Although PADOVA did not meet the primary endpoint, prespecified exploratory evidence suggests clinical activity of prasinezumab in early-stage Parkinson's disease, supporting continued investigation in the ongoing phase 3 PARAISO trial (NCT07174310). FUNDING: F Hoffmann-La Roche.
1 June 2026
Read appraisal →Brain structure & function
Automated interictal epileptic spike detection from simple and noisy annotations in MEG data
In drug-resistant epilepsy, presurgical evaluation can be considered for suitable candidates. Magnetoencephalography (MEG) has been shown to be an effective exam to inform the localization of the epileptogenic zone through the localization of interictal epileptic spikes. Manual detection of these pathological biomarkers remains a fastidious task due to the high dimensionality of MEG recordings, and inter-rater agreement has been reported to be only moderate. Current automated methods are unsuitable for clinical practice, either requiring extensively annotated data or lacking robustness on non-typical data. Considering a database of 82 patients, we demonstrate that deep learning models can be used for detecting interictal spikes in MEG recordings, even when only temporal and single-expert annotations are available, which represents real-world clinical practice. We propose two model architectures: a feature-based artificial neural network (ANN) and a convolutional neural network (CNN), evaluated against a state-of-the-art model to classify short time windows of signal. In addition, we employ an interactive machine learning strategy to iteratively improve our data annotation quality using intermediary model outputs. Both proposed models outperform the state-of-the-art model (F1-scores: CNN = 0.46, ANN = 0.44) when tested on 10 holdout test patients. The interactive machine learning strategy demonstrates that our models are robust to noisy annotations. Overall, results highlight the ability of our models to analyze complex and imperfectly annotated data. Our method of interactive machine learning offers great potential for faster data annotation, while our models represent useful and efficient tools for automated interictal spikes detection.
1 June 2026
Read appraisal →Journal of neural engineering
An online brain-computer interface for detecting incongruity in augmented reality applications
Objective.Augmented reality can provide digital information about physical entities presented within its real-world context. However, this information might disagree with the user's expectations due to factual errors in the data or cognitive biases. Such incongruity can impair user experience and undermine trust in the AR system. To address this issue, we propose detecting inconsistencies between physical objects and digital information through hybrid brain-computer interfaces.Approach.We conducted two complementary experiments. First, we implemented a strategy that integrates eye-tracking and brain signals for incongruity detection in an offline study. Subsequently, we assessed our approach in an online study in which participants received immediate feedback on the classification.Main results.The grand average event-related potentials revealed consistent electroencephalographic responses to incongruent augmentations, specifically a centroparietal N400 effect, across both experiments. We could further distinguish between congruent and incongruent information with an average balanced accuracy of 70% in the online study.Significance.These findings demonstrate the feasibility of detecting incongruity online, allowing for autonomous system adaptation, like presenting information in a more accessible format or providing contextual support.
31 May 2026
Read appraisal →Lancet (London, England)
Efficacy and safety of ocrelizumab in primary progressive multiple sclerosis, including older patients and those with more advanced disease (ORATORIO-HAND): a multicentre, double-blind, randomised, placebo-controlled, phase 3b study
BACKGROUND: The ORATORIO trial showed that ocrelizumab reduced the risk of disability progression versus placebo in patients with primary progressive multiple sclerosis (PPMS). We aimed to elucidate the effect of ocrelizumab in older and more disabled patients with PPMS, particularly regarding hand function preservation. METHODS: ORATORIO-HAND was a multicentre, double-blind, randomised, placebo-controlled, phase 3b study with 138 sites across 22 countries. Patients with PPMS aged 18-65 years and Expanded Disability Status Scale (EDSS) score of 3·0-8·0 were randomly assigned 1:1 to intravenous ocrelizumab 600 mg or placebo every 6 months for 144 weeks or until a prespecified number of progression events occurred. Masking was achieved by use of a placebo solution administered in the same manner as ocrelizumab. Double-blinding across all periods was maintained through separation of investigators responsible for efficacy and safety assessments. MRI scans were evaluated by a masked central reader, and laboratory parameters that could reveal treatment allocation were masked to site personnel until the primary analysis. Two coprimary estimands were defined, with the endpoint of time to onset of 12-week composite confirmed disability progression (12W-cCDP) in 9-Hole Peg Test or EDSS evaluated in all randomly assigned patients, and the same endpoint evaluated in a subset of patients with MRI activity at baseline. This study is registered with ClinicalTrials.gov, NCT04035005 and is ongoing and not recruiting. FINDINGS: Between Aug 12, 2019, and Dec 10, 2024, of 1360 patients assessed for eligibility, 1013 were randomly assigned (ocrelizumab [n=505]; placebo [n=508]). The proportion of patients with 12W-cCDP was 165 (33%) of 505 with ocrelizumab and 205 (40%) of 508 with placebo (hazard ratio, 95% CI 0·70 0·57-0·86; relative risk reduction=30%; p=0·0007). In the MRI-active subgroup, a significant risk reduction was also observed in 12W-cCDP (risk reduction=55%; p<0·0001). The overall safety profile was similar in both groups. More infections (245 [48%] of 506 vs 226 [45%] of 506) were observed with ocrelizumab, but not after COVID-19 was excluded (38% vs 37%). Rates of serious adverse events and serious infections were similar between groups. INTERPRETATION: Ocrelizumab was superior to placebo in delaying disability progression, with stronger effect on hand function, in a broad PPMS population including older patients and those with more advanced disease, while maintaining a manageable safety profile. FUNDING: F Hoffmann-La Roche.
31 May 2026
Read appraisal →Journal of medical Internet research
Acceptability of Technologies to Support Early Dementia Detection: Qualitative Study With the Boston University Alzheimer's Disease Center Cohort
BACKGROUND: Dementia is on the rise globally due to increasing life expectancies and population growth. Digital technologies may help detect early signs, enabling timely interventions to slow or reverse cognitive decline. However, to support the successful implementation of these digital technologies into health care settings, they must be acceptable to target users. Older adults and those with mild cognitive impairment (MCI) are at risk of developing dementia in later life and need to be able to use these technologies in order for this intervention to be approved and implemented in clinical practice. OBJECTIVE: This study explored the perspectives of older adults and those living with a clinical diagnosis of MCI on the acceptability of using various digital technologies that have the potential to support early dementia detection. METHODS: Participants were recruited from Boston University's Alzheimer's Disease Research Center. Participants selected at least 2 technologies from 9 different wearables and software to use for 2 weeks, at 3-month intervals, over a total duration of 2 years. A subgroup of self-selecting participants was interviewed after the first 2 weeks of use to gather initial perspectives regarding the acceptability of using the digital technologies. An inductive framework thematic analysis approach was used, assisted by NVivo (version 14.23.2; QSR International). RESULTS: In total, 13 individuals living with a clinical diagnosis of MCI and 11 adults aged 65 years and older were interviewed. Our analysis identified five key themes: (1) gamification, (2) wearability, (3) user guidance, (4) burden of use, and (5) usefulness. Gamified apps were generally liked, although users with little experience of digital games needed time to adjust. Wearables resembling everyday accessories (eg, watches) were preferred, but complaints about tight or uncomfortable straps were frequently reported. Clear instructions were critical to support correct use, but many participants would have liked more troubleshooting support when technical issues arose. The use of 5 or more devices led to a high burden, especially when devices had practicality issues such as not being waterproof. Devices offering personal feedback were perceived as useful to satisfy personal interests, though some questioned their usefulness within health care. Participants raised concerns about losing valued personal interactions with health care professionals and questioned how their existing health conditions and treatment for such conditions may affect the validity of the data collected by the devices. CONCLUSIONS: These findings can guide researchers in choosing appropriate devices and minimizing burden. Future work should explore the views of those experiencing digital exclusion to ensure equitable access to dementia-detection technologies.
31 May 2026
Read appraisal →Lancet (London, England)
Comparison of [18F]flortaucipir and [18F]MK6240 for the detection of tau pathology in Alzheimer's disease (HEAD): a multicentre, prospective, cross-sectional, within-participant study
BACKGROUND: Tau PET imaging has emerged as a critical biomarker for Alzheimer's disease, informing diagnosis, staging, and therapeutic selection. We investigated whether PET tracer selection alters tau detection. METHODS: We conducted a prospective, multicentre, non-randomised, within-participant comparison of [18F]flortaucipir (Tauvid), currently used in clinical settings in the USA and Europe, and [18F]MK6240, an investigational tau PET tracer. Participants were recruited from eight north American sites and underwent tau PET, amyloid-β (Aβ) PET, and detailed cognitive assessments. Tau PET with both agents was acquired within a 45-day window. Coprimary outcomes were the discriminative accuracy for Alzheimer's disease-related cognitive impairment and the frequency of tau positivity in early medial temporal lobe (MTL) and late neocortical regions. The study is registered with ClinicalTrials.gov, NCT05361382. FINDINGS: Between March 2, 2022, and Aug 27, 2025, 775 individuals were enrolled, with 682 completing all procedures (373 [55%] female, 309 [45%] male; 38 [6%] aged 19-27 years, 214 [31%] aged 50-65 years, and 430 [63%] aged 65-89 years). 32 (5%) participants identified as Hispanic or Latino. 637 (93%) identified as White, 24 (4%) as Black or African American, 16 (2%) as Asian, and five (1%) as other. In addition, 49 (7%) individuals were identified as being from a rural area. [18F]MK6240 showed greater accuracy than [18F]flortaucipir in distinguishing Alzheimer's disease from non-Alzheimer's disease impairment (area under the curve 0·93, 95% CI 0·89-0·95 vs 0·86, 0·75-0·91; p<0·0001). Among the older adults, tau positivity status was concordant in 560 (87%) for MTL and 603 (94%) for neocortical regions. In cognitively unimpaired participants, [18F]MK6240 identified twice as many MTL-positive cases as [18F]flortaucipir (n=54 [15%] vs n=23 [6%]). Prevalence ratio in Aβ-positive was 2·43 (95% CI 1·50-3·94; p=0·0003), identifying 23 additional cases per 100. Among discordant cases, 75 (89%) were [18F]MK6240-positive only and had higher Aβ burden (p<0·0001), APOEε4 frequency (p<0·0001), and cognitive impairment (p=0·0043) than those negative on both tracers. Neocortical tau positivity was more frequent with [18F]MK6240 than with [18F]flortaucipir in cognitively impaired individuals (80 [28%] vs 46 [16%]). Prevalence ratio in Aβ-positive was 1·74 (95% CI 1·32-2·29; p<0·0001), identifying 15 additional mild cognitive impairment and 21 dementia cases per 100. INTERPRETATION: Tau PET tracer selection influences the frequency of detection of tau pathology across the ageing and Alzheimer's disease spectrum. Compared with [18F]flortaucipir, [18F]MK6240 identified more individuals with tau pathology in cognitively unimpaired and cognitively impaired individuals, with direct implications for patient stratification in clinical trials and more precise guidance for therapeutic decision-making. FUNDING: National Institute on Aging.
31 May 2026
Read appraisal →Medicine
Machine learning-based identification of multidimensional predictors of quality of life in individuals with multiple sclerosis
This study aimed to identify independent predictors of quality of life in patients with multiple sclerosis (MS) using machine learning approaches. One hundred and one individuals diagnosed with MS were included in this cross-sectional study. Quality of life was assessed using the MS Quality of Life-54 (MSQoL-54). Demographic variables, clinical characteristics, disability level (Expanded Disability Status Scale [EDSS]), fatigue severity, sleep quality (Pittsburgh Sleep Quality Index), depression level (Beck Depression Inventory), and functional mobility (Timed Up and Go test) were evaluated. Multiple machine learning regression models, including Linear Regression, Lasso, Elastic Net, Support Vector Machines, Random Forest, and XGBoost, were developed and compared. Five-times repeated five-fold cross-validation was applied for internal validation. Model performance was evaluated using root mean squared error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). Among all models, Lasso regression demonstrated the best predictive performance (RMSE = 5.02, MAE = 4.04, R2 = 0.86). Disability level (EDSS) emerged as the strongest negative predictor of quality of life, followed by fatigue severity, sleep quality, and impaired functional mobility. Although disease duration was not significantly correlated with quality of life in univariate analyses, it showed a positive contribution in the multivariable model. Depression showed a strong bivariate association but had a relatively lower weight in the final predictive model. Quality of life in individuals with MS is strongly influenced by disability, fatigue, sleep quality, and functional mobility. Machine learning approaches provide an effective and interpretable framework for identifying key determinants of quality of life and may support personalized rehabilitation and clinical decision-making in MS.
31 May 2026
Read appraisal →Medicine
Common biomarkers of Alzheimer disease and postoperative delirium
Alzheimer disease (AD) and Postoperative delirium (POD) may share a common mechanism, but their shared genes and potential novel therapeutic targets remains unknown. We selected datasets GSE48350, GSE4226, and GSE16759 containing AD and control samples from the GEO database for AD model development, and use the GSE63061 dataset for AD model validation. The GSE163943 dataset containing PODs and controls was used to identify common genes. Differentially expressed genes (DEGs) associated with POD and AD respectively, as well as common DEGs between 2 diseases were analyzed. Shared DEGs were assessed using gene ontology and KEGG enrichment and PPI interaction networks. Machine learning algorithms, nomogram and decision tree analyses were used to interpret top 5 genes and visualize the models. 259 DEGs in POD with 148 being up-regulated and 111 down-regulated, and 199 DEGs in AD with 25 up-regulated and 174 down-regulated were found, of which 23 were common to POD and AD. Gene ontology analysis indicated that DEGs were primarily concentrated in 9 distinct biological processes, while KEGG results revealed 3 major metabolic pathways. Five hub genes emerged as potential diagnostic biomarkers for both diseases, Bruton tyrosine kinase (BTK), NCF2, CRH, FCGR3A, and SERPINA3. The LASSO model determined that the 23 common genes could serve as biomarkers for both POD and AD patients, and the Random Forest model is the the most suitable model. POD or AD decision trees showed BTK and NCF2 were sufficient to distinguish between POD or AD patients and healthy controls. BTK and NCF2 were identified common biomarkers of POD and AD. Further investigations are required to verify correlations between gene expression levels and pathological characteristics.
31 May 2026
Read appraisal →Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology
Concurrent de novo glioblastoma and meningiomatosis: a case report and systematic review of clinical, molecular, and topographical characteristics
INTRODUCTION: The co-occurrence of glioblastoma (GBM) and meningioma in a single patient is an exceptionally rare clinical phenomenon, often associated with prior irradiation or genetic syndromes. This study presents a unique case of concurrent de novo GBM and meningiomatosis and provides a systematic review of the literature to characterize this rare association. CASE PRESENTATION AND METHODS: A 78-year-old female with a known history of untreated meningiomatosis presented with refractory seizures and aphasia. Imaging revealed multiple stable extra-axial lesions and a new, distinct intra-axial right temporal lesion. The patient underwent a temporal lobectomy with gross total resection. Histopathology confirmed WHO grade 4 GBM (IDH-wildtype). Following multidisciplinary review, she completed adjuvant radiotherapy. At 6-month follow-up, she remained clinically stable with no recurrence. A systematic review was conducted, identifying 34 cases of concurrent GBM and meningioma. Data regarding demographics, topography, molecular profiles, and survival were analyzed. RESULTS: The median age was 63 years (range 30-86), with a slight male predominance (52.9%). Tumors were synchronous in 88.2% of cases. Topographically, 47.1% were "collision" or adjacent tumors, while 32.3% occurred in different hemispheres. Most meningiomas were WHO grade I (79.4%), while 97.1% of glial tumors were grade 4 GBM. Molecular analysis revealed classic independent drivers: 22q loss/NF2 mutations in meningiomas and 10q loss/EGFR amplification in GBMs. Median overall survival was 7 months (range 0.5-24 months), with modern trimodal therapy (Stupp protocol) showing a trend toward extended survival compared to older cohorts (Fig. 1). CONCLUSION: The coexistence of GBM and meningioma appears mostly coincidental, driven by distinct molecular pathways rather than a common progenitor. Despite the benign nature of the associated meningioma, prognosis is dictated by the aggressive GBM component. Early surgical intervention and modern adjuvant therapy remain essential for optimizing survival in these complex cases.
31 May 2026
Read appraisal →Medicine
Prevalence and long-term outcomes of brain fog and cognitive impairment in individuals with long COVID: A systematic review
BACKGROUND: Long COVID is increasingly recognized as a complex multisystem condition, with brain fog and cognitive impairment emerging as some of its manifestations. Despite growing literature, the pooled prevalence, subgroup differences, and underlying mechanisms remain incompletely understood. METHODS: We systematically reviewed 47 studies (2000-2025) encompassing over 25,000 patients to evaluate the prevalence of brain fog and cognitive impairment among long COVID populations. Data were extracted on study design, patient demographics, follow-up duration, and subgroup variables including gender, hospitalization, vaccination, and geographic region. Risk of bias was assessed using the Newcastle-Ottawa Scale (NOS v9.0) and JBI checklists. Quantitative synthesis was performed with subgroup and temporal analyses, presented in forest plots and summary figures. RESULTS: The pooled prevalence of brain fog was 30% (95% CI: 28-32), while cognitive impairment was 25% (95% CI: 23-27). Female patients consistently showed higher rates compared to males (34% vs 23% for brain fog; 29% vs 21% for cognitive impairment). Community-managed patients demonstrated higher prevalence compared to hospitalized cohorts, and unvaccinated individuals had a greater burden than vaccinated ones. Temporal analyses indicated that prevalence increased with longer follow-up, suggesting symptom persistence or late manifestation. Pathophysiological explanations include neuroinflammation, microvascular injury, immune dysregulation, and psychosocial stressors. CONCLUSION: Brain fog and cognitive impairment are common, persistent, and clinically significant features of long COVID. Gender differences, vaccination status, and follow-up duration influence prevalence. Future studies should focus on mechanisms, preventive strategies, and targeted interventions to mitigate long-term cognitive sequelae.
31 May 2026
Read appraisal →JMIR medical informatics
Advancing Alzheimer Disease Prediction With Large Language Model-Based Linguistic Feature Analysis: Development and Validation Study
BACKGROUND: Alzheimer disease (AD) is a progressive neurodegenerative disorder with rapidly growing global prevalence. Early detection is critical for timely intervention; yet, conventional diagnostic methods remain costly and invasive. Speech-based assessment has emerged as a noninvasive alternative, as AD characteristically impairs linguistic abilities including fluency, coherence, and informational content. Recent advances in large language models (LLMs) offer new opportunities to extract structured linguistic features from transcribed speech for automated AD classification. However, existing LLM-based approaches often lack transparency and clinical interpretability, limiting their adoption in clinical workflows. OBJECTIVE: This study aims to investigate the influence of linguistic features extracted from transcribed speech, as analyzed by LLMs, on the accuracy and interpretability of AD prediction. METHODS: We propose a framework that leverages LLMs to analyze linguistic features extracted from transcribed speech for AD classification. Our approach focuses on 4 key aspects, including readability, fluency, richness of detail, and keyword relevance. To enhance classification accuracy, the framework integrates transcript embeddings with feature explanation embeddings, forming a comprehensive linguistic representation. We conducted extensive ablation studies to evaluate the contributions of individual features and benchmarked our framework against existing LLM-driven methodologies through pairwise explainability evaluations. Output stability was assessed across 3 independent pipeline runs. A fully local configuration (Llama 3 8B + nomic-embed-text) was tested to evaluate privacy-preserving deployment feasibility. Explainability was assessed via LLM-based pairwise comparison (Gemini-3.1-flash-lite) against the method of Bang et al across 54 correctly classified cases and by blinded evaluation from 2 neurologists. RESULTS: The proposed framework achieved a mean precision of 91.52%, a sensitivity of 91.08%, a specificity of 96.29%, and F1-score of 91.05% across 3 independent runs on the ADReSSo 2021 dataset, outperforming existing LLM-based approaches. A fully-local configuration (Llama 3 8B+nomic-embed-text, requiring no cloud application programming interface access) achieved an F1-score of 81.58%, demonstrating framework transferability to privacy-preserving deployment environments. Keyword relevance was the most influential feature (F1-score drop of 13.22 pp when removed). Explainability evaluations showed our method was preferred in 49 out of 54 cases via Gemini-3.1-flash-lite, with human experts preferring our method in 89 of 108 blinded assessments. CONCLUSIONS: These findings highlight that a structured linguistic feature analysis using LLMs provides a robust and interpretable framework for preliminary AD detection. Our approach offers a scalable and accessible solution that bridges artificial intelligence-driven text analysis with clinical applications, supporting early detection of cognitive decline through noninvasive assessment methods.
31 May 2026
Read appraisal →Purinergic signalling
Turning failure into success: how artificial intelligence can help personalize therapies and re-use patient data.
Despite robust preclinical evidence, many clinical trials, including several that targeted the purinergic system, fail to demonstrate efficacy in humans. Failure may stem from inability to accurately identify patient subgroups responding similarly to treatments. Here, we explore the potential of artificial intelligence to revolutionize how we group and classify patients in clinical studies. We introduce a new framework using Large Language Models-generated embeddings of detailed patient data, to create a semantic-aware latent space, enabling us to identify truly meaningful patients' clusters. Large Language Models can provide explainable groupings, giving clear reasons why certain patients respond similarly to treatments. We present an example of successful application of this approach through the re-analysis of the AMARANTH clinical trial (NCT02245737, involving ~ 2200 patients and completed in 2018) testing Lanabecestat, a BACE1 inhibitor decreasing β-amyloid production in Alzheimer's disease, for which traditional analysis reported no efficacy. As in the original trial, our simulation showed no overall benefit. However, re-analysis per patients' clusters and subjects' re-stratification by semantic similarities (shared symptom profiles, progression patterns) identified a patients' subgroup in one of the clusters showing Lanabecestat-associated slower disease worsening, thus succeeding where the full trial had failed. By making a new therapy available to at least a subset of patients with a defined disease, this new approach may help maximize the return on drug development and reduce the burden on healthcare. Moreover, it will significantly improve the precision, efficiency and interpretability of clinical trials, paving the way for a new era of personalized medical treatments.
31 May 2026
Read appraisal →Lancet (London, England)
Efficacy and safety of oral semaglutide 14 mg (flexible dose) in early-stage symptomatic Alzheimer's disease (evoke and evoke+): two phase 3, randomised, placebo-controlled trials
BACKGROUND: Evidence, including animal, clinical, and real-world studies in individuals with type 2 diabetes and/or obesity, suggests reduced risk of dementia and Alzheimer's disease after GLP-1 receptor agonist exposure. The evoke and evoke+ trials aimed to investigate the efficacy and safety of oral semaglutide in individuals with early Alzheimer's disease. METHODS: evoke and evoke+ were multicentre, randomised, double-blind, placebo-controlled phase 3 trials conducted across 566 sites in 40 countries. The trials assessed the efficacy and safety of oral semaglutide up to 14 mg once daily in participants with amyloid-confirmed Alzheimer's disease, aged 55-85 years, with mild cognitive impairment or mild dementia due to Alzheimer's disease. In evoke+, participants with significant small vessel pathology were included. Participants were randomly assigned (1:1) to once-daily semaglutide 14 mg (flexible dose) or placebo for up to 156 weeks. The primary endpoint was change in Clinical Dementia Rating-Sum of Boxes (CDR-SB) score from baseline to week 104, assessed in all randomised participants. Safety was assessed in all randomised participants and reported for those receiving at least one dose of study drug. These trials were registered at ClinicalTrials.gov (NCT04777396 and NCT04777409); both trials have been discontinued due to negative clinical outcome. FINDINGS: Between May 18, 2021, and Sept 8, 2023, 9981 participants were screened, of whom 3808 were randomly assigned; 1855 in evoke (semaglutide, n=928; placebo, n=927) and 1953 in evoke+ (semaglutide, n=976; placebo, n=977). Mean age was 72·2 years (SD 7·1), and mean CDR-SB score was 3·7 (SD 1·6) at baseline. In evoke+, 54 (2·8%) participants had small vessel pathology. In evoke and evoke+, mean changes in CDR-SB score from baseline to week 104 were 2·3 (SE 0·1) and 2·2 (0·1) with semaglutide, compared with 2·3 (0·1) and 2·1 (0·1) with placebo (estimated difference -0·08 [95% CI -0·35 to 0·20], p=0·57 in evoke and 0·10 [-0·17 to 0·38], p=0·46 in evoke+). Treatment-emergent adverse events were reported in 1729 (91·2%) of 1896 participants receiving semaglutide versus 1613 (84·8%) of 1902 receiving placebo. There were five fatalities considered treatment-related by the investigators (one in the semaglutide group and four in the placebo group). INTERPRETATION: Oral semaglutide was not efficacious in slowing clinical progression in participants with early Alzheimer's disease. Safety and tolerability of semaglutide in early Alzheimer's disease is consistent with studies in other indications. FUNDING: Novo Nordisk.
31 May 2026
Read appraisal →Journal of neurology
Artificial intelligence for gait and balance in neurological disorders: a scoping review of clinical applications and technologies
BACKGROUND: Artificial intelligence (AI) has rapidly emerged within healthcare systems and neurological rehabilitation with the potential to revolutionize clinical decision-making and therapeutic strategies. However, a comprehensive understanding of how AI is currently applied to gait and balance rehabilitation in stroke, Parkinson's disease (PD), and multiple sclerosis (MS) is still lacking. OBJECTIVE: To map the current use of AI in neurological rehabilitation, focusing on clinical purposes, geographical distribution, and applied technologies for gait and balance rehabilitation. METHODS: Following the PRISMA-ScR statement, we conducted a literature search through MEDLINE, Cochrane CENTRAL, EMBASE, and Google Scholar up to July 2025 to identify studies applying AI-based methods to gait and balance outcomes in adults with stroke, PD, or MS. Study characteristics, AI methods, validation strategies, clinical purpose, and motor outcomes were extracted and synthesized narratively. RESULTS: Eighteen studies published from 2009 to 2025 were included. Most studies were conducted in Asia (50%) and involved people with stroke (77.8%). AI was predominantly used for prognostic purposes (72.22%), such as predicting falls, gait recovery, or treatment response and diagnostic applications (33.3%). Machine learning was the most common approach (88.9%) with Random Forest, Support Vector Machine, logistic regression, and eXtreme Gradient Boosting being the most frequently applied algorithms. None of the included studies performed prospective or external validation on independent datasets. CONCLUSIONS: This scoping review provides a comprehensive overview of current AI applications, highlighting promising but still immature approaches in the neurological rehabilitation of gait and balance. However, substantial methodological limitations remain major barriers to clinical translation.
30 May 2026
Read appraisal →The Cochrane database of systematic reviews
Botulinum toxin type A for subacute/chronic neck pain
RATIONALE: Botulinum toxin type A (BoNT-A) intramuscular injections are commonly administered for neck pain unresponsive to other therapies, but the benefit-harm profile remains uncertain. OBJECTIVES: To assess the benefits and harms of intramuscular BoNT-A injection for people with subacute to chronic neck pain. SEARCH METHODS: We searched CENTRAL, MEDLINE, Embase, CINAHL and trial registers, together with reference checking, citation searching and contact with study authors up to 4 March 2026. ELIGIBILITY CRITERIA: Randomised controlled trials (RCTs), including cross-over, pragmatic, or cluster designs, were included for adults with subacute to chronic neck disorder without radicular findings, with or without cervicogenic headache, and compared BoNT-A to placebo or no-treatment. OUTCOMES: Our outcomes were pain, function-disability, health-related quality of life, participant-reported treatment success, adverse events and costs at both short-term (< 24 weeks) and long-term (≥ 24 weeks) follow-up. RISK OF BIAS: We assessed the risk of bias (RoB) using the ROB 2 tool. SYNTHESIS METHODS: We synthesised results using meta-analyses with random-effects models to calculate mean differences (MD) and risk ratios (RR) with 95% confidence intervals (CIs), or using SWiM (Synthesis Without Meta-analysis) methods. We assessed certainty with GRADE. INCLUDED STUDIES: This update included 16 trials (855 participants) compared to six (252 participants) in the previous review: 13 parallel RCTs and three cross-over RCTs. One other trial is awaiting classification. Short-term follow-up ranged from 3 to 12 weeks post-treatment, and long-term follow-up was six months. Participants (mean age 45 years; 64% female; pain severity 56 on the Visual Analogue Scale (VAS 0 to 100)) were from ambulatory care settings in North America (five trials), Central America (one trial), Europe (six trials), the Middle East (one trial), Central Asia (one trial), and the East Asia & Pacific region (two trials), from upper (12 trials) or upper-middle (four trials) income economies. Disorder classifications included non-specific neck pain - myofascial (eight trials), whiplash-associated disorder (four trials), and cervicogenic headache (four trials). Overall, 88% of participants had chronic pain. The dose varied from 10 to 400 units over 1 to 10 trigger points. The RoB was high or raised some concern across all outcomes. SYNTHESIS OF RESULTS: Since 2011, evidence has shifted from no benefit to a potentially slight-to-small short-term benefit for BoNT-A compared with placebo. No trials compared BoNT-A with no treatment, and evidence was limited by bias (no studies with an overall low RoB), imprecision, and potential publication bias. For pain intensity measured on the VAS (scale 0 to 100), BoNT-A may result in a slight-to-small improvement in the short term (MD 5.95 points lower, 95% CI 10.89 lower to 1.02 lower; I2 = 0%; 12 studies, 393 participants; low-certainty evidence; downgraded two levels due to high overall RoB and indirectness), but not in the long term (MD 11.74 points lower, 95% CI 35.63 lower to 12.16 higher; I² = 50%; 2 studies, 43 participants; low-certainty evidence; downgraded two levels due to high RoB and imprecision). Mean pain in the placebo group was 46.66 points compared to 40.71 in the BoNT-A group, representing an absolute improvement of 6% (95% CI 11% better to 1% better). Additionally, there may be a moderate risk reduction in pain events in the short term (RR 0.68, 95% CI 0.56 to 0.83; I² = 0%; 2 studies, 292 participants; low-certainty evidence; downgraded two levels due to high overall RoB and dissemination bias, including potential conflict of interest from funding sources that may overestimate effects). Assuming a 70% risk with placebo, the corresponding risk with BoNT-A is 47%, representing a 23% absolute risk reduction. Five people (95% CI 3 to 9) would need BoNT-A treatment for one person to benefit. For physical function measured on the Neck Disability Index (scale 0 to 100), BoNT-A may have little to no effect in the short term, but the evidence is very uncertain (MD 2.49 points higher, 95% CI 9.72 lower to 14.70 higher; I² = 0%; 3 studies, 76 participants; very low-certainty evidence; downgraded three levels due to high overall RoB, indirectness, and wide confidence intervals). For health-related quality of life (change score), BoNT-A may make little to no difference in the short term (MD 2.30 points higher, 95% CI 12.57 lower to 17.17 higher; 1 study, 28 participants; low-certainty evidence; downgraded two levels due to imprecision). For participant-reported treatment success, BoNT-A may provide a small improvement in the global rating of change. Short-term results: MD 1.16 points lower on a 1 to 5 Verbal Rating Scale (95% CI 1.86 lower to 0.46 lower; 1 study, 31 participants; low-certainty evidence), corresponding to an absolute improvement of 20% (95% CI 10% to 36%). Long-term results were similar: MD 2.00 points lower on a 0 to 10 Patient Global Assessment (95% CI 3.80 lower to 0.20 lower; 1 study, 19 participants; 20% absolute risk reduction, low-certainty evidence). Evidence was downgraded by two levels for RoB and imprecision. No studies evaluated participant satisfaction or serious adverse events. For non-serious adverse events, participants treated with BoNT-A may have a small, negligible increased risk (RR 1.51, 95% CI 1.04 to 2.20; I² = 28%; number-needed-to-treat for an additional harmful outcome (NNTH) 6, 95% CI 4 to 12; 7 studies, 489 participants; low-certainty evidence; downgraded two levels due to RoB and indirectness). Participants treated with BoNT-A have a 16% increased absolute risk of experiencing non-serious adverse events lasting up to three weeks, such as injection soreness, muscle fatigue, and headache. AUTHORS' CONCLUSIONS: BoNT-A may provide slight-to-small short-term pain relief (6%) and perceived treatment success (20%) for subacute and chronic neck pain, but may offer no long-term physical function or health-related quality of life benefits. The moderate (23%) risk reduction in pain events may be overestimated due to publication bias. Non-serious adverse events are 16% more frequent. Uncertainty remains due to wide confidence intervals, short-term single-dose trials, and dose variation, highlighting the need for larger, high-quality studies with comprehensive adverse event reporting. FUNDING: This Cochrane review had no dedicated funding. REGISTRATION: Protocol and previous versions available via doi.org/10.1002/14651858.CD008626, doi.org/10.1002/14651858.CD008626.pub2 and doi.org/10.1002/14651858.CD008626.pub3 (withdrawn).
30 May 2026
Read appraisal →Behavioural brain research
Efficacy of repetitive transcranial magnetic stimulation at different frequencies for post-stroke cognitive impairment: A network meta-analysis
PURPOSE: Post-stroke cognitive impairment (PSCI) affects nearly half of stroke survivors, necessitating effective interventions. This Bayesian network meta-analysis evaluated the efficacy of various repetitive transcranial magnetic stimulation (rTMS) protocols for PSCI. METHODS: We systematically searched PubMed, Cochrane Library, and Chinese databases up to December, 2025, including 37 randomized controlled trials (2543 patients) comparing rTMS protocols with different frequencies (low/high) and sites (left/right). Outcomes were measured using the Montreal Cognitive Assessment (MoCA), Mini-Mental State Examination (MMSE), and Modified Barthel Index (MBI). RESULTS: Results indicated that high-frequency left dorsolateral prefrontal cortex stimulation (HFL-rTMS) most effectively improved MoCA scores (MD=3.38, SUCRA=87.6%), high-frequency right-sided stimulation (HFR-rTMS) ranked highest for MMSE improvement (MD=2.93, SUCRA=79.7%), and high-frequency bilateral stimulation (HFB-rTMS) yielded the greatest MBI enhancement (MD=13.47, SUCRA=89.6%). CONCLUSIONS: High-frequency rTMS demonstrated superior efficacy in cognitive and functional recovery. HFL-rTMS, HFR-rTMS, and HFB-rTMS are recommended as optimal interventions for MoCA, MMSE, and MBI outcomes, respectively. Further studies are warranted to validate stimulation parameters and expand multicenter evidence for clinical application.
30 May 2026
Read appraisal →Journal of neuro-oncology
Predicting late radiation-associated neurocognitive and endocrine toxicity in patients with brain tumors
PURPOSE: Neurocognitive and endocrine dysfunction are potential complications of cranial irradiation. However, risk factors are poorly understood, impeding accurate prognostication and exploration of potential preventive interventions. The objective of this study was to evaluate the prognostic value of various vascular and genotypic risk factors for the development of radiation-related toxicities. METHODS: This single-institution retrospective cohort study included patients with metastatic and malignant primary brain tumors who received cranial irradiation as part of their initial tumor-directed therapy. Demographic and treatment characteristics were collected, as well as putative vascular and genotypic risk factors. Univariate, multivariate, and machine-learning analyses were performed using five pre-specified measures of radiation-related toxicity. The primary outcome was change in mini-mental status exam (MMSE). RESULTS: Eighty patients (53% male, mean age 55.7, 53% primary and 44% metastatic brain tumors) were included. Elevated homocysteine and ApOE4 genotype were the strongest predictors of MMSE decline in the multivariate model (OR 3.96 [6.5-200, p < 0.001 and 2.85 [1.92-27.6], p = 0.004). Elevated homocysteine was associated white matter change on MRI and both physician and patient assessment. ApoE4 allele was associated with new endocrine deficiency, and physician assessment. An online nomogram provides risk predictions for each of the five late toxicity outcomes: ( https://afranklin22.shinyapps.io/PRMMSERadiationRiskFactors/ ) CONCLUSION: Two pre-treatment laboratory values (elevated homocysteine and ApOE genotype) were strongly associated with post-radiation neurocognitive and endocrine dysfunction using a variety of domains. Our predictive algorithm can aid clinicians in stratifying baseline risk and should be validated in prospective trials and with additional metrics of ND.
30 May 2026
Read appraisal →ACS applied materials & interfaces
A High-Sensitivity MXene Tactile Sensor with Dynamic Point-Contact Networks for a Wearable Healthcare Device
The self-stacking of the MXene material can diminish its interlayer spacing and active surface area, thereby diminishing the sensitivity and response speed of pressure sensors. Here, we introduce a high-performance flexible tactile sensor utilizing a composite of MXene, polystyrene (PS) microspheres, and bacterial cellulose (BC). PS microspheres are integrated within the MXene nanosheet layers to mitigate the self-stacking. By employing an innovative point-to-point conductive network design, the self-aggregation issue of MXene materials is effectively mitigated, leading to the formation of dynamic point-contact structures among PS microspheres. This configuration notably enhances detection sensitivity within the low-pressure range to 568.8 kPa-1 (20-200 Pa). Furthermore, the incorporation of BC enhances the interfacial bonding strength through hydrogen bonding, thereby enhancing sensor stability. The sensor exhibits a rapid response time of 29 ms, a wide detection range of up to 0-50 kPa, a low limit of detection of 2 Pa, and consistent signal stability over 20,000 cycles. It has demonstrated the capability to monitor a broad spectrum of physiological activities, including joint movements, facial microexpressions, and radial pulse. Integration with wireless transmission technology enables remote health monitoring, flexible touch keyboard functionality, and human-computer interaction. Additionally, the electromyography (EMG) skin sensor based on the MXene/PS/BC (MPB) composite film accurately distinguishes muscle movements, gestures, acoustic vibrations, and facial microexpressions by detecting surface EMG signals. This comprehensive approach offers a comprehensive solution for wearable medical devices that combine high sensitivity with practicality.
30 May 2026
Read appraisal →JMIR cardio
Barriers and Facilitators to Maintaining Physical Activity After an mHealth Intervention for People Poststroke or Transient Ischemic Attack: Reflexive Thematic Analysis
BACKGROUND: Regular physical activity is critical for preventing secondary stroke following a stroke or transient ischemic attack (TIA). Although mobile health (mHealth) interventions have shown promise for promoting short-term increases in physical activity, evidence on their long-term effects and the mechanisms that support sustained behavior change remains limited. In particular, little is known about how people poststroke or TIA integrate the skills, knowledge, and habits gained through mHealth interventions into their daily lives once structured intervention support ends. OBJECTIVE: This study aimed to explore the perceived barriers and facilitators of maintaining physical activity among individuals poststroke or TIA after completing an mHealth intervention. METHODS: A qualitative approach was used, involving a strategic sample of 12 participants recruited after they had completed a 6-month mHealth intervention for people poststroke or TIA. The intervention included supervised physical therapy with mHealth support for physical exercises and behavior change (eg, counseling, goal-setting, and self-monitoring), followed by a 6-month postintervention period with access to self-managed mHealth support. To enable richness and depth in participants' accounts, the dataset consisted of 2 semistructured interviews conducted 3 months and 6 months after completing the intervention, along with participant-generated photographs. Between the interviews, participants took photos to reflect their experiences of maintaining physical activity after the intervention. These images served as prompts for dialog and reflection during the second interview. Data were analyzed using reflexive thematic analysis. RESULTS: We generated 3 themes: building experience and knowledge to maintain physical activity, staying physically active in a complex life situation, and the meaning of context for maintaining physical activity after the intervention. Barriers and facilitators were conceptually integrated into a comprehensive understanding of maintaining physical activity, symbolized by the metaphor of a soap bubble, which requires persistence and a supportive environment to stay afloat. The complexity of participants' health and life situations created barriers to maintaining physical activity. To overcome these barriers, developing personal strategies within a supportive social and physical context was crucial. This development was facilitated by the mHealth intervention, which enabled knowledge acquisition of the principles of physical activity after stroke or TIA, along with increased awareness of its health benefits. CONCLUSIONS: mHealth interventions for people poststroke and TIA can serve as a catalyst for physical activity engagement and an enhancer of the knowledge and experience necessary to maintain physical activity after the intervention. Despite health-related and contextual barriers, participants may use personalized strategies, supported by their social and environmental context, to navigate these challenges. These insights highlight opportunities for future mHealth interventions to strengthen the interaction between individuals and their environment and empower tailored strategies for behavior change and long-term physical activity maintenance.
30 May 2026
Read appraisal →Science advances
MR-AIV reveals in vivo brain-wide fluid flow with physics-informed AI
The circulation of cerebrospinal and interstitial fluid plays a vital role in clearing metabolic waste from the brain, and its disruption has been linked to neurological disorders. However, directly measuring brain-wide fluid transport, especially in the deep brain, has remained elusive. Here, we introduce magnetic resonance artificial intelligence velocimetry (MR-AIV), a framework featuring a specialized physics-informed architecture and optimization method that reconstructs three-dimensional fluid velocity fields from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). MR-AIV unveils brain-wide velocity maps while providing estimates of tissue permeability and pressure fields, quantities inaccessible to other methods. Applied to the brain, MR-AIV reveals a functional landscape of interstitial and perivascular flow, quantitatively distinguishing slow diffusion-driven transport [∼0.1 micrometers per second (μm/s)] from rapid advective flow (∼3 μm/s). This approach enables new investigations into brain clearance mechanisms and fluid dynamics in health and disease, with broad potential applications to other porous medium systems, from geophysics to tissue mechanics.
29 May 2026
Read appraisal →Progress in biomedical engineering (Bristol, England)
Wearable sensors for monitoring neurogenic dysphagia: a scoping review
Neurological disorders, such as Parkinson's disease and stroke, often lead to neurogenic dysphagia, a swallowing disorder that compromises nutrition and increases the risk of malnutrition, aspiration of food, and even death. Although instrumental assessments remain the gold standard for evaluating swallowing function, their invasiveness, cost, and reliance on specialized clinical facilities limit their routine use. A systematic search was conducted in PubMed, Scopus, IEEE Xplore, and Web of Science, including only peer-reviewed studies using non-invasive wearable devices for assessing and monitoring dysphagia in neurological disorders. The initial search identified 1669 records, and an updated search retrieved 128 additional records. After screening, 24 studies met the inclusion criteria. Accelerometers and surface electromyography sensors were the most frequently used, either alone or in multimodal configurations. Multimodal approaches generally provided more accurate and comprehensive assessments than single-sensor systems. However, validation with gold-standard methods was performed in only half of the studies, and considerable heterogeneity was observed in sensor placement, study protocols, and signal-processing strategies. Wearable sensing technologies represent a non-invasive and scalable solution for assessing neurogenic dysphagia. To enable clinical translation, future research should focus on standardized methodologies, robust validation, and the integration of artificial intelligence for more precise swallowing assessment in individuals with neurological disorders.
29 May 2026
Read appraisal →The New England journal of medicine
Decompression with or without Duraplasty for Chiari I and Syringomyelia
BACKGROUND: In children with Chiari type I malformation and syringomyelia, neurosurgical posterior fossa decompression (PFD) provides clinical improvement, but whether duraplasty (incising the dura and placing a dural graft) improves outcomes is unclear. METHODS: We conducted a multicenter, cluster-randomized, controlled trial of PFD with duraplasty (PFD-D) as compared with PFD alone. Persons 21 years of age or younger with cerebellar tonsillar ectopia of at least 5 mm and a maximum syrinx diameter of 3.0 to 9.9 mm were enrolled at 38 centers. Centers were cluster-randomized: all the participants within each center underwent the same intervention. The primary outcome was surgical complications within 6 months. Secondary outcomes were clinical improvement, syrinx reduction, and repeat decompression at 10 to 24 months and the change in overall health-related quality of life at 6 to 24 months. RESULTS: A total of 162 participants were included in the trial, of whom 78 were assigned to undergo PFD-D and 84 to undergo PFD alone. The percentage of participants with complications within 6 months was 14% with PFD-D and 6% with PFD (adjusted odds ratio, 2.59; 95% confidence interval [CI], 0.86 to 7.84; P = 0.11). At 24 months, the percentage of participants with clinical improvement was 58% with PFD-D and 46% with PFD; the mean (±SD) syrinx reduction was 3.08±2.33 mm and 1.22±1.79 mm, respectively; and the percentage of participants with repeat decompression was 3% and 14%. Changes in health-related quality of life were similar in the two groups. CONCLUSIONS: The percentage of participants with surgical complications did not differ significantly between those who underwent PFD-D and and those who underwent PFD alone. Larger trials are needed to determine the relative benefits and risks of these two procedures. (Funded by the Patient-Centered Outcomes Research Institute and others; ClinicalTrials.gov number, NCT02669836.).
29 May 2026
Read appraisal →Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology
Exercise effects on cognitive function and molecular markers in post-stroke patients with cognitive impairment: a systematic review and meta-analysis
INTRODUCTION: Exercise may improve cognitive function in stroke survivors. However, no study has yet examined the most effective exercise modalities and associated changes in biomarkers. This study aims to investigate the effects of different exercise types on cognitive function and biomarker trends, providing evidence-based guidance for early rehabilitation interventions. METHODS: Data from randomized controlled trials were collected from January 1, 2000, to March 6, 2025, using medical subject headings combined with free-text terms from PubMed, Cochrane Library, Embase, Wanfang, CNKI, CSTJ, and the China Medical Information Database. The Cochrane Risk of Bias Tool was used to assess the quality of the studies. Review Manager version 5.4.1 was employed to conduct all statistical syntheses. RESULTS: The forty-four original studies involving 4,450 patients with PSCI(Post-stroke cognitive impairment) were analyzed. Subgroup analysis by exercise intervention type revealed larger effect sizes when combining aerobic exercise with other task-based interventions(cognitive-motor dual-tasking and functional motor training) [SMD = 1.18, 95% CI (0.68, 1.68)]. Patients also showed improvements in motor function [MD = 13.98, 95% CI (10.06, 17.89)] and activities of daily living [MD = 16.78, 95% CI (12.97, 20.58)]. Concerning molecular biomarkers, serum levels of brain-derived neurotrophic factor (BDNF), glutathione (GSH), and neuron-specific enolase (NSE) increased significantly, whereas interleukin-6 (IL-6) levels decreased notably in PSCI patients following exercise therapy. CONCLUSION: Structured exercise effectively improves cognitive recovery, motor skills, and daily functioning in patients with PSCI. These clinical gains are accompanied by favorable peripheral biomarker modulations, characterized by significantly increased serum BDNF and GSH, as well as markedly decreased IL-6 levels.
28 May 2026
Read appraisal →European journal of psychotraumatology
Biological and neurocognitive correlates of comorbid post-traumatic stress disorder and alcohol use disorder: a systematic review
Background: Post-traumatic stress disorder (PTSD) and alcohol use disorder (AUD) frequently co-occur, leading to greater clinical burden than either disorder alone. Despite this, little is known about the biological pathways linking these two disorders.Objective: We conducted a systematic review to synthesize evidence on molecular, genetic, neural, and cognitive mechanisms contributing to comorbid PTSD & AUD.Method: Following PRISMA guidelines, we performed a comprehensive search of five databases using PTSD-related, AUD-related, biological, and neurocognitive terms. Participants had to meet diagnostic criteria for both PTSD and AUD, and studies were required to have a comparator including controls, PTSD only, or AUD only. A critical appraisal was completed for all studies.Results: From 3904 identified papers, 14 met the inclusion criteria. Four studies examined the same molecular marker, with three papers derived from the same cohort, investigating baseline and stress-induced cortisol and adrenocorticotropic hormone, and found no differences unique to PTSD & AUD. One study linked low brain-derived neurotrophic factor and hazardous drinking to PTSD onset over 2 years following hospital admission. Genetic studies showed considerable overlap (72%) between PTSD and AUD in female twins, whereby the DRD2 A1 allele and the absence of the APOE ϵ2 allele were strongly associated with PTSD and drinking. Studies also reported lower neurometabolites, white matter integrity, and hippocampal volume in PTSD & AUD. Critical appraisal of these studies highlighted prominent selection bias (predominantly male and veterans) and limited justification of sample size.Conclusions: These findings suggest that PTSD & AUD may be characterized by distinct neurobiological alterations and genetic vulnerabilities relative to comparator groups. However, as there are currently insufficient data to support or refute these findings, this highlights the need for further research. PTSD and AUD frequently co-occur, leading to greater clinical burden than either disorder alone. Despite this, little is known about the biological pathways linking these two disorders.The current study aimed to review the existing molecular, genetic, neural, and cognitive evidence, and suggests similar impacted stress regulation across PTSD-only, AUD-only, and PTSD & AUD, yet distinct neural and genetic substrates among individuals with PTSD & AUD.Future research should include more female participants and a broader range of trauma types (currently dominated by veteran samples), and use large-scale neuroimaging and genetic datasets to help to identify mechanisms underpinning PTSD & AUD.
27 May 2026
Read appraisal →Journal of neurology
Man vs. Machine: can ML algorithms diagnose headaches as accurately as clinicians? A systematic review.
BACKGROUND: Headache disorders are frequently misdiagnosed. We aimed to systematically evaluate the diagnostic accuracy, methodological quality, and clinical applicability of artificial intelligence (AI) and machine learning (ML) models for classifying adult headache disorders against clinician diagnoses using the International Classification of Headache Disorders (ICHD) criteria. METHODS: In this systematic review, we searched PubMed, Embase, and the Cochrane Library (January 2015-December 2025) for AI/ML diagnostic headache studies. Two reviewers extracted data and assessed risk of bias using the QUADAS-2 tool with the QUADAS-AI extension. Main outcomes were sensitivity, specificity, area under the receiver operating characteristic curve (AUC-ROC), and risk of bias. RESULTS: We included 74 studies encompassing 154,856 participants. Models utilized traditional ML (n = 47), deep learning (n = 18), and hybrid or rule-based approaches (n = 9). Data inputs included neuroimaging (n = 27), multimodal datasets (n = 20), neurophysiological signals (n = 17), and clinical questionnaires (n = 10). Only 4 studies performed independent external validation. Overall sensitivity ranged from 47.5% to 100.0%, specificity from 50.4% to 100.0%, and AUC-ROC from 0.658 to 1.000. Models using structured questionnaires reported realistic accuracies (74-86%), whereas neuroimaging models frequently produced near-perfect, likely overfit estimates. Most studies (65 of 74) exhibited a high risk of bias driven by artificial case-control designs, data leakage, and absent external validation. CONCLUSIONS: AI-based headache diagnostic systems demonstrate promising but highly variable accuracy. Pervasive methodological flaws-specifically severe spectrum bias, data leakage, and a lack of independent external validation-currently preclude clinical implementation. Future studies require prospective, real-world validation to safely integrate these tools into practice.
27 May 2026
Read appraisal →ACS nano
All-Printed MXene-Based Springs for Concurrent Bidirectional Hand Motion Capture
Accurately capturing hand movements remains a challenge because the skin undergoes stretching, compression and bending at the same time, yet most flexible sensors respond only to tension. Achieving high sensitivity and a wide range often means making the sensor thick, which causes the sensing layer to shift under complex deformations and distorts the signal. Here, we address this by placing the strain-accommodating space within a plane parallel to the strain direction. Using a MXene/polyurethane ink with glycerol as a molecular bridge, we create a dense hydrogen-bonding network that enables high-resolution printing of a planar spring architecture. The resulting all-printed sensor achieves a gauge factor of 83.7 across a linear bidirectional range of ±60% strain. With just two sensors, we track and decode complex wrist motions with ±10° accuracy. When attached to finger joints, they enable real-time, dexterous control of a virtual hand. This work provides an alternative approach for skin strain capture through synergistic innovation in materials and architecture.
27 May 2026
Read appraisal →Journal of neural engineering
Non-invasive somatotopic proprioceptive feedback for closed-loop robotic hand aperture control
Objective.Current robotic prostheses developed for individuals with transradial amputation often lack physiological feedback, particularly proprioceptive information, which limits control precision and increases reliance on vision.Approach.This study investigates the effect of introducing non-invasive somatotopic feedback using transcutaneous electrical nerve stimulation (TENS) to convey hand aperture information, as an artificial form of proprioceptive feedback. Twenty healthy participants with intact limbs were divided into two groups: one receiving TENS-based feedback and one without feedback. Participants performed an aperture control task under visual and non-visual conditions, with some trials including a concurrent Stroop task to assess cognitive load. We hypothesized that providing non-invasive somatotopic proprioceptive feedback via TENS mitigates key control and integration challenges, leading to improved accuracy, faster learning, and reduced reliance on vision, without increasing cognitive demands.Main results.Under visual deprivation, participants receiving TENS feedback achieved significantly smaller aperture control errors than those without feedback, both under alternating visual conditions (p<0.001) and under prolonged visual deprivation conditions (p=0.017). From the very first trial, TENS-based feedback enabled control accuracy comparable to that with visual input. Although perceptual shifts affected control accuracy under dual-task conditions, the +TENS group maintained high cognitive performance and effective control toward perceived functional targets, suggesting that the artificial feedback nonetheless supported hand control in the presence of a secondary task.Significance.These findings highlight the potential of non-invasive somatotopic proprioceptive feedback delivered through TENS to provide physiologically meaningful information and support future strategies for restoring proprioceptive function in prosthetic hand users.
27 May 2026
Read appraisal →Journal of perinatal medicine
Functional neurobehaviour of the human fetus: a comprehensive framework for prenatal assessment using 4D ultrasound and AI
OBJECTIVES: To evaluate current evidence on prenatal neurobehavioural assessment using four-dimensional ultrasound (4D-US), structured scoring systems, and emerging artificial intelligence (AI) platforms, and to develop an integrated framework for early identification of neurodevelopmental vulnerability. METHODS: A narrative review of the literature from 2000 to 2025 was conducted following PRISMA-guided organisational principles to enhance transparency. Studies were eligible if they used 3D/4D ultrasound to assess fetal neurobehaviour, including spontaneous motor activity, facial expressions, or behavioural transitions. Specific tools examined included the Kurjak Antenatal Neurodevelopmental Test (KANET), prenatal General Movements Assessment (GMA), and AI-assisted behavioural analysis systems. Methodological quality was appraised using the Newcastle-Ottawa Scale and the Joanna Briggs Institute checklist. Data extraction focused on imaging protocols, behavioural parameters, scoring systems, and associations with neonatal neurological outcomes. RESULTS: Fifty eligible studies demonstrated that fetal motor sequences, movement variability, and facial expressions exhibit hierarchical maturation consistent with developmental progression of brainstem, subcortical, and cortical neural circuits. KANET parameters showed reproducible scoring and meaningful correlation with neonatal neurodevelopment, particularly in high-risk pregnancies. Prenatal general movement patterns displayed continuity with postnatal repertoires and contributed to early neurological prediction. AI-based classifiers provided objective quantification of fetal movement and facial activity, supporting automated or semi-automated assessment workflows. CONCLUSIONS: Functional neurobehavioural assessment using 4D-US, structured scoring tools, and AI-enhanced analysis is feasible, reproducible, and clinically informative. Integrating behavioural markers with neurosonographic findings and computational modelling may strengthen early detection of neurological risk and improve long-term neurodevelopmental care pathways.
27 May 2026
Read appraisal →Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology
How can social cognition be improved in neurological patients? a systematic review.
BACKGROUND: The neural network underlying social cognition (SC) may be vulnerable to different types of lesions, but also amenable to recovery through interventions that stimulate brain plasticity. This review assessed current non-pharmacological interventions (NPIs) and their effects on SC in neurological patients. METHODS: A systematic review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement. Randomized controlled studies, cohort studies, cross-sectional studies, case series, case reports, and literature reviews were included if they reported a precise description of the NPI in subjects older than 6 years, with brain or cerebellar lesion. RESULTS: Among 5.077 screened articles, 17 fulfilled the eligibility criteria. These articles provided 19 face-to-face, online or hybrid methods, involving 344 patients with multiple sclerosis, stroke, Parkinson disease, cerebellar ataxia, traumatic brain injuries or mixed acquired brain injuries. The methods were applied in observational and randomized study designs using group or individual settings, involving SC, general cognition, and relational aspects. These NPIs, classifiable into training, rehabilitation or combined interventions, improved SC in patients with various diagnosis and age. CONCLUSIONS: Results show that NPIs for SC in neurological patients is being addressed with insightful methods and study designs with improvement possibility in children and adults suffering different pathologies. This is not sufficient for thorough consensus but can trigger a range of broader high-quality and interdisciplinary clinical and research applications.
27 May 2026
Read appraisal →Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology
Genetic Creutzfeldt-Jakob disease associated with 5-octapeptide repeat insertion in the PRNP gene: case and pedigree report and literature review
BACKGROUND: Genetic prion diseases caused by octapeptide repeat insertions in the PRNP gene are rare. 5-octapeptide repeat insertion (5-OPRI) is an uncommon subtype that usually manifests as Creutzfeldt-Jakob disease (CJD) but shows marked variability in clinical phenotype and disease course. DISCUSSION: We report a patient with rapidly progressive gCJD caused by a 5-OPRI mutation. A systematic PubMed review identified genetically confirmed 5-OPRI cases with available clinical data. Demographic characteristics, clinical features, ancillary investigations, disease course, and PRNP codon 129 polymorphisms were analyzed. A total of 25 patients from 14 families were included. Age at onset ranged from 26 to 63 years (mean 44.8 ± 9.9 years). Disease duration ranged from 4 to 192 months. Dementia was present in all patients, and 84% showed frontotemporal dementia-like features. Cerebellar signs, extrapyramidal symptoms, and myoclonus occurred in 68%, 60%, and 44% of patients, respectively. Periodic sharp wave complexes were detected in 29.4% of EEGs, and cerebral atrophy was observed in all patients with neuroimaging. Most patients carried the MM genotype at PRNP codon 129 (83.3%). A short disease course was associated with more frequent akinetic mutism and myoclonus, while MM carriers had a younger age at onset than MV carriers. CONCLUSIONS: 5-OPRI-associated CJD exhibits marked heterogeneity in clinical and disease duration. The PRNP codon 129 polymorphism appears to influence disease onset and progression.
26 May 2026
Read appraisal →Neurology
Glucagon-like Peptide-1 Receptor Agonists and Risk of Nonarteritic Anterior Ischemic Optic Neuropathy: Systematic Review and Meta-Analysis
BACKGROUND AND OBJECTIVES: Recent observational studies have reported conflicting evidence regarding an association between glucagon-like peptide-1 receptor agonists (GLP-1RAs), particularly semaglutide, and nonarteritic anterior ischemic optic neuropathy (NAION). We aimed to synthesize the pooled evidence assessing the association between GLP-1RA use and NAION risk. METHODS: Embase, Medline, and Cochrane CENTRAL databases were searched from inception to April 5, 2025. Observational studies and randomized controlled trials comparing NAION risk in GLP-1RA users with non-GLP-1RA users were included. Title/abstract and full-text screening were conducted in duplicate by 2 independent reviewers. Discrepancies were resolved through discussion or adjudication by a third reviewer. Risk of bias was assessed using the Risk of Bias In Nonrandomized Studies of Interventions (ROBINS-I) tool, and the certainty of evidence was evaluated using the Grading of Recommendations Assessment, Development, and Evaluation framework. Pooled relative risks (RRs) were estimated using Bayesian random-effects models, incorporating half-normal, between-study, and weakly informative priors. The RR with 95% credible interval (CrI) was computed to analyze the development of NAION associated with GLP-1RA exposure. RESULTS: Five studies were included in the primary analysis, encompassing 1,593,554 patients (682,456 semaglutide users and 911,098 non-GLP-1RA users). Semaglutide use was associated with a high probability of increased risk of NAION compared with non-GLP-1RA use (RR: 2.52, 95% CrI [1.56, 4.72], I2: 57.8%, P(RR > 1): 99.9%). This association was particularly pronounced among patients with diabetes (RR: 2.41, 95% CrI [1.57, 4.10], I2: 47.2%, P(RR > 1): 99.9%). The overall incidence of NAION across 5 studies and 7 comparisons, encompassing 1,460,760 patients (semaglutide: 1,108,542; dulaglutide: 326,282; and exenatide: 25,936) and 272 NAION events (semaglutide: 198; dulaglutide: 54; and exenatide: 20) was 85 cases per 100,000 GLP-1RA users (95% CrI [29, 263], I2: 98.8%, P(incidence >0.00001): 100%). The incidence of NAION across 1,108,542 semaglutide users was 118 cases per 100,000 (95% CrI [32, 451], I2: 98.8%, P(incidence >0.00001): 100%). Leave-one-out sensitivity analyses consistently supported these findings. DISCUSSION: Low-to-moderate certainty evidence indicates that semaglutide significantly increases risk of NAION relative to non-GLP-1RAs, particularly among patients with diabetes. These findings warrant further investigation and should inform clinical risk-benefit discussions.
26 May 2026
Read appraisal →Journal of rehabilitation medicine
The effect of combined motor and cognitive rehabilitation on motor performance in Parkinson's disease: a systematic review and meta-analysis
OBJECTIVE: To evaluate the effectiveness of sequential and simultaneous motor and cognitive treatment on motor functions, activities of daily living, and quality of life in people with Parkinson's disease. DESIGN: Systematic review and meta-analysis. SUBJECTS/PATIENTS: Patients with Parkinson's disease. METHODS: A search was conducted in PubMed, Embase, Scopus, Web of Science, and Cochrane Library. Functional mobility was assessed as the main outcome, and balance, gait, activities of daily living, and quality of life as secondary outcomes. Meta-analyses were conducted using mean difference or standardized mean difference with 95% confidence intervals and fixed or random effect models. Heterogeneity was explored, setting a cut-off value of I2 = 50%. RESULTS: Sixteen studies were included, with 8 -eligible for meta-analysis. Results showed that sequential combined treatment offered no clear advantage over motor rehabilitation alone for functional mobility, although a significant benefit was found for activities of daily living. Results on simultaneous combined treatment suggested improvements in quality of life, but no significant differences between treatments for functional mobility. CONCLUSION: Motor-cognitive approaches may enhance prefrontal cortex efficiency, supporting complex motor tasks and reducing the risk of falls. Future research should elucidate neural mechanisms and compare simultaneous and sequential strategies to develop personalized, multidisciplinary rehabilitation protocols.
26 May 2026
Read appraisal →European journal of clinical pharmacology
Ondansetron augmentation for obsessive-compulsive disorder: A systematic review and meta-analysis of randomized placebo-controlled trials
BACKGROUND: A substantial proportion of adults with obsessive-compulsive disorder (OCD) experience persistent symptoms despite optimized first-line treatment with exposure and response prevention and/or serotonin reuptake inhibitors (SRIs). Ondansetron, a selective 5-hydroxytryptamine-3 (5-HT3) receptor antagonist with plausible circuit-level relevance to cortico-striato-thalamo-cortical dysfunction, has been evaluated as an augmentation strategy. Still, evidence is dispersed across small randomized trials. METHODS: We conducted a PRISMA 2020-adherent systematic review and random-effects meta-analysis (PROSPERO: CRD420261302132). Embase (Ovid and Embase.com), MEDLINE, PubMed, and Scopus were searched from inception to 12 February 2026. Eligible studies were randomized, double-blind, placebo-controlled trials of oral ondansetron augmentation in adults (≥ 18 years) with OCD on stable background treatment. The primary outcome was change in Yale-Brown Obsessive-Compulsive Scale (Y-BOCS) total score, pooled at prespecified acute windows (week 4, ~week 8 primary, week 12). Secondary outcomes included Y-BOCS obsession and compulsion subscales. Risk of bias was assessed using RoB 2. Random-effects models (DerSimonian-Laird) were used; Wan's method was applied when required. RESULTS: Five trials (total N = 289) were included. Ondansetron augmentation was associated with greater improvement in Y-BOCS total score versus placebo at week 4 (MD 2.63; 95% CI 0.87-4.39; I²=62%); heterogeneity resolved after excluding one influential study (MD 1.94; 0.37-3.51; I²=0%). At week 8 (four trials; N = 244), benefit was larger (MD 5.53; 3.45-7.60; I²=48%) and remained robust in sensitivity analysis (MD 6.76; 5.81-7.71; I²=0%). At week 12 (three trials; N = 200), effects persisted (MD 6.41; 4.28-8.53; I²=55%) with reduced heterogeneity after excluding one study (MD 5.06; 2.82-7.30; I²=0%). In two trials reporting subscales (N = 214), obsessions improved at weeks 4 and 8 (overall MD 2.67; 1.66-3.67; I²=0%), while compulsions showed a smaller, later signal (overall MD 1.02; 0.19-1.86; I²=0%). Safety reporting was variably detailed across trials; known QT-prolongation considerations remain relevant for off-label use. CONCLUSIONS: In randomized, placebo-controlled trials, ondansetron augmentation was associated with greater improvement in clinician-rated OCD severity than placebo over short-term follow-up, with signals emerging by week 4 and persisting through 8-12 weeks. However, these findings should be interpreted cautiously because the evidence base is small, geographically concentrated, and partly influenced by individual studies. The current evidence should therefore be considered preliminary and hypothesis-generating rather than definitive. Larger multicenter trials with standardized background SRI optimization, preregistered outcomes, and rigorous safety monitoring are required before routine clinical adoption can be considered.
25 May 2026
Read appraisal →Neurosurgical review
The role of preoperative thrombocytic factors on survival in patients with glioblastoma: a meta-analysis and synopsis of the literature
Glioblastoma (GBM) is the most common malignant primary brain tumour and is associated with poor prognosis. Platelet-related factors have been linked to outcomes in GBM, but evidence remains inconsistent. This review evaluates their prognostic value for survival. We conducted a systematic review and meta-analysis of studies on preoperative thrombocytic factors in GBM. PubMed, MEDLINE, and Embase were searched from inception to January 2025 using MeSH terms. Eligible studies included observational cohort studies of adults (≥ 16 years) with WHO grade 4 diffuse astrocytoma (GBM), ≥ 30 participants, and ≥ 3 months' follow-up after resection. Random-effects meta-analysis using restricted maximum likelihood with Hartung-Knapp adjustment was used to pool hazard ratios (HRs) of high vs. low parameters on overall survival (OS). Study quality was assessed with the Newcastle-Ottawa Scale. 21 studies were included. 13 (n = 2609) reported HRs of high vs. low platelet: lymphocyte ratio (PLR, median threshold = 150), with high PLR significantly associated with worse OS (pooled HR = 1.46, 95% CI [1.23, 1.74]). Eight studies (n = 1921) assessed platelet count (PC, median threshold = 208*109/L), showing high PC was a borderline predictor of worse OS (pooled HR = 1.38, 95% CI [1.00, 1.90]). Four studies (n = 1234) examined mean platelet volume (MPV, median cut-off = 9.05 fL), which qualitatively demonstrated worse OS in patients with high MPV or high MPV: PC ratio. One study assessed platelet distribution width (PDW, threshold = 14.7 fL), finding worse OS in the high PDW group. High preoperative PLR is associated with poor prognosis in GBM, while high PC demonstrates borderline increased risk, which may help refine risk stratification and guide future mechanistic studies.
25 May 2026
Read appraisal →Scientific reports
From sleep staging to spindle detection: a case study on end-to-end automated sleep analysis
Automation of sleep analysis, including both macrostructural (sleep stages) and microstructural (e.g., sleep spindles) elements, promises to enable large-scale sleep studies and to reduce variance due to inter-rater incongruencies. While individual steps, such as sleep staging and spindle detection, have been studied separately, the feasibility of automating multi-step sleep analysis remains unclear. In this case study, we evaluate whether a fully automated analysis using validated machine learning models for sleep staging (RobustSleepNet) and subsequent spindle detection (SUMOv2) can replicate findings from an expert-based study of bipolar disorder. The automated analysis qualitatively reproduced key findings from the expert-based study, including significant differences in fast spindle densities between bipolar patients and healthy controls, accomplishing in minutes what previously took months to complete manually. While the results of the automated analysis differed quantitatively from the expert-based study, possibly due to biases between expert raters or between raters and the models, the models individually performed at or above inter-rater agreement for both sleep staging and spindle detection. Our results demonstrate that fully automated approaches have the potential to facilitate large-scale sleep research. We are providing public access to the tools used in our automated analysis by sharing our code and introducing SomnoBot, a privacy-preserving sleep analysis platform.
25 May 2026
Read appraisal →Journal of neuro-oncology
Therapeutic effects of selumetinib on diffuse neurofibroma and optic pathway glioma in neurofibromatosis type 1.
PURPOSE: Selumetinib is approved for the treatment of inoperable plexiform neurofibromas (PN) in patients with neurofibromatosis type 1 (NF1). However, its efficacy in treating NF1-associated diffuse neurofibromas (NF1-DN) or optic pathway gliomas (NF1-OPG) remains unclear. We evaluated the efficacy and safety of selumetinib in these subgroups. METHODS: This was a sub-analysis of a Korean phase II open-label trial focusing on non-target treatment effects on NF1-DN and NF1-OPG. A total of 88 pediatric and adult patients with NF1-PN (59 children and 29 adults) in this trial had been treated for at least 2 years (~ 26 cycles, 28-day cycle) with oral selumetinib (20 or 25 mg/m², or 50 mg/dose every 12 h). Tumor volume, quality of life (QoL), and visual acuity were assessed. RESULTS: Among the 88 included patients, NF1-DN was diagnosed in 25 (28%), and NF1-OPG in 3 (3%). All NF1-DN patients exhibited disfigurement, two experienced pain, and a partial response (PR; ≥20% tumor reduction at a single time) was achieved in 9 of these cases (36%). The median time to PR was 6 cycles (range, 6-12), and the median time to best response was 18 cycles (range, 6-26), with a median volume change of - 11.9% (range, - 55.4% to + 36.3%). Confirmed PR (cPR; PR sustained for > 6 cycles) was observed in 6 NF1-DN patients (24%), stable disease (SD) was observed in 9 of these patients (36%), and progressive disease (PD) in 10 cases (40%). In a paired comparison, cPR was significantly lower for NF1-DN than for NF1-PN (24% vs. 88%, P < 0.001), and the median best volume reduction was also smaller (- 11.9% vs. -42.1%, P < 0.001). For the 3 NF1-OPG patients, visual impairment was present in all cases at baseline. One patient achieved PR at cycle 12 (- 36.1% from baseline) and complete response (CR) at cycle 26 but the visual acuity did not improve in this case. SD was maintained in the other two patients. Within these subgroups, QoL improved in 16 patients (57%) by cycle 26. No serious adverse events or treatment discontinuations were reported. CONCLUSIONS: Selumetinib shows therapeutic potential against NF1-DN, although with a lower response than that observed against NF1-PN. The NF1-OPG findings herein for this drug were exploratory only because of the small number of cases, but they may provide an additional clinical context for evaluating the broader effects of selumetinib across NF1-associated lesions. TRIAL REGISTRATION INFORMATION: KCT0003700, cris.nih.go.kr/cris/search/detailSearch.do/19,080, first submission March 4, 2019; first patient enrolled May 14, 2019.
25 May 2026
Read appraisal →Chemico-biological interactions
Carbon quantum dots exacerbate Parkinson's disease-like pathology through microglia-mediated neuroinflammatory responses with toxicological implications
The global burden of Parkinson's disease (PD) is projected to double by 2050, with early-onset cases demonstrating accelerated progression and limited therapeutic options. Recently, carbon quantum dots (CQDs) emerge as promising nanoplatforms for PD theragnostic, but their paradoxical role in exacerbating neuroinflammation and neurodegeneration shows the urgent need for mechanistic safety evaluations to bridge their therapeutic potential and neurotoxic risks. This study conducted a neuro-safety assessment of CQDs based on MPP+-induced mice and cell PD models to stimulate the theragnostic scenarios of CQDs in PD patients in the real-world. The results showed that CQDs not only promoted the onset and progression of PD-like damages in mice and cells but also exacerbated PD-related neurological dysfunction and structural damages in PD models. The microglia-mediated neuroinflammatory reactions were identified to be pivotal mediators of CQDs exacerbating PD-like damages based on machine learning (ML) approaches. According to these key toxic targets, we established molecular docking technology and network pharmacology to propose interventional strategies, including functional modification of CQDs and anti-neuroinflammatory drugs to alleviate the exacerbation of PD-like damages induced by CQDs, thereby ensuring the safe application of CQDs in the diagnosis and treatment of PD.
25 May 2026
Read appraisal →Neuroscience
MSCMH-Net: A multi-scale channel-mixing hybrid network for whole-brain segmentation
Whole-brain segmentation constitutes a fundamental task in medical image analysis, providing quantitative assessment of fine-grained brain regions and serving as a cornerstone for both clinical practice and neuroscience research. Despite its importance, the task is inherently challenging given the numerous brain regions, pronounced inter-class heterogeneity, and sophisticated inter-class spatial dependencies. Accurate whole-brain segmentation requires not only precise delineation of local features but also comprehensive modeling of long-range dependencies and global contextual information. To tackle these challenges, we propose the Multi-Scale Channel-Mixing Hybrid Network (MSCMH-Net), a CNN-MLP hybrid framework integrating convolutional and MLP modules at multiple hierarchical levels. The framework leverages the strengths of CNNs to capture local features and spatial structures, while employing MLPs to model long-range dependencies and global contextual information. For integrating global and local information, a channel-mixing module incorporating an exponential moving average (EMA) fusion strategy is employed. A composite dataset of 106 brain MR scans was used, including 36 from MICCAI-2012, 30 from ADNI and 40 from OASIS. Ground truth labels were annotated and double-checked by experts. Comprehensive experiments conducted on the composite dataset validate that MSCMH-Net achieves competitive results relative to existing approaches.
25 May 2026
Read appraisal →Neuroscience
Therapeutic efficacy of engineered exosomes in Alzheimer's disease: A systematic review and meta-analysis of preclinical animal models
Engineered exosomes are modified extracellular vesicles designed to enhance targeting and cargo delivery, and they have been proposed as a therapeutic strategy for Alzheimer's disease. We systematically reviewed preclinical animal studies evaluating engineered exosomes, synthesized evidence from comparisons with disease models and with natural exosomes, and reported the study in accordance with the PRISMA 2020 checklist. Outcomes included spatial learning and memory assessed by the Morris water maze, amyloid beta pathology, tau phosphorylation, and neuroinflammatory markers. Random effects meta-analyses suggested that engineered exosomes improved Morris water maze performance and reduced amyloid beta burden and pro-inflammatory cytokines compared with natural exosomes, whereas evidence regarding tau phosphorylation was limited and largely qualitative, and the overall certainty of evidence was low to very low. These findings support further investigation of engineered exosomes, but conclusions should be interpreted cautiously until confirmed by rigorously designed and blinded preclinical studies and clinical trials with standardized protocols.
25 May 2026
Read appraisal →European journal of clinical pharmacology
Dysesthesia associated with GLP-1 agonist therapies: data-mining analysis and literature review
PURPOSE: An increasing number of anecdotal reports on social media platforms and medical blogs describe dysesthesia, particularly burning skin sensations, in association with glucagon-like peptide-1 receptor (GLP-1R) agonists. We performed a pharmacovigilance data-mining analysis to characterise cases of dysesthesia related to GLP-1R. METHODS: We conducted a disproportionality analysis using VigiBase data on GLP-1R (Anatomical Therapeutic Chemical classification: ATC code A10BJ) and tirzepatide, focusing on the HLT (High Level Term) "Paraesthesia and dysesthesia", with the Information Component (IC). Additionally, we reviewed all narratives of dysesthesia cases in the French Pharmacovigilance database to extract clinical and pharmacological characteristics. A literature review complemented this analysis. RESULTS: Exenatide was significantly associated with hypoesthesia or oral paraesthesia, semaglutide and tirzepatide with hyperaesthesia, and semaglutide with dysesthesia and burning sensation. Dysesthesia appears to be dose-dependent, occurring more frequently at higher doses and with more potent GLP-1R, whether used for weight management or type 2 diabetes. Discontinuation was often performed, followed by spontaneous favourable outcomes, and cases of rechallenge were observed. Skin burning sensations represent a distinctive form of dysesthesia. CONCLUSION: Pharmacovigilance quantitative and qualitative data strengthens evidence for dysesthesias associated with GLP-1R agonists already observed in clinical trials of semaglutide, tirzepatide, and retatrutide.
25 May 2026
Read appraisal →Medical science monitor : international medical journal of experimental and clinical research
Utility of Acupuncture Therapy for Adult Chronic Daily Headache Prophylaxis: A Systematic Review and Meta-Analysis
BACKGROUND Chronic daily headache (CDH) management remains challenging due to limited efficacy of standard preventive pharmacotherapies. Acupuncture has shown promise in chronic headache management. This study evaluated its sustained prophylactic efficacy for CDH. MATERIAL AND METHODS Following PRISMA guidelines, we systematically searched PubMed, EMBASE, the Cochrane Library, CNKI, VIP, Sinomed, and Wanfang Data (inception to September 2025) for randomized controlled trials (RCTs) comparing acupuncture with other interventions for CDH. Primary outcomes included headache frequency, days, intensity, duration, and analgesic use; subgroup analyses covered treatment modality, CDH subtype, and duration. RESULTS Twenty-two RCTs (1449 patients) were included. Compared with control, acupuncture significantly reduced headache frequency (mean difference [MD], -0.32; P=0.001), headache days (MD, -0.72; P<0.00001), intensity (standardized mean difference [SMD], -0.63; P=0.001), duration (SMD, -1.18; P=0.0001), and analgesic use (MD, -0.52; P<0.00001) after the intervention. These benefits persisted during follow-up: headache days (standardized mean difference [SMD], -0.70; P<0.00001), intensity (SMD, -1.11; P=0.008), duration (SMD, -1.83; P=0.003), and analgesic use (SMD, -0.60; P=0.007) remained reduced; headache frequency showed a trend toward reduction (SMD, -0.47; P=0.05). Subgroup analyses revealed consistent efficacy across various CDH subtypes, including chronic migraine and chronic tension-type headache, treatment durations (4-12 weeks), and intervention strategies (acupuncture alone or combined with medication), indicating broad clinical applicability. CONCLUSIONS Acupuncture yields clinically meaningful, sustained improvements in CDH, supporting its role as an effective routine and adjunctive prophylaxis and broader application in this population.
25 May 2026
Read appraisal →Lancet (London, England)
Endovascular thrombectomy for patients with large-core ischaemic stroke presenting up to 24 h after onset (ATLAS): a systematic review and individual patient data meta-analysis with central imaging adjudication
BACKGROUND: Patients with extensive ischaemic change are often excluded from endovascular thrombectomy. We aimed to synthesise the evidence from recent trials in these patients by performing a systematic review and individual patient data meta-analysis to estimate treatment benefit, including within clinical and imaging subgroups. METHODS: In this systematic review and meta-analysis, we searched PubMed and Embase for randomised trials published between March 1, 2018, and March 1, 2025, that evaluated efficacy and safety of endovascular thrombectomy compared with medical management in patients with large-core ischaemic stroke (based on an Alberta Stroke Program Early CT Score [ASPECTS] of ≤5 or estimated ischaemic core ≥50 mL) presenting within 24 h of onset. Individual patient-level data from all eligible trials were obtained. A central imaging core laboratory readjudicated ASPECTS and reanalysed ischaemic core volume. A two-stage meta-analysis with random-effects model was used to evaluate the distribution of 90-day modified Rankin Scale (mRS) scores (the primary outcome) using adjusted pooled generalised odds ratios (aGenORs). Missing data were handled by multiple imputation. Safety outcomes were all-cause mortality within 90-day follow-up and neurological worsening within 24-48 h of randomisation, reported as adjusted pooled relative risk (aRR); and symptomatic intracerebral haemorrhage within 36 h of randomisation (reported as risk difference). Subgroup analyses based on clinical and imaging characteristics were done, including subgroups defined by ischaemic core volume, ASPECTS, and time window from onset to randomisation. The meta-analysis was registered with PROSPERO (CRD420251058584). FINDINGS: We included 1886 patients (944 assigned to endovascular thrombectomy and 942 assigned to medical management) from six trials. Baseline characteristics were similar between treatment groups. At day 90, the distribution of mRS scores was improved in patients in the endovascular thrombectomy group (median score 4 [IQR 3-6]; n=940) versus those in the medical management group (5 [4-6]; n=931; aGenOR 1·63 [95% CI 1·42-1·88], p<0·0001). The endovascular thrombectomy group also had reduced mortality (292 [31·1%]) compared with the medical management group (347 [37·3%]; aRR 0·82 [95% CI 0·70-0·97], p=0·022). No significant differences were observed in symptomatic intracranial haemorrhage (ten [1·1%] of 944 vs nine [1·0%] of 942 patients; pooled unadjusted risk difference -0·17 percentage points [95% CI -1·01 to 0·67], p=0·69) or neurological worsening (197 [22·0%] of 896 patients vs 161 [17·9%] of 899; aRR 1·19 [0·87-1·62], p=0·27). Improved functional outcomes with endovascular thrombectomy were consistent across clinical and imaging subgroups, except for those with an estimated ischaemic core volume of 150 mL or greater, in whom point estimates favoured endovascular thrombectomy, particularly in the early time window (0-6 h), but wide 95% CIs limited interpretation. INTERPRETATION: Endovascular thrombectomy was associated with improved functional outcomes and reduced mortality versus medical management in patients with large-core ischaemic stroke presenting within 24 h of onset. With the exception of very extensive ischaemic changes (core volume ≥150 mL) presenting beyond 6 h, where evidence remains limited, benefit was sustained across ASPECTS and ischaemic core strata for patients presenting up to 24 h after onset. FUNDING: None.
24 May 2026
Read appraisal →Journal of neurology
External sensory cueing on gait in Parkinson's disease: a systematic review and network meta-analysis
OBJECTIVE: This systematic review and network meta-analysis compares the individual and synergistic efficacy of external sensory cueing modalities to establish a treatment hierarchy for optimizing spatiotemporal gait parameters in individuals with Parkinson's disease (PD). METHODS: A systematic literature search was conducted across Scopus, PubMed, Cochrane CENTRAL, and ClinicalTrials.gov. Randomized controlled trials (RCTs) evaluating visual, auditory, somatosensory, or combined cueing modalities in individuals with PD were eligible. Trials were required to compare at least two active cueing modalities or an active modality against a control condition and report gait velocity and/or stride length. RESULTS: Thirty-three RCTs (n = 1118 participants) were included. For gait velocity, somatosensory (MD = 0.12 m/s; 95% CI 0.04-0.19) and visual cues (MD = 0.11 m/s; 95% CI 0.02-0.20) demonstrated significant improvements over control. For stride length, visual (MD = 11.46 cm; 95% CI 6.40-16.51), somatosensory (MD = 10.74 cm; 95% CI 7.04-14.44), and auditory cues (MD = 5.25 cm; 95% CI 0.43-10.06) all yielded statistically significant benefits. P-score rankings identified somatosensory cues as the top-ranked modality for gait velocity (P-score = 0.71) and visual cues for stride length (P-score = 0.83). Global heterogeneity was substantial (I2 = 64-90%), and no significant publication bias was detected. CONCLUSIONS: External sensory cueing improves spatiotemporal gait parameters in PD, with somatosensory and visual cues emerging as the most effective modalities for enhancing gait velocity and stride length. Both modalities outperformed standalone auditory stimulation, providing a data-driven hierarchy to guide evidence-based cueing selection in clinical practice.
24 May 2026
Read appraisal →Studies in health technology and informatics
AI-Integrated EEG Decision Support for Neurocritical Care: A Conceptual and Feasibility Framework
The clinical use of electroencephalography (EEG) for neuro-prognostication in neurocritical care remains limited, despite its ability to provide non-invasive insight into brain activity. This study presents a conceptual framework and early-stage prototype of an AI-assisted EEG decision-support system. Using a literature-informed design and system architecture approach, a web-based prototype was developed to support automated EEG analysis and interpretable brain response reporting. Feasibility testing with four users demonstrated reliable system operation and generation of within-patient decision-support indicators. We highlight the system design and feasibility of operationalizing EEG-based analytics into a clinically interpretable decision-support framework for neurocritical care.
24 May 2026
Read appraisal →Studies in health technology and informatics
Usability of Mobile Health Apps from the Patient Perspective: A Systematic Review
Mobile Health (mHealth) apps are widely used to support care, but patient-focused usability evidence remains fragmented. We conducted a systematic review (January 2012-September 2025) of PubMed, IEEE Xplore, and Scopus to identify studies assessing usability from the patient perspective using standardized questionnaires or structured interviews. Of 490 records retrieved, 146 studies met the inclusion criteria. Fifteen disease categories were represented, most commonly mental health, neurology, metabolic/endocrine, and oncology. Overall, 24 distinct usability assessment methods were reported, and most studies relied on a single method. Among standardized instruments, the System Usability Scale (SUS) was most common (50 studies, 34.3%); the mean SUS score across these studies was 76.37, above the acceptability benchmark of 68. Overall, mHealth apps were generally rated as usable by patients, but future work should adopt mHealth-specific standardized measures, enroll larger samples, and report methods transparently to enable benchmarking and inform better app design.
24 May 2026
Read appraisal →Studies in health technology and informatics
Machine Learning Applications Within the Earlier Medicine Framework for Stroke: A Scoping Review
This scoping review explores how machine learning (ML) has been applied to stroke research within the Earlier Medicine framework, which promotes proactive and personalized care through primary (preventive care), secondary (acute care), and tertiary (chronic care) prevention levels. Following PRISMA-ScR guidelines, studies were identified from PubMed and Scopus, yielding 105 eligible studies. Most studies focused on tertiary prevention, such as predicting in-hospital mortality, complications, and functional outcomes, while fewer addressed early risk prediction or acute-phase detection. Common predictors included age, NIHSS score, glucose, stroke volume, BMI, hypertension, and diabetes, reflecting reliance on routine clinical data. Traditional ML models (e.g., logistic regression, random forest, SVM) remain dominant, although Ensemble/Hybrid and Deep Learning models have increased steadily since 2021. ML demonstrated promising predictive accuracy and clinical potential, though challenges remain in data heterogeneity, model transparency, and external validation.
24 May 2026
Read appraisal →JMIR human factors
Support, Monitoring, and Reminder Technology for Mild Dementia (SMART4MD) for People With Mild Cognitive Impairment and Their Informal Caregivers: Cost-Effectiveness Analysis
BACKGROUND: Support, Monitoring, and Reminder Technology for Mild Dementia (SMART4MD), a customized tablet app, was developed to improve or maintain the quality of life of people with mild cognitive impairment (MCI) and their informal caregivers. OBJECTIVE: This study conducts an 18-month economic evaluation of the SMART4MD app, in addition to standard care, compared with standard care alone in Sweden and Spain, from a health care provider perspective. METHODS: In a pragmatic randomized controlled trial, people with MCI and their informal caregivers were randomized to the intervention and control groups. Health care costs, quality-adjusted life-years (QALYs), and incremental cost-effectiveness ratios (ICERs) were measured in 345 Swedish people with MCI and their informal caregivers, and in 347 Spanish people with MCI. RESULTS: The analysis showed higher incremental costs and lower QALYs for Swedish people with MCI than for controls, whereas higher incremental costs and higher QALYs were observed for Spanish people with MCI. The intervention was not found to be cost-effective for Swedish informal caregivers, with an ICER of €78,000/QALY (€1=US $1.16). CONCLUSIONS: The differing findings regarding cost-effectiveness for people with MCI in Sweden and Spain highlight the need for further research with extended follow-up, ideally involving a larger sample size and conducted across different national contexts. TRIAL REGISTRATION: ClinicalTrials.gov NCT03325699; https://clinicaltrials.gov/ct2/show/NCT03325699.
23 May 2026
Read appraisal →Journal of neurology
A machine learning-derived speech index as a biomarker for Huntington's disease severity
BACKGROUND: The development of disease-modifying therapies for Huntington's disease (HD) necessitates sensitive, scalable, and objective biomarkers for patient stratification and tracking. Current clinical scales are rater-dependent and time-consuming, while neuroimaging is costly and inaccessible. We aim to develop and validate a novel Speech Index, derived from automated acoustic analysis, to stratify HD stages and predict the severity of disease. METHODS: We recruited 141 HTT gene carriers (37 premanifest, 104 manifest HD) and 69 healthy controls. Participants read a standardized passage, and the recordings were processed to extract 28 speech features. A composite Speech Index was constructed using Bootstrap LASSO Regression for feature selection. Its performance was validated against the HD stages, clinical scale scores and the volume of the caudate and putamen from MRI. RESULTS: The Speech Index significantly increased across HD stages (p < 0.001). The Index showed strong correlations with clinical measures (cUHDRS: ρ = -0.67; TMS: ρ = 0.57; SDMT: ρ = -0.63; all p < 0.001) and neuroimaging biomarkers (caudate: ρ = -0.55; putamen: ρ = -0.62; all p < 0.001). Generalized additive models confirmed the Index's high predictive value for these outcomes (pseudo-R2 from 0.430 to 0.596). CONCLUSION: We developed a fully automated, interpretable Speech Index that serves as a valid digital biomarker for HD severity. It holds promise for remote monitoring, clinical trial enrichment, and objective assessment of therapeutic efficacy.
23 May 2026
Read appraisal →Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology
Prediction and diagnosis of post-stroke epilepsy using artificial intelligence approaches: a systematic review and meta-analysis
INTRODUCTION: Post-stroke epilepsy (PSE) is a common complication following a stroke and is a major cause of epilepsy in the elderly. Artificial intelligence (AI) is currently developing rapidly in the medical field and has a promising outlook in disease diagnosis, treatment, and prognosis. METHODS: We screened five studies that fully met the requirements from the PubMed, Web of Science, and EMBASE databases using relevant search terms such as PSE and AI, and analyzed the role of AI in predicting and diagnosing PSE in these studies. RESULTS: The results showed that the sensitivity of AI in predicting and diagnosing PSE was 88% (95% CI 0.78-0.94), and the specificity was 83% (95% CI 0.79-0.86). The area under the summary receiver operating characteristic (SROC) curve was 0.90 (95% CI 0.87-0.92). CONCLUSION: These results indicate that using AI to predict and assist in diagnosing PSE demonstrates high specificity and sensitivity, and has certain prospects in the future auxiliary diagnosis of PSE.
23 May 2026
Read appraisal →Journal of medical systems
Advances in Technology-Assisted Visual Rehabilitation Therapies: Scoping Review
Visual impairment (VI) is a growing condition associated with aging, neurological diseases, and chronic eye conditions. In 2023, more than 2.2 billion people experienced VI, and at least 1 billion could have been prevented or remain untreated. Visual rehabilitation (VR) is essential for optimizing residual vision and promoting functional independence; however, only a minority have access to these services. In recent years, the development of technologies such as artificial intelligence (AI), mobile applications, and virtual reality (VRt) has transformed VR, offering accessible and personalized alternatives. This scoping review aimed to map and categorize the available evidence on technology-assisted visual rehabilitation therapies, identifying the types of technologies used, their applications, and reported clinical and functional outcomes. A scoping review was conducted following the Joanna Briggs Institute (JBI) methodology and PRISMA-ScR guidelines to map the literature published between 2015 and 2025 on technology-assisted VR therapies in adults with VI worldwide. Twenty-five studies, mainly from developed countries, were included. Interventions included videoconferencing, computerized systems, portable devices, VRt, artificial vision (AV), and smart sensors. Overall, digital technologies were shown to improve visual function, autonomy, and quality of life, with high patient satisfaction and adherence. Telerehabilitation (TR), understood as the remote delivery of visual rehabilitation services through videoconferencing and digital monitoring platforms, has emerged as a viable and cost-effective strategy. In contrast, immersive virtual reality technologies (VRt), head-mounted display systems, and multisensory integration devices have demonstrated potential to stimulate neuroplasticity and enhance functional visual outcomes.Telerehabilitation platforms, immersive virtual reality systems, portable electronic vision enhancement devices, artificial vision technologies, and sensor-based monitoring tools are emerging as promising interventions. These technologies have demonstrated improvements in visual acuity, reading performance, visual field sensitivity, patient adherence, and functional autonomy, although most studies remain small-scale and geographically concentrated in high-income settings.
23 May 2026
Read appraisal →Journal of neurology
Effectiveness of telemedicine-based rehabilitation after stroke: a systematic review
BACKGROUND: Access to timely post-acute stroke rehabilitation remains inconsistent, contributing to persistent disability despite advances in acute stroke care. Telemedicine has been proposed as a strategy to improve continuity and access to rehabilitation services, but its effectiveness across functional domains remains incompletely defined. OBJECTIVE: To evaluate the effectiveness of telemedicine-delivered post-acute follow-up and rehabilitation on functional recovery and disability outcomes among adult stroke survivors. METHODS: A systematic review was conducted in accordance with PRISMA guidelines. PubMed, Embase, Scopus, and Web of Science were searched for studies published between January 2015 and December 2025. Eligible studies included adult stroke populations receiving telemedicine-based post-acute follow-up or rehabilitation and reporting functional, motor, balance, quality-of-life, or feasibility outcomes. Randomized controlled trials and observational studies were included. Risk of bias was assessed using validated tools, and findings were synthesized narratively without quantitative pooling due to heterogeneity. RESULTS: Twenty-eight studies comprising approximately 1237 participants were included. Telerehabilitation was associated with improvements in activities of daily living, although between-group differences varied across studies. Activities of daily living improved post-discharge (Barthel Index + 23.3 vs + 9.3; p < 0.001), and Functional Independence Measure scores increased in tele-CIMT and wearable-assisted interventions. Upper limb performance improved (WMFT time - 2.28 s; Box and Block Test + 5.57 vs 1.0; Nine-Hole Peg Test - 71.6 s; Jebsen-Taylor - 26.6 s). Spasticity decreased (MAS) with wrist improvements maintained at 6 months. Mobility and balance also improved, with intervention groups walking farther (6MWT 141.6 ± 8.7 m vs 129.5 ± 7.1 m; p < 0.001) and showing comparable improvements in BBS outcomes across groups. Technology-assisted telerehabilitation programs were more frequently associated with statistically significant improvements; however, these findings were not consistently superior to standard rehabilitation across all studies. Most randomized trials had some concerns for bias, and half of non-randomized studies were rated as serious risk, limiting overall certainty. CONCLUSIONS: Telemedicine-based post-acute stroke rehabilitation is feasible and associated with functional improvements comparable to in-person care. Reported adverse events were infrequent and generally minor, though safety outcomes were inconsistently measured across studies. High feasibility, adherence, and patient satisfaction support its role as a scalable component of post-stroke rehabilitation pathways.
23 May 2026
Read appraisal →Lasers in medical science
Functional and ultrasonographic evaluation of high intensity laser therapy and Kinesio taping in stroke patients with hemiplegic shoulder pain: a randomized controlled trial
Hemiplegic shoulder pain (HSP) is a common post-stroke complication that limits functional recovery and rehabilitation participation. The objective of this study was to evaluate and compare the clinical, functional, and ultrasonographic superiority of High-Intensity Laser Therapy (HILT) and Kinesio Taping (KT) over each other, as well as in comparison to conventional therapy (COT). The study was designed as a prospective, double-blind, randomized controlled trial. Participants were allocated into three equal groups (n = 11 each group): Group 1 (Control Group) received COT with sham HILT and sham KT, Group 2 (HILT Group) received COT with active HILT and sham KT, and Group 3 (KT Group) received COT with sham HILT and active KT. COT was performed five days per week for three weeks, HILT was administered in 10 sessions, and KT was applied every other day for three weeks (nine sessions). The primary outcome measure of the study was the Shoulder Pain and Disability Index (SPADI) score. Secondary outcome measures included pain intensity assessed using the Visual Analog Scale (VAS), passive shoulder range of motion (pROM), ultrasonographic findings, Brunnstrom Recovery Stage (BRS), the Functional Independence Measure (FIM), and the Health Assessment Questionnaire (HAQ). All measures were evaluated at baseline (pre-treatment) and immediately following the intervention period (post-treatment). Data were analyzed using repeated measures ANOVA and McNemar tests. Groups were sociodemographically comparable (p > 0.05). Post-treatment, all groups exhibited significant improvements in shoulder flexion, extension, external rotation, and BRS, VAS, SPADI, FIM, and HAQ scores (p < 0.05). No significant changes occurred in ultrasonographic findings, shoulder abduction, or internal rotation. Notably, HILT demonstrated superior VAS and SPADI improvements compared to other groups (p < 0.05). HILT provides rapid, clinically significant pain relief and functional recovery in HSP without adverse effects, offering a safe and superior therapeutic option.
23 May 2026
Read appraisal →ACS sensors
Diagnosis of Primary Open-Angle Glaucoma Using Spectral Profiling of Aqueous Humor-Derived Exosomes
Primary open-angle glaucoma (POAG) is one of the most common neurodegenerative diseases that cause irreversible optic nerve damage. POAG diagnosis requires multimodal assessments; however, current methods like intraocular pressure (IOP) and optical coherence tomography (OCT) imaging suffer from low sensitivity and inter-patient variability, respectively. Liquid biopsy provides objective molecular signatures, offering a robust alternative to overcome limitations of conventional clinical hallmarks. Here, we present an efficient and highly sensitive diagnostic platform operating on spectral profiles from aqueous humor (AH)-derived exosomes. Following morphological and compositional validation of exosome presence in AH samples, antibody-functionalized substrates were fabricated for selective capture. Immobilized AH-derived exosomes are then coated with gold nanoparticles to generate molecular fingerprint Raman signals. A total of 7600 spectra are acquired and used to train and evaluate the convolutional neural network (CNN) model for binary classification between healthy controls and glaucoma patients. The trained CNN model achieved an AUC of 0.96 and a prediction accuracy of 91%. The system enables rapid, individualized diagnosis, overcoming sample volume limitations via integrated immunoassay-based isolation and artificial intelligence (AI)-driven classification, highlighting the potential of ocular fluid-derived EVs in the diagnosis of neurodegenerative diseases.
23 May 2026
Read appraisal →BMJ evidence-based medicine
Comparative effects of early physical interventions on preventing intensive care unit-acquired weakness: a systematic review and component network meta-analysis
OBJECTIVE: To compare the effects of early physical interventions on the prevention of intensive care unit-acquired weakness (ICUAW) and the improvement of relevant clinical outcomes in patients with critical illness. METHODS: We systematically searched the Web of Science, PubMed, Embase and the Cochrane Central Register of Controlled Trials from their inception until 20 August 2024, to identify randomised controlled trials (RCTs) enrolling patients ≥18 years old and implementing early physical intervention that commenced at any time point during mechanical ventilation (MV) use or within 7 days after intensive care unit (ICU) admission for review. We synthesised data using a random-effects model and analysed through network meta-analysis (NMA) and component network meta-analysis (CNMA). MAIN OUTCOME MEASURES: Primary outcome is the incidence of ICUAW. Secondary outcomes included Medical Research Council sum score, length of stay in the ICU or hospital, duration of MV and mortality rates in the ICU or hospital. RESULTS: Our analyses included 63 RCTs involving 24 treatments and eight components. The NMA results revealed systematic early mobilisation (SEM) combined with neuromuscular electrical stimulation (NMES), SEM alone and NMES alone may lead to a moderate to large reduction in the incidence of ICUAW (odds ratios [ORs]=0.03, 0.09 and 0.12, 95% confidence intervals [CIs]=0.00 to 0.42, 0.01 to 0.97 and 0.03 to 0.44, respectively) and improved relevant clinical outcomes compared with routine care. The CNMA results further indicated that SEM (OR=0.14, 95% CI=0.02 to 0.83) and NMES (OR=0.22, 95% CI=0.09 to 0.52) effectively mitigated the ICUAW incidence. CONCLUSIONS: SEM and NMES are optimal interventions for preventing ICUAW. Healthcare providers in ICUs should implement early mobilisation with structured protocols and patient assessments or apply NMES to specific muscle groups to prevent ICUAW in critically ill patients and improve relevant clinical outcomes. PROSPERO REGISTRATION NUMBER: CRD42024581173.
23 May 2026
Read appraisal →The Cochrane database of systematic reviews
Artificial intelligence for detection and staging of Alzheimer's disease using retinal images
This is a protocol for a Cochrane Review (diagnostic). The objectives are as follows: To determine the diagnostic accuracy of AI algorithm-based retinal image reading for detecting referral-requiring Alzheimer's disease in adults who have behavioral symptoms or cognitive decline in the primary care setting. Secondary objectives Where data are available, we will investigate heterogeneity by incorporating covariates in a metaregression.
22 May 2026
Read appraisal →Clinical infectious diseases : an official publication of the Infectious Diseases Society of America
Dolutegravir Reduces Human T-Cell Lymphotropic Virus Type 1 Proviral Load and Improves Neurological Outcomes in a Phase 2 Controlled Trial
BACKGROUND: No specific therapy is approved for human T-cell lymphotropic virus type 1 (HTLV-1) infection. We tested the effect of dolutegravir (DTG) on clinical outcomes and on HTLV-1 proviral load (PVL) in adults with HTLV-1 infection. METHODS: We conducted an open-label, randomized controlled phase 2 trial in Salvador, Brazil. Symptomatic adults with HTLV-1 infection were randomized 1:1 to DTG 50 mg once daily or vitamin C (VTC) 500 mg once daily for 48 weeks. We evaluated changes in gait performance (timed 10-meter walk test [T10MWT]). Key secondary endpoints included change in PVL and prespecified neurofunctional and urinary outcomes. Primary analyses emphasized intragroup and between-group comparisons; exploratory per-protocol analyses were performed for neurofunctional measures. Asymptomatic participants were included for PVL and immunological evaluations. RESULTS: Eighty-three participants (53 symptomatic) were randomized (DTG, n = 43; VTC, n = 40). At week 48, there was no between-group difference in T10MWT. However, DTG use significantly reduced PVL over 48 weeks compared with VTC (P < .001). Exploratory per-protocol analyses found significant improvements associated with DTG use in lower limb spasticity (P < .001), sensory function (light touch, P = .035; pinprick, P = .001), and median nocturia frequency (P = .012). Network analysis of cytokine expression showed a change in the DTG group from baseline to week 48, with loss of negative correlations and a more balanced pattern. DTG was well tolerated, with no unexpected safety signals. CONCLUSIONS: In this pilot randomized trial, DTG use was associated with significantly reduced PVL and improvement in neurofunctional and immunological characteristics of participants in exploratory analyses. These findings provide evidence supporting the use of integrase inhibitors for HTLV-1 infection. Clinical Trials Registration. Brazilian Clinical Trial Register (ReBEC), code RBR-5x7ccxp.
22 May 2026
Read appraisal →Clinical infectious diseases : an official publication of the Infectious Diseases Society of America
Effects of Semaglutide on Cognitive Function in People With HIV: A Randomized, Controlled Trial
BACKGROUND: People with human immunodeficiency virus (HIV, PWH) are at higher risk for visceral adiposity, enhanced inflammation, and cognitive decline than controls who do not have HIV. We previously demonstrated that PWH with lipohypertrophy had a decrease in weight, visceral adipose tissue, and several inflammatory markers after receiving semaglutide, a glucagon-like peptide-1 receptor agonist. Our aim was to investigate the effect of semaglutide on cognitive function in PWH and the possible mediation of this effect by changes in adiposity or inflammation. METHODS: In this randomized, double-blind, placebo-controlled phase 2b clinical trial, PWH on antiretroviral therapy were randomized 1:1 to receive 32 weeks of subcutaneous semaglutide or placebo. The primary outcome was the change in cognitive function at 32 weeks. Secondary measures included changes in body composition and inflammatory markers. Causal mediation analysis assessed semaglutide's direct and indirect effects on Cognivue scores through changes in adiposity and inflammation. RESULTS: 108 participants were included (54 per arm); 65% were non-White, 40% were female, and median age was 53 years. Compared with placebo, PWH on semaglutide significantly increased visuospatial, naming/language, and delayed recall scores at 32 weeks (P = .01, .05, and .04, respectively). After adjusting for sex and absolute CD4 count, only visuospatial score remained statistically significant (P = .05). Semaglutide's total natural direct effect maintained a positive effect on the visuospatial score while accounting for potential changes in high-sensitivity C-reactive protein and soluble CD163 levels (P = .04). CONCLUSIONS: Semaglutide may have a beneficial impact on visuospatial cognitive function in PWH through its effect on inflammation. Clinical Trials Registration . NCT04019197.
22 May 2026
Read appraisal →Clinical infectious diseases : an official publication of the Infectious Diseases Society of America
Pharmacokinetics of Dexamethasone in Tuberculous Meningitis
BACKGROUND: Dexamethasone is recommended as adjunctive therapy for tuberculous meningitis (TBM). Co-administration with rifampicin is expected to reduce dexamethasone exposure in TBM, an effect that may be more pronounced with the higher rifampicin doses currently being evaluated in clinical trials. METHODS: This pharmacokinetic study was nested in a randomized controlled trial comparing the safety of high-dose rifampicin (oral, 35 mg/kg; intravenous, 20 mg/kg) plus linezolid, with or without aspirin, versus standard-dose rifampicin (10 mg/kg) for adults with HIV-associated TBM. All participants received adjunctive oral dexamethasone every 12 hours starting at a dose of 0.4 mg/kg/day. Dexamethasone concentrations were measured on intensively sampled plasma on day 3 after study enrollment and analysed using nonlinear mixed-effects modeling. RESULTS: In total, 261 dexamethasone concentrations from 43 participants were available for model development. Eight (18%) participants were on efavirenz-based ART and five (11%) were on a lopinavir/ritonavir-based regimen. The median duration of rifampicin therapy at the time of pharmacokinetic sampling was 4 days (range: 0-7). Dexamethasone pharmacokinetics was best described by a one-compartment disposition model with first-order absorption and elimination. Typical oral clearance (CL/F) was 131 L/h, reduced to 11.5 L/h with concomitant lopinavir/ritonavir. High-dose rifampicin had no significant additional effect on dexamethasone pharmacokinetic parameters compared with the standard-dose. CONCLUSIONS: In adults with HIV-associated TBM, there was high dexamethasone clearance, likely related to a drug-drug interaction with rifampicin. High-dose rifampicin had no additional effect on dexamethasone exposure.
22 May 2026
Read appraisal →JMIR formative research
Comparing Stakeholders' Perspectives on Parkinson Disease Management and Digital Technologies: Exploratory International Survey
BACKGROUND: Parkinson disease (PD) is a progressive neurodegenerative disorder that poses complex challenges for persons with PD, informal caregivers, and health care professionals. With growing interest in digital and predictive artificial intelligence (AI) tools for disease management, understanding the needs and digital readiness of these stakeholder groups is crucial. OBJECTIVE: This work aims to (1) identify digital practices for PD management among persons with PD, at-risk individuals, caregivers, and health care professionals; (2) compare these practices across groups; (3) explore stakeholder desires for AI-based tools; and (4) assess alignments and gaps to inform tailored AI solutions. METHODS: An anonymous cross-sectional online survey of an exploratory nature was distributed (from December 2024 to October 2025) in 5 languages and completed by 255 respondents. Descriptive statistics summarized responses to 41 questions, including stakeholder-specific items. χ2 tests were performed to examine stakeholder differences in desired AI features. RESULTS: Interest in predictive AI was high across stakeholder groups. Symptom tracking was the most desired feature (selected by more than 76% of the respondents), and personalized treatment recommendations came second for both persons with PD and health care professionals; however, stakeholder priorities diverged in other areas. Health care professionals rated improving patient and informal caregiver engagement as significantly more important than persons with PD did, χ21 (n=205)=34.78, P<.001, and Cramer V=0.41. Despite considerable interest, the reported use of digital tools was limited, as most persons with PD did not use symptom-tracking apps or wearables, nor were they currently monitoring their condition, although many expressed intentions to begin. CONCLUSIONS: While predictive AI tools were viewed positively across groups, there were significant gaps in stakeholder preferences, highlighting the importance of tailored, context-aware design. Early diagnosis was not prioritized by persons with PD or health care professionals, likely reflecting the complexity of diagnosing PD in the absence of disease-modifying therapies. Coupled with the emphasis placed on preventive lifestyle guidance by persons with PD and those at risk, this highlights the importance of actionability in AI-based monitoring and prediction. Such actionability may also enhance perceived relevance and uptake, given that reported interest in digital health tools and self-tracking exceeded actual use. These findings offer early-stage insight to guide the development of future AI-based solutions for PD.
21 May 2026
Read appraisal →Brain topography
fNIRS-STCT: A Novel Hybrid Spatial and Temporal CNN and Transformer Network for fNIRS Signal Classification
Functional near-infrared spectroscopy (fNIRS) is a non-invasive brain imaging method that detects neural activity by monitoring blood oxygen levels and circulation changes in the brain. Some current fNIRS classification models are simply adapted from deep learning models such as Transformer and fail to effectively leverage both temporal and spatial information from fNIRS. In this paper, we develop a hybrid deep learning model for fNIRS classification, fusing spatial convolutional neural network (CNN), a temporal CNN, and Transformer, named as fNIRS-STCT. Primarily, the architecture employs dual large convolutional kernels - one for temporal feature extraction and another for spatial feature extraction. Second, spatial and temporal CNNs are respectively integrated with Transformer by feeding CNN-extracted features as input to Transformer and then concatenating CNN and Transformer features. Third, spatial and temporal features are fused by element-wise addition. Finally, we exploit cross-entropy as the loss function, which integrates both online label smoothing and flooding strategies to avoid overfitting. The model's effectiveness is demonstrated through testing on two public fNIRS datasets. Across subject-dependent, subject-semi-dependent, and subject-independent evaluation schemes, fNIRS-STCT model consistently outperforms other compared models for all given performance metrics. Specifically, on the unilateral finger- and foot-tapping dataset, our model achieves accuracies of 83.29%, 81.73%, and 79.60% across three evaluation schemes, surpassing the second-best model by margins of 3%, 2%, and 1%, respectively. The findings underscore fNIRS-STCT's effectiveness in cross-subject mental state and brain activity interpretation.
21 May 2026
Read appraisal →Neurosurgical review
Preoperative predictors of seizure outcomes after epilepsy surgery for mesial temporal sclerosis: A systematic review
Mesial temporal lobe epilepsy (MTLE) due to hippocampal sclerosis (HS) is a leading cause of drug-resistant epilepsy. Surgical resection, particularly anterior temporal lobectomy (ATL), achieves high rates of seizure freedom, but outcomes vary. Identifying reliable preoperative predictors may optimize patient selection and surgical success. To systematically evaluate preoperative clinical, neuroimaging, and neuropsychological factors associated with postoperative seizure outcomes in MTLE. A systematic review was conducted according to PRISMA 2020 guidelines. PubMed, Scopus, and Web of Science were searched for cohort studies (2010-2025) reporting predictors of seizure freedom after MTLE-HS surgery. Eligible studies assessed one or more preoperative factors (clinical, EEG, imaging, neuropsychological) in relation to postoperative outcomes. Data were extracted for qualitative synthesis, and methodological quality was assessed with the Newcastle-Ottawa Scale (NOS). From 615 records, 24 studies were included, encompassing patients treated with ATL, selective amygdalohippocampectomy (SAHE), or stereotactic laser ablation. Seizure freedom rates (Engel I/ILAE 1) ranged from 50% to 75%, with ATL achieving ~ 70% at 1-2 years. Multimodal concordance-MRI-visible HS, unilateral seizure onset on EEG, and concordant PET hypometabolism-was the strongest predictor of seizure freedom. Bilateral EEG/PET abnormalities consistently predicted poorer outcomes, whereas structural measures, such as hippocampal subfield volumes and HS subtypes, showed little prognostic value. Multimodal presurgical concordance is a robust predictor of favorable outcomes in MTLE-HS surgery, whereas bilateral or discordant findings indicate a higher risk of failure. Comprehensive presurgical evaluation remains essential to optimize surgical decision-making.
21 May 2026
Read appraisal →ACS applied materials & interfaces
Breathable Bioadhesive Electronic Skin for Precise Multimodal Electrophysiological Monitoring with AI-Assisted Physiological-State Prediction
Accurate and stable monitoring of electrophysiological (EP) signals is essential for wearable healthcare applications. However, conventional commercial electrodes suffer from poor conformal contact, limited breathability, and weak adhesion under sweaty skin and human motion condition, leading to unstable skin-electrode interface and low-quality signal acquisition. Moreover, single-modality EP signal measurements are insufficient for reliable physiological and health evaluation. Herein, we develop a multimodal electrophysiological signal monitoring electrode (MESME) featuring a fiber membrane combining thermoplastic polyurethane (TPU) and poly(acrylic acid) functionalized with N-hydroxysuccinimide ester (PAA-NHS), seamlessly integrated with a liquid metal (LM) serpentine conductive path. Compared with commercial electrodes (gel-based wet electrode), this MESME exhibits high mechanical flexibility and conformability, superior breathability, robust bioadhesion, stable electrical performance under perspiration, and motion conditions and lower interfacial impedance, enabling high-fidelity multimodal signal acquisition. Leveraging this capability, the multimodal signals generated by MESME can be further integrated with deep learning for drowsiness prediction, achieving a validation accuracy of 97.14%. This platform offers a practical strategy for multimodal electrophysiological monitoring and supports future development of data-driven health assessment models.
21 May 2026
Read appraisal →Scientific reports
Use of machine learning and voice for multiclass classification of Parkinson's disease, chronic obstructive pulmonary disease, and healthy controls.
Parkinson's disease (PD) and chronic obstructive pulmonary disease (COPD) are prevalent conditions with substantial impact on quality of life and health care systems. Both disorders affect voice production through different physiological mechanisms, yet neither condition has a widely adopted objective biomarker for routine clinical use. Voice analysis has emerged as a non-invasive digital biomarker candidate, but existing studies have largely focused on binary classification within a single disorder or language. This study aimed to evaluate whether an unified multiclass machine learning (ML) framework applied to sustained vowel "a" phonation can discriminate between PD, COPD, and healthy controls (HC) across linguistically distinct cohorts. Sustained vowel recordings were analyzed from Swedish speaking individuals with COPD and HC, and English-speaking individuals with PD and HC, collected under comparable mobile recording conditions. Acoustic features included baseline voice measures and Mel Frequency Cepstral Coefficients. A soft voting ML framework integrating support vector machine, random forest, CatBoost, and light gradient boosting classifiers was trained using nested cross validation with hyperparameter optimization. Data were partitioned at the participant level into a development cohort and an independent test cohort. Model performance was evaluated using accuracy, macro averaged precision, recall, F1 score, receiver operating characteristic analysis, and confusion matrices. Model interpretability was assessed using Shapley additive explanations and vowel space analysis. The final soft voting classifier achieved robust multiclass discrimination on the participant disjoint independent test set, with an overall accuracy of 0.842 and a macro averaged F1 score of 0.839. Classification performance differed across groups, with the highest performance observed for PD, intermediate performance for HC, and lower performance for COPD. Misclassifications occurred primarily between HC and COPD, while confusion between PD and COPD was minimal. Feature attribution analysis revealed class dependent relevance patterns, and vowel space analysis demonstrated subtle but consistent group level differences. These findings demonstrate the feasibility of using an explainable soft voting machine learning framework applied to sustained vowel phonation to distinguish between neurologically and respiratory driven voice impairments across linguistic contexts. The study supports voice as a promising digital biomarker modality for multiclass clinical discrimination using mobile recordings.
21 May 2026
Read appraisal →Neurosurgical review
Short-and long-term responsiveness of deep brain stimulation on motor and cognitive outcomes in GBA vs. Non-GBA parkinson's disease: a systematic review and meta-analysis of observational studies
Deep brain stimulation (DBS) is an established therapy for motor complications in Parkinson's disease (PD). Patients carrying glucocerebrosidase (GBA) mutations exhibit distinct disease trajectories, raising questions regarding potential differences in clinical outcomes following DBS compared with non-carriers. To evaluate short- and long-term motor, medication, and cognitive outcomes following DBS in patients with GBA-PD compared with non-GBA PD. We conducted a systematic review and meta-analysis of studies reporting clinical outcomes in PD patients with and without GBA mutations who underwent DBS and had a minimum follow-up of one year. Random-effects inverse variance models were applied, with subgroup analyses according to GBA status. DBS was associated with significant improvements in motor function in the off-medication state and sustained reductions in levodopa equivalent daily dose in both GBA carriers and non-carriers, with no significant between-group differences. Cognitive performance declined over long-term follow-up in both groups. At five years, greater cognitive decline, assessed using the Mattis Dementia Rating Scale, was observed among GBA-PD mutation carriers compared with non-carriers. Motor improvement and medication reduction following DBS were comparable between PD patients with and without GBA mutations. Over long-term follow-up, greater cognitive decline was observed among GBA-PD carriers.
21 May 2026
Read appraisal →BMC neurology
The impact of caffeine consumption on migraine: a systematic review.
BACKGROUND: Migraine is a common, disabling neurological disorder affecting over one billion people worldwide, characterized by recurrent unilateral, pulsating headaches lasting 4-72 h. Caffeine, a widely consumed psychoactive alkaloid found in coffee, tea, and other beverages, is frequently implicated as both a trigger and a treatment for attacks. OBJECTIVES: We aim to comprehensively evaluate the association between caffeine exposure and migraine by integrating observational evidence with Mendelian Randomization (MR) studies. METHODS: A comprehensive search was conducted through PubMed, Scopus, Web of Science, and the Cochrane Library till August 2025. We included primary studies assessing the impact of caffeine exposure on migraine risk. RESULTS: 19 studies (nine cross-sectional, seven MR, and three cohort) were included in our review. MR studies included over one million participants, while cross-sectional studies involved over 43,000 participants, and the cohort studies included 421 migraine cases. MR reflected lifelong genetic liability (population-level, chronic exposure) and associated with a reduced risk with overall odds ratio (OR) ranging from 0.53 to 0.71, with stronger associations reported in migraine with aura (ORs as low as 0.37-0.39). Observational studies captured short-term, acute effects and showed that abrupt withdrawal or acute excessive intake (≥ 3 drinks/day) can trigger attacks in some people (P = 0.024). While habitual moderate use was generally not linked to higher average migraine burden. CONCLUSIONS: Lifelong genetic liability to higher coffee/caffeine intake is associated with a reduced risk of migraine especially for migraines with aura. However, acute excessive intake or sudden changes in caffeine habits can trigger attacks in some people. More future large, well prospective cohorts and carefully designed MR studies are needed to confirm our results and clarify the precise impact of caffeine on migraine risk.
20 May 2026
Read appraisal →BMJ open
Measurement of quality of stroke care with national electronic health records: a prospective cohort study during and after the COVID-19 pandemic
OBJECTIVES: To evaluate the value of linked electronic health records (EHRs) for measuring stroke care quality in England before and after the COVID-19 pandemic, focusing on metrics not routinely captured: stroke incidence, dispensing of secondary prevention medications and a proxy of disability-time spent at home after stroke ('home-time'). DESIGN: Prospective cohort study using national linked datasets. SETTING: England-wide health data linkage including the Sentinel Stroke National Audit Programme (SSNAP), primary and secondary care, dispensed medications and mortality records, accessed via National Health Service (NHS) England's Secure Data Environment. PARTICIPANTS: 425 675 adults with a first stroke between 1 January 2020 and 31 December 2023; data were available for 304 210 in primary care, 279 825 in hospital admissions, 220 470 in SSNAP and 59 465 in death records. MAIN OUTCOME MEASURES: Annual stroke incidence; first-year medication dispensing rates for antiplatelets, anticoagulants, antihypertensives and lipid-lowering agents (with a 1-month washout period) and home-time at 180 days post stroke. RESULTS: Stroke ascertainment was highest when combining all sources, with 10.8% of non-fatal ischaemic strokes recorded exclusively in primary care and 19.4% of fatal strokes identified solely through death records. Standardised annual stroke incidence rose from 227.6 (95% CI 226.1 to 229.0) to 244.8 (95% CI 243.4 to 246.3) per 100 000 over the study period including the COVID-19 pandemic. During the COVID-19 lockdown, non-fatal stroke recordings decreased while stroke-related deaths rose, indicating that recording quality was sensitive to shifts in healthcare-seeking behaviour during the pandemic. Among people with ischaemic stroke, 89.1% received an antiplatelet or anticoagulant, 44.5% an antihypertensive and 80.5% a lipid-lowering therapy. For haemorrhagic stroke, these proportions were: for anticoagulants 13.5%, antiplatelets 13.2%, antihypertensives 46.6% and lipid lowering 41.1%. Medication dispensing for stroke prevention declined with increasing age and comorbidity, but varied little by ethnicity, region or pandemic period. Mean home-time within 180 days of stroke was 166.6 (95% CI 166.4 to 166) days, decreasing with greater age (141.4 days for 90 years or older (95% CI 140.7 to 142.1)), deprivation (166.4 days (95% CI 166.1 to 166.6) for most deprived quintile) and stroke severity (137.4 days for National Institutes of Health Stroke Scale (NIHSS) score on arrival over 22 (95% CI 135.8 to 139.1)) and increasing with years from the COVID-19 pandemic 2023 (169.3 days (95% CI 169.0 to 169.5) vs 2020 164.4 days (95% CI 164.1 to 164.7)). CONCLUSIONS: Standardised stroke incidence increased significantly over the study period, highlighting a growing public health burden that persisted despite disruptions due to the pandemic although variation in case ascertainment and stroke coding practices was observed. While secondary prevention coverage for antiplatelets and lipids was high, lower rates of dispensing of antihypertensives, particularly in older and comorbid populations, potentially signal a target for improvement. Home-time represents a sensitive, person-centred outcome that exposes disparities linked to socioeconomic deprivation and clinical severity that can be used to enhance routine stroke audits. These findings justify the expansion of linked EHR infrastructure and the modernisation of governance frameworks to enable the longitudinal evaluation of care quality beyond the COVID-19 era.
20 May 2026
Read appraisal →PloS one
Deep Learning outperforms physicians in myopathy and neuropathy classification based on Needle Electromyography Signal
Needle electromyography (nEMG) is a valuable tool for diagnosing patients with neuromuscular diseases. However, it is labor-intensive and is prone to diagnostic inaccuracies stemming from human biases. To address these challenges, we validated an nEMG diagnosis-aiding system with minimal preprocessing using deep learning model to classify patients into three categories: normal, myopathy, and neuropathy. Using 376 nEMG signals from 57 patients from a tertiary university hospital database through nested k-fold cross validation, deep learning model surpassed the classification performance of six electromyographers. The median patient classification accuracy, precision, sensitivity, and specificity of the deep learning model was 0.70, 0.70, 0.70, and 0.85, respectively, whereas those of the physicians were 0.55, 0.60, 0.54, and 0.78, respectively. Model interpretability and failure analysis showed that the deep learning model classifies based on relevant signal features. Despite higher accuracy of DL model, the number of unanimously misclassified cases were higher in the DL model than physicians. Our study validates deep learning is a fast, accurate, and practical application to aid physicians in diagnosing patients using nEMG signals.
20 May 2026
Read appraisal →Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology
Parkinson disease severity detection based On OPtFuzNet with fused features
BACKGROUND: Parkinson's disease (PD) is a progressive neurodegenerative disorder affecting millions of people worldwide. It severely impairs patients' mobility. For effective treatment strategies, it is essential to determine the severity of the disease at an early stage. METHODS: In this study, an optimized feature-fusion framework is developed to detect the severity of Parkinson's disease using gait data via an Optimized Fuzzy Neural Network (OPtFuzNet). This feature-fusion framework can be categorized into three stages: pre-processing, feature extraction, and classification. In the pre-processing stage, a median filter is employed to reduce noise present in the gait images. During feature extraction, local and global features of the gait images are captured using SAE and IDCN, respectively. Subsequently, these features are fused and fed into the OPtFuzNet classifier, where parameter optimization is performed using IGWO-Lévy. RESULTS: The proposed model is evaluated on the GAIT-IT and GAIT-IST benchmark datasets using standard metrics, achieving superior performance with accuracies of 98.08% and 98.12%, respectively. Furthermore, a feature importance analysis is conducted to identify the most influential gait characteristics that contribute to determining the severity of the impairment, thereby enhancing the clinical interpretability of the model.The proposed model is evaluated on the GAIT-IT and GAIT-IST benchmark datasets using standard metrics, achieving superior performance with accuracies of 98.08% and 98.12%, respectively. Furthermore, a feature importance analysis is conducted to identify the most influential gait characteristics that contribute to determining the severity of the impairment, thereby enhancing the clinical interpretability of the model. CONCLUSION: These results validate the efficacy of the proposed feature fusion and optimization strategy in identifying discriminative gait patterns associated with the severity of Parkinson's disease. However, as the datasets are based on simulated gait patterns rather than real clinical data, further validation on real-world datasets is necessary for practical applicability.
20 May 2026
Read appraisal →Revista do Instituto de Medicina Tropical de Sao Paulo
Molecular biomarkers associated with ATLL and HAM progression in HTLV-1 infection: a systematic review
Human T-lymphotropic virus type 1 (HTLV-1) infects approximately five to 10 million individuals worldwide, although only a minority develop severe outcomes such as adult T-cell leukemia/lymphoma (ATLL) or HTLV-1-associated myelopathy (HAM). Proviral load (PVL), while widely used, shows limited sensitivity and specificity, reinforcing the need for complementary biomarkers. Omics-based approaches have emerged as promising tools to improve risk prediction. We conducted a systematic review following PRISMA guidelines, searching PubMed, Web of Science, Virtual Health Library (BVS), and CAPES Periodicals databases for studies published between May 2020 and May 2025. Eligible studies included original observational designs investigating genomic, proteomic, and metabolic biomarkers associated with progression to ATLL or HAM. Methodological quality was assessed using tools from the National Heart, Lung, and Blood Institute (NHLBI). In total, 35 studies met the inclusion criteria, most conducted in Brazil, Japan, and Iran. A total of 67 biomarkers were identified: 37 genomic, 27 proteomic, and three metabolic with potential clinical applications in risk stratification, prognosis, and therapeutic monitoring of HTLV-1 infection. PVL remained the most frequently investigated marker but lacked predictive power in isolation. Additional candidates with strong potential included IFN-γ, CXCL10, Neopterin, AnxA1, and sTNFR2. This review highlights the potential of integrated multiparametric panels-combining PVL with omics-derived biomarkers-as a promising strategy to improve risk stratification, prognosis, and therapeutic monitoring in people living with HTLV-1 (PLHTLV-1). However, further longitudinal and clinically validated studies are needed to confirm their applicability and support their translation into early intervention strategies, particularly during the asymptomatic phase.
20 May 2026
Read appraisal →PloS one
Facilitating the measurement and treatment of Behavioral and Psychological Symptoms of Dementia (BPSD) and understanding caregiver burden using wearable devices in Rural Taiwan-Protocol for a dyadic feasibility pilot study
INTRODUCTION: Alzheimer's disease (AD) prevalence rises with societal ageing. In clinical care, behavioral and psychological symptoms of dementia (BPSD)-including depression, agitation/aggression, apathy, and sleep disturbance-worsen patients' quality of life and substantially increase caregiver burden, more significantly than the cognitive symptoms. Standard BPSD assessments rely on caregiver-rated questionnaires that are cross-sectional and may be biased when caregivers are themselves older adults. Device-based measures (e.g., research-grade wrist actigraphy) can provide objective longitudinal data and novel features. In parallel, therapeutic wearables may improve sleep and mood in adults, and might improve BPSD if accepted by people living with dementia. METHODS: This dyadic pilot study will recruit 20 participants (n = 10 AD patients; n = 10 caregivers) from outpatient services and affiliated day-care/dementia hubs in rural Taiwan. Participants will wear Geneactiv continuously for 8 weeks and Re-Timer ≥30 min/day for 4 weeks. Device-based data will be processed with GGIR, a well validated R-package designed for processing accelerometer data. Questionnaire assessments include Pittsburgh Sleep Quality Index (PSQI), Neuropsychiatry Inventory Questionnaire (NPI-Q), Caregiver Burden Inventory (CBI), and a semi-structured interview based on the Taiwanese version of Quebec User Evaluation of Satisfaction with Assistive Technology (T-QUEST) at prespecified timepoints. DISCUSSION: Wearable devices may facilitate the measurement and treatment of specific BPSD, as well as reduce caregiver burden. If proven feasible even in rural Taiwan where both digital and health literacy and resources are limited, this model will inform how device-based dementia care model can be considered and applied in the context of global ageing.
19 May 2026
Read appraisal →Aesthetic surgery journal
Therapeutic Efficacy and Safety of Recombinant Botulinum Toxin Type A for Moderate to Severe Glabellar Lines: A Randomized, Controlled, Double-blind, Phase III Trial in Chinese Adults
BACKGROUND: Conventional botulinum toxin type A (BoNT/A) products include non-therapeutic accessory proteins vary in composition and manufacturing processes, which may contribute to differences in purity and biochemical characteristics. YY001 (Retoxin®) is a novel recombinant BoNT/A manufactured via E. coli expression to reduce impurities, improve purity, and minimize immunogenic risk. OBJECTIVES: To evaluate the efficacy, safety, and immunogenicity of YY001, a recombinant botulinum toxin type A (Chongqing Claruvis Pharmaceutical Co., Ltd., Chongqing, China), in the treatment of moderate-to-severe glabellar lines. METHODS: In this multicenter, randomized, double-blind, vehicle- and active-controlled Phase III trial, 529 adults aged 18-65 with moderate to severe glabellar lines were randomized 4:2:1 to receive a single 20 U injection of YY001, Botox®(Allergan Pharmaceuticals Ireland, Westport, Ireland), or vehicle. The primary endpoint was composite response rate (CRR) at Week 4, defined as a ≥2-grade improvement of glabellar line severity assessed by both investigator and participant. Secondary endpoints included onset time, duration, satisfaction, and safety. RESULTS: YY001 achieved a CRR of 86.7% at Week 4, significantly outperforming Botox® (66.7%, P < .0001) and vehicle (1.3%, P < .0001). It showed faster onset, longer median duration (97 days), and high participant satisfaction. Adverse events were mild and comparable across groups. Only one case of anti-drug antibody was observed; no neutralizing antibodies were detected. A 12-week follow-up and ethnically homogenous population may limit generalizability. CONCLUSIONS: YY001 demonstrated strong efficacy and a favorable safety profile than Botox®. Longer-term and multicenter studies are needed to assess durability, immunogenicity, and applicability across broader populations. LEVEL OF EVIDENCE: 1 (THERAPEUTIC): For image description, please refer to the figure legend and surrounding text.
19 May 2026
Read appraisal →Journal of medical Internet research
Efficacy of Digital Speech Therapy for Poststroke Dysarthria: Randomized Noninferiority Trial
BACKGROUND: Poststroke dysarthria, a common speech impairment, affects up to half of all stroke survivors, often reducing their ability to communicate, and adversely affecting their quality of life. Although conventional speech therapy for poststroke dysarthria is effective, access is often limited by time and geographical constraints. Here, digital speech therapy may serve as a remotely deliverable alternative for selected patients. However, few trials have assessed its efficacy, safety, and usability. OBJECTIVE: This study aimed to evaluate whether a smartphone-based speech therapy app is noninferior to conventional workbook-based therapy in improving speech intelligibility among individuals with poststroke dysarthria. METHODS: This single-blind, randomized controlled, noninferiority trial was performed at 3 hospitals in South Korea. Adults (≥19 y) with poststroke dysarthria who were cognitively intact, without aphasia, and able to use a smartphone were eligible. Participants were enrolled between July 20, 2023, and April 15, 2024. Participants were randomly assigned (1:1), stratified by stroke phase, using a block randomization method, to receive either a smartphone-based digital therapy app or a conventional workbook-based therapy for 4 weeks. The primary outcome was speech intelligibility (0-100 perceptual rating) after the intervention. Primary analysis was intention-to-treat using analysis of covariance. A noninferiority margin of 19 points was pre-defined. RESULTS: A total of 73 participants were enrolled (median age 62.00 years). Among them, 38 were assigned to the digital speech therapy group and 35 to the control group. Intelligibility scores improved from 80.48 (SD 18.92) to 92.08 (SD 12.38) in the intervention group, and from 80.94 (SD 16.74) to 88.11 (SD 18.06) in the control group. The adjusted between-group difference was 4.49 (95% CI 0.61-8.37), and the lower bound of the 95% CI was above the prespecified noninferiority margin (-19), which supported noninferiority. No significant between-group differences were observed in the secondary outcomes related to speech function or psychological status. The system usability score was 89.6, and adherence in the digital speech therapy group was 64.6% based on app logs, with no treatment-related adverse events. CONCLUSIONS: Digital speech therapy was noninferior to conventional workbook-based therapy in improving speech intelligibility and was feasible across acute to early subacute and chronic stroke phases in cognitively intact stroke survivors with predominantly mild-to-moderate dysarthria. However, feasibility and efficacy in older stroke survivors with cognitive deficits or co-occurring aphasia, or in those unable to use smartphones, remain to be established.
19 May 2026
Read appraisal →Cerebellum (London, England)
Inhibitory Circuits of the Sensorimotor Network are Not Modulated By Cerebellar Transcranial Direct Current Stimulation in Isolated Cervical Dystonia
Impaired cerebellar influence on motor cortical excitability and plasticity has been reported in cervical dystonia (CD) patients, accompanied by the absence of cerebellar brain inhibition (CBI). Polarity-specific modulation of CBI in healthy individuals using cerebellar transcranial direct current stimulation (ctDCS) suggested that ctDCS could normalize abnormal cerebellar output and improve clinical symptom severity in CD. The objective of this study was to determine whether anodal or cathodal ctDCS can modulate neurophysiological parameters of cortico-cortical, cerebello-cortical or afferent inhibition and improve motor symptom severity in CD patients. Fifteen patients with isolated CD participated in a randomized, double-blinded crossover study consisting of three sessions of anodal, cathodal, or sham ctDCS. Before and after each intervention, motor symptom severity and inhibitory circuits of the sensorimotor network were investigated using transcranial magnetic stimulation (TMS), including short-interval intracortical inhibition (SICI), short-latency afferent inhibition (SAI) and CBI. Baseline TMS measurements showed inhibitory influence of SICI (p < 0.001) and SAI (p < 0.001) in CD patients, whereas CBI had no inhibitory effect (p = 0.281). The different ctDCS interventions caused no significant modulation in any of the inhibitory TMS paradigms investigated. Similarly, motor symptom severity remained unchanged after the ctDCS interventions. A single session of ctDCS was not effective to modulate inhibitory circuits of the sensorimotor network in isolated CD patients. Future studies focusing on repetitive ctDCS interventions or multifocal stimulation protocols are needed to assess the influence on network excitability and connectivity as well as on motor symptoms.
18 May 2026
Read appraisal →Annals of medicine
Endovascular thrombectomy versus best medical therapy for acute vertebrobasilar artery occlusion in patients with low NIHSS scores: a meta-analysis
OBJECTIVE: The efficacy of endovascular thrombectomy (EVT) for acute vertebrobasilar artery occlusion (VBAO) presenting with mild symptoms (National Institutes of Health Stroke Scale [NIHSS] score ≤10) remains uncertain. This meta-analysis aimed to compare the effectiveness and safety of EVT versus best medical therapy (BMT) in this population. METHODS: We systematically searched PubMed, Embase, and the Cochrane Central Register of Controlled Trials from inception to September 2025 for comparative studies. The primary outcome was 90-day excellent functional outcome (modified Rankin Scale [mRS] score 0-1). Secondary outcomes included functional independence (mRS 0-2), symptomatic intracranial hemorrhage (sICH), and all-cause mortality. Pooled odds ratios (OR) with 95% confidence intervals (CI) were calculated using a random-effects model. RESULTS: Seven observational studies involving 3,107 patients were included. In unadjusted analyses, EVT was associated with a higher rate of excellent functional outcome (OR 2.17; 95% CI 1.58-2.96) but not with functional independence (OR 1.58; 95% CI 0.90-2.77). After adjustment for confounders, EVT was associated with higher rate of excellent functional outcome (OR 2.86; 95% CI 1.89-4.31) and functional independence (OR 1.91; 95% CI 1.01-3.62). Safety outcomes including sICH and mortality did not differ significantly between groups. CONCLUSION: In patients with acute VBAO and mild symptoms, EVT may be associated with superior functional outcomes compared to BMT alone, without a significant increase in procedural risks. These findings suggest a potential role for EVT in selected patients with low NIHSS scores and underscore the need for confirmation in randomized trials.
18 May 2026
Read appraisal →Journal of neurology
Diagnostic and prognostic value of blood neurofilament light chain in ischemic stroke: an individual patient data meta-analysis
BACKGROUND: We aimed to conduct an individual patient data meta-analysis on blood neurofilament light chain (NfL) in ischemic stroke (IS) to enhance its clinical applicability. METHODS: We performed a systematic literature search of studies on blood NfL measured in adult patients within 30 days after IS onset and derived age- and BMI-adjusted Z-scores based on a previously published reference population of healthy controls. We collected clinical, radiological and biochemical parameters of IS patients and tested associations of NfL at defined timepoints after IS onset (D1: < 24 h; D2: 24-48 h; D3: 48-72 h; D4-5: 72-120 h; D6-7: 120-168 h; D8-30: > 168 h) with baseline characteristics and 3-month follow-up outcomes (modified Rankin Scale, mRS; survival). RESULTS: We included 4081 blood NfL values from 2872 participants (IS n = 1985, transient ischemic attack n = 88, healthy controls n = 799) of 18 published studies and 3 unpublished cohorts. In patients with IS, NfL Z-score progressively increased from D1 [median: 2.0 (IQR: 0.9-2.9)] to D6-7 [median: 3.5 (IQR: 3.0-3.8)], with discriminative ability being high for IS vs. controls (AUC: 0.79-0.97) and fair for IS vs. TIA (AUC: 0.64-0.80). Higher NfL Z-score at D1 was associated with greater risk of symptomatic intracranial hemorrhage (aOR = 1.33, p = 0.014) and, from D2 onwards, with larger infarct lesion volume (highest Spearman's rho: 0.795 at D6-7). NfL independently predicted a mRS > 2 (aOR = 1.31, p < 0.001) and mortality (aOR = 1.67, p < 0.001) at 3 months. CONCLUSIONS: Blood NfL level was progressively elevated after IS, could discriminate IS from healthy controls with high accuracy and had prognostic value for intra-hospital complications and 3-month clinical outcomes in IS.
18 May 2026
Read appraisal →The Journal of infectious diseases
Safety, Tolerability, and Pharmacokinetics of 6-Diazo-5-Oxo-L-Norleucine in Malawian Adults With and Without Malaria: A Phase 1 Dose-Escalation Clinical Trial
BACKGROUND: Cerebral malaria (CM) is a common cause of febrile coma among African children. Despite treatment with highly efficacious intravenous artesunate, CM remains associated with high mortality and neurologic sequelae. In a mouse CM model, the glutamine antagonist 6-diazo-5-oxo-L-norleucine (DON) demonstrated robust efficacy as a potential adjuvant therapy. Before testing DON in children with CM, we investigated tolerability in African adults, including individuals with malaria. METHODS: We conducted an open-label, prospective, dose-escalation, phase 1 clinical trial of single-dose intravenous DON in Blantyre, Malawi. Participants included healthy adults and adults with uncomplicated malaria, enrolled in dose-cohorts of 10 and administered DON at 0.1, 1.0, 5.0, or 10 mg/kg. We assessed adverse events (AEs) and plasma pharmacokinetics. RESULTS: Forty healthy adults and 38 adults with uncomplicated malaria received DON. Transient, asymptomatic creatinine elevation occurred 12 hours postinfusion in 18% of all participants. Nausea and vomiting were uncommon at 0.1 and 1.0 mg/kg but occurred more frequently at 5.0 and 10 mg/kg DON. All DON-related AEs resolved rapidly, without sequelae, and did not significantly differ between healthy and uncomplicated malaria participants. In healthy adults, maximum plasma concentrations increased proportional to dose, but adults with malaria had greater than dose-proportional increases. Terminal half-life ranged from 1.7 to 4.1 hours. CONCLUSIONS: DON was well tolerated in healthy adults and adults with uncomplicated malaria. Higher doses were associated with transient gastrointestinal AEs. Pharmacokinetics were minimally influenced by malaria disease. These results provide critical data to guide dosing strategies for adjunctive DON in children with CM. Clinical Trials Registration. NCT05478720.
18 May 2026
Read appraisal →Lancet (London, England)
Ticagrelor with aspirin dual antiplatelet therapy combined with intravenous thrombolysis in patients with ischaemic stroke in China (TAPIS): a multicentre, double-blind, randomised controlled trial
BACKGROUND: Evidence supporting the early addition of antiplatelet therapy to intravenous thrombolysis in patients with acute ischaemic stroke remains inconclusive. We aimed to investigate the efficacy and safety of early oral dual antiplatelet therapy (DAPT), started within 6 h of onset, as an adjunct to intravenous thrombolysis. METHODS: TAPIS was a randomised, double-blind, placebo-controlled trial done in 60 hospitals across China. We enrolled patients treated with intravenous thrombolysis for ischaemic stroke, with a National Institutes of Health Stroke Scale score of 4-10. We randomly assigned (1:1) patients to receive oral aspirin plus ticagrelor (DAPT group) or corresponding placebo within 6 h of stroke onset, either before, during, or after receiving thrombolysis. Ticagrelor or placebo was continued for days 2-7 in each group, with open-label aspirin administered for days 2-90. Patients, clinicians, and investigators were masked to the group assignment. The primary efficacy outcome was an excellent functional outcome (modified Rankin Scale score 0-1) at 90 days. The primary safety outcome was symptomatic intracranial haemorrhage within 36 h. This trial was registered with ClinicalTrials.gov (NCT06316570) and is completed. FINDINGS: Between April 3, 2024, and Sept 30, 2025, we randomly assigned 1382 patients to the early DAPT (n=690 [49·9%]) or placebo (n=692 [50·1%]) groups. The median age was 65·6 years (IQR 58·3-72·0), 991 (71·7%) were men, and 391 (28·3%) were women. At 90 days, 474 (68·7%) patients in the early DAPT group and 429 (62·0%) in the placebo group achieved excellent functional outcomes (risk ratio 1·11 [95% CI 1·03-1·20; p=0·0089). Symptomatic intracranial haemorrhage within 36 h occurred in six (0·9%) patients in the early DAPT group versus five (0·7%) in the control group (risk ratio 1·20 [95% CI 0·37-3·93; p=0.76). INTERPRETATION: Among patients treated with intravenous thrombolysis for moderate ischaemic stroke, initiation of oral DAPT within 6 h of onset improved the likelihood of excellent functional outcomes at 90 days. Although no significant between-group difference in symptomatic intracranial haemorrhage was detected, wide CIs precluded exclusion of a small increased risk. FUNDING: National Natural Science Foundation of China, Capital's Funds for Health Improvement and Research, Noncommunicable Chronic Diseases-National Science and Technology Major Project, Beijing Municipal Science & Technology Commission, and the New Cornerstone Science Foundation.
18 May 2026
Read appraisal →Biological psychiatry
Overview of Challenges in Brain-Based Predictive Modeling: Toward Meaningful Predictive Insights
Predictive analytics based on machine learning (ML) and artificial intelligence is a powerful tool enabling precision psychiatry and providing insights into brain-behavior relationships. However, given the mixed results observed in the field so far, making meaningful progress requires careful consideration of several key challenges to ensure the validity of models and findings, including overfitting, confounding biases, site effect harmonization, and interpretability, among others. First, we highlight limitations of cross-validation, a ubiquitous ML strategy used to prevent overfitting and obtain generalization estimates, emphasizing the risk of performance inflation and the need for independent validation. Next, we introduce different types of so-called third variables that can influence the examination of a brain-behavioral relationship of interest in different ways, using causal inference principles. We emphasize the biasing impact of confounding variables on ML models and summarize common mitigation strategies. We then discuss site-specific effects in multisite datasets, reviewing different harmonization strategies to reduce unwanted variability and site-specific noise. Finally, we explore post hoc model interpretation methods to enhance model transparency while cautioning against misinterpretation. By integrating rigorous result validation, confounder control, and interpretability techniques, researchers can ensure that ML models produce more reliable and generalizable findings and avoid spurious associations.
18 May 2026
Read appraisal →Journal of molecular neuroscience : MN
Transcriptomic Analysis and Multiple Machine Learning Approaches Identify ZDHHC20 and Its Highly Correlated Gene AK5 as Biomarkers in Multiple System Atrophy
Multiple system atrophy (MSA) is a fatal neurodegenerative disorder lacking effective diagnostic tools. While protein palmitoylation is crucial for neuronal function, its specific role in MSA pathogenesis remains unexplored. We integrated bulk and single-nucleus RNA sequencing (snRNA-seq) data from postmortem MSA brain tissues. Eight machine learning algorithms were utilized to screen palmitoylation-related genes. Downstream analyses, including functional enrichment, cellular deconvolution, and pseudotime trajectory inference, were then conducted. ZDHHC20 and its highly correlated gene, AK5, were identified as hub genes. Both demonstrated significant downregulation in MSA, particularly within the cerebellar white matter. Functional enrichment analysis linked this expression pattern to mitochondrial dysfunction and impaired energy metabolism. Furthermore, snRNA-seq revealed that ZDHHC20 and AK5 are predominantly expressed in oligodendrocytes and exhibit lower expression levels along the developmental trajectory in MSA compared to healthy controls. ZDHHC20 and AK5 represent promising biomarkers for MSA. These findings provide new insights into the diagnosis and treatment of MSA.
18 May 2026
Read appraisal →Pituitary
Pituitary adenoma and intracerebral aneurysms: case series, systematic review and meta-analysis
PURPOSE: Intracranial aneurysms (IAs) and Pituitary Adenoma (PA) coexistence has been reported for decades, yet available evidence remains fragmented. We performed a systematic review and meta-analysis following PRISMA 2020 guidelines to assess clinical features of patients with both conditions and quantify the prevalence of IAs in PA patients. METHODS: PubMed and Embase were searched up to December 15, 2025. Studies reporting coexistence of IAs and PAs were included. Individual patient-level data (Group 1) and aggregate prevalence data (Group 2) were analyzed separately. Risk of bias (RoB) was assessed using Joanna Briggs Institute tools. All analyses were performed using metaprop function from the meta package in R software. RESULTS: Seventy-one studies met inclusion criteria. Most studies have a low RoB. In Group 1 (74 patients), PAs were predominantly macroadenomas (91.9%), most frequently non-functioning (40.5%). IAs were mainly in the cavernous segment of the internal carotid artery (33%). IA diagnosis occurred significantly later than PA diagnosis (p < 0.01). In Group 2 (5264 patients), the pooled prevalence of IAs among PAs was 4% (95% CI 2-4%; I² = 92.1%, τ² = 1.1050, p < 0.0001). Sex-stratified prevalence was 8% (95% CI: 6%-9%) in females and 6% (95% CI: 4%-7%) in males. IAs sex-specific prevalence was significantly higher in both male and female PA patients as compared to the general population (p < 0.01). CONCLUSION: Patients with PAs show a higher IAs prevalence compared with the general population, likely reflecting combined hemodynamic, anatomical, and endocrine influences.
18 May 2026
Read appraisal →Computers in biology and medicine
LADNET: An MRI-based deep learning approach for Alzheimer's disease detection.
Alzheimer's disease (AD) is considered a leading form of dementia, found in the majority of individuals, and it accounts for the highest prevalence of all the types of dementia. It is a gradually progressing condition which can begin with mild memory impairment and may lead to the inability to engage in meaningful conversations in the future. In addition to that, it is also capable of reacting appropriately to the environment around it. According to official data, there has been an increase in reported cases of AD-related mortality in recent years, along with rising rates of the disease itself. Therefore, it is beneficial for patients to be diagnosed with AD as early as possible in order to maximize their chance of survival. In medical field, deep learning (DL) is employed in many applications, especially the identification of Alzheimer's disease, has shown considerable success. Based on the fact that these methods are capable of acquiring and extracting features from extensive datasets autonomously, they are highly suitable for analyzing intricate medical images with the aid of these methods. Design the Lightweight Alzheimer's Disease Net (LADNET) model for the current task. Accurate detection of AD using magnetic resonance imaging (MRI)-based scans is presented in this work. The model classifies the three dementia types, namely Mild Dementia (MD), Moderate Dementia (MDD), and Very Mild Dementia (VMD), while also accurately categorizing the non-demented (ND) category based on the severity of the condition. We measure the performance of the model we recommend against a publicly available Kaggle dataset that contains over six thousand images from four different types of images. LADNET model achieves 99.4% accuracy and 99% AUC, outperforming existing methods. With a lightweight design of approximately 1.2 million parameters and fast inference (4.2 ms per image), LADNET demonstrates strong potential for practical clinical deployment, where it could help extract biomarker information from conventional MRIs, potentially reducing patient burden and diagnostic costs.
17 May 2026
Read appraisal →Biological psychiatry
Multimodal Representation Learning for Parsing Biological Heterogeneity in Psychiatric Neuroimaging
For decades, psychiatric neuroimaging has searched for biomarkers of depression and other disorders, but they remain elusive in clinical practice. While the last 5 years have seen rapid progress, other large-scale correlative studies have found only small, unreliable links between brain measures and clinical symptoms. Growing evidence suggests that such limitations are not just about sample size but depend critically on how models represent data. This review traces a recent shift away from univariate methods to multivariate/multiview approaches that learn more effective representations of biological and symptom measures by flexibly learning multimodal latent representations. First, we review how linear multiview embedding methods have revealed reproducible biological depression subtypes but do not perform well in small samples or samples enriched for mild symptoms. Then, we consider newer work exploring more sophisticated representations for neuroimaging data, including deep-learning and graph-based representations, and multimodal extensions that uncover complex latent patterns that single-modality studies miss. Then, we review recent developments in foundation models, which, once trained on large corpora, can "transfer learn" readily to small clinical cohorts, potentially bringing the advantages of large-scale learning to small, privacy-limited data. Finally, we highlight emerging representation tools that treat the brain as a dynamic, stateful multivariate process. Taken together, these advances point to a future in which the value of neuroimaging will be determined not only by ever-larger sample sizes but also by data quality and by how well our algorithms capture the distributed, multimodal, and evolving nature of psychiatric disorders.
17 May 2026
Read appraisal →Computer methods and programs in biomedicine
Integrating multimodal data and deep learning for functional assessment and rehabilitation prediction after cerebral hemorrhage
BACKGROUND: Intracerebral hemorrhage (ICH) is a leading cause of long-term disability, particularly in China. Post-stroke motor recovery exhibits considerable heterogeneity, presenting substantial challenges for clinicians in establishing realistic goals. While integrative AI paradigms have shown success in other complex medical domains, existing models often rely on single-modality data, limiting their predictive accuracy and clinical utility. This study aims to develop and validate a multimodal predictive model integrating CT imaging, clinical data, and rehabilitation assessments to simultaneously predict motor recovery and global rehabilitation outcomes following cerebral hemorrhage. METHODS: We conducted a retrospective study involving 739 patients (315 for motor function prediction and 424 for rehabilitation outcome assessment) who received rehabilitation therapy after cerebral hemorrhage. To predict motor function, we constructed a late-fusion deep learning model leveraging 3D-DenseNet for CT neuroimaging and Multi-Layer Perceptron (MLP) for clinical and laboratory features. To predict rehabilitation outcomes, a Gradient Boosting Decision Tree (GBDT) model was developed and validated using 5-fold and 10-fold cross-validation, comparing it against other machine learning algorithms, including SVR, Random Forest and AdaBoost. Model performance was assessed using metrics including AUC and R². Additionally, univariate and multivariate regression analysis were performed to identify significant factors influencing motor recovery and rehabilitation outcomes. RESULTS: A total of 739 patients were included. The multimodal fusion model achieved an AUC of 0.856 (95 % CI: 0.741-0.971) and an F1 score of 0.897 (95 % CI: 0.819-0.975), significantly outperforming the imaging-only (AUC: 0.833) and clinical-only (AUC: 0.749) models. For rehabilitation outcome prediction, the GBDT model achieved an R2 of 0.849 (95 % CI: 0.803-0.887), demonstrating superior stability and accuracy over other models. Additionally, multivariate analysis revealed that serum albumin (ALB), neutrophil percentage (NEUT%), triglycerides (TG), and thrombin time (TT) were independent predictors of motor recovery, while age, admission mBI, and time to start rehabilitation significantly influenced functional outcomes. CONCLUSION: This study confirms that a multimodal deep learning framework integrating routinely available CT imaging and clinical biomarkers provides high predictive value for simultaneously forecasting motor recovery and global functional outcomes after ICH. This proof-of-concept approach offers a reproducible, data-driven tool for early risk stratification, facilitating the formulation of individualized rehabilitation strategies and optimizing resource allocation in clinical workflows.
17 May 2026
Read appraisal →Journal of affective disorders
Right orbitofrontal cortex rTMS targets anxiety, not depressive, symptoms in first-episode schizophrenia.
BACKGROUND: Anxiety is highly prevalent and undertreated in first-episode schizophrenia(FES), but repetitive transcranial magnetic stimulation(rTMS) data for this comorbidity are scarce. The orbitofrontal cortex(OFC) is a key anxiety-regulating node, supporting its potential as a target. This study aimed to explore 1-Hz right OFC-rTMS's symptom-specific effects on FES mood. METHODS: This is a secondary analysis of a randomized controlled trial. Participants were drug-naive FES patients randomized to the active rTMS group(n = 51) or sham group(n = 45). All completed 20 intervention sessions, 20 sessions of active OFC-rTMS or sham, with 4-week follow-up, initiating oral olanzapine(10-20 mg/day) concurrently with the first rTMS session. Mood symptoms were assessed using the 24-item Hamilton Depression Rating Scale(HAMD) and the 14-item Hamilton Anxiety Rating Scale(HAMA). Psychopathological symptoms were assessed using the Positive and Negative Syndrome Scale(PANSS). The main outcome was the changes in HAMD and HAMA scores from baseline to 2 weeks and 4 weeks. RESULTS: The active group showed greater PANSS total(t = -3.260, p = 0.002; Cohen's d = 0.672) and subscale improvements vs. the sham group. Repeated-measures ANOVA(controlling for covariates) revealed significant Time×Group interactions for HAMA total(F = 4.698, p = 0.010; partial η2 = 0.059) and psychic anxiety(F = 5.735, p = 0.004; partial η2 = 0.072), but not somatic anxiety. For HAMD, only anxiety/somatization(F = 8.397, p = 0.031; partial η2 = 0.099) and cognitive impairment(F = 6.240, p = 0.002; partial η2 = 0.076) showed interactions, with no specific effects on overall depressive symptoms. In the sham group, HAMD anxiety/somatization correlated with all PANSS subscales(r = 0.311-0.477, p < 0.05), but this correlation was absent in the active group(all p > 0.05). CONCLUSIONS: Right OFC-rTMS improves FES anxiety (not depression), supporting it as a targeted non-pharmacological option.
17 May 2026
Read appraisal →Philosophical transactions. Series A, Mathematical, physical, and engineering sciences
Unexpected benefits of self-modelling in neural systems
Self-models have been a topic of interest for decades in human cognition and more recently in machine learning. Yet what benefits do self-models confer? Here we show that when an artificial network learns to predict its internal states as an auxiliary task, the network becomes simpler, more regularized and more parameter efficient. To test the hypothesis of self-regularizing through self-modelling, we used a range of network architectures performing three classification tasks across two modalities. In all cases, adding self-modelling caused a significant reduction in network complexity. The reduction was observed in two ways. First, the distribution of weights was narrower when self-modelling was present. Second, a measure of network complexity, the real log canonical threshold (RLCT), was smaller when self-modelling was present. These results support the hypothesis that self-modelling has a restructuring effect, reducing complexity and increasing parameter efficiency. This self-regularization may help explain some of the benefits of self-models reported in recent machine learning literature, as well as the adaptive value of self-models to biological systems. In particular, these findings may shed light on the possible interaction between the ability to model oneself and the ability to be more easily modelled by others in a social or cooperative context. This article is part of the theme issue 'World models in natural and artificial intelligence'.
17 May 2026
Read appraisal →Journal of affective disorders
Sex differences in cognitive function trajectories and influencing factors in older adults: A machine learning study based on CHARLS and HRS
BACKGROUND: Identify the influencing factors that respectively affect the differences in cognitive function trajectories between China and the United States, and explore the reasons for gender and transnational differences. METHODS: Five waves of data from the China Health and Retirement Longitudinal Study (CHARLS) and the Health and Retirement Study (HRS) were utilized, and Latent Class Growth Models (LCGMs) was employed to identify cognitive trajectories. The optimal machine learning (ML) model was adopted and the Boruta algorithm was used to screen the variables. The SHAP value is used to explain the ranking of feature importance. RESULTS: The XGBoost model performs best among the six ML models. Education was the top influencing factor across groups. Family factors mattered more in China, while U.S. medical and endowment insurance had stronger effects. Men were more affected by external social networks; women were influenced by family responsibilities and community environments. CONCLUSION: Differences were linked to public services, social roles, and traditional culture. These results support the development of tailored interventions targeting gender-specific and culture pathways at the individual, family, society and community levels, offering actionable insights for promoting cognitive health in aging populations globally.
16 May 2026
Read appraisal →Journal of neurology, neurosurgery, and psychiatry
Comparison of neuropsychological side effects between contemporary radiofrequency ablative neurosurgery for psychiatric disorders and conventional neurosurgical procedures: systematic review and meta-analysis
BACKGROUND: Psychiatric disorders are increasingly contributing to global disability. Despite advances in conservative management, the prevalence of treatment-resistant cases remains high. Meanwhile, neurosurgery for psychiatric disorders (NPD) remains underused, largely due to strict regulations and historical concerns, particularly those related to neuropsychological side effects (NPSE). OBJECTIVE: To address this issue, we conducted a systematic review with meta-analysis to compare NPSE associated with radiofrequency ablative NPD to those observed in neuro-oncological, neurovascular and epilepsy surgeries. METHODS: PubMed, Embase and LILACS databases were searched in April 2024 for articles published in English/Spanish from 1990 to 2022, following Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines. RESULTS: A total of 48 articles with 2678 participants were included. The frequency of transient and permanent NPSE in the NPD group ranged from 0.94% to 11.50% and 0.94% to 2.03%, respectively, comparable to the other surgical groups (epilepsy: 0.31-11.70%; vascular: 0.52-22.90%; oncology: 0.94-17.60% for transient NPSE; epilepsy: 0.31-12%; vascular: 0.40-1.96% and oncology: 0.84-1.48% for permanent NPSE). Regarding permanent NPSE, arguably the most critical consideration, the NPD group showed better outcomes in memory, language and social cognition than the epilepsy group, but worse outcomes in executive and perceptual-motor functions. Compared with the vascular group, the NPD group had better executive function but worse complex attention. Finally, the NPD group had fewer permanent deficits than the oncology group in executive function, complex attention and perceptual-motor domains, although language performance was lower. CONCLUSIONS: Contemporary NPD apparently carries a similar risk of NPSE as other conventional neurosurgical procedures, challenging misconceptions and this unjustified barrier to its broader use.
16 May 2026
Read appraisal →The Journal of neuroscience : the official journal of the Society for Neuroscience
Stable Mechanoreceptive Somatotopy in Chronic Low Back Pain: Is Cortical Map Reorganization a Myth?
Chronic low back pain (CLBP) has been linked to maladaptive cortical plasticity in sensorimotor regions, which may contribute to pain persistence through less distinct ("smudged") somatotopic maps of afferent input from the back. However, empirical support for such functional reorganization, especially in the primary somatosensory cortex (S1), is limited. To delineate precise sensory representations of the back in humans and test for altered somatotopic processing in CLBP, we utilized an MR-compatible pneumatic vibration device and applied frequency-specific vibrotactile stimuli at nine thoracolumbar paraspinal sites in 45 patients with CLBP and 41 healthy controls (53 females in total). Representational similarity analysis was employed via a whole-brain searchlight method, and the neural representational patterns were contrasted against theoretical models including a segmental (based on anatomical proximity), simple (upper vs lower back division), and a random model. In addition, a machine learning classifier was trained to predict afferent input from the upper versus lower back in healthy controls and tested in CLBP patients. Both groups displayed well-organized segmental representations spanning somatosensory, motor, and posterior parietal cortices. Posterior parietal regions exhibited the best model fits, followed by S1. No evidence for differences in representational patterns between groups was found. In CLBP patients, these patterns did not show associations with pain duration, severity, and self-reported back perception. The classifier, trained on healthy controls, accurately predicted upper versus lower back afferent input in CLBP patients. These findings indicate preserved cortical maps of the back in CLBP, challenging the hypothesis of sensory cortical reorganization in this condition.
16 May 2026
Read appraisal →Journal of neurointerventional surgery
Long-term outcomes of the Woven EndoBridge device for intracranial aneurysms: systematic review and meta-analysis
BACKGROUND: The Woven EndoBridge (WEB) device is widely recognized as a safe and effective treatment for intracranial aneurysms (IAs). However, data on the long-term evolution of aneurysm occlusion remain limited. This meta-analysis evaluates the long-term (≥1 year) safety and efficacy outcomes of patients treated with the WEB device. METHODS: A comprehensive literature search was conducted in PubMed, Web of Science, and Scopus for studies published between January 1, 2011 and August 1, 2024 following PRISMA guidelines. Studies reporting outcome for IAs treated with the WEB device and a follow-up period of ≥1 year were included. Data were pooled using a random-effects model to account for variability across studies. RESULTS: A total of 25 studies including 1735 patients and 1768 aneurysms were analyzed (30.1% ruptured). The pooled adequate occlusion rates were 87.3% (95% CI 83.5% to 91.1%) at 1 year, 90.7% (95% CI 86.2% to 95.2%) at 1-2 years, 93.3% (95% CI 87.7% to 99.0%) at 3 years, and 88.4% (95% CI 80.5% to 96.4%) at 5 years. Adequate occlusion was achieved in 89.2% (95% CI 83.0% to 93.2%) of unruptured aneurysms and 85.9% (95% CI 80.2% to 90.1%) of ruptured aneurysms. The overall retreatment rate at the last long-term follow-up was 7.2% (95% CI 4.8% to 9.6%). No procedure- or device-related adverse events were reported beyond 1-year follow-up. Additionally, no cases of delayed rupture or aneurysm rebleeding were observed. CONCLUSION: This systematic review and meta-analysis confirms the high long-term efficacy and safety of the WEB device in treating both ruptured and unruptured aneurysms. The findings support the WEB device as a reliable and effective treatment option for IAs.
16 May 2026
Read appraisal →Journal of neurology, neurosurgery, and psychiatry
Intravenous methylprednisolone as add-on induction therapy for chronic inflammatory demyelinating polyradiculoneuropathy: a randomised controlled trial
BACKGROUND: Most patients with chronic inflammatory demyelinating polyradiculoneuropathy (CIDP) require maintenance treatment. We aimed to determine whether combined treatment with intravenous immunoglobulin (IVIg) and intravenous methylprednisolone (IVMP) increases remission rates compared with IVIg and placebo. METHODS: We performed a randomised, double-blind, placebo-controlled study. Adults with CIDP who were treatment naive, relapsed after a remission or required continued treatment after a single IVIg dose were included. Participants were treated with IVMP (1000 mg) or placebo (sodium chloride) every 3 weeks for 18 weeks. All participants received IVIg treatment consisting of a loading dose (2 g/kg) and serial maintenance treatments (1 g/kg). The primary outcome was remission, defined as sustained improvement on disability scales at 52 weeks without need for further treatment. RESULTS: Enrolment was halted after the inclusion of 77 of the 96 planned participants because of safety concerns: four thromboembolic events occurred in the IVIg/IVMP group compared with none in the IVIg/placebo group. 14 of 37 (38%) participants in the IVIg/IVMP group were in remission at 52 weeks compared with 11 out of 40 (28%) in the IVIg/placebo group, with a difference of 10% (95% CI -11 to 31; p=0.47). Several secondary outcomes showed a greater magnitude of improvement in disability and impairment at 18 and 52 weeks in the IVIg/IVMP group. CONCLUSIONS: The study was prematurely stopped due to safety concerns after four thromboembolic events in the IVIg plus IVMP group. Combined treatment did not lead to significantly more frequent remissions than IVIg alone in the doses used in this study. TRIAL REGISTRATION NUMBER: ISRCTN15893334.
16 May 2026
Read appraisal →Journal of neurointerventional surgery
Comparative safety and efficacy of invasive therapies for carotid artery stenosis: a systematic review and network meta-analysis
BACKGROUND: Carotid artery stenosis is a major cause of stroke and is commonly treated with carotid endarterectomy (CEA) or carotid artery stenting (CAS). Trans-carotid artery revascularization (TCAR) has emerged as a newer alternative designed to reduce the risk of procedural stroke. However, comparative evidence on the safety and efficacy of these interventions is limited. This study evaluates the clinical outcomes of CEA, CAS, and TCAR in patients with carotid artery stenosis. METHODS: A comprehensive search of multiple databases was conducted to identify relevant studies. The outcomes were stroke, mortality, and myocardial infarction, cranial nerve injury, hematoma, infection, transient ischemic attack, and length of hospital stay. Meta-analysis and a frequentist graph-theoretical approach network meta-analysis were performed using the netmeta package in R platform. Risk of bias was assessed using RoB 2 Cochrane and ROBINS-I. RESULTS: Fifty-five studies were included in the review. The short-term stroke risk was significantly lower with CEA than with CAS. TCAR had a potential long-term mortality benefit over CAS, while no significant differences were observed in short-term mortality or myocardial infarction across treatments. CAS was associated with lower risks of cranial nerve injury, hematoma, and infection compared with CEA. CONCLUSION: CEA and TCAR offer better short-term stroke protection than CAS, with TCAR showing promise for long-term mortality benefits. CAS has advantages in reducing cranial nerve injury, hematoma, and infection risks. Overall, the choice of treatment should consider both efficacy and safety profiles. Further high-quality randomized controlled trials, particularly focusing on TCAR, are needed to validate these comparative outcomes. PROSPERO REGISTRY NUMBER: CRD420251055287.
16 May 2026
Read appraisal →Journal of neurointerventional surgery
Intravenous tenecteplase is associated with a higher incidence of thrombus movement before endovascular treatment for acute ischemic stroke: a secondary analysis of the AcT randomized controlled trial
BACKGROUND: Thrombus migration can occur in patients receiving IV thrombolysis for acute stroke. This study compared the effects of IV tenecteplase (0.25 mg/kg) versus alteplase (0.9 mg/kg) on thrombus movement in patients undergoing endovascular treatment (EVT) in the AcT trial. METHODS: Patients with intracranial occlusion on baseline CT angiography (CTA) who underwent EVT were included. Thrombus movement from baseline CTA to first angiographic EVT run was classified as no thrombus movement, thrombus extension (TE, movement to a proximal location on DSA compared with CTA), thrombus migration (TM, movement to a more distal location), or recanalization. Outcomes were 90-day modified Rankin Scale (mRS) score 0-2 and final extended Thrombolysis in Cerebral Infarction score 2b-3. Mixed-effects logistic regression was performed. RESULTS: Of the 1577 patients in the AcT trial, 496 patients with intracranial occlusions underwent EVT (median age 73 years, 50.8% female). No thrombus movement was seen in 398 patients (80.2%), TE in six (1.2%), TM in 77 (15.5%), and complete recanalization in 15 (3.0%). Tenecteplase recipients (n=252) had higher TM rates than alteplase recipients (19.4% vs 11.5%, adjusted OR (aOR) 1.83, 95% CI 1.10 to 3.07). TM did not impact final successful reperfusion (aOR 0.76, 95% CI 0.40 to 1.38) but was associated with increased odds of 90-day mRS 0-2 (aOR 1.77, 95% CI 1.05 to 3.06). Thrombolytic type did not affect the relationship between thrombus movement and study outcomes (P>0.05). CONCLUSION: IV tenecteplase is associated with higher thrombus migration rates before EVT compared with alteplase, which is linked to better functional outcomes. However, the type of thrombolytic agent did not affect final reperfusion and functional outcomes.
16 May 2026
Read appraisal →Journal of neurology
The effects of computerized cognitive training on cognitive function in patients with mild cognitive impairment: a systematic review and meta-analysis
BACKGROUND: We sought to quantify the effects of computerized cognitive training (CCT) on patients with mild cognitive impairment (MCI), to inform the development of clinical non-pharmacological intervention strategies. METHODS: We searched PubMed, Cochrane Library, Embase, and Web of Science databases from 2007 to February 2026 for randomized controlled trials (RCTs) of CCT in patients with MCI. Outcomes including global cognition, specific cognitive domains, psychosocial functioning, and activities of daily living were analyzed separately by stata 18 and Review Manager 5.4. RESULTS: We included 30 studies and 2056 participants. Our meta-analysis revealed that, compared with control conditions, CCT led to a significant moderate improvement in overall cognitive function, standardized mean difference (SMD) = 0.62, 95% confidence interval (CI) = 0.43-0.81; I2 = 73%. Subgroup analysis showed that intervention characteristics-including session duration, training device, total intervention length, weekly frequency, sample size, and assessment tools-yielded similar improvements in global cognition, with no statistically significant differences between subgroups. These findings indicate that the cognitive benefits of CCT are consistent and robust. CONCLUSION: This systematic review and meta-analysis shows CCT significantly improves global cognitive function in MCI patients, with stable benefits across subgroups. Despite clear overall effects, high heterogeneity, varying training protocols and assessment tools exist. Further large, long-term RCTs are needed to clarify CCT's long-term effects and support its standardized clinical application. TRIAL REGISTRATION: PROSPERO International prospective register of systematic reviews CRD420261328762; https://www.crd.york.ac.uk/PROSPERO/view/CRD420261328762 .
15 May 2026
Read appraisal →Journal of affective disorders
Systematic review and meta-analysis of intranasal esketamine for treatment-resistant depression: Evidence from real-world studies
BACKGROUND: Intranasal esketamine has demonstrated efficacy and safety for treatment-resistant depression in randomized controlled trials, however the generalizability of these findings to clinical practice remains unclear. This systematic review and meta-analysis aimed to synthesize real-world evidence of intranasal esketamine in patients with treatment-resistant depression. METHODS: A systematic search was conducted in PubMed, Embase, and PsycINFO up to April 2025 to identify observational studies reporting clinical outcomes of intranasal esketamine in adults with treatment-resistant depression. Standardized mean changes in depressive symptoms and odds ratios for remission were calculated. Random-effects meta-analyses were performed, followed by meta-regression to explore potential moderators. The study protocol was prospectively registered in PROSPERO (CRD420251025075). RESULTS: Nine studies were included in the final synthesis. The analyses demonstrated that intranasal esketamine showed substantial effectiveness, with an effect size of Hedges' g = -1.98 (95% CI -2.94 to -1.03; p < 0.0001). Patients were about five times more likely to achieve remission at three months compared with the induction phase (OR = 5.1, 95% CI 2.29-11.39). Treatment effectiveness was not influenced by factors such as gender or the presence of comorbidities, such as anxiety, personality, or substance use disorders. The pooled prevalence of any adverse event, based on studies that reported safety data, was 82% (95% CI 69-92). The most frequent was dissociation, reported in 49% of patients (95% CI 37-62). CONCLUSIONS: This study provides consistent real-world evidence supporting that Intranasal esketamine was associated with meaningful symptom reduction and rising remission rates over time in real-world settings. Given the observational nature of the evidence and the absence of control groups, effect sizes should be interpreted with caution. Nonetheless, these findings provide valuable insight into esketamine's effectiveness and tolerability in routine practice and highlight the need for controlled comparative studies to guide long-term treatment strategies.
15 May 2026
Read appraisal →Computers in biology and medicine
Dual-model deep learning for Alzheimer's prognostication
Disease-modifying therapies for Alzheimer's disease demand precise timing decisions, yet current predictive models require longitudinal clinical observations and provide no uncertainty quantification-rendering them impractical at the critical first-visit encounter when treatment decisions must be made. We developed PROGRESS (PRognostic Generalization from REsting Static Signatures), a dual-model deep learning framework that transforms a single baseline cerebrospinal fluid (CSF) biomarker assessment into actionable prognostic estimates without requiring prior clinical history. The framework addresses two complementary clinical questions: a probabilistic trajectory network predicts individualized cognitive decline parameters with calibrated uncertainty bounds that achieve near-nominal coverage, enabling honest prognostic communication rather than false precision; and a deep survival model estimates time-to-conversion from mild cognitive impairment to dementia. Using data from over 3000 participants across 43 Alzheimer's Disease Research Centers in the National Alzheimer's Coordinating Center database, PROGRESS substantially outperforms existing approaches including Cox proportional hazards, Random Survival Forests, and gradient boosting methods for survival prediction. Risk stratification identifies patient groups with seven-fold differences in conversion rates, enabling clinically meaningful treatment prioritization. Leave-one-center-out validation demonstrates robust generalizability, with survival discrimination remaining strong across all held-out clinical sites despite heterogeneous measurement conditions spanning four decades of assay technologies. By combining superior survival prediction with trustworthy trajectory uncertainty quantification, PROGRESS bridges the gap between biomarker measurement and personalized clinical decision-making-providing the prognostic timeline that current staging approaches cannot offer.
15 May 2026
Read appraisal →Philosophical transactions. Series A, Mathematical, physical, and engineering sciences
Goals and the structure of experience
Purposeful behaviour is a hallmark of natural and artificial intelligence. Its acquisition is often believed to rely on world models, comprising both descriptive (what is) and prescriptive (what is desirable) aspects that identify and evaluate state of affairs in the world, respectively. Canonical computational accounts of purposeful behaviour, such as reinforcement learning, posit distinct components of a world model comprising a state representation (descriptive aspect) and a reward function (prescriptive aspect). However, an alternative possibility, which has not yet been computationally formulated, is that these two aspects instead co-emerge interdependently from an agent's goal. Here, we describe a computational framework of goal-directed state representation in cognitive agents, in which the descriptive and prescriptive aspects of a world model co-emerge from agent-environment interaction sequences, or experiences. Drawing on Buddhist epistemology, we introduce a construct of goal-directed, or telic, states, defined as classes of goal-equivalent experience distributions. Telic states provide a parsimonious account of goal-directed learning in terms of the statistical divergence between behavioural policies and desirable experience features. We review empirical and theoretical literature supporting this novel perspective and discuss its potential to provide a unified account of behavioural, phenomenological and neural dimensions of purposeful behaviours across diverse substrates. This article is part of the theme issue 'World models in natural and artificial intelligence'.
15 May 2026
Read appraisal →Philosophical transactions. Series A, Mathematical, physical, and engineering sciences
Empowerment gain and causal model construction: children and adults are sensitive to controllability and variability in their causal interventions
Learning about the causal structure of the world is a fundamental problem for human cognition. Causal models and especially causal learning have proved to be difficult for large pretrained models using standard techniques of deep learning. In contrast, cognitive scientists have applied advances in our formal understanding of causation in computer science, particularly within the causal Bayes net formalism, to understand human causal learning. In the very different tradition of reinforcement learning (RL), researchers have described an intrinsic reward signal called 'empowerment' which maximizes mutual information between actions and their outcomes. Empowerment may be an important bridge between classical Bayesian causal learning and RL and may help to characterize causal learning in humans and enable it in machines. If an agent learns an accurate causal world model, they will necessarily increase their empowerment, and increasing empowerment will lead to a more accurate causal world model. Empowerment may also explain distinctive features of children's causal learning, as well as providing a more tractable computational account of how that learning is possible. In an empirical study, we systematically test how children and adults use cues to empowerment to infer causal relations and design effective causal interventions. This article is part of the theme issue 'World models in natural and artificial intelligence'.
15 May 2026
Read appraisal →Neurology. Clinical practice
Clinical Meaningfulness of Donanemab in Early Symptomatic Alzheimer Disease: Data From the Randomized Phase 3 TRAILBLAZER-ALZ 2 Trial
BACKGROUND AND OBJECTIVES: Understanding the meaningfulness of clinical trial outcomes is essential for people living with Alzheimer disease (AD) and their clinicians to make evidence-based shared treatment decisions in real-world clinical care. Donanemab, a monoclonal antibody targeting the insoluble form of β-amyloid found in plaques, significantly slows cognitive and functional decline of AD in participants with mild cognitive impairment (MCI) or mild AD-related dementia. This analysis reviews the efficacy of donanemab across various clinical outcome assessments, using both published data and new complementary analyses to provide context on its potential benefits for patients and caregivers. METHODS: We present findings from prespecified and post hoc analyses from the TRAILBLAZER-ALZ 2 trial. Clinical outcomes assessed were Integrated AD Rating Scale (iADRS), comprising the 13-item AD Assessment Scale-Cognitive Subscale (ADAS-Cog13) and AD Cooperative Study-Instrumental Activities of Daily Living (ADCS-iADL); Clinical Dementia Rating (CDR)-Sum of Boxes (CDR-SB) for clinical severity and individual cognitive and functional domains; CDR-Global for clinical severity stage progression; meaningful within-patient change (MWPC); and ADCS-Activities of Daily Living dependence score. RESULTS: Donanemab reduced the risk of progression from MCI to mild AD by 33% (hazard ratio [HR] = 0.67; 95% CI 0.52-0.87; p = 0.003) and from mild to moderate AD by 50% (HR = 0.50; 95% CI 0.33-0.78; p = 0.002). In addition, donanemab reduced MWPC risk over 76 weeks by 38% for CDR-SB (HR = 0.62; 95% CI 0.52-0.75; p < 0.001) and 30% for iADRS (HR = 0.70; 95% CI 0.58-0.84; p < 0.001). Donanemab-treated participants exhibited significant slowing of clinical progression across multiple ADAS-Cog13 and ADCS-iADL items and all CDR-SB cognitive and functional domains. Donanemab also slowed progression of dependence least-squares mean change difference, -0.14 [95% CI -0.24 to -0.04; p = 0.007]), representing 23% slowing of progression (95% CI 6.17%-40.32%), and reduced risk of progression to requiring in-home support by 27% (HR = 0.74; 95% CI 0.59-0.91; p = 0.005). DISCUSSION: These results add to the evidence and further support clinically meaningful donanemab-mediated effects on cognition and function for patients and their caregivers and may aid communication of realistic treatment expectations and informed decision-making. TRIAL REGISTRATION INFORMATION: ClinicalTrials.gov NCT04437511. Submitted: June 17, 2020; First patient enrolled: June 19, 2020. clinicaltrials.gov/study/NCT04437511 EudraCT Number 2020-000077-25. Start date of recruitment: June 19, 2020. clinicaltrialsregister.eu/ctr-search/trial/2020-000077-25/results.
15 May 2026
Read appraisal →Neurology. Clinical practice
Optimizing Antiseizure Medication Taper in the Epilepsy Monitoring Unit
BACKGROUND AND OBJECTIVES: Inpatient video-EEG epilepsy monitoring aims to provide a safe environment for antiseizure medication (ASM) tapering to precipitate seizures, which is often required for diagnosis and treatment. However, there is no standardized taper protocol across institutions, which may contribute to increased morbidity through longer hospitalizations if the taper is too mild and bilateral tonic-clonic seizures (BTCSs) if too aggressive. This study investigates how ASM taper influences length of stay (LOS) and patient outcomes in the epilepsy monitoring unit (EMU), aiming to develop guidelines for medication taper strategies to maximize diagnostic yield while minimizing patient risk. METHODS: We used natural language processing to extract data from electronic medical records of 639 patients admitted to the University of Pennsylvania EMU between 2017 and 2023. We estimated ASM load, the total normalized sum of all ASM serum concentrations based on dose-dependent pharmacokinetic models, throughout each hospitalization. We applied LASSO (Least Absolute Shrinkage and Selection Operator) regression and mixed-effects models to identify predictors of LOS and BTCS risk as a function of taper speed and percentage of baseline ASM load in patients with (hBTCS) and without (nBTCS) a history of BTCS. RESULTS: Taper speed did not correlate with BTCS risk, although absolute ASM load did. Median ASM loads at seizure occurrence were 51% of baseline in the hBTCS group, 41% in the nBTCS group, and 39% in the unclear group. BTCS occurred at higher ASM loads in patients with hBTCS vs nBTCS. Median ASM loads at first EMU seizure were 33% for both hBTCS and nBTCS groups, while the unclear group had first seizures at lower loads (25% of baseline). Time to first seizure and number of baseline ASMs were positively correlated with longer hospital stays, suggesting that sequential medication taper delayed seizures and prolonged LOS without altering BTCS risk. DISCUSSION: Patient history and ASM load are critical factors in predicting BTCS. Rapid taper to approximately 50% baseline ASM load may more efficiently produce seizures without increased BTCS risk in patients without BTCS history. Individualized taper protocols remain advised, particularly for severe epilepsies. This study provides a foundation for quantitative, evidence-based guidelines to optimize ASM reduction strategies for safely precipitating seizures in EMUs.
15 May 2026
Read appraisal →The New England journal of medicine
Endovascular Treatment of Medium-Vessel-Occlusion Strokes
BACKGROUND: Endovascular thrombectomy for acute ischemic stroke due to medium-vessel occlusion has had varying results across trials. Whether thrombectomy improves functional outcomes in patients with medium-vessel occlusion and moderate-to-severe deficits is unclear. METHODS: We conducted an open-label, randomized trial with blinded outcome assessment at 48 centers in China. Eligible patients were adults who presented within 24 hours after the onset of a moderate-to-severe stroke (National Institutes of Health Stroke Scale [NIHSS] score, ≥6; scale, 0 to 42, with higher scores indicating greater neurologic deficits) due to occlusion of a medium vessel. Patients were assigned in a 1:1 ratio to thrombectomy plus medical management (thrombectomy group) or medical management alone (control group). The primary outcome was functional disability as measured by the shift in the modified Rankin scale score (scale, 0 [no disability] to 6 [death]) at 90 days. Violation of the proportional-odds assumption precluded the use of shift in the modified Rankin scale score, so as prespecified, functional independence (modified Rankin scale score of 0, 1, or 2) at 90 days was used as the primary outcome. Safety outcomes were symptomatic intracranial hemorrhage and 90-day mortality. RESULTS: Among 280 patients in the thrombectomy group and 283 in the control group, the median age was 71 years, the median NIHSS score was 10 (range, 3 to 36), and 42.8% were women; 36.6% received intravenous thrombolysis. Functional independence at 90 days was seen in 58.6% of the patients in the thrombectomy group and in 46.6% of those in the control group (adjusted rate ratio, 1.24; 95% confidence interval, 1.07 to 1.44; P = 0.004). The incidence of symptomatic intracranial hemorrhage was 4.7% in the thrombectomy group and 2.2% in the control group; 90-day mortality was 11.1% and 10.2%, respectively. CONCLUSIONS: Among patients with acute ischemic stroke due to medium-vessel occlusion and moderate-to-severe deficits, thrombectomy led to a greater likelihood of functional independence than medical management alone but also to a higher risk of symptomatic intracranial hemorrhage. (Funded by the National Natural Science Foundation of China and the Noncommunicable Chronic Diseases-National Science and Technology Major Project; ORIENTAL-MeVO ClinicalTrials.gov number, NCT06146790.).
15 May 2026
Read appraisal →Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology
When bad turns good: a systematic review on cholesterol and LDL in longitudinal patient cohorts with Parkinson's disease
BACKGROUND: While the aetiology of Parkinson's disease (PD) involves genetic and environmental factors, emerging evidence has suggested a surprising link between lipid metabolism - particularly serum total cholesterol (TC) and low density lipoprotein cholesterol (LDL-C) - and the pathogenesis of PD. Cholesterol plays a vital role in neuronal membrane integrity, myelination, and synaptic function. However, its peripheral concentration and regulatory mechanisms in the central nervous system (CNS), remain incompletely understood in the context of PD. OBJECTIVE: This review aims to systematically examine the current literature on the relationship between TC and LDL-C with the risk and progression of PD in longitudinal patient cohorts. METHODS: A systematic literature search was conducted using PubMed with the keywords "Parkinson's disease" AND "cholesterol" and "Parkinson's disease" AND "LDL." Inclusion criteria encompassed English-language longitudinal human studies published before October 2024, with data on cholesterol levels in relation to PD incidence or progression. Fourteen studies met the eligibility criteria. RESULTS: Twelve of the 14 included studies reported an inverse association between lower serum total cholesterol (TC) and low-density lipoprotein cholesterol (LDL-C) levels and increased risk for PD onset as well as faster disease progression. Notably, this pattern was not uniform across all subgroups: age, sex, BMI, and statin use modulated the strength and direction of associations. CONCLUSION: Despite some heterogeneity across studies, there is growing evidence that lower TC and LDL-C may be associated with a higher risk and more rapid progression of PD. Future research should focus on mechanistic studies and stratified analyses to clarify whether and how cholesterol modulation could contribute to neuroprotective strategies in PD.
15 May 2026
Read appraisal →PloS one
The impact of walking on creative thinking: A systematic review and meta-analysis
BACKGROUND: Walking is associated with many benefits, from improved mental health to a reduced risk of mortality - but can it boost creative thinking? Current evidence suggests a positive effect of physical activity on creative thinking, but the specific effect of walking has not been adequately explored. METHODS: We conducted a systematic search of the PsycINFO, MEDLINE, Scopus, and ProQuest databases from inception to 8 January 2025 using search terms related to walking and creativity. We then performed meta-analyses of Cohen's d effect sizes to assess the effect of walking on creative thinking, as represented by divergent thinking (the ability to generate novel and useful ideas) and convergent thinking (the ability to analyze and select ideas to find the best solution). We assessed study quality using a creativity-specific tool, and the certainty of the evidence with the Grading of Recommendations Assessment, Development and Evaluation Framework. RESULTS: We identified 23 studies (12 randomized experimental studies, nine non-randomized experimental studies, two observational studies) from 16 articles including a total of 1,036 participants, most of whom were post-secondary students. We found moderate certainty evidence of a large effect of walking on divergent thinking (d = 0.93 [95% CI 0.44, 1.42]), and very uncertain evidence of a null effect of walking on convergent thinking (d = 0.16 [95% CI -0.31, 0.63]). Sensitivity analyses of randomized trials only found similarly large effects of walking on divergent thinking ability (d = 0.82 [95% CI 0.35, 1.28]). CONCLUSION: Results suggest that walking likely results in a large increase in divergent thinking, indicating its potential as an intervention to stimulate creative idea generation.
15 May 2026
Read appraisal →Annals of human biology
Machine learning combined with WGCNA reveals potential calcium uptake-associated biomarkers in Parkinson's disease
BACKGROUND: Parkinson's disease (PD) is the second most common neurodegenerative disease, characterised by progressive loss of dopaminergic neurons in the substantia nigra pars compacta. Although previous studies have suggested a correlation between calcium signal disruption and the development of PD, the role of genes associated with store-operated calcium entry (SOCE) in PD remains unclear. AIM: To combine machine learning methods with Weighted Gene Co-expression Network Analysis (WGCNA) to systematically screen and identify SOCE-associated candidate genes and evaluate their potential for assisting in discrimination. SUBJECTS AND METHODS: This study is based on the GSE6613 dataset, using ssGSEA and Weighted Gene Co-expression Network Analysis (WGCNA) to screen for co-expression modules related to SOCE, and combining LASSO, SVM-RFE, and random forest algorithm to identify feature genes. Subsequently, validation was conducted in GSE20163 and GSE22491, and in vitro experiments were carried out in 6-OHDA and MPP+ models of dopaminergic neurons derived from LUHMES. RESULTS: A total of 5 SOCE-associated feature genes (LPCAT3, CLCNKB, TXLNA, etc.) were identified, and their combined model showed moderate discriminative ability in external validation. Immune analysis showed immune dysfunction in PD patients, and in vitro experiments also observed consistent expression trends with patient samples. CONCLUSIONS: In summary, this study identified potential PD feature genes associated with SOCE, providing clues for further exploration of the molecular mechanisms of SOCE-associated calcium signalling in PD.
15 May 2026
Read appraisal →Fluids and barriers of the CNS
Cognitive assessment methods and outcomes following shunt surgery in idiopathic normal pressure hydrocephalus (iNPH): a systematic review and meta-analysis
INTRODUCTION: Core cognitive deficits in iNPH include slowed information processing, psychomotor slowing and executive dysfunction. However, cognitive outcomes following shunt surgery are not well understood. This review synthesised evidence on cognitive assessment methods and outcomes following shunt surgery in iNPH. METHODS: PubMed, Scopus, PsycINFO and Web of Science were searched for peer-reviewed studies including adults with iNPH who underwent shunt surgery and had within-subject cognitive evaluations pre- and post-operatively. Key data were extracted and study quality was assessed. Random-effects meta-analyses were performed on pooled baseline and post-shunt difference scores for frequently reported cognitive tests with comparable outcome data. RESULTS: Of 1,876 records, 195 met the inclusion criteria, comprising 11,445 patients. Cognitive evaluation methods ranged from subjective reports and NPH grading scales to brief screening tools and comprehensive test batteries. Over 193 distinct tests were reported and 54.4% of studies did not formally assess any core iNPH cognitive deficits. Post-shunt improvement rates, follow-up times and criteria for defining improvement varied widely. Eighty-five studies contributed data to meta-analyses of ten outcomes. Pooled estimates indicated post-shunt cognitive improvement, with Trail Making Test-A, Grooved Pegboard-Dominant and Trail Making Test-B showing changes exceeding thresholds for clinically significant improvement. CONCLUSIONS: Cognitive assessment in iNPH is highly heterogeneous and frequently omits core domains, limiting detection of treatment effects. When domain-relevant cognitive measures are used, shunt surgery is associated with statistically and clinically significant cognitive improvement. These findings highlight the need for standardised iNPH-appropriate cognitive evaluation tools with validated criteria for detecting clinically meaningful change and have direct implications for clinical assessment, interpretation of shunt response and the selection of cognitive endpoints in future interventional studies.
15 May 2026
Read appraisal →The Cochrane database of systematic reviews
Pharmacological treatments for Friedreich ataxia
RATIONALE: Friedreich ataxia (FRDA) is a rare inherited autosomal recessive neurological disorder, characterised initially by unsteadiness in standing and walking, and slowly progressing to wheelchair dependency, usually in the late teens or early twenties. Scoliosis, pes cavus and cardiomyopathy are often present at diagnosis. As the disease progresses, individuals usually develop slurred speech, auditory impairment (especially in a noisy environment), urinary tract morbidity, diabetes, anxiety, depression, muscle spasticity and visual disturbances. Cardiac abnormalities cause premature death in 60% of people with FRDA. There is no easily defined clinical or biochemical marker to assess progression and no known curative treatment. This is the third update of a review published in 2009 and updated in 2012 and 2016. OBJECTIVES: To assess the effects of pharmacological treatments for people with Friedreich ataxia (FRDA) after 12 months of treatment. SEARCH METHODS: To identify studies for inclusion in this review, we searched CENTRAL, MEDLINE, Embase, CINAHL Plus, TRIP, Orphanet, WHO International Clinical Trials Registry Platform (ICTRP) and ClinicalTrials.gov. We also checked reference lists of relevant studies and contacted study authors. The most recent search was on 4 February 2025. ELIGIBILITY CRITERIA: We were interested in randomised controlled trials (RCTs) and quasi-RCTs of pharmacological treatments (including vitamins) in people with genetically-confirmed FRDA. To be included in the review, studies had to last at least 12 months. OUTCOMES: We assessed the following outcomes after 12 months of treatment: change in score on a validated ataxia rating scale; change in interventricular septal thickness in diastole (IVSTd) by cardiac magnetic resonance imaging or echocardiogram; change in activities of daily living (ADL) using a validated questionnaire; change in upper limb dexterity; and change in cardiopulmonary exercise testing (CPET). We also assessed treatment-related adverse events with medication continued for the study period, and treatment-emergent adverse events leading to cessation of medication or death during the study period. RISK OF BIAS: Using the Cochrane risk of bias tool RoB 2, we assessed the risk of bias in the seven outcomes reported in our summary of findings tables. SYNTHESIS METHODS: We synthesised the results of the studies for each outcome using meta-analysis, where possible. We calculated the mean difference (MD) or standardised mean difference (SMD) for continuous outcomes, and the risk ratio (RR) for dichotomous outcomes, all with 95% confidence intervals (CIs). We used GRADE to assess the certainty of evidence for our prespecified outcomes as high, moderate, low or very low. INCLUDED STUDIES: We included eight RCTs in this updated review. We included seven of them in meta-analysis, and these studies enroled a total of 574 participants, with sample sizes ranging from 29 to 232 participants per study. One study was international, with centres in North America, Europe and Australia. There were four other multicentre studies (three in Europe and one in North America). Single-centre studies were conducted in the UK, Italy and France. Participants in the studies ranged from eight to 70 years of age at enrolment, with the mean age in each study being between 18 years and 35 years (with an unweighted pooled mean age across studies of 25.9 years). All of the studies enroled both males and females, with the proportion of female participants ranging from 21% to 58%. Sixty-eight per cent of participants had severe ataxia, while the other 32% had moderate ataxia. The studies tested epoetin alpha, CoQ10 and vitamin E, idebenone, leriglitazone, omaveloxolone, and RT001. One study tested pioglitazone but has not published any results. Four of the studies were pharmaceutical-industry-controlled. SYNTHESIS OF RESULTS: Meta-analysis of seven studies demonstrated that pharmacological treatment probably makes little or no difference to scores on the ataxia rating scale after 12 months of treatment (SMD 0.02, 95% CI -0.23 to 0.26; I² = 42%; 7 studies, 513 participants; moderate-certainty evidence). The evidence was very uncertain about the effects of treatment on IVSTd (MD -0.51, 95% CI -1.10 to 0.09; I² = 80%; 2 studies, 72 participants; very low-certainty evidence) and on ADL (MD -0.59, 95% CI -1.39 to 0.21; I² = 24%; 3 studies, 167 participants; very low-certainty evidence). Meta-analysis of three studies showed that treatment probably improves upper limb dexterity (SMD -0.42, 95% CI -0.73 to -0.11; I² = 0%; 3 studies, 166 participants; moderate-certainty evidence). We are very uncertain about the effect of pharmacological treatment on CPET (SMD -0.16, 95% CI -0.46 to 0.13; I² = 0%; 3 studies, 181 participants; very low-certainty evidence). We are very uncertain whether pharmacological treatment has any effect on treatment-related adverse events (RR 0.88, 95% CI 0.63 to 1.22; I² = 0%; 2 studies, 104 participants; very low-certainty evidence). There were only 104 participants in this analysis out of a total of 545 participants, so the result may have little relevance. Meta-analysis of six studies found that there may be little to no difference between pharmacological treatment and placebo in treatment-emergent adverse events leading to cessation of medication or death (RR 1.24, 95% CI 0.44 to 3.48; I² = 0%; 6 studies, 313 participants; low-certainty evidence). Our certainty in the evidence ranged from very low to moderate. We downgraded all outcomes by one or two levels for imprecision, with further downgrades for inconsistency (for the outcomes of IVSTd, ADL, CPET) and suspected publication bias (for the outcomes of IVSTd, ADL, CPET and adverse events). AUTHORS' CONCLUSIONS: In this updated Cochrane systematic review, meta-analysis of results on the ataxia rating scale showed that pharmacological treatments probably make little or no difference compared with placebo after 12 months of treatment. Given this result, the probable improvement that we found in upper limb dexterity was unexpected. Treatment-emergent adverse events leading to cessation of medication or death may be no more common in treatment groups than placebo groups as there were few adverse events detected in the treated groups. However, the studies may not have detected all rare and serious adverse events. FUNDING: The authors in this review received no funding. REGISTRATION: Protocol (2009) DOI: 10.1002/14651858.CD007791 Original review (2009) DOI: 10.1002/14651858.CD99791.pub2 Update (2012) DOI: 10.1002/14651858.CD007791.pub3 Update (2016) DOI: 10.1002/14651858.CD007791.pub4.
14 May 2026
Read appraisal →Behavior research methods
A stroke-level large-scale database of Chinese character handwriting and the OpenHandWrite_Toolbox for handwriting research
Understanding what linguistic components (e.g., phonological, semantic, and orthographic systems) modulate Chinese handwriting at the character, radical, and stroke levels remains an important yet understudied topic. Additionally, there is a lack of comprehensive tools for capturing and batch-processing fine-grained handwriting data. To address these issues, we constructed a large-scale handwriting database in which 42 Chinese speakers each handwrote 1200 characters in a handwriting-to-dictation task. Additionally, we enhanced the existing handwriting package and provided comprehensive documentation for the upgraded OpenHandWrite_Toolbox. This toolbox allows users to easily modify the experimental design, capture the stroke-level handwriting trajectory, and batch-process handwriting measurements (e.g., latency and duration). Analysis of our large-scale database indicates that orthographic predictors affect handwriting preparation and execution at the character, radical, and stroke levels. Phonological factors influence handwriting at character and radical levels. Importantly, these lexical effects demonstrate hierarchical attenuation: they were most pronounced at the character level, followed by the radical, and weakest at the stroke level. These findings demonstrate that handwriting preparation and execution at the radical and stroke levels are closely intertwined with linguistic components. This database and toolbox offer valuable resources for future psycholinguistic and neurolinguistic research on the handwriting of characters and sub-characters across different languages.
14 May 2026
Read appraisal →PloS one
Oral carbohydrate sensing enhances prefrontal cortex oxygenation, reduces perceived exertion, and improves high-intensity cycling performance: A randomized crossover trial
PURPOSE: Carbohydrate mouth rinsing (CHO-MR) and music listening (MUS) are non-metabolic interventions proposed to attenuate cognitive and perceptual fatigue during exercise. However, their comparative effects on prefrontal cortical oxygenation, executive function, and perceived exertion during high-intensity endurance performance remain unclear. This study examined the effects of CHO-MR and MUS on dorsolateral prefrontal cortex (DLPFC) oxygenation, cognitive performance, perceived exertion, and cycling performance during a 4-km time trial (TT). METHODS: Eleven trained cyclists (7 men, 4 women) completed a randomized, single-blind, crossover trial under three conditions: CHO-MR, MUS (120 beats/min), and placebo mouth rinse (PLA). Bilateral DLPFC oxygenation was assessed using functional near-infrared spectroscopy at rest, during Stroop testing, post-intervention, and post-TT. Stroop performance, rating of perceived exertion (RPE); every 500 m, completion time, power output, heart rate, and blood lactate were measured. Outcomes were analyzed using generalized estimating equations adjusted for period, sequence, and first-order carryover effects with robust standard errors. RESULTS: CHO-MR increased bilateral DLPFC oxygenation compared with MUS and PLA at post-intervention and post-TT time points (p < 0.05), with no differences at rest or baseline. Stroop performance was superior in CHO-MR relative to MUS and PLA following the TT (p < 0.001), without between-condition differences at earlier assessments. RPE was lower in CHO-MR than PLA across all intervals (p ≤ 0.01) and lower than MUS at mid-trial distances (p < 0.05). CHO-MR resulted in faster completion times, greater mean power output, and higher mean speed compared with MUS and/or PLA (p < 0.05), whereas peak power, heart rate, and blood lactate did not differ among conditions. Pooled change-score analyses demonstrated positive associations between bilateral ΔDLPFC oxygenation (p < 0.001) and between ΔDLPFC oxygenation and ΔStroop performance (p < 0.05). CONCLUSION: CHO-MR enhances bilateral DLPFC oxygenation, preserves executive function, and reduces perceived exertion during high-intensity endurance exercise, translating into improved performance without detectable peripheral metabolic alterations. These findings support a central neurocognitive mechanism underlying the ergogenic effects of oral carbohydrate sensing. TRIAL REGISTRATION: ClinicalTrials.gov NCT07099807.
14 May 2026
Read appraisal →Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology
Metabolic, demographic, and behavioral risk factors predict brain structural variability in the general population: an analysis of the human connectome project young adults
BACKGROUND: Understanding whether commonly available metabolic, demographic, and behavioral factors can explain variability in brain structure may support the development of accessible predictive approaches. This study aims to evaluate the ability of machine learning regression models to predict brain structural measures in young, neurologically healthy adults using clinically accessible risk factors. METHODS: Data were drawn from the Human Connectome Project Young Adults dataset, including 1082 participants aged 22-37. Predictors comprised metabolic measures (systolic and diastolic blood pressure, hematocrit, body mass index, hemoglobin A1C, thyroid stimulating hormone) and demographic/behavioral variables (age, gender, race, education level, smoking history, alcohol use). Brain structural parameters from MRI included gray matter (GM) and white matter (WM) volumes, surface areas, and cortical thickness. Distance correlation (dcor) guided feature selection, followed by machine learning modeling. Model performance was evaluated using normalized mean absolute error (NMAE), normalized root-mean-squared error (NRMSE), and coefficient of determination (R²). SHAP values were used for feature interpretation. RESULTS: XGBoost consistently outperformed other algorithms, showing strong predictive accuracy for total brain segmentation, supratentorial, and total GM volumes (NMAE < 0.35, R² > 0.80). In contrast, cortical surface predictions (left superior frontal) showed moderate performance (NMAE > 0.50, R² < 0.60). SHAP analysis highlighted age, gender, education level, and diastolic and systolic blood pressure as the most influential predictors of brain structure. CONCLUSIONS: The findings highlight the methodological value of regression-based approaches for estimating brain structural variability and support their potential role as complementary tools for brain health research.
14 May 2026
Read appraisal →Journal of molecular neuroscience : MN
Development and Validation of a SHAP-Interpretable Machine Learning Model for Stroke Risk Prediction Using Circulating MicroRNA Biomarkers
BACKGROUND/OBJECTIVE: Stroke remains a leading cause of morbidity and mortality worldwide. Circulating microRNAs (miRNAs) have emerged as promising non-invasive biomarkers for cardiovascular disease diagnosis and risk stratification. However, their integration into predictive models remains limited by challenges in feature selection, model robustness, and interpretability. This study aims to develop an interpretable machine learning framework for predicting stroke incidence using serum miRNA signatures. METHODS: We analyzed serum miRNA expression profiles from 1,785 human samples (173 stroke patients and 1,612 non-stroke controls) obtained from the GEO dataset GSE117064. Differential expression analysis was performed to identify significantly dysregulated miRNAs using linear modeling and empirical Bayes moderation. LASSO logistic regression was then applied to select predictive miRNA features. Five supervised machine learning classifiers-logistic regression, support vector machine (SVM), random forest, XGBoost, and k-nearest neighbors-were evaluated using 10-fold cross-validation. The best-performing model (SVM) was further interpreted using Shapley Additive exPlanations (SHAP) to assess individual miRNA contributions to prediction. To validate the robustness and clinical generalizability of the identified signatures, quantitative real-time PCR (qPCR) and Next-Generation Sequencing (NGS) were performed on independent sets of serum samples. The pre-trained SVM model was applied to the NGS data to verify its classification performance in an external cohort. RESULTS: A total of 604 miRNAs were differentially expressed between stroke and non-stroke groups, including 206 downregulated and 398 upregulated candidates. LASSO regression identified 66 non-zero-coefficient miRNAs with potential predictive value. Among the classifiers tested, the SVM model achieved the highest average accuracy of 0.9983 and perfect AUC (1.0000), demonstrating superior performance and stability. SHAP analysis revealed that a subset of miRNAs, including hsa-miR-3648, hsa-miR-1290, and hsa-miR-6765-3p, had the greatest impact on classification outcomes, providing mechanistic insights and enhancing model interpretability. qPCR results preliminarily supported the differential expression patterns of key miRNAs. Furthermore, NGS analysis of an independent validation cohort (n = 10) demonstrated distinct hierarchical clustering of the selected biomarkers. The pre-trained SVM model preliminarily supported its cross-platform validity by classifying these external samples with an overall accuracy of 80% (sensitivity 80%, specificity 80%), suggesting the potential generalizability of the identified miRNA panel across different quantification platforms. CONCLUSIONS: This study demonstrates the utility of integrating differential expression analysis, regularized feature selection, and machine learning with SHAP-based interpretation to identify and validate serum miRNA signatures predictive of stroke. The successful external validation using NGS highlights the robustness of the identified biomarkers and their potential for cross-platform application. The results provide a transparent, data-driven framework for biomarker discovery in clinical risk prediction and support the development of non-invasive diagnostic tools for stroke detection.
14 May 2026
Read appraisal →Journal of molecular modeling
Exploring human GABA transporter 3 binders for epilepsy through quantum reactivity analysis, molecular dynamics, machine learning prediction, and network pharmacology
CONTEXT: Drug-resistant epilepsy (DRE) remains a major therapeutic challenge, affecting millions of patients globally who do not respond to conventional antiepileptic medications. Recent evidence highlights GABA transporter 3 (GAT3), a glial subtype responsible for regulating extracellular GABA levels, as a promising molecular target for seizure control. Despite its therapeutic potential, selective and potent GAT3 inhibitors remain largely unexplored. This study aims to identify novel GAT3 inhibitors through an integrative computational strategy combining quantum chemical, molecular dynamics, and machine learning approaches to accelerate antiepileptic drug discovery. The cryo-EM-resolved human GAT3-SNAP-5114 complex structure was used for virtual screening, leading to the identification of three highest-ranking compounds, viz. Comp_1, Comp_2, and Comp_3, from the diverse chemically optimized screening library. Analysis of their quantum chemistry profiles indicated HOMO-LUMO energy gap values of 4.14 eV, 4.21 eV, and 3.26 eV, respectively, suggesting them to be maximally reactive with favorable electronic properties for interacting with biological molecules. Molecular dynamics simulations were performed in a fully hydrated POPC lipid bilayer using the AMBER force field, providing a physiologically relevant environment for assessing ligand binding and transporter dynamic, revealed stable binding modes, with the highest structural integrity or least deviations in the structure with Comp_2, having RMSD values ranging from 2.5 to 3.0 Å, with strong hydrogen bonding, while the binding mode was more flexible with strong anchorage for Comp_3, with its mode involving diverse interactions in the complex. Principal component and free energy landscape analyses identified energetically favorable, ligand-induced conformational states. Machine learning-based pIC₅₀ predictions using the AdaBoost regressor yielded values of 7.95 (Comp_1), 7.98 (Comp_2), and 7.63 (Comp_3), all comparable or superior to the reference inhibitor (7.89). A novel approach for network pharmacology integrating detailed drug-gene interaction profiling was additionally used to improve systems-level understanding of the discovered compound. These findings validate the potential of Comp_1-3 as lead GAT3-targeting antiepileptic agents. METHODS: A docking-based virtual screening of a structurally diverse compound library was performed against the cryo-EM structure of human GAT3 in complex with the reference inhibitor SNAP-5114. The top-scoring ligands were subjected to density functional theory (DFT) analysis to assess electronic properties such as HOMO-LUMO energy gaps, reflecting potential binding reactivity. Lead candidates were refined, docked, and evaluated through 500 ns molecular dynamics (MD) simulations, analyzing structural stability via RMSD, RMSF, hydrogen bonding, and interaction network profiles. Principal component analysis and free energy landscape evaluations were used to explore ligand-induced conformational dynamics. Furthermore, machine learning (ML) models-particularly the AdaBoost algorithm-were employed to predict pIC₅₀ values, confirming bioactivity trends. Finally, network pharmacology analysis was conducted to elucidate gene targets and pathway interconnections, revealing the multi-pathway involvement of top candidates in seizure regulation. This integrative computational framework underscores the potential of GAT3-focused modelling and ML-guided screening for rational antiepileptic drug design.
14 May 2026
Read appraisal →Sleep
Testing affect regulation theories of dreaming
STUDY OBJECTIVES: Dreaming is theorized to regulate waking affect via fear extinction learning, similar to exposure therapy. We tested whether fear in dreams was associated with lower negative affect (NA) and higher positive affect (PA) on subsequent mornings and whether these effects were moderated by emotion regulation strategies or mixed dream emotion states. Additionally, we examined whether individuals with higher average dream fear showed more adaptive emotion regulation. METHODS: We employed a natural language processing tool and Bayesian multilevel modeling using online daily survey data in a community sample (n = 536; 85.6% women; Mage = 39.3 ± 17.3; days = 4715). RESULTS: Frightening dreams were associated with more negative affect the following morning (+7% NA, pd = 100%), which was amplified for individuals with more adaptive emotion regulation (+3% NA, pd = 98.69). Dreams concurrently high in fear and joy were related to 20% higher odds of zero NA the next morning (pd = 97.82%). Higher average dream joy was related to 9% higher morning PA (pd = 98.79%). Individuals with higher average dream fear showed greater adaptive emotion regulation (β = 0.18, pd = 99.93%). CONCLUSIONS: Findings supported affect continuity from dream to wake, yet affect regulation at the individual level. Frightening dreams were related to worse morning affect, with a more pronounced effect among high-regulation individuals. High-regulation individuals tended to experience more fearful dreams. Mixed dream emotions were related to mornings without NA, and dream joy was related to higher morning PA. Our findings suggest adaptive, though dynamic, effects of dream emotions on psychological functioning.
14 May 2026
Read appraisal →Progress in biomedical engineering (Bristol, England)
Cross-subject generalization for EEG decoding: a survey of deep learning methods
Deep learning for cross-subject electroencephalography (EEG) decoding is hindered by high inter-subject variability, which introduces a severe domain shift between training and unseen test subjects. This survey presents a comprehensive review of deep learning methodologies specifically engineered to address this cross-subject generalization challenge. To ground this analysis, we formalize the cross-subject setting as a multi-source domain problem and delineate the rigorous, subject-independent evaluation protocols required for valid assessment. Central to this survey is a systematic taxonomy of the current literature into discrete methodological families, including feature alignment, adversarial learning, feature disentanglement, and contrastive learning. We conclude by examining three critical elements for advancing robust, real-world decoding: the theoretical limitations of current methodologies, the structural value of subject identity, and the emergence of EEG foundation models.
14 May 2026
Read appraisal →Biomedical physics & engineering express
Differentiating depression subtypes through three-electrode EEG and emotional stimuli: a machine learning approach
We propose an EEG-based framework for depression subtype assessment using emotion-modulated neural dynamics elicited by immersive virtual reality (VR). EEG was recorded from 70 participants (31 first depressive episode, FDE; 18 recurrent depressive episode, RDE; 21 control participants, HC) using a compact frontal montage (Fp1/Fpz/Fp2) during positive and negative VR conditions, focusing on the immediate post-stimulus regulation period. Subject-level features were analyzed with a linear mixed-effects model to probe group-by-condition interaction patterns, and a Bi-Emotional Siamese Network (BESN) was developed to model baseline-anchored within-subject deviations by fusing positive/negative reactivity streams with path-signature-based temporal encoding. Feature-level analyses revealed interaction signals suggestive of differential emotion-related modulation across groups. In subject-wise evaluation, BESN achieved 83.1% accuracy for HC versus FDE discrimination and 70.6% accuracy for three-class classification (HC versus FDE versus RDE), outperforming conventional machine-learning baselines. Robustness was further supported on an external public resting-state EEG dataset (MODMA), achieving 78.2% accuracy. These results suggest that baseline-anchored, emotion-modulated EEG dynamics combined with path-signature modeling provide an objective and generalizable computational approach for depression-related group discrimination.
14 May 2026
Read appraisal →Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology
Efficacy and safety of CGRP monoclonal antibodies for cluster headache: a systematic review and network meta-analysis of randomized controlled trials
INTRODUCTION: Cluster headache (CH) is a severe unilateral trigeminal autonomic cephalalgia with limited well tolerated therapies. Calcitonin gene related peptide monoclonal antibodies, including galcanezumab, fremanezumab, and eptinezumab, have established efficacy in migraine and are under evaluation in CH. This study assesses their efficacy and safety using pairwise and network meta analysis. METHODS: PubMed, Embase, Scopus, and ClinicalTrials.gov were searched through April 2025. Randomized controlled trials enrolling adults with episodic or chronic CH were included. The primary endpoint was change in weekly attack frequency. Secondary outcomes included subtype specific effects and adverse events. Frequentist random effects network meta analysis and pairwise meta analysis were performed in R. RESULTS: Five trials with approximately 1,000 participants were included. No agent demonstrated statistically significant reduction in weekly attack frequency versus placebo, although consistent numerical improvements were observed. Dose specific network estimates showed mean differences of - 0.81 for galcanezumab 300 mg, - 0.17 for eptinezumab 400 mg, 0.71 for fremanezumab 675/225 mg, and - 1.27 for fremanezumab 900/225 mg. Pooled dose estimates were - 0.81 for galcanezumab, - 0.27 for fremanezumab, and - 0.17 for eptinezumab. Pairwise meta analysis yielded a pooled mean difference of - 0.41. Subgroup analysis indicated minimal change in chronic CH and greater numerical reduction in episodic CH. CONCLUSION: CGRP monoclonal antibodies demonstrate modest directional reductions in attack frequency without statistical significance. Signals appear more pronounced in episodic CH, supporting the need for adequately powered, subtype specific trials.
14 May 2026
Read appraisal →Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology
Oral antipyretics for fatigue alleviation and exercise enhancement in adults with multiple sclerosis: a systematic review and meta-analysis
AIMS: This review aims to explore the potential role of oral antipyretics (aspirin (ASA)/ acetaminophen), commonly known for fever and pain control, in managing fatigue, temperature regulation, and exercise capacity in patients with Multiple Sclerosis (MS), with a focus on nursing implications for symptom management. DESIGN: A systematic review of existing clinical studies assessing the effects of aspirin/ acetaminophen on MS-related fatigue, thermoregulation, and exercise performance. DATA SOURCES: Electronic databases including Cochrane Central Register of Controlled Trials (CENTRAL), EMBASE, PubMed, Scopus, Web of Science, Wiley, Google Scholar, and ClinicalTrials.gov were searched up to March 2024. REVIEW METHODS: Quality assessment was conducted using the Cochrane risk of bias tool 2. to evaluate the methodological rigor of included studies. Outcomes analyzed included clinically assessed fatigue scores, exercise endurance, and post-exercise thermoregulation, with attention to potential risks associated with aspirin use. RESULTS: After assessment of 57 reports for eligibility, only seven studies met inclusion criteria; results indicated that aspirin pretreatment significantly improved Time to Exhaustion (TTE) in heat-sensitive MS patients (p = 0.013), though one study reported no significant effect. Aspirin reduced post-exercise temperature rise by 56%, but this was not statistically significant in one trial (p = 0.178), while another showed significant reductions (p = 0.002). CONCLUSION: Aspirin and acetaminophen may offer benefits in alleviating fatigue, enhancing thermoregulation, and improving exercise endurance in MS patients. These findings suggest that nurses should consider the potential role of aspirin in symptom management, with further research needed to confirm efficacy and safety. IMPACT: This review highlights a potential adjunct therapy for nurses to incorporate into comprehensive MS care, emphasizing symptom control and quality of life improvements. PATIENT OR PUBLIC CONTRIBUTION: Not applicable. TRIAL REGISTRATION: This review is PROSPERO registered having ID CRD420251000875.
14 May 2026
Read appraisal →Molecular neurobiology
Gut Microbiota Dysbiosis and Neuroinflammation in Alzheimer's Disease: a Systematic Review of Mechanistic Insights
This systematic review aims to identify microbial species associated with Alzheimer's disease and clarify how they influence Alzheimer's disease pathology, including immune system interactions and blood-brain barrier integrity. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, 18 studies were analyzed, revealing significant differences in gut microbiota composition between patients with Alzheimer's disease and controls. Key findings include reduced beneficial bacteria, such as Faecalibacterium prausnitzii and Roseburia hominis and increased pro-inflammatory species like Escherichia coli. Mechanistically, Escherichia coli produces lipopolysaccharides, triggering the release of pro-inflammatory cytokines such as tumor necrosis factor-alpha. This response correlates with elevated YKL-40 levels and reduced beneficial bacteria like Turicibacter. Additionally, reductions in butyrate-producing bacteria impair blood-brain barrier integrity, while bile acid metabolism disruptions impair signaling pathways, exacerbating amyloid-beta aggregation. These findings highlight the interplay between gut microbiota-derived metabolites and neuroinflammation, suggesting that targeting the gut microbiota for novel diagnostic and therapeutic approaches in Alzheimer's disease.
14 May 2026
Read appraisal →Experimental brain research
Subthalamic nucleus deep brain stimulation and associations with attention and processing speed in Parkinson's disease
Although subthalamic nucleus deep brain stimulation (STN DBS) is an effective treatment for the motor symptoms of Parkinson's disease (PD), its effects on cognitive functions are controversial. This cross-sectional study aimed to compare attention and processing speed in PD patients with STN-DBS and those receiving medical therapy, using a multimodal assessment combining P300 event-related potentials and neuropsychological tests. The study included 20 PD patients who underwent STN DBS and 17 PD patients who were followed with medical treatment. Demographic and clinical data for all patients were recorded. Cognitive functions were assessed using the P300 potential, the Stroop test (ST), and the Serial Digit Learning Test (SDLT). P300 potential latency and amplitude values, as well as neuropsychometric test results, were statistically compared between the two groups. P300 latency was shorter in the DBS-treated group than in the non-DBS group (p = 0.004). Performance times on Part III of the ST were shorter in the DBS-treated group (p = 0.03). In addition, a higher proportion of patients in the DBS-treated group performed normally on Part I of the ST compared to the non-DBS group (p = 0.03). There was no significant difference between the two groups in SDLT results. Shorter P300 latency and subtle differences in ST performance suggest a potential association between STN-DBS and the cognitive domains of attention and processing speed. Combining neuropsychological and electrophysiological assessments could provide a more comprehensive evaluation, and future large-scale, longitudinal studies are needed to clarify domain-specific cognitive effects.
14 May 2026
Read appraisal →Journal of medical systems
Artificial Intelligence in Physical, Occupational and Neuro-Rehabilitation: Clinical Effectiveness, Prognostic Performance, and Pre-Implementation Feasibility - A Systematic Review
BACKGROUND: Global rehabilitation needs far exceed capacity, and artificial intelligence (AI) is proposed to extend access, personalise therapy, and support adherence. We aimed to synthesise evidence on clinical effectiveness, prognostic performance, and implementation feasibility of AI-enabled rehabilitation across conditions and care settings. METHODS: We conducted a mixed-methods systematic review of AI-enabled rehabilitation and rehabilitation-led prevention relevant to physiotherapy practice. MEDLINE, Embase, Web of Science, CINAHL, Scopus, and IEEE Xplore were searched. Methodological quality was appraised with MMAT and PROBAST + AI (prediction/diagnostic models), with a priori AI-reporting minimums checklist. Given heterogeneity, evidence was integrated via prespecified thematic synthesis. FINDINGS: Thirty studies from diverse regions met inclusion, comprising randomised trials, non-randomised comparisons, cohorts, surveys, qualitative work, and prediction/diagnostic models. Clinical effects were modest and heterogeneous. Most AI-enabled interventions were comparable to conventional rehabilitation; signals of benefit appeared in selected musculoskeletal and telerehabilitation contexts but rarely persisted beyond short follow-up. Internal validity was frequently limited by asymmetric adherence measurement objective telemetry in AI arms versus self-report or attendance in controls alongside low uptake and declining engagement over time. Safety and usability were generally favourable within short horizons, although surveillance and explicit attribution to AI components were inconsistently reported. Prognostic and adaptive models showed encouraging discrimination in development settings but lacked multicentre external validation, calibration, subgroup-error profiling, and prospective impact evaluation, leaving them unready for clinical use. Stakeholders reported willingness to adopt AI while highlighting gaps in training, governance and information technology support, costs, and digital equity. Overall certainty for comparative clinical outcomes was low to moderate, for models, very low. CONCLUSION: AI in rehabilitation presently acts more as a behavioural amplifier structuring home programmes and supporting execution than as a replacement for dose-matched therapist-delivered care. Credible scale-up should position AI as an adjunct and hinge on arm-symmetric capture of adherence and execution, clinically meaningful outcomes paired with verified behaviour change over treatment and longer-term follow-up, and model deployment only after transparent development, external validation with calibration and subgroup-error characterisation, and demonstration of clinical impact. Embedding equity and economic evaluation, together with living oversight and version control, will be essential to convert promising prototypes into trustworthy, durable, and widely accessible services.
14 May 2026
Read appraisal →Physiological measurement
Turning glasses into sensors: capacitive facial expression recognition under frame geometry constraints
Objective.Wearable facial expression recognition requires sensing modalities that are unobtrusive, illumination-independent, and compatible with everyday eyewear form factors. Existing optical and electromyographic approaches are limited by line-of-sight requirements, skin contact, or restricted action unit (AU) coverage. This work investigates the feasibility of non-contact capacitive sensing for facial muscle activity monitoring within the strict geometric constraints of standard eyewear.Approach.We developed FERGlasses, integrating eight loading-mode capacitive channels via a custom flexible PCB and active shielding to detect skin-to-frame displacement. A validation study with 40 participants was conducted to elicit seven facial action coding system-defined AUs. Signal fidelity was assessed using independent Random Forest regressors and benchmarked against commercial optical-proximity (OCOsense) and computer-vision (Py-Feat) standards.Main results.The system demonstrated high agreement for upper-face activity, achieving median cosine similarities of 0.97 for the Outer Brow Raiser (AU02) and 0.87 for the Nose Wrinkler (AU09), while lower-face actions such as the Chin Raiser (AU17) yielded a median of 0.33, consistent with mechanical signal attenuation along the frame. Channel-ablation analysis quantified the specific anatomical contributions of each electrode group, identifying eyebrow and rhinion sensors as primary signal drivers for upper-face and perinasal actions, respectively, and temple sensors as critical for cheek raiser detection.Significance.This study provides the first metrological validation of multi-channel capacitive sensing for AU-level recognition in eyewear. The results establish the modality as a robust, privacy-preserving, and illumination-independent alternative to optical methods for longitudinal physiological monitoring, with the current prototype serving as a wired research platform and full consumer-grade integration identified as a future engineering objective.
14 May 2026
Read appraisal →Cerebellum (London, England)
Quantitative Ocular Motor / Vestibular Assessment in Patients with Spinocerebellar Ataxia Type 3 (SCA3, Machado Joseph Disease) - Systematic Review of the Literature
Spinocerebellar ataxia type 3 (SCA3 or Machado-Joseph disease) is the second most common autosomal-dominant hereditary ataxia and often presents with ocular motor and vestibular impairment. The value of ocular motor testing for early disease detection, monitoring disease progression and as a potential treatment biomarker has been acknowledged in several studies. This systematic review aims to summarise the current evidence supporting quantitative eye movement testing in SCA3. Studies reporting on quantitative ocular motor and/or vestibular measurements in SCA3 were searched for in MEDLINE and Embase. Thirty studies reporting on 432 SCA3 patients (including 64 pre-symptomatic carriers) were included. Information on various ocular motor parameters were extracted and correlations with other parameters (including clinical scales, imaging, and genetic testing) were assessed. Ocular motor abnormalities observed comprised (horizontal) pursuit eye-movement (PEM) gain reduction (14/19 patients), saccadic intrusions (principally square-wave jerks (SWJ, 29/45)), gaze-evoked nystagmus (GEN, 65/82), and rebound nystagmus (8/10). Additionally, saccades to target displayed increased latency (13/29), reduced peak velocity (16/49) and dysmetria (33/49). Vestibular deficits measured included vestibulo-ocular reflex (VOR) gain-reductions during quantitative head-impulse testing (qHIT, 117/133) and abnormal responses during caloric irrigation (7/9), impaired visually-enhanced VOR-responses (5/6), and impaired cervical (19/66) and ocular (10/14) vestibular-evoked myogenic-potential testing. In summary, impaired VOR-gains (qHIT), GEN, horizontal PEM-gain decreases, dysmetric saccades, and SWJ were most frequently reported and therefore seem most suitable as potential disease markers. The quantitative ocular motor/vestibular assessment may facilitate early diagnosis, distinction of disease stages and also potentially allow monitoring of disease progression and treatment response.
14 May 2026
Read appraisal →Angewandte Chemie (International ed. in English)
A Novel Acetylcholine Nanosensor for Single Vesicle Storage and Sub-Quantal Exocytosis in Living Neurons and Organoids
Acetylcholine (ACh) is a critical neurotransmitter that regulates diverse physiological functions, such as cognition and muscle contraction, through synaptic transmission. However, in situ quantitative chemical analysis of single-vesicle storage and release dynamics at single-cell level remains a major technical challenge, hindering mechanistic understanding of cholinergic synaptic plasticity across physiological and pathological states. To address this, we developed an Ti3C2Tx MXene/enzyme-functionalized (M@E@CF) nanosensor, enabling real-time monitoring of vesicular ACh storage and exocytotic release in primary cholinergic neurons and human spinal cord organoids. Our findings reveal a sub-quantal release mode in both mouse and human-derived neurons. To further elucidate the regulatory principles of exocytosis kinetics, we classified single-vesicle exocytosis patterns based on signal peak shapes using a 1D convolutional neural network (1D-CNN) deep learning model, uncovering significant differences in the number of released molecules and kinetic parameters across modes. Critically, in Down syndrome models, we observed significantly reduced single vesicle ACh storage and release alongside an elevated release fraction, concurrent with shortened fusion pore durations during exocytosis. Thus, the M@E@CF nanosensor platform establishes a versatile tool for spatiotemporal investigation of neurotransmitter storage and release dynamics, providing a critical technical foundation for exploring physiological functions and pathological mechanisms of the cholinergic system.
13 May 2026
Read appraisal →Journal of chemical information and modeling
MechMemDyn: Coupling Frustration Analysis with Membrane Dynamics to Target the TREM2-DAP12 Complex Interface
The transmembrane complex formed by TREM2 (Triggering Receptor Expressed on Myeloid cells 2) and DAP12 (DNAX-activating protein of 12 kDa) constitutes a pivotal therapeutic target for Alzheimer's disease. However, the intrinsic plasticity of its interface poses a formidable challenge for structure-based discovery of protein-protein interaction stabilizers (PPI stabilizers). Conventional approaches, ranging from static molecular docking to geometric deep learning, fail to capture the subtle interfacial energetics required for stabilizing this assembly, often leading to erroneous activity predictions. Here, we present MechMemDyn, a novel predictive framework that uniquely integrates protein frustration analysis with membrane-embedded molecular dynamics (MD) simulations. To our knowledge, this is the first systematic application of frustration analysis to rationalize PPI-stabilizing activity and guide ligand design. By mapping the local frustration landscape, we identify critical, minimally frustrated contact networks at the protein interface, which are essential for stabilizing the PPI. We demonstrate that the ability of a ligand to dampen distance fluctuations within these key networks, a metric rooted in thermodynamic rigor, correlates strongly with experimental potency. This method outperforms conventional static docking, AI-driven dynamic docking approaches, and standard molecular simulations, providing a more accurate and reproducible basis for cross-ligand comparison. This work not only resolves the intractability of the TREM2-DAP12 complex but also establishes a physics-driven paradigm for targeting dynamic transmembrane interfaces via frustration-optimized PPI stabilizers.
13 May 2026
Read appraisal →Cerebellum (London, England)
Novel Imaging Phenotype in Spinal Cerebellar Ataxia Type 8: Symmetrical White Matter Changes without Cerebellar Atrophy
Spinal Cerebellar Ataxia Type 8 (SCA8) is a rare autosomal dominant neurodegenerative disorder characterized by progressive cerebellar dysfunction. It is caused by pathogenic expansions of a CTG/CAG trinucleotide repeat sequence within the ATXN8OS/ATXN8 gene locus on chromosome 13q21. Although cerebellar atrophy is widely recognized as a cardinal neuroimaging feature of SCA8, the phenotypic spectrum remains incompletely characterized. This study identified two cases of SCA8 through genetic analysis. Pedigree information was gathered and analyzed for all available family members. A systematic literature review was conducted to identify all published SCA8 cases with available neuroimaging data. The distinctive clinical and radiological features of SCA8 were analyzed through detailed case characterization integrated with a comprehensive review of existing literature. We report two genetically confirmed SCA8 cases exhibiting a previously undescribed neuroimaging phenotype. Case 1: A 36-year-old woman presented with a one-year history of progressive gait ataxia. Brain MRI revealed confluent, symmetric white matter signal abnormalities without cerebellar atrophy. Repeat expansion analysis detected a pathogenic CTG/CAG expansion of 9/77 in the ATXN8 gene. Case 2: A 64-year-old man (father of case 1) exhibited significant gait abnormalities, cognitive impairment and dysarthria. Brain MRI showed symmetric white matter lesions similar to case 1, with a CTG/CAG repeat length of 17/73. Notably, our systematic review revealed no prior reports of SCA8 presenting with isolated, symmetric white matter abnormalities in the absence of cerebellar atrophy. These findings delineate a novel neuroimaging phenotype of SCA8, characterized by symmetric white matter alterations without cerebellar volume loss. This atypical presentation expands the radiological spectrum of SCA8 and underscores the importance of considering repeat expansion disorders in the differential diagnosis of leukoencephalopathies, even in the absence of classic neurodegenerative imaging signatures.
13 May 2026
Read appraisal →Neurosurgical review
Deep brain stimulation for freezing of gait in Parkinson's disease: An updated systematic review, meta-analysis, and a single-center clinical experience.
Freezing of gait is a disabling and treatment-resistant manifestation of Parkinson's disease (PD). The effectiveness of deep brain stimulation (DBS) for freezing of gait remains inconsistent across stimulation targets, frequencies, and medication states. We conducted a systematic review and meta-analysis following PRISMA guidelines to examine how DBS affects freezing of gait in patients with PD. We searched Medline, Scopus, Web of Science, and Cochrane up to September 28, 2025. For synthesis, we combined mean differences and 95% confidence intervals for the Freezing of Gait Questionnaire (FOG-Q) and the Unified Parkinson's Disease Rating Scale (UPDRS) part III across different medication and stimulation settings to calculate the final effect size. Thirty-one studies with 905 patients were included. Of these, 21 provided FOG-Q data, and all reported UPDRS-III results. DBS led to a modest decrease in FOG-Q scores (mean difference [MD] = - 2.99; 95% CI = - 5.69 to - 0.29). The biggest improvement in FOG was seen when stimulation was used while patients were off medication (Med-OFF/Stim-OFF vs. Med-OFF/Stim-ON: MD - 5.88; 95% CI - 9.28 to - 2.47). Stimulation during the medication-ON state had smaller effects (Med-ON/Stim-OFF vs. Med-ON/Stim-ON: MD - 2.65; 95% CI - 4.99 to - 0.32), and there was no significant benefit when comparing Med-ON/Stim-OFF to Med-OFF/Stim-ON (MD - 0.70; 95% CI - 3.88 to 2.48). UPDRS-III scores improved substantially in the medication-OFF state with stimulation (MD - 14.35; 95% CI - 17.39 to - 11.32). High-frequency stimulation targeting the subthalamic nucleus provided more consistent benefits, yet substantial variation persisted across studies. The results of our small cohort showed significant improvement in FOG-Q and UPDRS-III (P values = 0.034, 0.022, respectively). DBS improves freezing of gait primarily in the medication-OFF state, with greater effects observed using high-frequency stimulation and subthalamic nucleus targets. Significant heterogeneity and limited data for alternative targets warrant cautious interpretation and further controlled studies.
13 May 2026
Read appraisal →PloS one
A modular deep learning architecture for interpretable disease prediction across tabular clinical and biometric datasets
Accurate disease prediction using clinical datasets is essential for improving early diagnosis and clinical decision-support systems; however, many existing deep learning approaches are disease-specific, computationally intensive, and difficult to generalize across heterogeneous biomedical datasets. This study addresses this challenge by proposing a unified and dataset-aware deep learning framework that enables accurate and interpretable disease prediction across diverse clinical datasets. The framework adopts a modular architecture that selects appropriate models based on dataset characteristics such as feature dimensionality, sample size, and class imbalance. It integrates multiple deep learning architectures, including MLP, one-dimensional CNN, FT-Transformer, autoencoder-based classifiers, and ensemble strategies. Robust preprocessing, fold-safe feature selection, and nested cross-validation are incorporated to ensure reliable performance evaluation. The framework is evaluated on three heterogeneous benchmark datasets: the UCI Heart Disease dataset (303 samples, 13 clinical features), the PIMA Indians Diabetes dataset (768 samples, 8 metabolic features), and the Parkinson's disease voice dataset (195 recordings, 22 acoustic features). Experimental results demonstrate competitive predictive performance relative to classical baselines across the diverse tasks. The FT-Transformer + autoencoder ensemble achieved an AUC of 0.8980 (±0.0483) for heart disease prediction, while the CNN + Autoencoder ensemble obtained an AUC of 0.8451 (±0.0270) for diabetes classification. For Parkinson's disease detection, the MLP achieved an AUC of 0.7538 with perfect specificity. Overall, all models achieved AUC values comparable to ML baselines. The study contributes a scalable and interpretable deep learning framework that improves reliability, generalization, and practical applicability for multi-disease prediction in real-world healthcare environments.
13 May 2026
Read appraisal →Child's nervous system : ChNS : official journal of the International Society for Pediatric Neurosurgery
Postoperative raised ICP after minimally invasive surgery for monosutural craniosynostosis
INTRODUCTION: Minimally invasive craniectomies (MIC) for monosutural craniosynostosis are more and more used. Their results are usually investigated in terms of surgical, esthetic, and neuropsychological outcomes but not specifically about the risk of postoperative raised intracranial pressure (ICP). The goal of this article is to review the literature and the personal experience looking for the possible risk of postoperative raised ICP after MIC. METHODS: A systematic review adhering to PRISMA guidelines was realized. In parallel, the personal experience concerning scaphocephalic children consecutively operated on between 2010 and 2013 by MIC was analyzed. Children operated on by open cranial vault remodeling (OCVR) in the same period were considered as controls. RESULTS: Overall, 3048 patients from 14 studies were included, 5% of them requiring a reoperation after a 59.9-month mean follow-up. A total of 2297 children were treated by MIC: 2.8% (65 cases) showed postoperative raised ICP, the rate of reoperation was 4.4% (102 cases). The risk of reoperation varies across the techniques, being higher in modified or spring-assisted strip craniectomies (2.4-14.6%) than in endoscopy-assisted craniectomies (2.4%). Among 751 cases treated by OCVR, this risk varied from 0.95 to 7.1%. Also in the personal experience, no significant differences were found between MIC (1.5% of reoperation for raised ICP) and OCVR (0.8%). CONCLUSIONS: The risk of postoperative raised ICP seems to be relatively low after surgery for monosutural synostosis without a significant difference between MIC and OCVR. Missing standardized methods for postoperative screening, missing information about the reason of reoperation in some studies, and the retrospective analysis methods in most of them are the main limitations of this study.
13 May 2026
Read appraisal →Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology
Efficacy and safety of GLP-1 receptor agonists in Parkinson's disease: a systematic review and meta-analysis of randomized clinical trials
BACKGROUND: Parkinson's disease (PD) is a progressive neurodegenerative disorder with no proven disease-modifying therapies to date. Because changes in cerebral glucose metabolism and insulin resistance have been linked to PD pathophysiology, glucagon-like peptide-1 receptor agonists (GLP-1RAs), widely used for diabetes, have been investigated as potential neuroprotective treatments. METHODS: This study systematically assessed the efficacy and safety of GLP-1RAs in PD through a systematic review and meta-analysis of randomized controlled trials identified in PubMed, Embase, and the Cochrane Library. The primary outcomes were motor function improvements measured by the MDS-UPDRS Part III in both on- and off-medication states at study endpoints and at intermediate timepoints of interest. Secondary outcomes included MDS-UPDRS Parts I, II, and IV, quality of life assessed by the PDQ-39, levodopa equivalent daily dose (LEDD), and the occurrence of adverse events. RESULTS: The meta-analysis found no statistically significant difference in favor of GLP-1RAs over placebo for motors and non-motors outcomes, except for PDQ-39 (MD: - 0.75; 95% CI: [- 1.34, - 0.17], P = 0.01). Regarding safety, GLP-1RAs were associated with a higher incidence of adverse events, especially gastrointestinal effects such as nausea, vomiting, and constipation. CONCLUSIONS: Overall, current evidence does not demonstrate consistent clinical benefit of using GLP-1RAs for treating motor or non-motor symptoms in PD nor support GLP-1RAs as disease-modifying therapy, underscoring the need for further research.
13 May 2026
Read appraisal →Brain topography
rTMS Modulates Static and Dynamic Brain Functional Networks in Children with Autism Spectrum Disorder: An EEG Microstate Study
Repetitive Transcranial Magnetic Stimulation (rTMS) shows promise for treating Autism Spectrum Disorder (ASD), but its impact on the temporal dynamics of large-scale brain networks remains unclear. This study investigated the modulatory effects of rTMS on static and dynamic brain functional networks in children with ASD. Thirty-two children were randomized into an active rTMS group (1 Hz over the dorsolateral prefrontal cortex) and a sham control group. Resting-state EEG and behavioral assessments were conducted before and after a 9-week intervention. We employed a multi-dimensional analysis approach, combining microstate temporal parameters, static functional connectivity based on the weighted Phase Lag Index (wPLI), and dynamic complexity measured by Fuzzy Entropy. Results indicated that intrinsic features of Microstate B were significantly correlated with social relating deficits. Although rTMS did not induce significant interaction effects in standard microstate temporal parameters, it significantly enhanced static functional connectivity strength and increased the dynamic complexity of brain networks across all microstates. These findings suggest that rTMS exerts its therapeutic effects by strengthening network integration and restoring neural flexibility rather than simply altering the duration of brain states. The study underscores the value of network-based EEG metrics in elucidating the neuroplastic changes induced by neuromodulation in ASD.
12 May 2026
Read appraisal →PloS one
Effects of galvanic vestibular stimulation on bodily ownership and postural control: An experimental examination with counterbalanced randomization of stimulus conditions
Vestibular stimulation influences both bodily ownership and postural control. Although previous studies in the literature have examined the effects of galvanic vestibular stimulation (GVS) on body ownership and balance separately, their combined and time-dependent effects remain insufficiently explored. This study investigated how GVS modulates multisensory integration over time by assessing bodily ownership and postural control within the same participants. A within-participant design was used with four conditions: Baseline (pre-GVS), Sham (60-min post-GVS placebo), 30-min post-GVS, and 60-min post-GVS. Forty-eight healthy adults completed all conditions. Balance performance was assessed via the Single-Leg Stance (SLS) and Fukuda Stepping Test (FST), while bodily ownership was measured using the Rubber Hand Illusion (RHI) questionnaire. Balance performance on the SLS showed a significant reduction at 30 minutes post-GVS, with values returning toward baseline by 60 minutes. In contrast, angular deviation on the FST decreased significantly at both 30 and 60 minutes post-GVS. RHI ownership scores increased at both post-stimulation time points, with the most pronounced increase observed at 60 minutes. The sham condition also elicited increases in RHI scores, indicating possible expectancy-related effects. Overall, these findings indicate time-dependent and task-specific effects of GVS on bodily ownership and postural control. The results are consistent with adaptive sensory reweighting processes that differentially affect static and dynamic balance measures. Further research in clinical populations and using longer stimulation protocols is required to determine the extent to which these short-term effects translate into sustained functional benefits.
12 May 2026
Read appraisal →PloS one
Effects of deconstructed Tai Chi step training combined with conventional rehabilitation on lower limb function in brunnstrom stage III stroke patients: A randomized controlled trial
OBJECTIVE: To evaluate whether a deconstructed Tai Chi stepping protocol adapted for patients with Brunnstrom Stage III stroke, when combined with conventional rehabilitation, improves lower limb motor function, walking ability, and joint mobility compared with conventional rehabilitation plus limb synergy training. METHODS: In this assessor-blinded randomized controlled trial, 52 patients with subacute stroke (Brunnstrom Stage III, ≤ 6 months post-stroke) were randomized in a 1:1 ratio to an experimental group (conventional rehabilitation plus adapted Tai Chi stepping training) or a control group (conventional rehabilitation plus limb synergy training). Both interventions were delivered 5 days per week for 8 weeks. Fifty participants completed the study and were included in the final per-protocol analysis (25 per group). Outcomes were assessed at baseline and after intervention. The primary outcomes were the Fugl-Meyer Assessment for Lower Extremity (FMA-LE) and Holden Walking Function Classification. Secondary outcomes were hip, knee, and ankle range of motion (ROM). RESULTS: Both groups improved after treatment, with greater improvement observed in the experimental group. FMA-LE scores increased by 5.6 ± 1.5 points in the experimental group and 1.8 ± 0.8 points in the control group (p < 0.001, Cohen's d = 1.32). In addition, 92% (23/25) of participants in the experimental group achieved Holden Grades II-III compared with 60% (15/25) in the control group (p < 0.001). The experimental group also showed larger gains in joint ROM, including hip flexion (+19.9° vs. + 4.2°), knee external rotation (+9.9° vs. + 3.7°), and ankle dorsiflexion (+7.6° vs. + 1.3°) (all p < 0.001). CONCLUSION: Deconstructed Tai Chi stepping training combined with conventional rehabilitation was associated with greater improvement in lower limb motor function, walking ability, and joint mobility than conventional rehabilitation plus limb synergy training in patients with Brunnstrom Stage III stroke. This stage-specific protocol may represent a practical adjunct to stroke rehabilitation, although confirmation in larger trials is still needed. CLINICAL TRIAL REGISTRATION: International Traditional Medicine Clinical Trial Registry (ITMCTR; a WHO ICTRP Primary Registry), registration number ITMCTR2025000972.
12 May 2026
Read appraisal →Neurology
IV Fosphenytoin for Acute Trigeminal Neuralgia: A Multicenter, Randomized, Double-Blind, Placebo-Controlled Trial
BACKGROUND AND OBJECTIVES: Trigeminal neuralgia (TN) is a debilitating condition characterized by sudden, severe facial pain. IV medications for acute TN exacerbations have not been investigated in randomized controlled trials (RCTs). Oral medications are often insufficient for acute attacks, and surgery is not immediately available. We conducted an RCT to evaluate IV fosphenytoin therapy (IFT) as a rapid-acting rescue treatment for acute TN exacerbations. We hypothesized that, compared with placebo, IFT would reduce acute TN pain. METHODS: This multicenter, double-blind, placebo-controlled phase 3 trial was conducted at 6 institutions (April 2023-April 2024). Patients with classical or idiopathic TN who were experiencing TN exacerbation (numerical rating scale [NRS] score ≥5) were recruited from emergency and outpatient settings, and all study drugs were administered during hospital admission. Participants were randomized 1:1 to IFT (initial dose: 18 mg/kg, maintenance: 7.5 mg/kg for 4 days) or placebo. The primary end point was the change in NRS pain scores from baseline to 120 minutes after the initial dose. Secondary end points included attack frequency, the proportion achieving ≥50% NRS score improvement, and adverse events. Data were collected by blinded personnel and analyzed using repeated-measures mixed-effects models. RESULTS: Of 31 participants screened, 22 were randomized; 1 placebo-assigned participant did not meet the NRS ≥5 criterion and was excluded, leaving 21 for analysis (IFT: 11; placebo: 10). Participants had a mean age of 65.2 years (range 42-83; 8 men, 13 women). Compared with placebo, IFT significantly reduced pain (NRS change: -6.1 vs -2.4; between-group difference -3.8; 95% CI -6.4 to -1.1; p = 0.008), decreased attack frequency within 24 hours (2.5 ± 5.3 vs 16.1 ± 19.6, difference -13.6; 95% CI -26.4 to -0.7), and increased the proportion achieving ≥50% NRS improvement (90.9% vs 40.0%, difference 50.9%; 95% CI 5.6%-81.6%). Adverse events (somnolence, hypotension, and nausea) were mild and transient. DISCUSSION: IFT provided rapid and well-tolerated pain relief in acute TN exacerbations. Although findings are preliminary because of the small sample size, IFT may be a viable short-term treatment option, meriting further investigation. TRIAL REGISTRATION INFORMATION: Japan Registry of Clinical Trials, jRCT2011220043. Registered March 3, 2023. First enrollment April 25, 2023. jrct.mhlw.go.jp/en-latest-detail/jRCT2011220043. CLASSIFICATION OF EVIDENCE: This study provides Class I evidence that in patients with acute TN exacerbations, IV fosphenytoin is superior to placebo in reducing pain intensity at 120 minutes.
12 May 2026
Read appraisal →Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology
Thrombolysis in spinal cord infarction: Case report and systematic review
INTRODUCTION: Spinal cord infarction (SCI) is a rare but severe cause of acute myelopathy, and evidence supporting reperfusion therapies remains limited. METHODS: We report a 73-year-old man with spontaneous cervical SCI treated with intravenous alteplase and perform a systematic review (database search from inception to 13 November 2025) of case reports/series describing intravenous (IV) or intra-arterial (IA) thrombolysis for SCI, extracting data about clinical presentation, diagnostic and therapeutic work-up and functional outcomes. RESULTS: Our patient improved after thrombolysis and achieved functional independence at 3 months (modified Rankin Scale [mRS] 2). The review identified 21 studies (19 case reports, 2 case series) totaling 25 patients; including our case, 26 patients were analyzed. Mean age was 57.4 years (range 14-83), and anterior spinal artery (ASA) syndrome was the most common presentation. Most patients received IV thrombolysis; 4 underwent IA therapy (alone or combined). Considering the last available assessment, 14/25 patients (56.0%) had a favorable outcome (mRS 0-2), 8/25 (32.0%) had mRS 3, and 3/25 (12.0%) had mRS 4-5. No symptomatic treatment-related hemorrhagic complications were reported. DISCUSSION AND CONCLUSION: Published experience suggests that thrombolysis in carefully selected patients with suspected SCI may represent a reasonably safe therapeutic option, but evidence is limited and subject to major bias. Prospective registries and multicenter studies are needed to clarify safety, efficacy, and selection criteria.
12 May 2026
Read appraisal →Journal of computer-aided molecular design
NeuroPred-GMC: a dual-branch deep learning architecture for neuropeptide prediction based on gated dilated convolutional network and multi-scale convolutional network
Neuropeptides are multifunctional signaling molecules in the nervous system. By modulating synaptic transmission and integrating physiological systems, they influence a broad range of functions from pain perception to emotional regulation. Predicting neuropeptides can rapidly expand the library of potential therapeutic targets, thereby providing novel candidate molecules for drug development in areas such as analgesics, anti-anxiety medications, and weight-loss drugs. Traditional experimental methods are extremely time-consuming, labor-intensive, these promising alternative computational methods have emerged. In this study, a dual-branch deep learning architecture for neuropeptide prediction known as NeuroPred-GMC are built up based on gated dilated convolutional network with ESM-2 feature representation and multi-scale convolutional network with Prot-T5 feature representation. Dilated convolution exponentially enlarges the receptive field via increased dilation rates, gating mechanism enables dynamic, selective feature enhancement and noise suppression, and multi-scale convolution captures multi-level contextual information. On the independence test set, the accuracy of 93.24%, Sn of 93.69%, Sp of 92.79%, Pre of 92.86%, MCC of 0.8649 and the auROC of 0.9667 are obtained. The experimental results through cross-validation and independent test demonstrate that the proposed model has good robustness and generalizability, and can serve as a supplemental candidate predictor. The source datasets and codes can be freely available at https://github.com/yunyunliang88/NeuroPred-GMC .
11 May 2026
Read appraisal →Molecular biology reports
Genetic modulation of pain pathways: toward a new era in pain therapy- a systematic review
Gene therapy represents a new strategy in pain management targeting the causes of pain rather than its symptoms. This review summarizes recent advances across RNA interference (RNAi), viral vector delivery systems, CRISPR/Cas9, gene replacement therapy, and endogenous opioid gene delivery, emphasizing efficacy, safety, and mechanisms of action in pain conditions. RNAi and gene replacement techniques remain powerful tools for reducing pain and improving the quality of life by modulating pain-associated genes. Long-term relief may also be achieved with CRISPR/Cas9 and site-directed delivery using nanoparticle systems. Safety profiles especially with CRISPR/Cas9 remains a concern. A systematic search of Web of Science, Medline, Scopus, and Google Scholar identified 512 records (January 2010-March 2023). Following PRISMA screening, 18 studies met inclusion criteria. These studies evaluated strategies of gene therapy in neuropathy, arthritis, fibromyalgia, and complex regional pain syndrome. The review outlines available options and highlights preclinical findings.
11 May 2026
Read appraisal →AJNR. American journal of neuroradiology
Hippocampal Subfield Susceptibility Alterations in Mild Cognitive Impairment Revealed by 7T MRI
BACKGROUND AND PURPOSE: Hippocampal magnetic susceptibility alterations may serve as early neurodegenerative biomarkers of Alzheimer disease (AD), but subfield-level evidence in mild cognitive impairment (MCI) remains limited. This study uses submillimeter high-resolution quantitative susceptibility mapping (QSM) to investigate hippocampal subfield susceptibility changes in MCI. MATERIALS AND METHODS: Thirteen individuals with MCI and 13 cognitively normal controls (NC) underwent 7T MRI scans including MP2RAGE and multiecho gradient echo imaging (for QSM). The visibility of hippocampal strata radiatum, lacunosum, and moleculare (SRLM) was assessed using a visual rating scale. Hippocampal subfields (Cornu Ammonis 1 [CA1], CA2, CA3, dentate gyrus [DG], and subiculums) were segmented, and magnetic susceptibility values (χ) were extracted for each subfield. The susceptibility difference between the DG and other subfields was also calculated. Associations between subfield susceptibility measures and cognitive performance were then assessed. RESULTS: Qualitative analysis revealed a distinct hyperintense curved pattern in the hippocampus on the 7T QSM image, corresponding to the SRLM. In NC, this pattern was clearly visible exhibiting consistently delineated boundaries, while it appeared blurred or absent in most subjects with MCI. The visual rating results showed that NC had significantly higher scores than subjects with MCI (Hedgesg = 1.43, 95% confidence interval) [95% CI, 0.60-2.35; P = .003], with most NC rating 3-4 and most subjects with MCI rating 1-2. Quantitative analysis of hippocampal subfield susceptibility revealed significantly elevated susceptibility [χ] in the left DG in those with MCI compared with NC (Hedgeg = 0.88, 95% CI, 0.10-1.72; P = .03), leading to reduced susceptibility contrast between the SRLM and adjacent CA1-CA3 or DG. Furthermore, regional differences of susceptibility between key subfields (left CA1 versus DG and subiculum versus DG) were significantly reduced in MCI, reflecting a loss of subfield contrast. These quantitative changes were significantly associated with lower cognitive performance, even when controlling for age. CONCLUSIONS: 7T QSM reveals susceptibility changes in hippocampal substructures in individuals with MCI, providing a potential early biomarker for AD.
11 May 2026
Read appraisal →Journal of medical Internet research
AI in Clinical Decision Support Systems: Promising Applications and Strategies for Managing Data Challenges
The translation of big data analytics and artificial intelligence (AI) into clinical decision support systems (CDSSs) has advanced from proof of concept to real-world clinical practice. AI-informed CDSSs show measurable improvements in diagnostic accuracy, risk stratification, resource use, and patient outcomes compared to traditional models, offering the potential to assist clinicians in managing symptom complexity and uncertainty in health care delivery. Despite this potential, access to large amounts of high-quality and granular data remains one of the most significant bottlenecks to AI-enabled CDSSs. We argue that as health care systems increasingly adopt data-driven decision support, addressing the challenges of data accessibility and protection is essential to realizing the full potential of AI in clinical medicine. We use selected case examples of AI-informed CDSSs in oncology, organ transplantation, diabetic retinopathy, epilepsy, spinal cord injury, rare disease diagnosis, and emergency medicine to illustrate opportunities and challenges related to AI's potential to improve patient outcomes. We discuss public and semipublic, medical institutional and commercial, and government and national data sources that are currently available for the development of CDSSs and highlight the practical and ethical constraints associated with these data. We consider alternative data resources and ways in which health care systems can strengthen data ecosystems to increase AI-driven CDSS efficacy and implementation to improve patient outcomes.
11 May 2026
Read appraisal →JMIR formative research
At-Home Sleep Electroencephalography Assessment in Young and Older Adults Using a Novel Wireless Soft Electronics Sleep Monitoring System: Experimental Study
BACKGROUND: Sleep quality declines with age and is a known contributor to multiple chronic health conditions, including Alzheimer disease. Emerging evidence suggests that certain electroencephalography (EEG) neural signatures measured during sleep may be predictive of cognitive decline in older adults. Sleep EEG signals are traditionally measured using bulky, rigid, and uncomfortable equipment in an unfamiliar laboratory setting, which can negatively impact sleep signals. Due to these limitations, sleep EEG data acquisition is typically limited to a single night. OBJECTIVE: This study aimed to validate our recently developed portable, skin-like EEG monitoring patch for 7 nights in the home environment in a pilot sample of young and older adults by evaluating usability and acceptance, and replicating age-related differences in sleep architecture observed in the polysomnography literature. METHODS: Eighteen young adults and 18 cognitively unimpaired older adults without sleep disorders were enrolled (data from 11 young adults and 12 older adults were included in the analyses) in a 7-night study during which they wore novel, gel-free, wireless, ultrathin, skin-conforming, sleep monitoring, fabric-based patches. These patches were self-applied to the forehead and face for optimal usability and comfort. The patches incorporate laser-cut mesh electrodes with low-profile electronics (including a rechargeable battery and amplifier) and transmit EEG signals to a participant-controlled, Bluetooth-enabled, tablet-based data acquisition app. An automated algorithm was used to stage sleep and assess microarchitecture features from the EEG commonly impacted for each participant. Averages across nights were computed for these sleep features for each participant. RESULTS: Young and older adults reported that the sleep patch was easy to use and comfortable to wear. There was no loss of signal power over 7 nights of wear across participants (retained-data signal-to-noise ratio over the 7-d period: young adult, mean 20.69, SD 12.78, maximum 52.13, minimum 5.19; older adult, mean 22.10, SD 9.39, maximum 49.96, minimum 13.79). Most datasets not retained were lost due to poor reference electrode adhesion on the nose (75/101, 74% of lost datasets in young adults and 57/88, 65% in older adults). Trained sleep technologists verified that the retained datasets were of sufficient quality to be scored without difficulty. Expected age-group differences in sleep features were observed, including age-related reductions in stage N3 sleep (young adult, mean 18.55, SD 6.70; older adult, mean 10.40, SD 6.43; Mann-Whitney U=42.0; P=.01) and reduced sleep spindle density (young adult, mean 2.92, SD 2.24; older adult, mean 0.94, SD 1.33; Mann-Whitney U=45.0; P=.006). CONCLUSIONS: This study demonstrates that our novel, comfortable, wearable patch can reliably measure physiological sleep data over multiple nights at home in adults across the lifespan, thereby making multinight sleep assessment in cognitive aging studies and clinical research more accessible than traditional polysomnography. In future studies, the small, lightweight system, which is highly scalable, can be shipped inexpensively to participants' homes, making this technology and research accessible to individuals who may have difficulty traveling or who are hesitant to travel to a laboratory or clinic.
11 May 2026
Read appraisal →Behavioral and brain functions : BBF
Neuropeptide S modulation of learning and memory: a systematic review
BACKGROUND: In the last two decades, Neuropeptide S (NPS) has been identified as a key bioactive peptide in the mammalian brain, influencing fear, anxiety, wakefulness, reward, and learning. While some reviews have addressed its role in reward-seeking and anxiety, few have addressed its particular role in learning and memory. The neuropeptide S receptor 1 is highly expressed in key areas for learning processing, such as the hippocampus, cortex, thalamus, and amygdala. This review aims to examine evidence from human and animal studies that focused on the NPS system's role in modulating learning and memory. A special focus is given to experiments addressing the impact of NPS on associative learning leading to addiction and in fear conditioning, pointing to its potential therapeutic value in associated pathologies. MAIN BODY: An advanced search was conducted using the databases PubMed, Google Scholar, Web of Science, and Scopus, focusing on memory and Neuropeptide S. The reviewed data suggest that NPS modulation occurs at all memory phases, including acquisition, consolidation, and retrieval, and in extinction learning, whether motivated by appetitive or aversive stimuli. The summarized evidence shows that the NPS system interferes with working and short-term memory, mitigates learning impairments, enhances spatial and object memory consolidation, supports fear extinction learning and inhibitory avoidance consolidation, and reinstates drug-seeking behaviors. The NPS system closely interacts with key neuromodulators, including orexinergic, dopaminergic, and noradrenergic systems, in influencing memory. CONCLUSION: The Neuropeptide S system emerges as a critical modulator of memory processes. The NPS signaling may preferentially influence learning that involves emotionally or motivationally relevant stimuli. This highlights the NPS system's potential as a target for therapeutic interventions for particular memory impairments.
11 May 2026
Read appraisal →Journal of neural engineering
Uncovering relationships in multi-channel EEG data using principal Hessian directions and Ricci flow
Objective.The high-dimensional nature of multi-channel EEG data poses major challenges for downstream classification. Uncovering connectivity relationships and network structure between EEG channels can improve high-dimensional EEG signal recovery by identifying redundant inputs and guiding more targeted feature selection. In this study, we aim to demonstrate a systematic framework for extracting useful interchannel structure in EEG data.Approach.We present two complementary methods for inferring network structure and connectivity in EEG data: (1) a new supervised algorithm for inferring classification-relevant community structure based on principal Hessian directions (pHds), and (2) a discrete Ricci flow-based unsupervised community detection algorithm. We demonstrate these systematic methods on high-dimensional real-world EEG datasets involving classifying imagined digits versus non-digits and detecting emotional valence.Main results.We show that our pHd and Ricci flow methods-when combined-can detect interchannel relationships that meaningfully hold on unseen test classification data. Moreover, we demonstrate that this interchannel structure extracted via pHd and Ricci flow can enable subsequent improvements in downstream EEG signal classification.Significance.Our combined pHd-Ricci flow method expands the existing EEG preprocessing toolkit by offering a systematic framework for extracting meaningful network structure from high-dimensional EEG data. By facilitating more targeted and effective feature selection, our method has the potential to improve EEG signal recovery in real-world applications.
11 May 2026
Read appraisal →AJNR. American journal of neuroradiology
Comparative Analysis of Artificial Intelligence-Based Quantification versus Visual Rating of Enlarged Perivascular Spaces in the Multi-Ethnic Study of Atherosclerosis Cohort
BACKGROUND AND PURPOSE: Enlarged perivascular spaces (PVS) are fluid-filled spaces surrounding small cerebral vessels. Current PVS research has been limited by the absence of efficient and scalable quantification tools. We compared visual ratings versus artificial intelligence (AI)-based quantification in identifying associations of PVS with vascular risk factors and cognitive performance. MATERIALS AND METHODS: This cross-sectional study included 235 participants from the Multi-Ethnic Study of Atherosclerosis who had undergone brain MRI and had available both visual ratings and AI-derived PVS quantification. Visual ratings were performed by an expert neuroradiologist (K.D.H.) using a semiquantitative scale. AI-derived counts were based on a fully automated deep learning algorithm. Visual and AI-derived counts were grouped into 4 a priori-defined anatomic locations: basal ganglia, frontoparietal cerebrum, midbrain, and cerebellum. The relationships of PVS counts with demographic characteristics, vascular risk factors, and global and domain-specific cognitive scores were examined using ordinal logistic regression (for ordinal categoric outcomes) and linear regression (for continuous outcomes) models. RESULTS: Mean age (SD) was 72.1 (6.8) years; 95 (40%) participants were men; and 126 (54%) self-reported as black. Means (SD) of AI-derived regional PVS counts were 63.7 (24.6) for basal ganglia, 414.9 (167.5) for frontoparietal cerebrum, and 9.8 (4.4) for midbrain. On visual ratings, the most prevalent count category for each region was 11-20 for basal ganglia (40%), 21-40 (31%) for frontoparietal cerebrum, and 1-5 (83%) for midbrain. For basal ganglia PVS, while both methods were associated with older age and white race/ethnicity, AI-derived counts exhibited additional associations with higher systolic blood pressure (β, 0.20; 95% CI, 0.05-0.36) and diabetes (unstandardized β coefficient [β], 11.51; 95% CI, 3.48-19.55), as well as poorer global cognition (β, -0.012; 95% CI, -0.023 to -0.0004), delayed memory (β, -0.005; 95% CI, -0.010 to -0.0005), and attention/processing speed (β, -0.005; 95% CI, -0.009 to -0.001) cognitive performance. CONCLUSIONS: In this cross-sectional study, AI-derived PVS quantification was more sensitive in detecting associations with vascular risk factors and cognitive outcomes than traditional visual ratings. AI-based quantification may aid in the analysis of large-scale epidemiologic data, advancing PVS research.
11 May 2026
Read appraisal →AJNR. American journal of neuroradiology
Neuroimaging Predictors of Futile Recanalization in Anterior Circulation Stroke: A Systematic Review and Meta-Analysis
BACKGROUND: Mechanical thrombectomy (MT) has transformed the management of large vessel occlusion stroke; however, a considerable proportion of patients fail to achieve functional recovery despite successful recanalization. This futile recanalization underscores the need to better understand imaging biomarkers that may predict poor outcomes after endovascular therapy. PURPOSE: To systematically review and synthesize evidence on neuroimaging biomarkers associated with futile recanalization after MT in acute ischemic stroke. DATA SOURCES: PubMed, Scopus, Web of Science, and EMBASE databases were searched up to March 2025, supplemented by manual reference screening. STUDY SELECTION: Eligible studies enrolled adult patients with anterior-circulation stroke who underwent thrombectomy, reported pretreatment neuroimaging findings, and evaluated functional outcomes despite successful reperfusion. Case reports, reviews, abstracts, and nonhuman studies were excluded. DATA ANALYSIS: Two reviewers independently screened, extracted data, and assessed study quality using the Prediction Model Risk of Bias Assessment Tool. Random-effects meta-analysis was performed where appropriate. Heterogeneity, publication bias, and sensitivity analyses were undertaken. DATA SYNTHESIS: Fifty-one studies were included. Lower ASPECTS scores were strongly associated with futile recanalization (OR = 0.81). Core infarct volume showed inconsistent associations, whereas perfusion and collateral markers, including elevated hypoperfusion intensity ratio, delayed venous outflow, and poor collateral scores, demonstrated strong predictive value. Chronic white-matter injury, leukoaraiosis, and cerebral atrophy were also consistently associated with worse outcomes. LIMITATIONS: Limitations included heterogeneity in imaging protocols and thresholds; predominance of retrospective single-center cohorts; limited direct comparison across imaging modalities; and variable adjustment for confounders. CONCLUSIONS: Neuroimaging biomarkers, particularly ASPECTS, perfusion-derived metrics, collateral status, and markers of chronic small vessel disease, provide important prognostic information for identifying patients at risk of futile recanalization despite successful thrombectomy.
11 May 2026
Read appraisal →Neurology
Intrathecal Mesenchymal Stem Cells in Progressive Multiple Sclerosis: A Randomized, Double-Blind, Placebo-Controlled Trial (SMART-MS)
BACKGROUND AND OBJECTIVES: There is an unmet need for neuroregenerative therapies in multiple sclerosis (MS). Mesenchymal stem cells (MSCs) have shown immunomodulatory and regenerative effects in preclinical models and early clinical studies. Intrathecal administration may enhance therapeutic potential by direct delivery to the CNS. However, randomized, placebo-controlled trials are needed to establish safety and efficacy. The objective of this trial was to assess whether a single intrathecal administration of autologous MSCs could provide evidence of a neuroregenerative effect in progressive MS. METHODS: In this randomized, double-blind, placebo-controlled phase I/II trial (NCT04749667), patients with progressive MS enrolled at 4 Norwegian tertiary hospitals received a single intrathecal injection of autologous bone marrow-derived MSCs (1 × 106 cells/kg) in a crossover design. The primary end point was the change in latency of combined evoked potentials at 6 months. Secondary end points included safety, brain MRI measures, functional and ophthalmologic assessments, and serum biomarkers at 6 and 12 months. Exploratory analyses comprised proteomic profiling of CSF. Outcomes were analyzed using baseline-adjusted regression models. RESULTS: A total of 18 patients were included (mean age 46.7 years; 55.6% female). No significant between-group difference was observed for the primary end point (β = -0.31, 95% CI -1.84 to 1.22, p = 0.668). At 6 months, patients in the MSC group showed reduced cerebral atrophy on MRI (β = 9.37, 95% CI 0.29 to 18.45, p = 0.044) and lower serum glial fibrillary acidic protein levels (β = -16.3 pg/mL, 95% CI -33.0 to 0.3, p = 0.054), but neither were sustained at 12 months. Exploratory CSF proteomics revealed reductions in multiple inflammation-related proteins at 6 months. One serious adverse event was deemed probably related to MSC treatment. Common adverse events included fever (n = 9) and low back pain (n = 10) after MSC administration, and spinal MRI abnormalities with fluid loculations and nerve root clumping (n = 7) at 6 months. One patient developed chronic coccygeal pain attributed to arachnoiditis. DISCUSSION: A neuroregenerative effect was not detected, although interpretation may be limited by the small sample size. Adverse events suggest an acute localized inflammatory reaction after MSC administration. Our findings suggest that intrathecal administration of MSCs in progressive MS should be approached with caution in future studies. CLASSIFICATION OF EVIDENCE: This study provides Class III evidence that, in patients with progressive multiple sclerosis, treatment with a single intrathecal administration of autologous mesenchymal stem cells does not provide neuroregenerative effect, as assessed by a composite evoked potential score. TRIAL REGISTRATION: ClinicalTrials.gov (NCT04749667); registered February 8, 2021; first patient enrolled August 9, 2021.
10 May 2026
Read appraisal →PloS one
Reducing annotation burden in medical imaging with ADGNET: A semi-supervised deep learning strategy
We propose ADGNET, a semi-supervised framework for Alzheimer's disease (AD) diagnosis that jointly optimizes image reconstruction and classification through shared feature representations. The architecture integrates a residual backbone with attention modulation for dynamic feature selection, an encoder-decoder reconstruction branch for unsupervised representation learning, and a classification branch with focal loss to address class imbalance. This dual-task design enables effective feature learning from limited annotations. On two public MRI datasets-KACD (2D, 6,400 images) and ROAD (3D, 532 scans)-ADGNET achieves average performance improvements of 4.1% and 7.2% over state-of-the-art methods (ResNeXt WSL, SimCLR) across six metrics. Interpretability analysis using Grad-CAM and attention visualization confirms that the model focuses on clinically relevant neuroanatomical structures, particularly the hippocampus and temporal lobes, with strong correlation to established AD pathology (r = 0.67, p < 0.001). These results validate the model's exceptional generalization capability and feature representation effectiveness across multi-modal medical imaging data, offering an efficient solution for few-shot medical image analysis.
10 May 2026
Read appraisal →Neurosurgical review
Deep brain stimulation for clozapine-resistant schizophrenia: a systematic review of target-specific outcomes and stereotactic technical considerations
Treatment-resistant schizophrenia (TRS) affects approximately 20-30% of patients, and a substantial proportion develop clozapine-resistant schizophrenia (CRS). Deep brain stimulation (DBS) has emerged as a potential neurosurgical intervention targeting dysfunctional cortico-striato-limbic circuitry. However, technical heterogeneity and limited clinical data constrain interpretation of outcomes. This systematic review was conducted in accordance with PRISMA guidelines and prospectively registered in PROSPERO (CRD420251080715). Seven electronic databases were searched from inception through January 31, 2025. Clinical studies investigating DBS in TRS or CRS were included. Methodological quality was assessed using Joanna Briggs Institute (JBI) tools. Data extraction emphasized stereotactic targeting methods, hardware configurations, stimulation parameters, and clinical outcomes. The total number of study is 6. We excluded paper by Manssuer 2023. The total study population comprises 21 patients. Seven studies involving 21 patients met inclusion criteria. Targets included the nucleus accumbens (NAcc; n = 11), substantia nigra (SNr; n = 1), habenula (HB; n = 2), subgenual cingulate (SCG; n = 4), subgenual anterior cingulate cortex (sgACC; n = 3). Reported PANSS total score changes ranged widely (11%-85.7%), reflecting substantial inter-individual variability and methodological limitations. Surgical complications occurred in 3 of 21 patients (14.2%), including infection and hemorrhage. All cases utilized open-loop stimulation and conventional cylindrical leads. Current evidence suggests a preliminary therapeutic signal for DBS in highly selected CRS patients, particularly with NAcc targeting. However, conclusions remain limited by small sample sizes, technical heterogeneity, and absence of controlled trials. Future investigations should prioritize standardized stereotactic reporting, volumetric lead reconstruction, and long-term safety assessment within specialized neurosurgical research settings. PROSPERO CRD420251080715.
10 May 2026
Read appraisal →Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation
Quality of life after tranexamic acid in subarachnoid hemorrhage: post-hoc analysis of the ULTRA trial
PURPOSE: The ULTRA trial evaluated the impact of ultra-early and short-term tranexamic acid (TXA) treatment in patients with subarachnoid hemorrhage (SAH) and found no clinical benefit at six months. This post-hoc analysis examines whether TXA improves quality of life (QoL) at three and six months. METHODS: The ULTRA trial was a randomized, controlled, multicenter study conducted from July 2013 to July 2019. Patients received either TXA or standard care. This analysis included patients who completed at least one QoL questionnaire. The primary endpoint was QoL, assessed using the EQ-5D-3L questionnaire at three and six months. Linear mixed models adjusted for confounders were used to analyze the association between TXA and QoL. RESULTS: Of the 955 ULTRA patients, approximately 25% died, and 63% completed at least one QoL questionnaire. At three months, the TXA group had a mean EQ-5D index score of 0.75 versus 0.71 in the control group (p = 0.11) and a mean EQ-5D Visual Analogue Scale (VAS) score of 89 versus 86 (p = 0.10). At six months, the mean EQ-5D index score was 0.84 in the TXA group compared to 0.82 in the control group (p = 0.23), and the mean VAS was 92 in the TXA group compared to 89 in the control group (p = 0.13). CONCLUSION: Ultra-early and short-term TXA did not result in a significant improvement in QoL at three or six months in patients with SAH. Given the lack of benefit on both clinical outcome and QoL, routine use of TXA is not recommended. TRIAL REGISTRATION: Netherlands Trial Register: NTR3272. CLINICALTRIALS: gov: NCT02684812.
10 May 2026
Read appraisal →Lancet (London, England)
Efficacy and safety of cemdisiran siRNA in myasthenia gravis (NIMBLE): a double-blind, randomised, placebo-controlled, phase 3 trial
BACKGROUND: Autoantibody-mediated complement activation drives pathology in acetylcholine receptor (AChR) antibody-positive generalised myasthenia gravis. Newer targeted therapies lower overall antibody concentrations or inhibit complement activity. We aimed to evaluate an siRNA (cemdisiran) targeting complement component 5 (C5) as monotherapy and in combination with a C5 antibody (pozelimab) in generalised myasthenia gravis. METHODS: NIMBLE is a randomised, double-blind, placebo-controlled, phase 3 trial conducted at 86 centres in 13 countries. We enrolled patients aged 18 years or older with a diagnosis of generalised myasthenia gravis, positive serology for anti-AChR or anti-LRP4 antibodies, and a Myasthenia Gravis-Activities of Daily Living (MG-ADL) score of 6 or greater. Patients were randomly allocated to receive cemdisiran monotherapy (600 mg every 12 weeks; cemdisiran group), pozelimab monotherapy (200 mg every 4 weeks; pozelimab group), combined cemdisiran (200 mg every 4 weeks) and pozelimab (200 mg every 4 weeks; combination group), or placebo, all administered subcutaneously, during a 24-week double-blind treatment period. The primary endpoint was change from baseline in MG-ADL scores at week 24, assessed in the modified intention-to-treat (mITT) primary analysis set (the first 245 randomly allocated patients who received any dose of study treatment and had at least one post-baseline assessment). A prespecified hierarchal statistical testing strategy was used; multiplicity was controlled for comparisons of cemdisiran versus placebo and combination versus placebo (pozelimab was not statistically tested against placebo and was used only to assess the contribution of components to the combination treatment). Safety was mainly assessed by recording of treatment-emergent adverse events in all patients who received any dose of study treatment. This study is registered with ClinicalTrials.gov (NCT05070858); the current status is active, not recruiting. FINDINGS: Between Jan 20, 2022 and July 18, 2025, 390 participants were screened and, as of the data cut-off presented in this report (July 8, 2025), 284 were randomly allocated to the cemdisiran group (79 [28%] patients), pozelimab group (50 [18%]), combination group (80 [28%]), and placebo group (75 [26%]). Of 277 patients who received any study treatment, 263 (95%) completed the double-blind treatment period. In the mITT primary analysis set, at week 24, the least-squares mean change from baseline in MG-ADL total score was -4·5 (SE 0·4) in the cemdisiran group (n=64), -4·0 (0·4) in the combination group (n=67), and -2·2 (0·5) in the placebo group (n=59). Placebo-adjusted least-squares mean differences in MG-ADL score at week 24 were -2·3 (SE 0·7; 95% CI -3·6 to -1·0; p=0·0005) in the cemdisiran group and -1·7 (0·7; -3·0 to -0·4; p=0·0086) in the combination group. The proportion of participants with at least one adverse event during the double-blind treatment period was 54 (69%) of 78 in the cemdisiran group, 65 (81%) of 80 in the combination group, 40 (82%) of 49 in the pozelimab group, and 54 (77%) of 70 in the placebo group. The most common adverse event in the cemdisiran group was upper respiratory tract infection (nine [12%] of 78 patients), which occurred at a similar incidence in the placebo group (eight [11%] of 70). No serious or meningococcal infections occurred in the cemdisiran group. Adverse events leading to treatment discontinuation occurred in two participants (3%) in the placebo group and one participant (2%) in the pozelimab group. No deaths occurred during the double-blind treatment period. Two deaths occurred after the double-blind treatment period, one of which was assessed as treatment-related by the investigator but not treatment-related by the sponsor. INTERPRETATION: Cemdisiran monotherapy and combination therapy were effective in the treatment of generalised myasthenia gravis, and were generally well tolerated. Subcutaneous dosing of cemdisiran, administered every 3 months, could provide a convenient treatment approach for generalised myasthenia gravis. FUNDING: Regeneron Pharmaceuticals.
10 May 2026
Read appraisal →Neurosurgical review
The efficacy of ventriculosubgaleal shunt in managing neonatal hydrocephalus due to intraventricular hemorrhage: a systematic review and meta-analysis
Intraventricular hemorrhage (IVH) is a severe complication in premature neonates, occurring in 25%-30% of cases and often leading to posthemorrhagic hydrocephalus (PHH). When blood clots in the cerebrospinal fluid (CSF), preventing permanent shunt placement, temporary interventions are considered. Ventriculosubgaleal shunt (VSGS) utilizes the subgaleal space to absorb and drain excess CSF, reducing infection risk and allowing hydrocephalus control until the neonate reaches an appropriate weight and CSF clarity. This systematic review and meta-analysis evaluate the safety and efficacy of VSGS in treating neonatal PHH. A systematic review was conducted using Medline, Embase, and Web of Science following Cochrane and PRISMA guidelines. Eligible studies included those with ≥ 4 neonates. The primary outcomes analyzed were VSGS-related infection, VSGS revision, VSGS catheter migration, catheter obstruction, VSGS-related CSF leakage, permanent ventriculoperitoneal shunt (VPS) placement, overall mortality, and procedure-related mortality. A total of nineteen studies, encompassing 562 neonates, were included in our analysis. The pooled VSGS-related infection rate was 9% (95%CI: 5% to 12%). The need for VSGS revision was observed in 4% of cases (95%CI: 0% to 8%). The catheter obstruction rate was 2% (95% CI: 0% to 5%), while VSGS catheter migration occurred in 1% of cases (95%CI: 0% to 5%). VSGS-related CSF leakage was reported in 6% of neonates (95%CI: 3% to 9%). Permanent VPS placement was required in 75% of patients (95%CI: 67% to 82%). The overall mortality rate was 10% (95%CI: 4% to 16%), and the procedure-related mortality rate was 1% (95%CI: 0% to 2%). This systematic review and meta-analysis identified VSGS as a safe and effective option for treating hydrocephalus caused by IVH in premature neonates.
10 May 2026
Read appraisal →Medical image analysis
Transfer learning from 2D natural images to 4D fMRI brain images via geometric mapping
Functional magnetic resonance imaging (fMRI) allows real-time observation of brain activity through blood oxygen level-dependent (BOLD) signals and is extensively used in studies related to sex classification, age estimation, behavioral measurements prediction, and mental disorder diagnosis. However, the application of deep learning techniques to brain fMRI analysis is hindered by the small sample size of fMRI datasets. Transfer learning offers a solution to this problem, but most existing approaches are designed for large-scale 2D natural images. The heterogeneity between 4D fMRI data and 2D natural images makes direct model transfer infeasible. This study proposes a novel geometric mapping-based fMRI transfer learning method that enables transfer learning from 2D natural images to 4D fMRI brain images, bridging the transfer learning gap between fMRI data and natural images. The proposed Multi-scale Multi-domain Feature Aggregation (MMFA) module extracts effective aggregated features and reduces the dimensionality of fMRI data to 3D space. By treating the cerebral cortex as a folded Riemannian manifold in 3D space and mapping it into 2D space using surface geometric mapping, we make the transfer learning from 2D natural images to 4D brain images possible. Moreover, the topological relationships of the cerebral cortex are maintained with our method, and calculations are performed along the Riemannian manifold of the brain, effectively addressing signal interference problems. The experimental results based on the Human Connectome Project (HCP) dataset demonstrate the effectiveness of the proposed method. Our method achieved state-of-the-art performance in sex classification, age estimation, and behavioral measurement prediction tasks. Moreover, we propose a cascaded transfer learning approach for depression diagnosis, and proved its effectiveness on 23 depression datasets. In summary, the proposed fMRI transfer learning method, which accounts for the structural characteristics of the brain, is promising for applying transfer learning from natural images to brain fMRI images, significantly enhancing the performance in various fMRI analysis tasks.
10 May 2026
Read appraisal →Epilepsy research
Independent evaluation of deep learning models for detecting focal cortical dysplasia
The purpose of this study is to perform an independent assessment of three state-of-the-art tools for the detection of focal cortical dysplasia (FCD) from Magnetic Resonance images (MRI). These tools include DeepFCD, the Multi-center Epilepsy Lesion Detection (MELD) Classifier, and MELDGraph. T1-weighted and fluid-attenuated inversion recovery MRIs from 101 epilepsy patients with FCD and 101 epilepsy patients without FCD were retrospectively included. Classifiers were evaluated at patient-level by their ability to correctly identify the presence of any FCD lesions, and at lesion-level by their capacity to identify lesions within regions delineated by neuroradiologists in MRI reports. A calibrated threshold for DeepFCD prediction probabilities was empirically determined to improve classifier specificity. Classifier test-retest consistency was measured using the Dice coefficient on repeated MRI scans of 21 individuals. At patient-level, MELDClassifier achieved 52% accuracy (sensitivity=91%, specificity=14%), MELDGraph reached 61% accuracy (sensitivity=76%, specificity=47%) and DeepFCD performed with 56% accuracy (sensitivity=62%, specificity=50%) at an empirically determined threshold of 0.90. At lesion-level, MELDClassifier performed with a sensitivity of 70% and a positive predictive value (PPV) of 13%. MELDGraph reached 53% sensitivity and PPV of 36%, whereas the DeepFCD performed with 30% sensitivity and PPV of 19%. Test-retest reliability was low, with an average [min, max] Dice coefficient of 0.28 [0.0, 1.0] for MELDClassifier, 0.38 [0.0, 1.0] for MELDGraph, and 0.35 [0.05, 0.54] for DeepFCD. This study highlights the current limitations of using deep learning models in FCD diagnosis and emphasizes the need to enhance the tools' accuracy, reliability, and interpretability to improve clinical utility.
9 May 2026
Read appraisal →Epilepsy research
Machine learning in epilepsy
Epilepsy is a complex neurological disorder characterized by pathological processes that unfold across multiple biological scales, from cellular excitability and synaptic integration to large-scale network dynamics observable in electroencephalographic (EEG) recordings. While traditional analytical approaches have provided valuable insights, they often fail to capture high-dimensional and nonlinear structure of contemporary electrophysiological and clinical datasets. Consequently, machine learning (ML) has emerged as a powerful analytical framework in epilepsy research, although its rapid adoption has revealed a growing gap between algorithmic performance and biological interpretability. This review examines ML methods operating across three analytically distinct yet interconnected levels: (i) unsupervised learning for cellular-level phenotyping using high-dimensional electrophysiological data; (ii) supervised learning for EEG-based seizure detection and prediction; and (iii) multiscale modeling frameworks integrating neuronal and network dynamics. Rather than providing an exhaustive catalog of algorithms, we focus on inferential assumptions underlying ML applications, the methodological pitfalls constraining generalization and clinical relevance, and how ML-derived representations can be interpreted within established neurophysiological theory. We highlight that unsupervised ML facilitates identification of latent excitability phenotypes and trajectories obscured in traditional univariate analyses, while supervised ML has substantially advanced automated seizure detection and prediction, despite persistent challenges related to data leakage, class imbalance, and ambiguous preictal labeling. We argue that the most promising direction lies in embedding ML within multiscale mechanistic models, where data-driven inference facilitates parameter estimation and hypothesis generation rather than black-box prediction. By prioritizing interpretability, rigorous validation, and cross-scale integration, ML-enhanced multiscale frameworks offer a path toward clinically actionable models of epilepsy.
9 May 2026
Read appraisal →Journal of medical systems
Interpretable Machine Learning to Anticipate the Diagnostic Yield of EEG in the Emergency department. The EMINENCE study.
INTRODUCTION: Emergent electroencephalography (emEEG) is increasingly employed in the emergency department (ED) for evaluating altered consciousness and seizure-related conditions, yet standardized criteria guiding its use remain limited. METHODS: We retrospectively analyzed 1,018 patients (mean age 66 ± 20 years; 48.4% female) undergoing emEEG at the ED of the Careggi Teaching Hospital (Florence, Italy) in 2023. Clinical, anamnestic, and neuroimaging data available at admission were used to train supervised machine-learning (ML) models. We evaluated tree-based ensembles (Random Forest and XGBoost) to predict abnormal and epileptiform emEEG, as well as confirmation or refutation of initial diagnosis. Ground-truth labels were derived from a multidisciplinary expert team including neurologists, neurophysiopathologists and intensivists. Model performance was assessed with 5 × 5 nested cross-validation, receiver operating characteristic (ROC) analysis, balanced accuracy, decision-curve analysis, and Shapley Additive Explanations (SHAP) interpretability. RESULTS: Abnormal emEEG occurred in 691 cases (67.9%), epileptiform activity in 192 patients (18.9%). emEEG ruled out the initial diagnostic suspicion in 514 cases (50.5%) and confirmed it in 188 cases (18.5%). Best performance was obtained with Random Forest for abnormal emEEG (AUC 0.79, 95% CI: 0.76-0.82) and diagnosis rule-out (0.84, 0.81-0.86), and with XGBoost for epileptiform emEEG (0.82, 0.78-0.85) and diagnosis confirmation (0.82, 0.79-0.85). Performance varied by initial diagnostic suspicion, but subgroup-stratified analyses showed overall consistent patterns. Key predictive features included altered consciousness, prior brain lesions, antiseizure therapy, and seizure-related presentations. Interpretability analyses revealed seizure-centric features drove confirmation, while systemic or nonspecific features favored refutation. CONCLUSIONS: Interpretable ML models using only admission data can predict emEEG outcomes and anticipate their diagnostic contribution, supporting triage and decision-making in emergency neurology without replacing clinical judgment. Models and explanations were easily usable on a freely-accessible website ( www.emergencyeeg.com ), where tools return probabilistic outputs for all four prediction tasks together with per-patient explanation plots, enabling transparent and reproducible use. CLINICAL TRIAL NUMBER: Not applicable.
9 May 2026
Read appraisal →Journal of medical systems
Prognostic Modeling Based on Post-Endovascular Thrombectomy Systolic Blood Pressure Trajectories Using Explainable Artificial Intelligence: A Secondary Analysis of the OPTIMAL-BP Trial
Blood pressure (BP) management following successful reperfusion after endovascular thrombectomy (EVT) is critical in achieving favorable clinical outcomes. Individualized BP management using predictive modeling by machine learning may further improve prediction of functional outcomes. This study was a retrospective analysis of data from the Outcome in Patients Treated with Intra-Arterial Thrombectomy-Optimal Blood Pressure Control (OPTIMAL-BP) trial, a randomized controlled trial comparing between intensive and conventional BP management during the 24 h after successful recanalization by EVT from June 18, 2020, to November 28, 2022. The trial was conducted across 19 centers in South Korea. Machine learning models were developed to predict functional independence (90-day modified Rankin Scale 0 to 2). Model performance was compared between clinical variables only and systolic blood pressure (SBP) metrics in addition to clinical variables. In addition, the Shapley additive explanations (SHAP) analysis was performed to provide model explanation and understand the importance of SBP metrics. A total of 288 patients (61.1% men, median age 75 years [interquartile range, 65-81]) were included. Among the six algorithms, the deep neural network model incorporating SBP metrics performed best on validation, achieving an area under the curve of 0.86 (95% confidence interval, 0.76-0.92) which was significantly better than the model using only clinical variables (area under the curve 0.80 [95% confidence interval, 0.69-0.88], P = .037). Among SBP metrics, SHAP analysis identified time rate of SBP and minimum SBP as important features, with time rate showing greater influence in the intensive group and minimum SBP in the conventional group. Integrating SBP metrics with clinical variables significantly improved machine learning performance in predicting functional outcomes after successful EVT. Explainable artificial intelligence (AI) identified time rate and minimum SBP as key predictors of outcome. Trial Registration Information: ClinicalTrials.gov (NCT04205305; registered December 17, 2019).
9 May 2026
Read appraisal →Pediatric neurology
Electroencephalography-Based Machine Learning Models for Predicting Ketogenic Diet Outcomes in Pediatric Drug-Resistant Epilepsy
BACKGROUND: Ketogenic diet therapy (KDT) is an established treatment for drug-resistant epilepsy (DRE); however, methods for predicting its effectiveness remain underdeveloped. This study evaluated various machine learning (ML) models in predicting responses to KDT among DRE patients based on electroencephalography data. METHODS: Using leave-one-out cross-validation, this study evaluated 19 ML classifiers in predicting the outcomes of 90 DRE patients based on absolute and relative power, as well as functional connectivity measures (phase-locking value, phase lag index [PLI], and weighted PLI) across standard frequency bands. KDT significantly reduced seizure frequency at three and 6 months after initiation. RESULTS: The most effective classifier at 3 months was a Coarse Tree classifier trained on absolute power (recall = 0.933, precision = 0.767, F2 = 0.894, area under the receiver operating characteristic curve = 0.607). The most effective classifier at 6 months was a Gaussian Naive Bayes classifier trained on weighted PLI + relative power (recall = 0.759, precision = 0.854, F2 = 0.776, area under the receiver operating characteristic curve = 0.603). CONCLUSIONS: This study identified the most effective ML models for predicting KDT outcomes in DRE patients. The results highlight the potential of electroencephalography-based ML tools for guiding KDT treatment in clinical practice.
9 May 2026
Read appraisal →IEEE transactions on bio-medical engineering
Multilevel Correlation-Aware and Modal-Aware Graph Convolutional Network for Diagnosing Neurodevelopmental Disorders
OBJECTIVE: Graph-based methods using resting-state functional magnetic resonance imaging demonstrate strong capabilities in modeling brain networks. However, existing graph-based methods often overlook inter-graph relationships, limiting their ability to capture the intrinsic features shared across individuals. Additionally, their simplistic integration strategies may fail to take full advantage of multimodal information. To address these challenges, this paper proposes a Multilevel Correlation-aware and Modal-aware Graph Convolutional Network (MCM-GCN) for the reliable diagnosis of neurodevelopmental disorders. METHODS: At the individual level, we design a correlation-driven feature generation module that incorporates a pooling layer with external graph attention to perceive inter-graph correlations, generating discriminative brain embeddings and identifying disease-related regions. At the population level, to deeply integrate multimodal and multi-atlas information, a multimodal-decoupled feature enhancement module learns unique and shared embeddings from brain graphs and phenotypic data and then fuses them adaptively with graph channel attention for reliable disease classification. RESULTS: Extensive experiments on two public datasets for Autism Spectrum Disorder (ASD) and Attention Deficit Hyperactivity Disorder (ADHD) demonstrate that MCM-GCN outperforms other competing methods, with an accuracy of 93.11% for ASD and 76.41% for ADHD. CONCLUSION: The MCM-GCN framework integrates individual-level and population-level analyses, offering a comprehensive perspective for neurodevelopmental disorder diagnosis, significantly improving diagnostic accuracy while identifying key indicators. SIGNIFICANCE: These findings highlight the potential of the MCM-GCN for imaging-assisted diagnosis of neurodevelopmental diseases, advancing interpretable deep learning in medical imaging analysis.
6 May 2026
Read appraisal →Magnetic resonance in medicine
Accelerating Multiparametric Quantitative MRI Using Self-Supervised Scan-Specific Implicit Neural Representation With Model Reinforcement
PURPOSE: To develop a self-supervised scan-specific deep learning framework for reconstructing accelerated multiparametric quantitative MRI (qMRI). METHODS: We propose REFINE-MORE (REference-Free Implicit NEural representation with MOdel REinforcement), combining an implicit neural representation (INR) architecture with a model reinforcement module that incorporates MR physics constraints. The INR component enables informative learning of spatiotemporal correlations to initialize multiparametric quantitative maps, which are then further refined through an unrolled optimization scheme enforcing data consistency. To improve computational efficiency, REFINE-MORE integrates a low-rank adaptation strategy that promotes rapid model convergence. We evaluated REFINE-MORE on accelerated multiparametric quantitative magnetization transfer imaging for simultaneous estimation of free water spin-lattice relaxation, tissue macromolecular proton fraction, and magnetization exchange rate, using both phantom and in vivo brain data. RESULTS: Under 4× and 5× accelerations on in vivo data, REFINE-MORE achieved superior reconstruction quality, demonstrating the lowest normalized root-mean-square error and highest structural similarity index compared to baseline methods and other state-of-the-art model-based and deep learning approaches. Phantom experiments further showed strong agreement with reference values, underscoring the robustness and generalizability of the proposed framework. Additionally, the model adaptation strategy improved reconstruction efficiency by approximately fivefold. CONCLUSION: REFINE-MORE enables accurate and efficient scan-specific multiparametric qMRI reconstruction, providing a flexible solution for high-dimensional, accelerated qMRI applications.
6 May 2026
Read appraisal →Annual review of biomedical engineering
Digital Twins for Biofluids
Digital twins-virtual representations dynamically linked to physical systems-have the potential to transform biomedical engineering by enabling real-time prediction, optimization, and personalization in health and disease. In biofluids, digital twins offer a framework for integrating physics-based models with data from clinical imaging, sensors, and physiological measurements to support diagnostics, therapeutic planning, and device design. This article reviews modeling approaches used in the construction of digital twins for biofluid applications. We survey high-fidelity numerical methods alongside emerging machine learning techniques, highlighting their respective strengths and limitations. Key requirements for digital twins are discussed, emphasizing the bidirectional interaction between physical and virtual assets and the importance of selecting modeling strategies tailored to specific biomedical contexts. While notable progress has been made over the past decade, significant challenges remain, particularly in integrating multiphysics models with data-driven methods and in establishing standardized protocols for data acquisition, interoperability, and sharing.
5 May 2026
Read appraisal →Journal of neuroradiology = Journal de neuroradiologie
Automated estimation of perivascular space and brain morphometry from deep learning-reconstructed three-dimensional T1-weighted MRI: comparison with the conventional technique
PURPOSE: To evaluate the utility of deep learning-based reconstruction (DLR) three-dimensional T1-weighted imaging (T1-WI) in improving fine structural delineation through the automated estimation of brain morphometry and perivascular space (PVS). MATERIALS AND METHODS: In this retrospective cohort study, participants who underwent T1-WI between August 2021 and June 2022 were included. Participants were categorized into normal and disease groups based on the presence of visible cerebral lesions. Cortical thicknesses and cerebral volumes from DLR T1-WI were estimated using FreeSurfer and compared to those derived from conventional T1-WI. Additionally, participants were grouped by MRI-visible PVS severity according to the number of enlarged PVS in centrum semiovale and basal ganglia. PVS volumes from DLR and conventional T1-WI were compared across these groups. Statistical comparisons were performed using a paired independent t-test with Benjamin-Hochberg adjustment. RESULTS: 240 participants (mean age: 40.8 ± 17 years) were included. In the normal group (n = 144), cortical thicknesses and volumes were significantly greater in DLR T1-WI compared to conventional T1-WI (P < 0.001). This trend was also observed in the disease group (n = 96) and in external datasets (n = 63). Across all three PVS severity groups (mild, n = 111; moderate, n = 71; severe, n = 58), DLR T1-WI significantly reduced PVS volumes and variability compared to conventional T1-WI. DLR T1-WI demonstrated PVS volumes (mild: 738.4 mm³ vs. 1408.1 mm³; moderate: 919.0 mm³ vs. 1895.8 mm³; severe: 1328.5 mm³ vs. 2711.7 mm³; all P < 0.001) and variability (mild: 224.4 mm3 vs. 420.9 mm³; moderate: 229.7 mm3 vs. 462.9 mm³; severe: 382.4 mm3 vs. 653.6 mm³) that were consistently smaller. CONCLUSION: DLR T1-WI yielded cerebral cortical thickness and volume measurements that were more consistent with expected anatomical values than those from conventional T1-WI.PVS volumes measured from DLR T1-WI exhibited less variability and better concordance with radiologists' visual assessments.
5 May 2026
Read appraisal →Journal of applied gerontology : the official journal of the Southern Gerontological Society
Anomaly Detection Technologies for Dementia Care: Monitoring Goals, Sensor Applications, and Trade-offs in Home-Based Solutions-A Narrative Review
Anomaly detection technologies are increasingly used to monitor people living with dementia (PLwD) in home settings, addressing critical behaviors such as wandering, sleep disturbances, and agitation. This narrative review examines technologies used for detecting behavioral anomalies, the activities they monitor, and the trade-offs between their benefits and limitations. A systematic search across MEDLINE, IEEE Xplore, ACM Digital Library, and Web of Science identified 78 studies, categorized through thematic analysis. Three primary motivations emerged: early diagnosis, safety monitoring, and reducing caregiver stress while promoting autonomy. Technologies include GPS tracking, wearables, environmental sensors, and smart home systems, each with benefits like real-time alerts and non-intrusive monitoring but also challenges such as user compliance, false positives, and privacy concerns. While these systems enhance safety and autonomy, improving sensor accuracy, integrating AI for personalized interventions, and addressing ethical concerns are essential for long-term effectiveness and supporting the well-being of both PLwD and caregivers.
5 May 2026
Read appraisal →Journal of psychiatric research
Predicting treatment response in psychosis using fMRI: A comprehensive review
In recent years, the use of functional Magnetic Resonance Imaging (fMRI) methods to predict treatment response in schizophrenia (SCZ) through statistical and machine learning (ML) algorithms has increased. We conducted a comprehensive literature review to assess the role of various fMRI measures in predicting pharmacological treatment response in psychosis. Literature available on PubMed from January 1990 to December 2023 was reviewed, and 21 fMRI studies were included. The results suggest that many studies have employed ML techniques, which may enhance the accuracy of treatment outcome predictions. Additionally, several studies utilizing resting-state fMRI have identified potential associations of functional connectivity patterns across multiple large-scale networks, including the default mode network (DMN), the salience network (SN), the central executive network (CEN), and sensory-motor circuits. These findings suggest that altered connectivity within and between these networks may be relevant for personalized treatment strategies in patients with psychosis, although further investigation is needed to confirm their predictive value. Future research should focus on developing robust and generalizable models to more reliably optimize treatment outcomes in psychosis.
5 May 2026
Read appraisal →Magnetic resonance in medicine
Dynamic-Guided Diffusion Probability Model for Cranial Nerves Segmentation
PURPOSE: Segmentation of cranial nerves (CNs) bundles using magnetic resonance imaging (MRI) provides a valuable quantitative approach for analyzing the morphology and orientation of individual CNs. Currently, the CN regions can be segmented directly using deep learning-based methods. However, existing methods overlook the unique characteristics of CNs, particularly their environmental features and representation in multimodal images that may lead to suboptimal segmentation outcomes. METHODS: We proposed a dynamic-guided diffusion probability model for CNs segmentation, which enhances segmentation performance by integrating the intrinsic characteristics of CNs. A dynamic-guided mechanism approach called the SE-A-NL module was proposed. The module is capable of addressing both the varying characterization abilities of multimodal images and the long-range connections of CNs within images. RESULTS: Quantitative and qualitative experiments demonstrate that the proposed method surpasses current state-of-the-art approaches, delivering accurate and effective segmentation of five pairs of cranial nerves. Notably, the method outperforms existing techniques in 16 out of the 20 evaluated metrics. CONCLUSION: The overall network model effectively integrates multimodal information and anatomical priors by combining multi-channel attention and non-local attention mechanisms, thereby improving CNs segmentation performance. Thorough comparative and ablation studies highlight the superior performance of the proposed method.
5 May 2026
Read appraisal →International journal of psychophysiology : official journal of the International Organization of Psychophysiology
Task-evoked brain network dynamics underlying cognitive control using source-localized EEG and machine learning
OBJECTIVE: Functional connectivity (FC) coordinates brain activity during cognitive tasks, yet the influence of demographic variables and health factors on task-related dynamic FC remains underexplored. We examined FC within and between seven core brain networks: Default Mode (DMN), Ventral Attention (VAN), Somatomotor (SMN), Dorsal Attention (DAN), Frontoparietal (FPN), Visual (VIN), and Limbic (LIN) during a modified Eriksen flanker task. We aimed to identify distinct network configurations linked to varying cognitive control demands and assess the influence of demographic and health factors. METHODS: Secondary analyses of source-localized EEG data from 255 adults (mean age 38.97 ± 11.65 years; BMI 30.21 ± 9.29 kg/m2) were conducted. A hidden Markov model extracted spatiotemporal dynamics, quantifying within- and between-network correlations. Extreme Gradient Boosting with recursive feature elimination identified connectivity metrics predicting reaction time (RT) of high performers (>80% accuracy). Regression models assessed effects of age, sex, BMI, and income on task-related FC. RESULTS: Classification accurately predicted RT in both congruent (AUC = 0.81) and incongruent (AUC = 0.86) trials. Key FC features included VAN-LIN, VIN-VAN, and SMN-DAN for congruent trials; SMN-DMN, LIN-FPN, and VIN-DAN for incongruent trials. Age was linked to reduced FC, while higher BMI showed modest positive FC associations in LIN-FPN and VIN-DAN. Sex and income were not significant predictors of RT or FC. CONCLUSIONS: These findings reveal the relevance of specific dynamic network interactions in cognitive control and highlight the need to consider age and BMI as contributors to brain connectivity during task performance.
4 May 2026
Read appraisal →International journal of neural systems
Data-related Ablation for Reinforcing Deep Learning in Explaining Complex Phenomena
Deep Learning (DL) models excel at automatically learning intricate patterns within complex data, but their black box nature undermines human trust. To address this, current validation strategies typically focus on the model itself, modifying its architecture to assess the role and importance of the components. However, this model-centric view overlooks the critical learning substrate, which is represented by the data, implicitly assuming that it accurately represents the target phenomenon. This implicit trust in data means that evaluation may fail to detect whether high performance stems from exploiting biases or data quirks rather than learning relevant patterns. We present a novel data-related ablation as a complement to the traditional architectural ablation. Using this framework for Electroencephalography (EEG) signals of Emotional Recognition (ER) and Motor Execution (ME) as a case study, we show that seemingly high-accuracy models often rely heavily on process-irrelevant features, maintaining performance even when key information is eliminated. This shows that a standard, data-independent evaluation can be misleading about whether a model truly captured the intended process; the proposed approach helps distinguish robust learning from leaning on incidental characteristics. Therefore, incorporating data-related ablation is essential for developing reliable and generalizable DL models in fields that rely on data derived from complex and often not completely known phenomena.
4 May 2026
Read appraisal →Journal of affective disorders
Plasma metabolomics identifies lipid mediators linking depression and cognitive decline in late-life depression.
BACKGROUND: Late-life depression (LLD) features recurrent episodes and frequently co-exists with cognitive impairment, which predicts worse outcomes and progression to dementia. Evidence indicates a bidirectional depression-cognition relationship, but objective biological tools to capture severity and this interplay are scarce. METHODS: This study compared 110 patients with LLD between depressive episodes and remission phases. Based on untargeted liquid chromatography-mass spectrometry (LC-MS) metabolomics analysis of plasma samples, we identified key metabolites and developed four predictive machine learning models (GLMNet, LDA, Naive Bayes, and cTree). Additionally, Spearman rank correlation analysis and mediation analysis were conducted to further investigate the relationships and mediating effects of the key metabolites. RESULTS: The diagnostic model based on key metabolites selected by the random forest algorithm showed good discriminatory performance in distinguishing LLD Episodes (AUC = 0.824). Tridecanoylcarnitine (Car(13:0)), PC(P-16:0/22:6), and SM(d18:1/22:0) were significantly downregulated during the depressive episode. Tridecanoylcarnitine (Car(13:0)) negatively correlated with depressive severity (p < 0.001) and positively with Mini-Mental State Examination scores. PC(P-16:0/22:6) was associated with both emotional and cognitive impairments. Mediation analysis supported that Tridecanoylcarnitine (Car(13:0)) and PC(P-16:0/22:6) partially mediated the depression-cognition relationship, explaining 22.0 % and 26.9 % of the variance, respectively (p < 0.05). CONCLUSION: This study reveals specific lipid metabolic dysregulation in LLD and identifies key metabolites significantly associated with both depressive severity and cognitive function. It further supports their mediating role in the comorbidity between depression and cognitive impairment. These metabolites may serve as potential targets for simultaneously regulating depression and cognition. The machine learning model developed provides a new auxiliary tool for the objective assessment of LLD.
4 May 2026
Read appraisal →PloS one
Interpretable multivariate survival models: Improving predictions for conversion from mild cognitive impairment to Alzheimer's disease via data fusion and machine learning
Accurately predicting which individuals with mild cognitive impairment (MCI) will progress to Alzheimer's disease (AD) can improve patient care. This study examines the role of quantitative MRI (qMRI), cognitive evaluations, apolipoprotein [Formula: see text]4 (APOE [Formula: see text]4), and cerebrospinal fluid (CSF) biomarkers in Cox survival models to predict progression from MCI to AD. Data from 564 participants in the ADNI study, who transitioned from MCI to AD, were analyzed. The data set included 330 features encompassing qMRI, cognitive assessments, CSF biomarkers, and APOE [Formula: see text]4 status. Advanced machine learning (ML) methods were applied to evaluate the importance of these data sources, select relevant features, and develop interpretable Cox survival models within a cross-validation framework. The top optimized model achieved a sensitivity of 0.69, 95% CI [0.63, 0.76], and a specificity of 0.87, 95% CI [0.83, 0.90], and used all data sources. The results demonstrated that combining qMRI features with cognitive assessments, CSF biomarkers, and APOE [Formula: see text]4 status, analyzed using the BSWiMS model, resulted in a substantial improvement in the ability to predict progression from MCI to AD, achieving 81% precision and 87% specificity. These results exceed those obtained with other models evaluated. Finally, biomarker analysis showed that cognitive scores are the most relevant features to predict conversion, followed by CSF and qMRI biomarkers. These findings highlight the value of integrating multiple data sources in highly interpretable Cox survival models for the early identification of individuals at risk for AD.
4 May 2026
Read appraisal →BMJ supportive & palliative care
Memantine efficacy in chemotherapy-induced peripheral neuropathy: double-blind placebo-controlled randomised clinical trial
BACKGROUND AND AIMS: Chemotherapy-induced peripheral neuropathy (CIPN) is a detrimental side effect of chemotherapeutic agents. If left untreated, CIPN can persist for several years following the chemotherapy. CIPN management may enhance the quality of life of the affected patients. This study evaluated the efficacy of memantine, an antagonist of N-methyl-D-aspartate, in CIPN management. METHODS: This double-blinded randomised clinical trial was performed on 176 individuals with CIPN. Patients were randomised to either the memantine or placebo groups (88 in each). The memantine group received a single daily dose of 5 mg for the initial 3 days. Subsequently, the dosage was increased to 20 mg per day, with 5 mg increments every 3 days. The placebo group received tablets of equal shape and size. All individuals received medications for 8 weeks. The McGill pain questionnaire and tuning fork test were used to evaluate the severity of neuropathic pain in patients, both at baseline and after the intervention ended. RESULTS: Based on tuning fork test results, significant improvement in neuropathy was observed among patients who received memantine compared with those who received placebo (p<0.001 vs 0.665). McGill pain questionnaire score revealed a significant difference between memantine and placebo groups (p<0.001 for both groups). This difference was also significant after performing intention-to-treat analysis. Moreover, a higher proportion of patients in the memantine group achieved minimal clinically important difference. CONCLUSION: The use of memantine mitigated the CIPN intensity. Thus, it appears that memantine is a promising option for the management of patients experiencing CIPN.
3 May 2026
Read appraisal →Journal of neurology
Identifying diagnostic biomarkers in functional motor disorders through multimodal behavioral, neurophysiological, and imaging assessment using explainable machine learning
BACKGROUND: Functional motor disorders (FMDs) represent a frequent and disabling neurological condition. The lack of reliable diagnostic biomarkers and their heterogeneity might affect diagnosis. We identified multimodal biomarkers distinguishing FMDs from healthy controls (HCs) using machine-learning approaches. METHODS: In this multicenter cross-sectional study, consecutive adults with a clinically established FMDs diagnosis (n = 75, 74.7% female; mean age 44.20 ± 12.92) and age- and sex-matched HCs (n = 75; 58.6% female; 48.42 ± 11.67) were recruited. All participants underwent standardized behavioral, neurophysiological, and brain MRI assessment exploring motor, exteroceptive, and interoceptive domains. A Random Forest (RF) classifier combined with repeated stratified k-fold cross-validation was trained on the collected features. Predictive performance was evaluated using accuracy, sensitivity, specificity, precision, F1-score, and AUC-ROC. SHapley Additive exPlanations interpreted feature importance. RESULTS: The strongest diagnostic biomarkers were lower dual-task effect scores for postural sway area under eyes-closed motor and cognitive conditions, and gait speed during the motor dual-task, followed by increased vDMN and basal ganglia networks functional connectivity, reduced baseline ipsilateral-contralateral R2 blink reflex area, and higher DNIC-to-baseline N2P2 amplitude ratios for the lower limb. The RF classifier achieved robust performance (accuracy 85.0%, sensitivity 83.9%, specificity 86.1%, F1-score 85.7%, AUC-ROC 0.921). CONCLUSIONS: Motor, functional neuroimaging, and neurophysiological markers demonstrated diagnostic value in distinguishing FMDs from healthy controls, addressing the current lack of objective tools and supporting more confident and accurate diagnosis of these heterogeneous conditions. TRIAL REGISTRATION: Trial registration number NCT06328790. Registered on 26 March 2024.
3 May 2026
Read appraisal →Journal of medical Internet research
Online Community Support for Stroke Survivors and Caregivers: Scoping Review
BACKGROUND: Previous studies found that online communities are critical in supporting stroke survivors and caregivers for stroke recovery. However, it is unclear how such online communities are designed, or could be designed. OBJECTIVE: This review aims to identify the key design elements of an online community to support stroke survivors and caregivers, that is, the actors, types of community support, and supporting technologies. METHODS: We used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines. We included journal articles related to online community support for stroke. Editorials, registers, opinion pieces, letters, and conference papers were excluded. Online databases PubMed/MEDLINE, Scopus, Web of Science, ScienceDirect, and ProQuest were searched for articles published from January 2015 to June 2025. Articles were screened based on the title, abstract, and full text using Covidence software. After screening and full-text review, we read the selected articles in detail to analyze and synthesize information on key actors, types of support, and technologies used to support stroke survivors and caregivers. RESULTS: A total of 77 articles were included. These articles discussed digital support technologies (52 articles), community functions and roles (18 articles), online stroke community systems (6 articles), and the stroke ecosystem (1 article). Our review found that the online community of support for stroke survivors and caregivers includes the caregivers themselves (46 articles), health workers (24 articles), and the local community/society (14 articles). Online communities mainly provide informational support, including giving advice (28 articles) and tangible aids (29 articles), followed by social support to create a sense of belonging (26 articles). Technologies discussed included mobile health (25 articles), web-based systems (12 articles), virtual/augmented reality (8 articles), sensors/wearable technology (8 articles), video-guided exercise apps (4 articles), and telehealth/telerehabilitation/teleconsultation/telestroke (4 articles). Only one examined how cultural differences influence technology. CONCLUSIONS: Although technologies are essential in online communities of support for stroke survivors and caregivers, this review shows a lack of studies that analyze the use and role of technologies in such online communities. This could be because the key actors of the online communities are the caregivers, who mainly seek social support and therefore do not require sophisticated technology. Nevertheless, technologies such as telerehabilitation and video-guided exercise apps could be important for other actors, including the local community and health workers, to enable them to support stroke survivors and their caregivers.
3 May 2026
Read appraisal →Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine
Visualization of the Trochlear Nerve Using Deep Learning-enhanced 3D T2-weighted MR Imaging at 3T
PURPOSE: Cranial nerve imaging with 3T MRI commonly uses 3D fast imaging employing steady-state acquisition (3D-FIESTA); however, this sequence has limitations in achieving higher spatial resolution and provides poor tissue contrast between cranial nerves and adjacent vascular structures. We evaluated 3D T2-weighted imaging (T2-CUBE) with deep learning-based reconstruction (DLR) for cranial nerve visualization, focusing on the trochlear nerve, the smallest cranial nerve with the longest intracranial course, and compared it with T2-CUBE without DLR and 3D-FIESTA. METHODS: Ten healthy male volunteers (age, 23-40 years; mean age, 32 years) underwent T2-CUBE with and without DLR, and 3D-FIESTA at 3T. Two neuroradiologists independently evaluated trochlear nerve visualization in 4 anatomical segments (origin from the midbrain, cisternal, tentorial, and anterior portion of its cavernous segments) using a 3-point scale, and SNR of the pons (SNRPONS) and cerebrospinal fluid (SNRCSF) were calculated. RESULTS: T2-CUBE with DLR achieved a 100% visualization across all trochlear nerve segments and demonstrated significantly better visualization than both T2-CUBE without DLR and 3D-FIESTA (P < 0.025). T2-CUBE without DLR showed 67.5%-100% visualization across the 4 segments. 3D-FIESTA showed 32.5%-80% visualization of the origin from the midbrain, cisternal, and tentorial segments, with no visualization of the cavernous segments. DLR increased SNRPONS and SNRCSF by factors of 1.8-2.5 (SNRPONS: 14.1 vs 5.7; SNRCSF: 31.8 vs 17.5, respectively; P < 0.001). T2-CUBE with DLR demonstrated significantly higher SNRPONS than 3D-FIESTA (14.1 vs 6.4, P < 0.001), while SNRCSF was comparable (31.8 vs 36.3, P = 0.20). CONCLUSION: T2-CUBE with DLR at 3T provided a significantly better trochlear nerve visualization than T2-CUBE without DLR and 3D-FIESTA. This technique may extend beyond the trochlear nerve to other cranial nerves and to the evaluation of neurovascular compression in the cistern, with the potential to become the new standard for cisternal imaging.
3 May 2026
Read appraisal →Journal of medical Internet research
Effects of a Soft Robotic Exoskeleton for Gait Training on Clinical Outcomes in Patients With Parkinson Disease: Randomized Controlled Pilot Study
BACKGROUND: Balance and gait disorders in Parkinson disease (PD) impair motor function and quality of life. OBJECTIVE: Evidence on soft exoskeleton robots (SERs) for PD rehabilitation is limited. This study evaluated the impact of SERs on motor dysfunction in PD. METHODS: A total of 56 people with PD (July 2023 to May 2024) were randomized to 2 groups: the control group (n=25, 44.6%) received conventional rehabilitation, and the experimental group (n=31, 55.4%) received conventional rehabilitation combined with SER training (ChiCTR2500111990). Training occurred 5 times per week for 20 minutes each session over 4 weeks. Primary outcomes included gait speed and stride length, while secondary outcomes assessed the percentage of swing phase, ankle joint range of motion, Unified Parkinson Disease Rating Scale total and motor scores, and Montreal Cognitive Assessment. Paired sample t tests (2-tailed) were used for within-group pre- and postintervention comparisons, and independent sample t tests (2-tailed) were used for between-group comparisons. Correlation analyses were conducted between gait parameters and improvements in ankle mobility. RESULTS: After 4 weeks, the experimental group showed significant improvements in gait and balance. Specifically, left stride length increased by a mean of 0.15 (SD 0.16; 95% CI 0.09-0.21) m (P<.001), right stride length by a mean of 0.15 (SD 0.15; 95% CI 0.10-0.21) m (P<.001), left ankle dorsiflexion by a mean of 2.84 (SD 1.46; 95% CI 2.32-3.36) degrees (P<.001), left swing phase percentage by a mean of 1.56% (SD 3.05%; 95% CI 0.44-2.68; P=.01), and right swing phase percentage by a mean of 1.6% (SD 2.72%; 95% CI 0.62-2.62; P=.002). The Unified Parkinson Disease Rating Scale Part III total score decreased by a mean of 2.80 (SD 3.98) points, and balance subscale scores decreased by a mean of 0.40 (0.58) points (P<.001). Montreal Cognitive Assessment scores increased by a mean of 1.23 (1.23; 95% CI 0.77-1.68) points (P<.01), and Barthel Index scores increased by a mean of 6.84 (7.14; 95% CI 4.22-9.46) points (P<.001). Other measures such as balance reaction time, reaction speed, maximum movement distance, and movement direction control showed significant improvement (P<.01). Compared to the control group, the experimental group demonstrated greater improvements in gait speed (P=.04), balance reaction time (P=.04), and maximum movement distance (P=.048). Correlation analysis revealed that improvements in left ankle dorsiflexion were positively correlated with improvements in gait speed, stride length, and swing phase duration (P<.05). CONCLUSIONS: SER-assisted training significantly improves gait, balance, and PD symptoms. Our work integrates multidimensional assessments (gait analysis, balance metrics, and clinical scales) and reveals that gains in ankle mobility directly correlate with gait improvements, suggesting a key mechanism. This study contributes by establishing SER as an effective adjunct to conventional therapy, supported by comprehensive quantitative data.
3 May 2026
Read appraisal →Circulation
Prognostic Factors for Long-Term Risk of Stroke After Transient Ischemic Attack or Minor Stroke: A Systematic Review and Meta-Analysis
BACKGROUND: Patients with a transient ischemic attack (TIA) or minor stroke have an increased risk of subsequent stroke that persists for at least 10 years. We aimed to identify prognostic factors associated with long-term risk of stroke in this patient group and estimate their population attributable fraction (PAF). METHODS: A systematic review was performed of MEDLINE, Embase, and Web of Science for cohort studies including patients with TIA or minor stroke that evaluated factors for subsequent stroke over a follow-up period of ≥1 year. We pooled hazard ratios adjusted for relevant confounders using random-effect meta-analysis and determined the PAF of factors based on their pooled prevalence and adjusted hazard ratio. We assessed certainty of evidence using the grading of recommendations, assessment, development, and evaluation approach. The study is registered in PROSPERO (CRD42023476551). RESULTS: From 14 732 identified citations, we included 28 cohort studies comprising 86 810 patients with TIA or minor stroke (median age, 69 years [interquartile range, 65-71]; 52%-60% male patients). Factors that had high certainty evidence of association with increased long-term risk of stroke included older age (adjusted hazard ratio, 1.04 per year increase [95% CI, 1.02-1.05]); male sex (1.25, 1.15-1.36; PAF 13.0% [95% CI, 7.8-18.7]); atrial fibrillation (1.34, 1.18-1.52; 3.8% [95% CI, 0.3-9.9]); diabetes (1.52, 1.32-1.75; 7.7%, 3.1-14.1); hypertension (1.60, 1.31-1.94; 19.3%, 8.4-31.6); ischemic heart disease (1.67, 1.28-2.18; 10.7%, 2.8-22.9); history of stroke or TIA before the index event (1.70, 1.43-2.02; 12.0%, 5.2-21.4); smoking (1.29, 1.05-1.60; 11.2%, 1.0-30.7); ABCD2 (age, blood pressure, clinical features, duration of TIA, and presence of diabetes) score of ≥4 (1.59, 1.31-1.94; 18.0%, 2.9-39.9); presence of acute infarct on neuroimaging (1.97, 1.41-2.74; 19.0%, 5.2-38.9), including diffusion-weighted imaging-positive lesions (1.86, 1.02-3.37; 14.0%, 7.0-25.0); minor stroke as index event (1.75, 1.35-2.27 versus TIA; 28.0%, 10.2-47.6); presentation with aphasia or dysarthria (1.45, 1.24-1.69; 19.2%, 0.2-53.1); presentation with paresis (1.45, 1.15-1.84; 22.0%, 3.8-43.4); and etiologic stroke subtypes, including cardioembolism (2.16, 1.53-3.05; 14.6%, 3.1-33.5), large-artery atherosclerosis (2.19, 1.68-2.86; 13.2%, 5.1-25.5), and small-vessel disease (1.69, 1.14-2.49; 16.8%, 5.0-34.3). CONCLUSIONS: These findings can help identify patients with a particularly enduring risk of stroke who are most likely to benefit from ongoing monitoring and treatment and facilitate the development and implementation of targeted stroke prevention strategies.
3 May 2026
Read appraisal →Neurology
Hypnotic Cognitive Therapy for Chronic Pain in Spinal Cord Injury: A Randomized Controlled Trial
BACKGROUND AND OBJECTIVES: Chronic pain is highly prevalent, disabling, and difficult to treat in people with spinal cord injury (SCI). Our objective was to determine whether hypnotic cognitive therapy (HYP-CT) significantly reduces average pain intensity. METHODS: This single-blind, randomized, parallel-group trial investigated the efficacy of HYP-CT in treating moderate-to-severe chronic pain in community-residing adults with SCI. Participants were recruited nationwide through SCI organizations and randomized 1:1 to HYP-CT vs usual care (UC). We used computerized permuted block randomization with variable block sizes and stratified by sex and worst pain type. HYP-CT was delivered by a psychologist over the telephone or through Zoom and consisted of 6 weekly 60-minute sessions plus instructions to practice self-hypnosis daily independently by listening to session recordings. Controls were encouraged to continue current treatments or seek additional pain treatments. The primary outcome was average pain intensity assessed 4 times per week on a 0-10 numerical rating scale and averaged. The primary end point was at 6 weeks after randomization, with a follow-up assessment at 12 weeks. All outcomes were assessed through structured telephone interviews performed by blinded assessors. RESULTS: A total of 127 participants were randomized to HYP-CT (n = 64) vs UC (n = 63). Forty-eight percent were treated over the telephone and 52% through Zoom. The group was 42% female, had a mean age of 51.3 years (15.4 years after SCI), and had a mean baseline pain intensity of 5.93. The worst pain was neuropathic in 57% of cases. The primary outcome, average pain intensity, decreased more in the HYP-CT group compared with UC controls at 6 (-0.55, 95% CI -1.04 to -0.06) and 12 (-0.79, 95% CI -1.28 to -0.29) weeks. Depression declined significantly more in the HYP-CT vs UC groups at 6 and 12 weeks. Planned exploratory analyses suggested that the effect of HYP-CT on pain intensity was similar when delivered over the telephone vs through Zoom, and that pain intensity decreased more in those with all neuropathic pain vs those with mixed pain at 12 weeks. DISCUSSION: The study suggests that HYP-CT is an effective adjunctive treatment for SCI-related pain. Replication studies are needed. TRIAL REGISTRATION INFORMATION: The study was registered on ClinicalTrials.gov (NCT03857672) on February 28, 2019 and study enrollment commenced on April 24, 2019. CLASSIFICATION OF EVIDENCE: This study provides Class III evidence that, in patients with SCI, HYP-CT improves average pain intensity at 6 weeks compared with UC.
3 May 2026
Read appraisal →Neurology
Idiopathic Intracranial Hypertension Prevalence and Hormonal Contraception: A Meta-Analysis
BACKGROUND AND OBJECTIVES: Women's health advice in people living with idiopathic intracranial hypertension (IIH) is inconsistent, primarily due to an enduring concern that hormonal contraception (HC), including oral contraceptive pills (OCPs) and hormonal intrauterine devices (IUDs), might be associated with a higher prevalence of IIH. This systematic review and meta-analysis examines whether a meaningful association exists between HC use and IIH prevalence. METHODS: Systematic searches of Ovid MEDLINE, Embase, and the Cochrane Library were conducted from their inception to May 14, 2025. Gray literature and citation lists were also reviewed. Observational studies comparing HC use among women with IIH to controls without IIH were included. Noncomparative studies and pharmacovigilance analyses were excluded. Two independent reviewers conducted data extraction, assessed the quality of studies using the Newcastle-Ottawa Scale, and appraised the certainty of evidence using the Grading of Recommendations, Assessment, Development, and Evaluation framework. A random-effects meta-analysis using the Mantel-Haenszel method in Review Manager 5.4 was conducted to evaluate the association between HC use and IIH prevalence, the primary outcome of interest, with leave-one-out sensitivity analyses and subgroup analyses stratified by HC modality. RESULTS: A total of 13 studies (nIIH = 5,351 and ncontrol = 669,260; mean age = 33.3 years; 100% female) were included. HC use was not significantly associated with IIH prevalence (odds ratio [OR] 0.93, 95% CI 0.69-1.24, p = 0.60, I2 = 82%; n = 12 studies). Subgroup analyses revealed no significant associations for OCPs (OR 0.91, 95% CI 0.72-1.16, p = 0.45, I2 = 25%; n = 6 studies), IUDs (OR 1.45, 95% CI 0.61-3.44, p = 0.39, I2 = 92%; n = 5 studies), or medroxyprogesterone acetate injections (OR 1.21, 95% CI 0.84-1.74, p = 0.30, I2 = 2%; n = 2 studies). Findings were largely robust in sensitivity analyses. Quality assessments ranged from poor to good, with the certainty of evidence rated as very low to low. DISCUSSION: The meta-analysis found no significant association between HC use and IIH prevalence, with consistent results across various HC modalities. This evidence provides confidence to reassure patients with IIH and clinicians that the directed use of HC should be continued as appropriate. The limited quality of available evidence and considerable heterogeneity highlights the necessity for large-scale, well-designed studies, particularly in diverse patient populations, to validate these findings.
3 May 2026
Read appraisal →JMIR aging
Machine Learning-Derived Cardiovascular Aging Phenotypes From Cardiac Function and Stroke Risk in the UK Biobank: Cohort Study
BACKGROUND: Cardiovascular magnetic resonance (CMR) is widely used across various cardiac conditions and systematically assesses cardiac anatomical structures and functional dynamics. Machine learning (ML) can accurately predict outcomes and understand the inherent features of clinical data. OBJECTIVE: This study aimed to derive CMR phenotypes related to cardiovascular aging, investigate the relationship between these phenotypes and stroke risk, and relearn these phenotypes using supervised ML. METHODS: We enrolled 36,467 participants without stroke and extracted CMR parameters from the UK Biobank, with follow-up data extending until September 30, 2023. Using the generative topographic mapping technique, we identified latent grid nodes among participants and then derived phenotypes through agglomerative hierarchical clustering. We used supervised ML models to predict cardiac function phenotypes and used Cox proportional hazards models to assess the association between these phenotypes and long-term stroke risk. RESULTS: We enrolled 36,467 participants in the study. The mean age was 54.9 (SD 7.5) years, with 17,442 (47.8%) male participants. During a mean follow-up time of 14.7 (SD 1.1) years, 500 (1.4%) participants developed stroke and 664 (1.8%) participants died, respectively. After generative topographic mapping modeling, we identified 2 distinct phenotypes: phenotype 1, characterized by adverse cardiac function and an accumulation of cardiovascular risk factors, reflecting cardiovascular aging; and phenotype 2, associated with a lower risk of stroke (hazard ratio 0.695, 95% CI 0.559-0.864; P=.001), which remained significant after accounting for competing mortality (hazard ratio 0.578, 95% CI 0.484-0.691; P<.001). We selected the random forest model as the optimal model for the phenotypes, demonstrating high accuracy (area under the curve 0.914, 95% CI 0.911-0.918 for training and 0.867, 95% CI 0.858-0.876 for validation) and calibration ability (Brier score 0.111, 95% CI 0.109-0.113 for training and 0.132, 95% CI 0.127-0.137 for validation). CONCLUSIONS: By integrating unsupervised and supervised ML methods, we identified cardiovascular aging-related phenotypes that demonstrate robust predictive ability for incident stroke, which may have the potential to improve preventive and therapeutic strategies for high-risk populations.
2 May 2026
Read appraisal →Stroke
Preterm Delivery and Long-Term Risk of Maternal Stroke: Systematic Review and Meta-Analysis
BACKGROUND: Adverse pregnancy outcomes are linked to increased risk of maternal cardiovascular disease. However, the association between preterm delivery (PTD) and long-term risk of stroke in the mother remains uncertain, particularly in the case of spontaneous PTD. METHODS: A systematic review and meta-analysis were performed to provide an up-to-date synthesis of the evidence on the association between PTD and long-term maternal stroke. PubMed, CINAHL, and Web of Science were searched for relevant articles published between January 1, 2000, and May 6, 2025. Eligible studies included cohort and case-control studies examining populations of parous women. Primary exposure was defined as any PTD; secondary exposures were spontaneous PTD and medically indicated PTD. The primary outcome was defined as any stroke; secondary outcomes were ischemic and hemorrhagic stroke. Two reviewers screened studies, extracted data, and performed quality assessments. Pooled unadjusted and adjusted effect estimates (as defined by the original study) were calculated separately using random effects inverse variance models. RESULTS: Eleven thousand twenty-five studies were screened for eligibility. Across 21 studies including a total of 8.7 million participants, PTD was consistently associated with increased stroke risk. The follow-up period ranged from 8 to 57 years. The primary meta-analysis demonstrated a positive association between any PTD and any stroke (adjusted risk ratio [aRR], 1.66 [95% CI, 1.34-2.05]). A high level of heterogeneity was observed (I2=97%, τ2=0.15), possibly due to variations in exposure definition, outcome ascertainment, and follow-up durations. Spontaneous PTD (aRR, 1.37 [95% CI, 1.15-1.64]) and medically indicated PTD (aRR, 2.08 [95% CI, 1.70-2.54]) were associated with any stroke. PTD was positively associated with ischemic (aRR, 1.59 [95% CI, 1.45-1.75]) and hemorrhagic stroke (aRR, 1.44 [95% CI, 1.06-1.94]). CONCLUSIONS: Women who have experienced a PTD, including spontaneous PTD, may be at increased risk of stroke in later life. These women may benefit from targeted cardio-metabolic preventive interventions postpartum to reduce their risk.
2 May 2026
Read appraisal →Psychological medicine
Intrinsic regional brain activity differences and its relationship with neurotransmitter and gene expression in postpartum depression: a voxel-based meta-analysis
BACKGROUND: Inconsistent findings persist across resting-state functional imaging studies of regional brain alterations in postpartum depression (PPD), while connections to transcriptional profiles and neurotransmitter systems remain largely uncharacterized. METHODS: We performed a whole-brain voxel-wise meta-analysis of resting-state functional imaging studies comparing PPD patients and healthy controls using SDM-PSI software. JuSpace toolbox analyzed atlas-based nuclear imaging-derived neurotransmitter maps, and transcriptional data were sourced from the Allen Human Brain Atlas. RESULTS: Our systematic review identified 12 functional imaging studies (475 PPD patients, 504 controls). Patients with PPD displayed increased resting-state functional activity in the left inferior occipital gyrus and left precuneus as well as decreased resting-state functional activity in the right amygdala and left precentral gyrus. These functional alterations spatially overlapped with serotonergic, dopaminergic, and VAChT systems. Transcriptional analysis revealed PPD-related gene enrichments in ion channel function (transmembrane transport, gated/passive channels) and channel complexes. CONCLUSIONS: The meta-analysis revealed functional alterations within the DMN, limbic, and primary sensorimotor systems in PPD patients. These changes were linked to neurotransmitter alterations and genetic modulations underlying brain dysfunction. Collectively, these findings advance mechanistic understanding of PPD pathophysiology.
2 May 2026
Read appraisal →Molecular neurobiology
Establishing a Diagnostic Model Based on Plasma CDR1as and Alpha-Synuclein for Mild Cognitive Impairment in Type 2 Diabetes
Emerging evidence implicates cerebellar degeneration-related protein 1 antisense transcript (CDR1as) and alpha-synuclein (α-Syn) in diabetes and cognitive decline, suggesting their potential to serve as biomarkers. This study aimed to evaluate their diagnostic efficacy for type 2 diabetes mellitus-associated mild cognitive impairment (T2DM-MCI) and establish a corresponding diagnostic model using machine learning. T2DM-MCI-associated differentially expressed genes were identified from the hippocampus using the Gene Expression Omnibus and predictive databases. The study involved 529 T2DM patients in two phases (training set = 408, test set = 121). Levels of plasma CDR1as, α-Syn, Aβ1-40/1-42, and ApoE genotype were detected. Three diagnostic models were identified using least absolute shrinkage and selection operator (LASSO) and logistic regression. Their performance was assessed using the area under the curve (AUC), net reclassification index (NRI), and integrated discrimination improvement (IDI). The best-performing model was selected from nine machine learning algorithms. Personalized risk assessment was achieved with the SHapley Additive exPlanations (SHAP). Bio-informatics analysis identified CDR1as and α-Syn as potential biomarkers. Their inclusion in multi-modal panels markedly improved diagnostic accuracy. The Random Forest (RF) model outperformed eight other machine learning classifiers (AUC = 0.975, 95% CI: 0.946-0.999) with the lowest Brier score (0.054). When evaluated on a test set, the RF model maintained robust performance (AUC = 0.901, accuracy = 0.850). An online diagnostic tool was established ( https://www.xsmartanalysis.com/model/list/predict/model/html?mid=6734&symbol=816GyBh9ag1Wr9367341 ). The study preliminarily identified CDR1as and α-Syn as promising biomarkers for T2DM-MCI. Integrating these markers with conventional blood tests showed potential clinical utility for improving diagnostic precision.
2 May 2026
Read appraisal →Behavioural brain research
Non-pharmacotherapy for craving in methamphetamine use disorder: A systematic review and network meta-analysis of randomised controlled trials
OBJECTIVES: We evaluated the effects of non-pharmacotherapy on quality of life (QOL) and craving in individuals with methamphetamine use disorder (MUD), ranking the efficacy of various protocols. METHODS: We searched multiple databases for randomized controlled trials from inception to Dec 2025. The analysis included 53 studies with 3174 participants, investigating interventions such as exercise, transcranial magnetic stimulation (TMS), transcranial direct current stimulation, behavioral therapy, and acupuncture. The protocol was prospectively registered in PROSPERO (CRD42023443291). RESULTS: For craving reduction, both exercise and TMS demonstrated statistically significant benefits compared to conventional rehabilitation. Combined aerobic/resistance exercise and 10 Hz repetitive TMS targeting the left dorsolateral prefrontal cortex emerged as the most effective specific protocols within their respective categories. Furthermore, preliminary evidence from a limited number of studies indicated that exercise, acupuncture, and behavioral therapy improved patients' QOL. CONCLUSION: This study confirms that several non-pharmacotherapies are effective for managing craving in MUD. Exercise and TMS are particularly promising for reducing craving. The findings, synthesized within a novel "Intervention-Circuit-Symptom" integrative framework, provide a robust evidence base to inform clinical decision-making and guide the future development of MUD treatment guidelines.
1 May 2026
Read appraisal →Behavioural brain research
Influences of high-definition transcranial direct current stimulation (HD-tDCS) and cognitive reappraisal on emotions and aggression following social exclusion
Social exclusion can trigger negative emotions and aggressive behavior, while emotion regulation strategies and neuromodulation techniques may mitigate these effects. This study employed a 2 × 2 factorial design to investigate the impacts of high-definition transcranial direct current stimulation (HD-tDCS) and cognitive reappraisal on emotions and aggression following social exclusion. Thirty-two university students were randomly assigned to four groups: active HD-tDCS + cognitive reappraisal, sham HD-tDCS + cognitive reappraisal, active HD-tDCS + no reappraisal, and sham HD-tDCS + no reappraisal. Results showed that active HD-tDCS significantly reduced aggressive behavior in both the Competitive Reaction Time Task and Job-relevant Evaluations Task, likely by enhancing prefrontal regulatory control. Cognitive reappraisal significantly decreased negative emotions but did not directly reduce aggressive behavior, suggesting a dissociation between emotional and behavioral regulation pathways. No significant interaction was found between HD-tDCS and cognitive reappraisal, possibly due to their independent neural mechanisms. These findings highlight the potential of HD-tDCS and cognitive reappraisal as interventions for social exclusion-induced aggression and emotional dysregulation.
1 May 2026
Read appraisal →Neurology
Association of MRI-Visible Perivascular Spaces With Longitudinal Cognitive Decline Over a Decade
BACKGROUND AND OBJECTIVES: Cerebral small vessel disease (SVD) is the most common vascular contributor to dementia. SVD markers often coexist, contributing to difficulty assessing their independent contributions to cognitive domains. MRI-visible perivascular spaces (PVSs) are an emerging SVD marker visualized by MRI. We previously showed that basal ganglia PVSs cross-sectionally contribute to worse cognition, independent of other SVD markers. To further characterize the clinical relevance of PVS, we studied its role as a unique SVD marker of longitudinal cognitive decline. METHODS: Participants without stroke or dementia were included in the Vanderbilt Memory and Aging Project, a longitudinal observational cohort study based in Nashville, TN. Participants completed 3T MRI at study entry to measure SVD burden (PVS volume fraction, white matter hyperintensities volume, lacune counts, and cerebral microbleeds counts). PVS volumes were segmented using a deep learning algorithm. Participants underwent comprehensive serial neuropsychological testing over an 11-year follow-up period (mean follow-up = 4.9 ± 3.1 years). Each SVD marker was related to longitudinal neuropsychological performances using a linear mixed-effects model adjusting for age, sex, race/ethnicity, education, baseline cognitive status, apolipoprotein E-ε4 presence, Framingham Stroke Risk Profile, and intracranial volume. Head-to-head comparisons simultaneously tested multiple statistically significant SVD markers. RESULTS: Among participants (n = 750, age 68 ± 9 years, 52% female), higher basal ganglia PVS burden at baseline was associated with worse longitudinal performances in Boston Naming Test (β = -29.63; 95% CI -56.66 to -2.60), Animal Naming (β = -33.09; 95% CI -65.66 to -0.51), Wechsler Adult Intelligence Scale IV Coding (β = -86.36; 95% CI -150.9 to -21.82), executive function composite (β = -9.51; 95% CI -14.33 to -4.68), Hooper Visual Organization Test (β = -26.06; 95% CI -50.53 to -1.59), and episodic memory composite (β = -7.05; 95% CI -11.9 to -2.21). In head-to-head comparisons, basal ganglia PVS remained independent associations with executive function composite (β = -7.47; 95% CI -12.84 to -2.10) and Hooper Visual Organization Test (β = -22.11; 95% CI -42.38 to -1.85). DISCUSSION: Basal ganglia PVS burden independently contributes to worse longitudinal executive function and visuospatial skills independent of other SVD markers, highlighting PVS as an emerging marker of domain-specific cognitive decline in aging. Although causation cannot be established, findings further support PVS as a vascular contributor to deep brain structure damage underlying cognitive decline over time.
28 Apr 2026
Read appraisal →Proceedings of the National Academy of Sciences of the United States of America
Neural reservoir control of a bio-hybrid soft arm
A long-standing engineering problem, the control of soft robots is difficult because of their highly nonlinear, heterogeneous, anisotropic, and distributed nature. Here, bridging engineering and biology, neural reservoirs are employed for the dynamic control of a bio-hybrid model arm made of multiple muscle-tendon groups enveloping an elastic spine. We show how the use of reservoirs facilitates simultaneous control and self-modeling across challenging tasks, outperforming classic neural network approaches. Further, through the use of spiking reservoirs on neuromorphic hardware, energy efficiency gains of up to 75 and 45 times are obtained relative to standard and high-efficiency CPUs, with implications for the on-board control of untethered, small-scale systems.
28 Apr 2026
Read appraisal →Scientific reports
LASSO-HHO two-stage hybrid gene selection framework for accurate Alzheimer's disease diagnosis
Selecting informative genes is essential for building accurate and efficient diagnostic models, especially for high-dimensional gene expression data such as those related to Alzheimer's disease. These datasets typically involve thousands of genes with limited samples, increasing the risk of overfitting and computational complexity. To address this, we propose a LASSO-HHO Gene Selection (LHGS) framework. LASSO is first applied to reduce dimensionality by filtering irrelevant genes. A conditional HHO-based optimization stage is then applied only when the LASSO-selected subset does not achieve sufficient accuracy or remains relatively large. Otherwise, the LASSO-selected features are directly used without further optimization. Experimental results show that the proposed method reduces the number of selected genes by up to 99.9% while maintaining high performance. The experimental results indicate that 100% accuracy can be achieved on specific datasets. In particular, GSE48350 and GSE36980 achieved 100% accuracy using LASSO alone, whereas GSE118553 and GSE132903 required the full LHGS framework to achieve the same performance. The framework also improves computational efficiency within a consistent experimental setup by reducing the optimization search space after LASSO filtering. Overall, LHGS provides a practical and efficient solution for gene selection in high-dimensional biomedical data.
28 Apr 2026
Read appraisal →Behavior research methods
Lexique 4: A major upgrade of the Lexique French lexical database
Lexique 4, an updated French lexical database, expands upon its predecessor, Lexique 3, by incorporating several significant improvements to enhance its utility in psycholinguistics, computational linguistics, and education. The new version is based on a larger corpus of 316 million words derived from 65,317 documents, including movie, TV show, and documentary subtitles, which offers more accurate frequency estimates and includes contemporary neologisms. Lexique 4 introduces new variables, such as orthographic surface frequency, contextual diversity (CD), and detailed morphological structure, which provide a more comprehensive view of lexical properties. We find that contextual diversity is a slightly better predictor than word frequency, in line with previous work. Moreover, the integration of lexical decision times from the French Lexicon Project into Lexique 4 facilitates more in-depth linguistic research. Enhancements to the user interface, including a redesigned web platform, enable dynamic searches and sorting capabilities, increasing accessibility and usability for researchers. Statistical analyses indicate that the updated frequency measures in Lexique 4 are better predictors of lexical decision times compared to Lexique 3, supporting the value of these enhancements. Overall, Lexique 4 represents a comprehensive and flexible tool for analyzing French lexical properties, making it an essential asset for a broad range of users.
27 Apr 2026
Read appraisal →Biomedizinische Technik. Biomedical engineering
Evaluation of an adaptive system for breathing control in closed-loop diaphragm pacing
OBJECTIVES: Existing diaphragm pacing (DP) system use an open-loop control method with a fixed stimulation mode to control breathing. It requires doctors to manually adjust stimulation parameters to meet the patient's ventilation needs. METHODS: A neural network adaptive controller was tested to control breathing in DP system. For the diaphragm motion model, the respiratory airflow of healthy adults and rabbits was collected and compared with the simulation calculation results to verify the accuracy of the model. The performance of the adaptive controller was evaluated comparatively with that of the PID controller. Adaptive controller consists of a neural network and adaptively adjusts stimulation parameters to produce the desired respiratory volume waveform. Superiority of the output performance of the adaptive controller was further studied by setting various diaphragm model parameters. We further verified the feasibility of the adaptive controller through animal testing. RESULTS: The adaptive controller is better than the PID controller in maintaining the stability of the desired breath volume. Application of the adaptive controller can reduce the root mean square (RMS) error between the desired breath volume and the actual value to less than 6 %. CONCLUSIONS: This study demonstrates the potential application of adaptive controllers in closed-loop DP systems.
27 Apr 2026
Read appraisal →PloS one
A secondary head-to-head comparison of low-intensity focused ultrasound and repetitive transcranial magnetic stimulation for motor recovery after stroke
BACKGROUND: Low-intensity focused ultrasound (LIFU) is a non-invasive neuromodulation technique with high spatial precision and the ability to reach deeper brain regions, offering potential advantages for post-stroke rehabilitation. Repetitive transcranial magnetic stimulation (rTMS) is a widely adopted non-invasive brain stimulation technique that modulates cortical excitability to promote neuroplasticity. However, direct head-to-head comparisons between these two modalities for post-stroke motor recovery remain limited. OBJECTIVE: To perform a secondary head-to-head comparison of LIFU and repetitive transcranial magnetic stimulation (rTMS) for motor recovery after stroke, based on a prospectively registered randomized controlled trial. METHODS: This secondary analysis included patients with subacute stroke who received two weeks of standard rehabilitation combined with either LIFU (n = 25) or rTMS (n = 25) targeting the ipsilesional primary motor cortex. LIFU parameters: 0.5 MHz, spatial-peak pulse-average intensity (ISPPA) 10.2 W/cm² (free-field), pulse duration 0.2 ms, duty cycle 20%, 20 minutes per session, five days per week for two weeks (10 sessions total). rTMS parameters: 10 Hz, 80% resting motor threshold, 1,000 pulses per session (20 trains of 5 seconds), 20 minutes per session, five days per week for two weeks (10 sessions total). Motor outcomes were assessed using the Fugl-Meyer Assessment (FMA; upper and lower extremities), Modified Barthel Index (MBI), and Brunnstrom stages. Resting-state functional near-infrared spectroscopy (fNIRS) was used to evaluate cortical activity and functional connectivity before and after the intervention. Primary analyses were conducted in the intention-to-treat (ITT) population (n = 50), with completer analyses (n = 43) performed as sensitivity analyses. RESULTS: Both groups showed significant within-group improvements in FMA and MBI after the intervention (all p < 0.001), and changes in Brunnstrom stages were also uniformly significant. No statistically significant between-group differences were observed in post-intervention FMA, MBI, or Brunnstrom stages (all p > 0.05), and completer analyses yielded consistent between-group conclusions. In contrast, change-from-baseline analyses demonstrated greater improvements in FMA scores in the LIFU group compared with the rTMS group (ΔFMA upper limb: median 7 [IQR 3-10.5] vs. 2 [1-3], p = 0.001; lower limb: 3 [1-4.5] vs. 1 [0-1.5], p < 0.001). Exploratory fNIRS analyses revealed modality-specific patterns: prefrontal fractional amplitude of low-frequency fluctuations (fALFF) increased significantly in the LIFU group (p = 0.002) but not in the rTMS group, while functional connectivity changes did not remain significant after correction for multiple comparisons. CONCLUSION: LIFU and rTMS were associated with comparable short-term motor outcomes in subacute stroke. Differences observed in change-from-baseline motor improvements and exploratory neuroimaging measures suggest potential divergence in recovery dynamics and cortical modulation, warranting further investigation in larger, longitudinal studies. TRIAL REGISTRATION: This study was derived from a prospectively registered, three-arm randomized controlled trial in the Chinese Clinical Trial Registry (ChiCTR2500114687). The present manuscript reports a secondary head-to-head comparison between the two neuromodulation intervention arms.
27 Apr 2026
Read appraisal →PloS one
Comparison of deep learning-based three-dimensional human pose estimation methods with motion capture for gesture research
Humans often make gestures while speaking. These gestures have been extensively researched in psychology and cognitive science, revealing their functions and roles in communication. Despite being produced in three-dimensional (3D) space, gestures and gesture space have primarily been measured and analyzed using two-dimensional planes. Optical motion capture (MoCap) is commonly used for 3D gesture measurement. However, MoCap is expensive, requires a large space, and the attachment of retroreflective markers can interfere with the natural generation of gestures by the speaker. Recent advances in deep learning-based human pose estimation (HPE) offer promising alternatives by enabling 3D keypoint estimation from standard video cameras. This study investigates the accuracy of four HPEs-two monocular and two stereo with triangulation methods-in estimating upper-body keypoints commonly used in natural scenarios for gesture research. Ten participants were recorded performing gesture-rich speech by both a MoCap system and three video cameras. We compared 13 keypoints, including the wrists, elbows, shoulders, fingers, and face, with MoCap data, using Euclidean distance as an error metric. Statistical analyses revealed that stereo methods significantly outperformed monocular methods across all keypoints. The most accurate method achieved an average error of 49.4 mm with respect to MoCap, suggesting sufficient accuracy for practical gesture analysis. Additionally, we visualized 3D gesture spaces and found a 75.4% overlap between HPE and MoCap at a voxel size of 50 mm, indicating high spatial agreement. Our findings demonstrate that stereo HPE methods are viable, cost-effective alternatives to MoCap in gesture-related applications. Our ultimate objective is to create a toolbox that is accurate and easy to use for measuring human gestures in 3D space by exploiting the recent advances in HPE as an alternative to MoCap. Such a toolbox will be suitable for non-experts in machine learning, and these results lay the foundation for it.
27 Apr 2026
Read appraisal →Journal of the International Society of Sports Nutrition
Effects of nutritional interventions on competitive performance and dose‒response relationships among esports players of different skill levels: a systematic review and three-level meta-analysis
BACKGROUND AND OBJECTIVES: Esports place exceptional demands on neuropsychological capacities; however, a systematic and quantitative synthesis of the specific efficacy of nutritional interventions across various tasks and competitive populations remains absent. This study aims to evaluate the impact of nutritional supplementation on cognitive function and competitive performance in esports players and to explore key moderating variables. METHODS: Electronic databases, including Web of Science, PubMed, and Scopus, were searched (up to January 25, 2026). Thirteen randomized controlled trials (RCTs) met the inclusion criteria. A three-level random-effects model was employed to calculate the pooled effect size (Hedges' g), with sources of heterogeneity explored through subgroup analyses and meta-regression. RESULTS: Nutritional interventions significantly enhanced esports-related performance (g = 0.92), with the most pronounced benefits observed in processing speed (g = 1.18) and executive function (g = 1.06). Subgroup analyses revealed significant gains in both professional (g = 0.97) and amateur groups (g = 0.92), while the improvement in first-person shooter (FPS) games (g = 0.96) appeared to surpass that in multiplayer online battle athlete (MOBA) games. Meta-regression indicated no significant linear correlation between caffeine dosage and effect size. GRADE assessment categorized the quality of evidence mostly as "low" to "moderate." CONCLUSIONS: Nutritional interventions demonstrate significant potential for optimizing multidimensional cognitive performance in esports players, particularly in enhancing fundamental reaction speed and high-level decision-making processes. While the findings exhibit a degree of robustness, their practical application should be cautiously evaluated within specific competitive contexts due to current limitations in evidence certainty and significant interstudy heterogeneity. Future research characterized by gender balance and mechanistic orientation with long-term intervention protocols is warranted to elevate the level of clinical evidence.
27 Apr 2026
Read appraisal →Behavior research methods
FaceTrack-AOI: An AI-driven tool for automated dynamic AOI placement and eye movement analysis in face perception studies
Face perception is fundamental to human social interaction, relying on the dynamic allocation of visual attention to facial features across natural variations in poses, expressions, lighting and context-critical factors for real-world face perception. While eye-tracking studies have advanced our understanding of this process, analyzing gaze data in naturalistic settings presents challenges due to the labor-intensive and often subjective process of manually defining areas of interest (AOI) in dynamic stimuli. We introduce FaceTrack-AOI, an open-source, artificial intelligence (AI)-driven tool that automates dynamic AOI construction using robust 68-point facial landmark detection. Featuring a user-friendly graphical interface, the tool allows researchers to define and export eye movement metrics without programming expertise. It supports both rectangular and polygon-based AOIs, with customizable buffers, blank zones, and hierarchical configurations, accommodating diverse theoretical and methodological needs. Validation on multi-ethnic naturalistic video frames, images from a standardized face dataset (Chinese Face and Body Dataset [CFBD]), and benchmarks against a published study revealed strong agreement with expert-defined manual AOIs, achieving a mean spatial overlap of 95.73%. A proof-of-concept validation confirmed high fixation capture: over 98.8% of dwell time was correctly directed toward key AOIs. In two experiments with infants (6-18 months) and adults viewing 28,800 dynamic video frames, and children (2-8 years), both neurotypical and with autism spectrum disorder (ASD), viewing 80 static faces, FaceTrack-AOI demonstrated enhanced sensitivity in detecting developmental and group differences in naturalistic face recognition. By integrating AI-powered tracking with an intuitive interface, FaceTrack-AOI streamlines gaze analysis for dynamic facial stimuli, offering researchers an efficient and scalable solution to advance face perception research in ecologically valid contexts.
26 Apr 2026
Read appraisal →Brain topography
Effects of Sleep Deprivation and Neuromodulatory Interventions on Olfactory-Related Brain Function
Sleep deprivation (SD) has been shown to impair sensory functions, including olfactory processing, which may be related to changes in brain activity. This study aims to investigate the effects of SD, short-term sleep recovery (RS), and neuromodulation interventions on the functional activity of olfactory-related brain regions. We hypothesize that these interventions could restore some of the neural activity disrupted by SD, particularly in areas associated with olfactory processing. In this study, 90 male participants first underwent a baseline sleep period of at least 8 h, followed by 36 h of sleep deprivation (SD36h). The participants were randomly assigned to four groups: control group (CON, n = 30, age 20.8 ± 1.6 years), magnetic stimulation group (MS, n = 30, age 21.3 ± 1.6 years), electrical stimulation group (ES, n = 17, age 20.3 ± 0.7 years), and combined stimulation group (CS, n = 13, age 20.8 ± 1.0 years). After SD36h, participants underwent an 8-hour restorative sleep period, before and after completing the 8-hour RS period, additionally received 30 min of MS(MS was applied over the occipital region using a rhythmic gamma magnetic-field device), ES(ES was delivered as tDCS targeting the left DLPFC, anode at F3, cathode at FP1, 2 mA), and CS. All participants underwent three resting-state functional MRI (fMRI) scans: at baseline, after sleep deprivation, and after intervention. This experiment analyzed the Amplitude of Low-Frequency Fluctuations (ALFF) and functional connectivity (FC) in olfactory-related regions. ALFF and FC were analyzed using a group (CON/MS/ES/CS) × time (baseline/SD36h/RS) repeated-measures ANOVA with FDR correction, followed by FDR-corrected post-hoc tests for significant main effects. No group × time interaction survived FDR correction for ALFF or FC. For ALFF, a main group effect was observed in Hippocampus_L and Olfactory_R, with the ES (and CS in Olfactory_R) showing lower ALFF than CON and/or MS. For time effects, ALFF in Hippocampus_L was reduced at SD36h and remained lower after RS relative to baseline, while Olfactory_L ALFF was lower after RS than baseline. For FC, main group effects were found in Hippocampus_L and Fusiform_R, with ES and CS showing higher FC than CON and MS. A main time effect cluster with a peak in Cingulum_Mid_R showed reduced FC after SD36h and increased FC after RS, returning to a level not significantly different from baseline. SD disrupts the ALFF and FC in olfactory-related brain regions, while RS can partially restore these functions. Short-term neuromodulation interventions, such as electrical stimulation and combined stimulation, may improve network-level connectivity but are insufficient to fully restore local neural activity.
26 Apr 2026
Read appraisal →ACS sensors
Vertically Oriented Assembly of a Few-Layer Ti3C2Tx Grid by Electric Field Inducing and Freeze Drying for Wearable Levodopa Sensing in Sweat
Compared with the parallel assembly, the vertical assembly of MXene nanosheets on the substrate exhibits open three-dimensional porous structure, which is beneficial to the construction of high-performance electrodes. Herein, the vertically oriented assembly of Ti3C2Tx nanosheets is proposed by a novel preparation process of electric field inducing and freeze drying, resulting in the Ti3C2Tx grid with thin-wall and open porous structure. First, the electric field induces the formation of Ti3C2Tx hydrogel layer on the substrate. Second, freeze drying causes the growth of ice crystals for squeezing few-layer Ti3C2Tx nanosheets to stand up. More importantly, such a Ti3C2Tx grid with large surface area and intrinsic conductivity can effectively load electrocatalysts, resulting in the Au-Ti3C2Tx grid as a high-performance working electrode for the electrochemical detection of levodopa. The main parameters include 1-30 μM and 30-160 μM two-stage linear ranges and 37 nM detection limit. The structural advantages of Ti3C2Tx grid-based electrode materials are their three-dimensional conductive network and interconnected porous structure, which contribute to the penetration of the electrolyte, the diffusion of the analyte, and the electrocatalytic reaction. Furthermore, the self-adapting shape of the Ti3C2Tx grid leads to the fan-shaped Au-Ti3C2Tx grid collaborated with a self-designed leaf-like microfluidic. Ultimately, a wearable levodopa sensor composed of Au-Ti3C2Tx grid, microfluidic, flexible electronic system, and mobile phone client is established for detecting levodopa in human sweat, which is expected to help Parkinson's patients control the drug dosage. Our work proposes the vertically assembled Ti3C2Tx nanosheet loaded noble metal electrocatalysts to develop high-performance electrodes for electrochemical sensing.
26 Apr 2026
Read appraisal →Reviews in the neurosciences
Microstructural alterations of the amygdala in neurodegenerative and neuroinflammatory disorders: insights from diffusion tensor imaging
Diffusion tensor imaging (DTI) is a valuable method for evaluating microstructural changes in the amygdala associated with neurodegenerative and neuroinflammatory disorders. This systematic review examines amygdala microstructural alterations in Alzheimer's disease (AD), Parkinson's disease (PD), amyotrophic lateral sclerosis (ALS), dementia with Lewy bodies (DLB), and multiple sclerosis (MS) using DTI metrics. Following PRISMA 2020 guidelines, we searched PubMed, Scopus, and Web of Science databases through August 2025, identifying 4,442 records. After screening and eligibility assessment, 13 studies were included, comprising 1,412 patients and 1,146 healthy controls. Due to sample heterogeneity and lack of standardized effect size measures, meta-analysis was not performed. Across disorders, elevated mean diffusivity (MD) emerged as the most consistent finding, present in 100 % of AD patients and observed in all examined conditions. Reduced fractional anisotropy (FA) was the second most frequent alteration, with 36.6 % of AD patients showing decreased FA. In MS, increased radial diffusivity (RD) was prominent, while longitudinal DLB studies revealed progressive free water (FW) increases. These DTI-based microstructural changes often preceded volumetric atrophy and correlated with clinical severity. Our findings demonstrate that DTI metrics, particularly MD and FA, serve as sensitive markers of amygdala pathology across neurodegenerative diseases and may facilitate early diagnosis, disease monitoring, and differential diagnosis of these conditions.
26 Apr 2026
Read appraisal →PloS one
RMETNet: A cross-subject motor imagery EEG signal classification model based on TSLANet and riemannian geometry features
Motor imagery electroencephalogram (MI-EEG) analysis is essential for natural interaction and autonomous control in brain-computer interfaces (BCIs). However, deep learning models often struggle with inter-subject variability, which limits their ability to generalize across subjects. This study proposes RMETNet, a novel framework that integrates TSLANet, a spatio-temporal convolution module, and a multi-scale Riemannian geometry feature module. TSLANet suppresses noise and captures complex temporal patterns for preliminary signal decoding, while the spatio-temporal convolution module extracts higher-order representations. The Riemannian branch learns geometry-based distribution features across subjects, and the fused features are used for classification. To address inter-subject distribution shifts, RMETNet incorporates Maximum Mean Discrepancy (MMD) loss for domain adaptation, aligning feature distributions between source and target domains. Experiments show that on the four-class BCI Competition IV 2a (BCICIV2a) dataset, RMETNet achieved accuracies of 71.39% in the cross-subject setting and 80.71% in the subject-dependent setting; on the two-class BCI Competition IV 2b (BCICIV2b) dataset, it achieved 80.93% and 86.76%, respectively. The model consistently outperformed baseline algorithms. Ablation and visualization analyses further validated its effectiveness in reducing inter-subject feature distribution disparities and enhancing MI-EEG decoding. The code is available at: https://github.com/rokanfeermecer486/RMETNet.
26 Apr 2026
Read appraisal →Journal of ethnopharmacology
Neuroprotective effects of Tongxinluo capsule in acute ischemic stroke: A systematic review and meta-analysis
ETHNOPHARMACOLOGICAL RELEVANCE: Tongxinluo capsule is widely used in patients with acute ischemic stroke (AIS), although the literature does not consistently agree on its efficacy and safety. AIM OF THE STUDY: We aimed to assess the efficacy and safety of Tongxinluo capsule for patients with AIS. MATERIALS AND METHODS: A systematic search of the databases PubMed, Embase, Cochrane Central Register of Controlled Trials, China National Knowledge Infrastructure, Wanfang data, VIP Chinese Journal Service Platform, and SinoMed was conducted from their inception dates to January 2025. Randomized controlled trials (RCTs) that enrolled patients with AIS who receive Tongxinluo capsule and compared effects with placebo or as an add-on therapy to usual care were included. Literature screening, risk of bias assessment, and data extraction were independently conducted in duplicate. Fixed-effects or random-effects models were used to perform meta-analysis for pooling data from eligible studies. The primary outcome was long-term functional independence (defined as modified Rankin Scale [mRS] grade less than 2). Secondary outcomes included all-cause mortality, improvements in activities of daily living (measured with the Barthel Index [BI]), and neurological impairment (measured with the National Institutes of Health Stroke Scale [NIHSS]). Incidence of adverse events were identified as safety outcomes. RESULTS: We included 4842 patients with AIS from 12 RCTs (2422 [50.02 %] treated with Tongxinluo capsule) in the final analysis. Tongxinluo capsule treatment in AIS significantly improved the proportion of patients with mRS less than 2 (RR, 1.21; 95 % CI, 1.02 to 1.44), improved the change in BI score (MD, 11.47; 95 % CI, 10.34 to 12.60) and NIHSS score (MD, 3.48; 95 % CI, 2.87 to 4.09). No significant differences were detected in all-cause mortality (RR, 0.57; 95 % CI, 0.17 to 1.95), or the incidence of overall adverse events (RR, 1.01; 95 % CI, 0.91 to 1.12). CONCLUSION: Administration of Tongxinluo capsule for AIS demonstrated potential benefits in improving the long-term functional outcomes and activities of daily living, and relieving neurological impairment, without observing major safety concerns.
25 Apr 2026
Read appraisal →Clinical chemistry and laboratory medicine
Serum GFAP as a biomarker for progression in multiple sclerosis: assay comparison and a large reference database of healthy controls
OBJECTIVES: Compare Elecsys (Roche) and Simoa (Quanterix) immunoassays for serum glial fibrillary acidic protein (GFAP) using our reference database and Z scores, and evaluate their prognostic value for progression independent of relapse activity (PIRA) in multiple sclerosis (MS). METHODS: Platform correlation was assessed in 612 samples from healthy controls (n=188; median [interquartile range, IQR] age 45.1 [36.4-61.7] years) and people with MS (n=424; 45.3 [35.2-53.9] years). Elecsys values were converted to Z scores via Passing-Bablok-derived regression and validated in fingolimod (n=414), and B-cell depleting therapy (BCDT; n=353) cohorts. Z scores and hazard ratios (HRs) for time-to-PIRA were compared using Cox regression. RESULTS: GFAPSimoa and GFAPElecsys measurements were correlated (r=0.94), with Elecsys values ∼54 % lower (GFAPElecsys, ng/L=2.847 [95 % confidence interval, CI: 1.335 - 4.98] + 0.457 [0.434 - 0.478] * GFAPSimoa, ng/L). In univariable Cox models, GFAPSimoa and GFAPElecsys Z scores were associated with time-to-PIRA in both validation cohorts. In multivariable Cox models, higher GFAPSimoa Z scores were associated with shorter time-to-PIRA in fingolimod cohort (HR: 1.27 [95 % CI 1.08 - 1.50], p=0.0031) and trended toward significance in BCDT (1.18 [0.99 - 1.41, p=0.0693). In contrast, GFAPElecsys Z scores were associated with time-to-PIRA in both cohorts (fingolimod: 1.27 [1.09 - 1.48], p=0.0023; BCDT: (1.19 [1.00 - 1.40], p=0.0487). CONCLUSIONS: Serum GFAP measured by Elecsys shows a comparable association with time-to-PIRA as Simoa, and GFAPSimoa Z scores can be successfully bridged to GFAPElecsys Z scores, supporting Elecsys`s potential for clinical implementation.
25 Apr 2026
Read appraisal →PloS one
The journey to diagnosis and care of functional neurological disorder (FND).
PURPOSE: Functional neurological disorder (FND) is a common condition, associated with high disability and healthcare costs, and poor treatment access. This qualitative study aimed to explore participants' experiences of being diagnosed with functional neurological disorder (FND), accessing treatment, and navigating life after diagnosis. Participants were drawn from a randomised feasibility study of eye movement desensitisation and reprocessing therapy for people with FND (MODIFI, Trial Registration: NCT05455450 (www.clinicaltrials.gov)). METHODS: Reflexive thematic analysis was used to analyse the data from eighteen semi-structured interviews with participants diagnosed with FND. RESULTS: Six main themes were identified: (1) the process of seeking a diagnosis and initial relief from the physical struggles, (2) receiving a diagnosis of FND, (3) treatment for FND, (4) the burden of FND in day-to-day life, (5) the need to assert agency and return to normal, and (6) hopes for the future. CONCLUSION: The findings emphasize the need for personalised and compassionate care for individuals suffering from FND, underpinned by increased service provision within the healthcare system.
25 Apr 2026
Read appraisal →American journal of physical medicine & rehabilitation
Optimal Strategy of Resistance Training Combined With Other Rehabilitation Interventions for Lower-Limb Dysfunction in Stroke Patients: A Systematic Review and Network Meta-Analysis
OBJECTIVE: Stroke frequently results in lower-limb dysfunction, severely limiting independence. This study compared the effectiveness of resistance training alone and resistance training combined with other rehabilitation modalities in improving poststroke lower-limb function. DESIGN: A systematic review and Bayesian network meta-analysis were conducted following PRISMA guidelines. Randomized controlled trials involving resistance training alone or in combination with other therapies versus daily care or resistance training alone were included. The primary outcomes were balance, lower-limb motor function, and walking ability. RESULTS: Forty-seven trials with 2448 participants were analyzed. Resistance training combined with electrical stimulation therapy produced the greatest improvements in balance and lower-limb motor function, while resistance training combined with stretching training showed the best enhancement in walking ability. Surface Under the Cumulative Ranking Curve rankings indicated resistance training plus electrical stimulation therapy as most effective for balance (91.13%) and lower-limb motor function (79.70%), and resistance training plus stretching training for walking ability (96.34%). CONCLUSIONS: Resistance training remains fundamental in stroke rehabilitation. Integrating resistance training with targeted adjunctive training particularly electrical stimulation therapy or stretching training yields superior therapeutic outcomes and supports individualized, evidence-based strategies for optimizing lower-limb recovery after stroke.
25 Apr 2026
Read appraisal →Biomedical physics & engineering express
A graph deep learning method for diagnosis of Parkinson's disease using brain functional connectivity features
Early and precise identification of Parkinson's disease (PD) is crucial for clinical intervention. Resting-state functional magnetic resonance imaging (rs-fMRI) provides a valuable approach for revealing PD-related differences in brain functional connectivity (FC). However, existing methods often focus solely on characterizing the spatial topology of FC while neglecting its time-varying dynamic fluctuations. Furthermore, they frequently exhibit limited generalization capability when dealing with small sample sizes, and their decision-making mechanisms lack interpretability. To address these limitations, this study proposes an interpretable graph convolutional network framework. This framework integrates both static and dynamic FC information to capture both the stable topological structure and the dynamic temporal characteristics of brain networks. Simultaneously, it models population relationships by constructing an inter-subject similarity graph to enhance the model's representational capacity. Additionally, this study incorporates interpretability analysis techniques to deeply dissect the model's decision-making mechanism and identify key brain regions critical for classification. Results demonstrate that the proposed model achieves superior performance in PD classification tasks and exhibits good generalization ability. More importantly, by interpreting the model's decisions, key brain regions associated with PD discrimination were successfully identified. This study provides an effective computational framework for PD identification and offers new insights into understanding its pathological mechanisms.
25 Apr 2026
Read appraisal →Cell reports. Medicine
Bed nucleus of the stria terminalis-nucleus accumbens stimulation for depression: A randomized, double-blind, crossover trial
Although deep brain stimulation (DBS) shows potential in treatment-resistant depression (TRD), the efficacy remains controversial. To assess the efficacy of DBS of the bed nucleus of the stria terminalis (BNST) and nucleus accumbens (NAc) for TRD, we conduct a randomized, double-blind, crossover trial after an open-label optimization stage. Stimulation leads to a mean decrease in the Hamilton Depression Scale-17 (HAMD) scores of 10.1 points (p < 0.001) with a 50% response rate at the end of the open-label stage. Anxiety, quality of life, and disability are also improved. The HAMD scores are significantly lower in patients during active DBS than during sham DBS (p < 0.001). Serious adverse events include suicide (1 patient) and seizure (1 patients). With individual stimulation analysis, we identify optimal stimulation sites, fiber tracts, and functional networks (false discovery rate [FDR] p < 0.05). This study demonstrates the efficacy and safety of BNST-NAc DBS for TRD and reveals optimal brain targets for stimulation. This study was registered at ClinicalTrials.gov (NCT04530942).
25 Apr 2026
Read appraisal →Antonie van Leeuwenhoek
Faecalibacterium prausnitzii and Akkermansia muciniphila: driving the multiple sclerosis-a systematic review
The gut microbiota (GM) is a complex microbial ecosystem, and its alteration contributes to the development of several diseases including multiple sclerosis (MS). Progressive research concerning neurodegenerative diseases and GM reveals that GM dysbiosis has been linked to fostering the development and progression of MS. Among existing bacteria, F.prausnitzii and A.muciniphila are key species implicated in this disease. Thus, the present study systematically reviewed and synthesized the research on the involvement of F. prausnitzii and A. muciniphila in MS patients versus healthy individuals. We systematically searched PubMed, MEDLINE, EMBASE, and Web of Science databases for relevant published articles from January 2010 till January 2023. Out of 400 articles, 16 met the required criteria. The included research investigations originated from the US, Germany, Norway, Egypt, Iran, Brazil, China, the UK, and Romania. Most publications reported decreased levels of F. prausnitzii and increased levels of A. muciniphila in MS patients compared to controls. This review underscored the importance of the F. prausnitzii and A. muciniphila in MS, which could explain the chronic inflammation that characterizes this disease and not only help in understanding its etiology and progression but also open new avenues for the treatment strategies focusing on gut health. Moreover, understanding the role of F. prausnitzii and A. muciniphila could lead to novel biomarkers for early detection and progression monitoring MS.
25 Apr 2026
Read appraisal →Neurology
Virtual vs In-Person Neurologic Ambulatory Care: A Case-Control Study of Subsequent Health Care Utilization
BACKGROUND AND OBJECTIVES: Implementation of telemedicine expanded options for outpatient neurology care. It remains uncertain which new neurology patients can be appropriately evaluated virtually. We compared subsequent health care utilization after virtual vs in-person new patient neurology visits across 3 academic medical centers. METHODS: We conducted a retrospective multicenter cohort study of adults with a new outpatient neurology visit from September 2020 through December 2021 using the Vizient Clinical Data Base and Clinical Practice Solutions Center databases. Virtual and in-person patients were matched 1:1 using propensity scores incorporating demographics, clinical characteristics, time period, and previous health care utilization. Outcomes were analyzed overall and stratified by neurologic chief complaint category and institution. We compared rates of subsequent neurologic clinic follow-up, emergency department (ED) visits, and hospitalizations after virtual and in-person encounters. Testing and all-cause ED visits/hospitalizations were also assessed. RESULTS: We identified 10,428 virtual and 36,767 in-person neurology new outpatient visits. After propensity score matching, 8,202 virtual visits were matched to 8,202 in-person visits. Neurology follow-up within 90 days did not differ between virtual and in-person visits (24.6% vs 23.7%, p = 0.18). Thirty-day neurology clinic follow-up was slightly lower after virtual visits, whereas follow-up at 6 months and 1 year was similar between groups. Neurologic ED visits and hospitalizations within 90 days were similar (0.9% vs 0.8%, p = 0.23 and 1.8% vs 1.7%, p = 0.47, respectively). All-cause ED visits and hospitalizations within 90 days were also comparable (1.8% vs 1.7%, p = 0.59 and 2.2% vs 1.8%, p = 0.13, respectively). Analyses by chief complaint found that 90-day follow-up was higher after in-person visits for dementia, whereas 30- and 90-day follow-up was higher after virtual visits for Parkinson disease and multiple sclerosis, and 90-day follow-up was higher after virtual visits for headache. Testing was more frequent after in-person visits for certain chief complaints. DISCUSSION: In this propensity score-matched multicenter cohort, new neurology patients seen virtually had similar downstream utilization as those seen in-person, including comparable 90-day follow-up and similar neurologic and all-cause ED visits and hospitalizations. Although follow-up varied modestly by chief complaint and testing was more frequent after some in-person visits, no major differences emerged overall.
25 Apr 2026
Read appraisal →Journal of neurology
Efficacy and safety of monoclonal antibodies in the treatment of relapsing remitting multiple sclerosis: a systematic review
INTRODUCTION: In relapsing-remitting multiple sclerosis (RRMS), conventional immunomodulatory and immunosuppressive therapies are widely used. However, in many cases, optimal control of inflammatory activity and disease progression is not achieved, which has led to the use of biological drugs such as monoclonal antibodies that act specifically on key components of the immune system. The aim was to evaluate the efficacy and safety of monoclonal antibodies compared to other drugs or placebo in adult patients with RRMS. METHODS: A systematic review was performed based on randomized, double-blind, phase III controlled clinical trials published between 2012 and 2025 in the PubMed, Cochrane Library, and Web of Science databases, assessing efficacy and safety in adult patients with RRMS. The review was carried out following the PICO methodology and PRISMA guidelines. RESULTS: A total of 11 studies were included, evaluating 5 monoclonal antibodies: alemtuzumab, daclizumab, ocrelizumab, ofatumumab, and ublituximab. These therapies showed superior efficacy compared to conventional treatments in reducing the annual relapse rate, MRI inflammatory activity and MRI activity, particularly in patients with highly active disease. However, effects on disability progression were heterogeneous across trials and not consistently significant. In addition, decreases in biomarkers of axonal damage were observed. Nevertheless, relevant adverse effects were identified, including infections, autoimmune reactions, hepatic and cutaneous toxicity, whose incidence varies depending on the drug, requiring close clinical monitoring. CONCLUSIONS: Monoclonal antibodies are an effective option in RRMS, with clinical and radiological benefits superior to those of conventional treatments. Their use requires individualized assessment and close follow-up due to the risk of adverse effects, especially in high-risk patients.
25 Apr 2026
Read appraisal →Science advances
Dual computational systems in the development and evolution of mammalian brains
Analyses of brain sizes across mammalian taxonomic groups reveal a consistent pattern of covariation between major brain components, including a robust inverse relationship between the limbic system and the neocortex. To find the functional basis of this relationship, we mapped the multidimensional representations of task-optimized artificial neural networks onto two-dimensional surfaces resembling the forebrain cortices. We found that networks optimized for visual, somatosensory, and auditory representations develop ordered spatiotopic maps where units draw information from localized regions of the sensory input. In contrast, networks optimized for olfactory and relational memory representations develop fractured maps with distributed patterns of information convergence. Evolutionary optimization of multimodal networks for varying task objectives results in inverse covariation between spatiotopic and disordered network components that compete for the representational space. These results suggest that the observed pattern of covariation between brain components reflects an essential computational duality in brain evolution.
24 Apr 2026
Read appraisal →Journal of Korean medical science
Comparison of Clinical and Radiological Outcomes Between Suction Aspiration and Combination Methods of Mechanical Thrombectomy in Patients With Acute Cerebral Infarction: The COMPETE Trial
BACKGROUND: Recent advances in suction catheter technology suggest that suction aspiration may prevent thrombus fragmentation during mechanical thrombectomy. We therefore hypothesize that the radiological outcomes of suction aspiration as a first-line treatment would not be inferior to those of the combination technique. This study aims to evaluate the radiological and clinical outcomes of first-line suction aspiration compared with those of the first-line combination technique for large artery occlusion amenable to both modalities. METHODS: The COMPETE trial is a prospective, randomized, multicenter, noninferiority, open-label trial. A total of 1,136 patients will be randomized at a 1:1 ratio to either the suction aspiration or combination technique groups. RESULTS: The primary endpoints are successful recanalization (modified Thrombolysis in Cerebral Infarction scale score ≥ 2b) and a good clinical outcome (modified Rankin Scale score of 0-2) at 90 days after thrombectomy. The technical outcome is the switching rate from the combination technique to suction aspiration and from suction aspiration to the combination technique. The safety outcomes include procedural morbidity, nonprocedural morbidity, procedural mortality, and nonprocedural mortality. CONCLUSION: The COMPETE trial is expected to determine whether the use of suction aspiration as a first-line approach in patients with acute ischemic stroke is noninferior to the combination technique in achieving successful radiological and good clinical outcomes. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT06254755; CRIS (KCT0007726).
23 Apr 2026
Read appraisal →Annals of medicine
Comparative evaluation of oxidative stress biomarkers F2-isoprostanes and 8-OHdG in Parkinson's disease and Type 2 Diabetes Mellitus: a systematic review and meta-analysis of human studies
BACKGROUND: Oxidative stress is central to type 2 diabetes mellitus (T2DM) and Parkinson's disease (PD). However, the utility of biomarkers for lipid peroxidation (F2-isoprostanes) and DNA damage (8-OHdG) in the comorbidity of PD and T2DM remains unclear. METHODS: We conducted a systematic review and meta-analysis of 54 unique studies of human subjects aged ≥ 50 years (n = 7,521: 3,522 with T2DM, 722 with PD, and 3,277 controls), measuring biomarkers in serum, plasma, or leukocytes. Mixed-effects models quantified standardized differences (Hedges' g) across subgroups. RESULTS: In T2DM, F2-isoprostanes (g = 1.60, 95% CI: 0.95-2.25) and 8-OHdG (g = 2.64, 95% CI: 2.13-3.14) were markedly elevated (p < 0.001). Stronger effects were observed in younger cohorts and serum/plasma samples, with complications like nephropathy exhibiting extreme oxidative stress (g = 5.24). In PD, 8-OHdG was moderately elevated (g = 0.78, 95% CI: 0.18-1.39; p = 0.011), particularly in randomized controlled trials and plasma samples, whereas F2-isoprostanes were not significantly elevated (g = 0.47, 95% CI: -0.43-1.38). High heterogeneity in T2DM (I2 > 90%) reflected methodological variability. CONCLUSION: Distinct profiles - both markers elevated in T2DM but only 8-OHdG in PD - underscore 8-OHdG's potential in PD-T2DM comorbidity. Future research should focus on standardized assays, multi-compartmental or multi-modal sampling, and longitudinal studies to clarify mechanisms and therapeutic targets.
21 Apr 2026
Read appraisal →Journal of neurology
Predicting disease progression in multiple sclerosis with clinically accessible information and technology
BACKGROUND: Predicting disease progression at the individual level is essential for personalized medicine. We previously developed machine-learning tools to estimate 5-year progression risk in people with multiple sclerosis (PwMS). Such models should account for disease-modifying therapy (DMT) and objective outcome definitions. METHODS: In a retrospective multicenter case-control study, we evaluated adults with relapsing-remitting multiple sclerosis (RRMS) at baseline. Using machine-learning, we developed two complementary tools for individualized 5-year risk estimation: DAAE-M, optimized for transparency, software-neutral use, and mitigation of indication bias, and ELIE, optimized for dynamic landmark-based modeling, complex treatment histories, and mitigation of immortal-time bias. Disease progression was defined using both a clinical outcome (RRMS-to-progressive MS) and an objective outcome (late-stage confirmed progression independent of relapse activity). RESULTS: Among 34,510 people with RRMS (72.6% female, mean age = 37.1, mean disease duration = 5.8), 9.8% and 21% met clinical and objective progression criteria, respectively, over five years. Both models demonstrated good calibration across risk-groups (Brier scores 0.06-0.16). DAAE-M provided patient-level risk estimates with monotonic risk escalation across risk-groups for clinical (3.1%/11.2%/22.6%/33.0%) and objective (8.4%/14.5%/23.3%/38.8%) progression. For DAAE-M, high-efficacy DMT was associated with approximately half the progression risk compared with low-efficacy DMT (risk-ratios: 0.42-0.59; p < 0.01). ELIE also showed good calibration across risk deciles with increasing incidence for both clinical (0.3%/1.2%/1.7%/2.5%/3.7%/5.5%/7.2%/10.2%/14.3%/21.5%) and objective (0.9%/1.6%/2.5%/4.0%/5.8%/7.8%/10.2%/15.3%/20.9%/32.5%) outcomes. CONCLUSION: We developed two well-calibrated machine-learning-based tools for individualized 5-year prediction of clinically- and objectively-defined MS progression, each with distinct strengths in usability, bias handling, and treatment modeling. These findings support future tool use in personalized risk stratification and secondary prevention.
21 Apr 2026
Read appraisal →Journal of neurology
The association of night shift work with the risk of acute ischemic cerebrovascular syndrome: a longitudinal study of 204,261 UK Biobank participants
BACKGROUND: Shift work is highly prevalent globally, yet evidence linking it to ischemic cerebrovascular disease remains inconsistent. Existing studies are limited by unsystematic quantification of night shift exposure and outcome definitions failing to capture the clinical continuum of ischemic events. We performed a large prospective cohort study using refined shift pattern classification and acute ischemic cerebrovascular syndrome (AICS) as the primary end point to enable a robust evaluation of the association between shift patterns and ischemic cerebrovascular disease. METHODS: 204,261 participants with employment information from the UK Biobank were included. Cox proportional hazards regression was used to assess associations between current shift work and AICS risk, with cross-classification by shift status and job duration. We further quantified average lifetime night shift frequency and cumulative duration among participants with complete occupational histories. Transient ischemic attack (TIA) and ischemic stroke (IS) were analyzed separately to identify the main driver of AICS and potential heterogeneity. RESULTS: During a median follow-up exceeding 14 years, irregular shift work was significantly associated with elevated AICS risk (HR 1.118, 95% CI 1.019-1.227, P = 0.019), while always night shift work was not (HR 0.904, 95% CI 0.707-1.157, P = 0.423). AICS risk was driven by irregular shift patterns rather than duration. Lifetime night shift frequency and duration showed significant dose-response relationships with AICS. CONCLUSIONS: Irregular shift work increases AICS risk relative to non-shift work, and lifetime night shift exposure exhibits a clear dose-response effect. IS constitutes the primary driver of the composite AICS end point.
20 Apr 2026
Read appraisal →Medicine
Drug-associated insomnia: A pharmacovigilance study based on FDA adverse event reporting system.
Insomnia has become an increasingly serious public health issue with complex causes, among which medications act as a significant factor. This study aims to systematically detect and evaluate drug adverse event signals associated with insomnia risk using the Food and Drug Administration Adverse Event Reporting System (FAERS) database. The analysis utilized data from the FAERS database covering January 2004 to December 2024. Disproportionality analysis was conducted using 4 algorithms: reporting odds ratio, proportional reporting ratio, information component (IC), and empirical Bayes geometric mean. Potential risk signals were deemed significant only when all 4 algorithms simultaneously met their thresholds. Subgroup analyses were further performed, stratified by age and sex, to assess the robustness of signals across different populations. From 2004 to 2024, there were 179,697 adverse event reports of insomnia in FAERS in which one or more medications were designated as the primary suspect. The top 30 medications with the strongest signal strength were predominantly nervous system medicines (18 types, 60%), followed by respiratory system medicines (3 types, 10%), and genitourinary system and sex hormones (3 types, 10%). The top 3 medications with the highest reporting frequency were mefloquine, viloxazine, and flibanserin. Subgroup analyses revealed distinct drug signal profiles across age groups and genders, with pediatric cases dominated by nervous system and anti-infective agents, adults and the elderly showing additional endocrine or hormonal signals, and sex specific signals such as finasteride in males and flibanserin in females. This pharmacovigilance study identifies insomnia risk signals across multiple drug classes, underscoring the need for clinical vigilance regarding drug-related sleep disturbances. Further prospective research is required to confirm these associations.
20 Apr 2026
Read appraisal →Journal of neural engineering
Physiologically inspired modeling of cortical dynamics through spiking neural networks
Objective. The characterization of neural activity underlying neurophysiological function presents a major challenge in computational neuroscience. Several methods have been proposed to investigate cortical network dynamics by reconstructing underlying neural activity from electroencephalography (EEG) signals. However, these methods generally pose significant mathematical challenges.Approach. This study introduces a novel framework to model the underlying brain activity network from a functional and physiologically-inspired perspective, combining spiking neural networks with EEG signal analysis. The dynamics of single neurons are described by the well-known Izhikevich model, and distinct populations of cortical inhibitory and excitatory neurons are employed to model experimental EEG recordings. Functional interactions among distinct populations are mathematically formalized through connective probabilities.Main results. The proposed framework is validated by testing it on synthetic data, as well as on two experimental datasets comprising data from 30 healthy subjects undergoing a cold-pressure test (CPT), and 36 subjects undergoing a mental arithmetic stressor. Experimental results suggest that the proposed framework provides novel and complementary insights into characterizing neuronal changes in comparison to standard EEG power analysis.Significance. The proposed framework constitutes a promising tool for functionally characterizing the underlying cortical dynamics under pathophysiological conditions.
20 Apr 2026
Read appraisal →PLoS computational biology
'Backpropagation and the brain' realized in cortical error neuron microcircuits
Neural responses to mismatches between expected and actual stimuli have been widely reported across different species. How does the brain use such error signals for learning? While global error signals can be useful, their ability to learn complex computation at the scale observed in the brain is lacking. In comparison, more local, neuron-specific error signals enable superior performance, but their computation and propagation remain unclear. Motivated by the breakthrough of deep learning, this has inspired the 'backpropagation and the brain' hypothesis, i.e., that the brain implements a form of the error backpropagation algorithm. In this work, we introduce a biologically motivated, multi-area cortical microcircuit model, implementing error backpropagation under consideration of recent physiological evidence. We model populations of cortical pyramidal cells acting as representation and error neurons, with bio-plausible local and inter-area connectivity, guided by experimental observations of connectivity of the primate visual cortex. In our model, all information transfer is biologically motivated, inference and learning occur without phases, and network dynamics demonstrably approximate those of error backpropagation. We show the capabilities of our model on a wide range of benchmarks, and compare to other models, such as dendritic hierarchical predictive coding. In particular, our model addresses shortcomings of other theories in terms of scalability to many cortical areas. Finally, we make concrete predictions, which differentiate it from other theories, and which can be tested experimentally.
19 Apr 2026
Read appraisal →Medicine
Safety assessment of intravenous immunoglobulins in pediatric population: A systematic review of adverse events
BACKGROUND: Intravenous immunoglobulin (IVIG) is a therapeutic intervention utilized in various disorders due to its antimicrobial, antiinflammatory, and immunomodulatory properties. The market offers a range of IVIG products, leading to variations in efficacy and safety profiles. This study aimed to systematically evaluate the safety profile of IVIG administration in pediatric patients. METHODS: A systematic search was conducted on PubMed, Embase, and Cochrane databases using predetermined MeSH terms, covering the period from January 1, 2000, to October 15, 2022. Two independent reviewers performed relevance screening. Inclusion criteria encompassed English-language studies involving patients below 18 years of age who received IVIG treatment. Included studies were limited to peer-reviewed full-text publications with a minimum of 5 patients. A total of 8 studies met the inclusion criteria, encompassing 1088 pediatric patients. RESULTS: Eight studies met the inclusion criteria (total 1088 pediatric patients). Five studies reported patient-level adverse event data suitable for pooling (688 patients). Among these 688 patients, 267 (38.8%) experienced adverse events (AEs). Among reported AEs, the most frequent were headache 68/267 (25.5%) and fever 47/267 (17.6%). Less common AEs included nausea 12/267 (4.5%) and fatigue 12/267 (4.5%). Four studies recorded a total of 2960 IVIG infusions, with 12.6% of infusions accompanied by AEs. The predominant AEs during infusions were headache (54.3%) and fever (29.4%). Cough (0.8%) and nausea (1.6%) were the least commonly observed AEs. Other AEs such as rash, vomiting, and abdominal pain were also reported. Of the 23 full-text articles reviewed, 8 were included. The remaining 15 were excluded due to the following reasons: 6 for duplicate data, 4 for inappropriate study design, 3 for not meeting the minimum number of patients, and 2 for incomplete adverse event reporting. These reasons are now detailed in the updated Preferred Reporting Items for Systematic Reviews and Meta-Analyses flow diagram. CONCLUSION: IVIG therapy demonstrates good tolerability in pediatric patients, with an overall favorable safety profile, aside from mild AEs associated with its administration. Serious AEs were infrequent and mainly observed in high-risk patients. Headache was the most prevalent AE, which seldom required hospitalization and could be effectively managed with acetaminophen. Other AEs were generally mild systemic reactions. Notably, AEs appear to be more frequent during the initial exposure to IVIG.
19 Apr 2026
Read appraisal →Pharmaceutical biology
Valeriana species and insomnia: multi-organ mechanisms and translational perspectives.
CONTEXT: Insomnia is a common sleep disorder that substantially impairs quality of life. Conventional treatments, including cognitive behavioral therapy and pharmacological agents, are often limited by side effects and insufficient long-term safety data. Valeriana species, such as Valeriana officinalis L. and Valeriana jatamansi Jones, have been traditionally used for their sedative and neuroprotective properties. OBJECTIVE: This review evaluates the therapeutic potential of Valeriana species for insomnia, emphasizing their pharmacological mechanisms, multi-organ effects, and evidence from preclinical and clinical studies. METHODS: A systematic literature search was performed in databases including PubMed and Web of Science. Studies addressing Valeriana-induced sleep regulation, neurotransmitter modulation, oxidative stress reduction, and neural, hepatic, and gastrointestinal interactions were included to assess efficacy and safety. RESULTS: Valeriana species modulate neurotransmission, mitigate oxidative stress, and influence hepatic and gastrointestinal functions. Preclinical studies support their sedative and neuroprotective effects, while clinical outcomes vary with formulation, dosage, and individual response. Overall, safety profiles are favorable, though inconsistencies remain. CONCLUSION: Valeriana species represent a promising multi-target strategy for insomnia, integrating neural and peripheral pathways. Future research should clarify the mechanisms underlying multi-organ effects, optimize formulations, and conduct rigorous clinical trials to establish standardized, effective, and safe interventions.
19 Apr 2026
Read appraisal →Journal of neurology
Iron accumulation in multiple sclerosis and implications for cognitive and clinical progression: a systematic review of advanced neuroimaging studies
BACKGROUND AND AIMS: Iron-related alterations are frequently implicated in multiple sclerosis (MS) neurodegeneration, but its relationship with clinical symptoms and cognitive impairment remains unclear. We systematically reviewed evidence linking iron-sensitive susceptibility-based magnetic resonance imaging (MRI) metrics to cognitive outcomes, in order to evaluate their potential role as diagnostic and prognostic biomarkers of cognitive decline in MS. MATERIALS AND METHODS: A systematic review was conducted using the PRISMA guidelines. 600 studies were identified by searching PubMed, Scopus, Web of Science, and Embase in October 2025. After screening, 12 studies fulfilled the eligibility criteria and were included. RESULTS: Two main patterns emerged: (i) higher basal ganglia susceptibility/iron-related metrics are consistently linked to worse cognitive performance, particularly information processing speed and executive control; and (ii) thalamic findings are more variable in direction and clinical meaning, with susceptibility often capturing a mixed signal that overlaps with thalamic atrophy. CONCLUSIONS: Iron-sensitive quantitative MRI captures clinically relevant deep gray matter (DGM) pathology associated with cognitive impairment in MS, with basal ganglia measures showing the strongest linkage with cognitive and clinical outcomes. Future studies should include harmonized methodology and a longitudinal component.
19 Apr 2026
Read appraisal →Sleep
Optimizing automated sleep stage scoring of 5-s mini-epochs: a transfer learning study
STUDY OBJECTIVES: Conventional sleep staging relies on 30-s epochs, potentially concealing transient sleep stage intrusion and reducing precision. Building on our previous study of mini-epochs, we investigated whether U-Sleep, an existing automatic deep learning-based sleep staging model with high performance in epochs, could be optimized to similar performance level in 5-s mini-epoch scoring, thereby enabling more detailed sleep characterization. METHODS: We created a dataset of 48 000 human-scored 5-s mini-epochs from 100 polysomnographies. We compared mini-epochs to human-scored epochs before U-Sleep was optimized using transfer learning and evaluated on a test set. Model performance was assessed using F1-scores, confusion matrices, stage distributions and transition rates comparing scorings of the original U-Sleep before, and the optimized U-Sleep after transfer learning to human-scored mini-epochs. RESULTS: Compared to human-scored epochs, human-scored mini-epochs captured significantly more transitions (1.70/min vs. 0.21/min, p < .001), and significantly more wake (8.4 per cent vs. 5.4 per cent), N1 (7.2 per cent vs. 5.4 per cent), and N2 (51.8 per cent vs. 40.9 per cent), less N3 (15.4 per cent vs. 25.2 per cent), and REM sleep (16.7 per cent vs. 23.0 per cent) (all p < .001). Optimizing U-Sleep improved its performance significantly from F1 = 0.74 to F1 = 0.81 (p < .05) and gave increased transition rates in the test set (original U-Sleep: 1.06/min, optimized U-Sleep: 1.34/min, human-scored mini-epochs: 1.70/min). Stage distributions did not differ between optimized U-Sleep's scorings and human-scored mini-epochs. CONCLUSION: After optimization, U-Sleep performance in mini-epochs matched the high performance levels previously reported in both human and automated 30-s epoch scoring. This demonstrates the feasibility of precise, automated high-resolution sleep staging. Future work should include external validation and application to full-night recordings. Statement of Significance Conventional 30-s epochs limit temporal resolution in sleep staging and may conceal transient intrusions of wake or sleep stages. However, no validated methods are available for high-resolution scoring. In this study, we trained and validated the state-of-the-art deep learning model U-Sleep for accurate automatic 5-s mini-epoch scoring using a large dataset of human-scored mini-epochs. The optimized model achieved a high performance, matching levels from previously reported automatic and human epoch scoring. Compared to epoch scoring, mini-epochs captured significantly more stage transitions, supporting their ability to uncover sleep dynamics that are otherwise lost. Our findings show the potential of high-resolution sleep staging for more detailed characterization of sleep architecture and demonstrate the feasibility of precise, automatic mini-epoch scoring.
19 Apr 2026
Read appraisal →PloS one
The association between RDW-to-platelet ratio and in-hospital mortality in critically ill stroke patients: A retrospective cohort study based on the eICU database
OBJECTIVE: The red cell distribution width-to-platelet ratio (RDW-to-platelet ratio, RPR) is a potential biomarker of inflammation and bone marrow function. This study aimed to investigate the association between RPR and in-hospital mortality in critically ill stroke patients in the eICU, and to evaluate mediating role of APACHE-IV score. METHOD: This retrospective cohort study utilized data from the 2014-2015 US multicenter eICU database. Of 200,859 initially included patients, 9,736 critically ill stroke patients were analyzed after excluding non-stroke cases and those with missing key variables. Multivariable logistic regression assessed the relationship between RPR and in-hospital mortality, adjusting for age, sex, comorbidities, and laboratory parameters, with multiple imputation for missing data. Subgroup analyses, mediation analysis, and restricted cubic spline modeling were performed. RESULTS: Patients with higher RPR exhibited significantly elevated in-hospital mortality (17.7% vs. 11.6%, P < 0.001). After adjustment, each unit increase in RPR was associated with 4.6% higher odds of death (adjusted OR=1.046, 95% CI: 1.032-1.061, P < 0.001), with consistent findings across subgroups. Restricted cubic spline analysis indicated a linear relationship. Mediation analysis showed that APACHE-IV score mediated 20.15% of the total effect (P < 0.001). CONCLUSIONS: Elevated RPR is independently associated in-hospital mortality in critically ill stroke patients, partly mediated by disease severity. As a simple and accessible marker, RPR shows promise for clinical prognostic risk stratification.
18 Apr 2026
Read appraisal →Journal of neurointerventional surgery
Systolic blood pressure within 24 hours after successful reperfusion correlates with outcomes in acute ischemic stroke: a secondary analysis of the EHCHANTED2/MT Trial
BACKGROUND: The optimal threshold or range for systolic blood pressure (SBP) control in patients with successful reperfusion after endovascular thrombectomy for acute ischemic stroke (AIS) remains undefined. This study investigated whether SBP within the first 24 hours after successful reperfusion correlates with functional outcomes in AIS. METHODS: In this secondary analysis of the ENCHANTED2/MT trial, patients were categorized into two groups (120-140 mm Hg and 140-180 mm Hg, respectively) based on achieved SBP within 24 hours after randomization. The primary outcome was the modified Rankin Scale (mRS) score at 90 days. Secondary outcomes included neurological deterioration at 7 days, major disability (mRS score of 3-5 at 90 days), hospitalization duration, and health-related quality of life assessed by the three-level EuroQoL 5-Dimension Self-Report Questionnaire (EQ-5D-3L) at 90 days. Safety outcomes included early neurological decline (END), 90-day mortality, symptomatic intracranial hemorrhage (sICH), and any intracranial hemorrhage (ICH). Treatment effects were expressed as ORs with 95% confidence intervals (CIs). RESULTS: A total of 611 patients (363 in the 120-140 mm Hg group and 248 in the 140-180 mm Hg group) were included. The mean (SD) age was 67 (12) years and 37.8% were female. After adjusting for confounders, the 120-140 mm Hg group was significantly associated with better functional outcomes (mRS: 2 (IQR 1-4) vs 2 (IQR 1-5); adjusted OR 1.54 (95% CI 1.10 to 2.17), P=0.013). Compared with the 140-180 mm Hg group, the 120-140 mm Hg group had lower rates of neurological deterioration at 7 days (adjusted OR 0.68 (95% CI 0.47 to 0.98), P=0.037) and 90-day mortality (47 (13.0%) vs 53 (21.4%); adjusted OR 0.48 (95% CI 0.27 to 0.86), P=0.013). There were no significant differences between groups in END, major disability at 90 days, hospitalization duration, EQ-5D-3L score, sICH, or ICH (all P>0.05). CONCLUSIONS: In patients with successful reperfusion after endovascular thrombectomy, an average SBP within 24 hours of 120-140 mm Hg was associated with a greater likelihood of functional independence compared with 140-180 mm Hg.
18 Apr 2026
Read appraisal →Journal of global health
Associations between haemoglobin-to-red cell distribution width ratio and mortality in intracerebral haemorrhage: a population-based analysis of the MIMIC-IV database
BACKGROUND: Intracerebral haemorrhage (ICH) is a leading cause of mortality and morbidity worldwide. Identifying early prognostic biomarkers is crucial to optimise clinical management of critically ill patients with ICH. The haemoglobin-to-red cell distribution width ratio (HRR) has recently emerged as a potential predictor in various critical illnesses, but its role in ICH remains unclear. This study aimed to evaluate associations between HRR and mortality in patients with ICH. METHODS: This retrospective cohort study included the data of adults (≥18 years) extracted from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database (version 1). HRR was calculated from the first available haemoglobin and RDW measurements within one day of each other. Patients were categorised into HRR quartiles. The primary outcomes were 28-day and 1-year all-cause mortality. Associations were assessed using Cox proportional hazards models. RESULTS: The data of 1915 patients (mean age 67.5 years) were analysed retrospectively. After adjusting for possible confounders, compared with the lowest HRR quartile (Q1), patients in Q3 and Q4 had significantly lower 28-day mortality (Q3: adjusted hazard ratio (aHR) = 0.72; 95% CI = 0.54, 0.97; Q4: aHR = 0.67; 95% CI = 0.49, 0.90). Similarly, higher HRR quartiles were associated with reduced 1-year mortality risk (Q3: aHR = 0.64; 95% CI = 0.49, 0.84; Q4: aHR = 0.56; 95% CI = 0.42, 0.75). CONCLUSIONS: Lower HRR at admission is independently associated with higher short-term and long-term mortality in ICU patients with ICH. HRR may serve as a candidate prognostic biomarker for early risk stratification in this high-risk population. Further prospective studies are warranted to confirm these findings.
18 Apr 2026
Read appraisal →PloS one
Statistically valid explainable black-box machine learning: applications in sex classification across species using brain imaging
Sex classification using neuroimaging data has the potential to revolutionize personalized diagnostics by revealing subtle structural brain differences that underlie sex-specific disease risks. Despite the promise of machine learning, traditional methods often fall short in providing both high classification accuracy and interpretable, statistically validated feature importance scores for high-dimensional imaging data. This gap is particularly evident when conventional techniques such as random forests, LIME, and SHAP are applied, as they struggle with complex feature interactions and managing noise in large datasets. We address this challenge by developing an integrated framework that combines Oblique Random Forests (ORFs) with a novel, permutation-based feature importance testing algorithm. ORFs extend traditional random forests by employing oblique decision boundaries through linear combinations of features, thereby capturing intricate interactions inherent in neuroimaging data. Our feature importance testing method, NEOFIT, rigorously quantifies the significance of each feature by generating null distributions and corrected p-values. We first validate our approach using simulated datasets, establishing its robustness and scalability under controlled conditions. We then apply our method to classify sex from both voxel-wise structural MRI and cortical thickness data in humans and macaques, facilitating direct cross-species comparisons. ORFs achieves AUC > 0.80 on human data, and >0.70 on macaque data, while NEOFIT identifies statistically significant features aligned with sex-dimorphic neuroanatomy. Our results demonstrate that the proposed framework not only enhances classification performance but also provides clear, interpretable insights into the neuroanatomical features that distinguish sexes. These methodological advancements pave the way for improved diagnostic tools and contribute to a deeper understanding of the evolutionary basis of sex differences in brain structure.
18 Apr 2026
Read appraisal →Medicine
Effectiveness of cognitive behavioral therapy for depression in patients with multiple sclerosis: A systematic review and meta-analysis
BACKGROUND: The efficacy and methodologies of cognitive behavioral therapy (CBT) for treating depression in multiple sclerosis (MS) require further comprehensive research. METHODS: Two researchers conducted independent literature searches across 5 databases, utilizing the keywords "multiple sclerosis," "cognitive behavioral therapy," and "depression," without any restrictions on publication year. The Cochrane Risk of Bias Tool 2.0 was utilized to assess the included studies. Statistical analyses were conducted using standardized mean difference as the effect size with Stata 17.0 software. RESULTS: This meta-analysis included 22 studies. The results indicate that, regardless of whether delivered face-to-face, online, or via telephone, both individual and group CBT effectively alleviate depression in patients with MS. Significant improvements in depressive symptoms were observed starting from the second month of intervention and persisted for up to 6 months post-intervention. CONCLUSION: Patients with MS can flexibly choose the delivery modality of CBT based on their individual circumstances while engaging in treatment for around 2 months. They are encouraged to reengage in CBT within 6 months post-intervention to ensure the continuity and stability of depression treatment outcomes. Healthcare providers can offer various CBT delivery modalities based on the individual preferences and circumstances of patients with MS, while developing corresponding plans to enhance the effectiveness of depression management.
18 Apr 2026
Read appraisal →Medicine
Publication trends of dexmedetomidine and perioperative cognitive dysfunction: A bibliometric analysis from 1995 to 2024
INTRODUCTION: Dexmedetomidine (Dex) has shown potential in mitigating perioperative cognitive dysfunction (POCD). This study aimed to systematically evaluate the research hotspots and development trends in Dex and POCD using bibliometric analysis. METHODS: A systematic search was conducted on the Web of Science Core Collection database to identify publications related to Dex and POCD from 1995 to 2024. Bibliometric analysis was performed using VOSviewer (V 1.6.20), CiteSpace (V 6.3.R1), and R V4.3.3. RESULTS: A total of 585 articles on Dex and POCD between 1995 and 2024 were analyzed. These articles involved 1688 authors from 1857 institutions across 140 countries or regions, published in 240 journals, and citing 12,881 sources. The annual growth rate of publications in this field was 16.21%, with a peak of 91 articles in 2021. China led in the number of articles (n = 379), multiple country publications (MCP = 29), and total citations = 4989). The USA ranked second with 74 articles, 18 MCPs, and 2608 citations, followed by South Korea with 29 articles and 533 citations. Harvard University published the most articles (n = 51). Wang Dong-Xin was the most influential author, with an h-index of 7. Anesthesiology led the high-impact journals with an H-index of 11. The keyword analysis identified "neuroinflammation," "risk," "injury," and "efficacy" as the latest research themes. CONCLUSION: This bibliometric analysis provides a comprehensive overview of the research trends regarding Dex and POCD. Future research should continue to explore the mechanisms, efficacy, and safety of Dex in preventing POCD.
18 Apr 2026
Read appraisal →Journal of neurointerventional surgery
Safety and efficacy of selective and superselective intra-arterial cerebral infusion with blood-brain barrier disruption for glioma: a systematic review and meta-analysis
BACKGROUND: Selective and superselective intra-arterial cerebral infusion (SIACI/SSIACI) delivers chemotherapy directly to tumor-supplying arteries, achieving high local drug levels with reduced systemic toxicity. Often combined with blood-brain barrier disruption (BBBd) to enhance penetration, these methods have been widely studied, yet a comprehensive evidence synthesis is lacking. This study systematically assesses the safety and efficacy of intra-arterial chemotherapy with BBBd for glioma treatment. METHODS: We searched PubMed, Embase, and Web of Science for studies on SIACI with BBBd in glioma patients. Safety outcomes included rates of cases with complications, procedure-related complications (major/minor), stroke, intracranial and intratumoral hemorrhage, and mortality. Efficacy was based on tumor response (complete, partial, stable disease, progression). A subanalysis of SSIACI-only cases was also conducted. RESULTS: Nine studies with 230 glioma patients were included. The pooled rate of cases with complications was 27.1% (95% CI 19.8% to 35.7%), with procedure-related complications occurring in 15.4% (95% CI 6.1% to 24.6%). Major and minor complication rates were 4.3% (95% CI 0.9% to 7.7%) and 9.7% (95% CI 1.0% to 18.7%), respectively. Stroke, intracranial hemorrhage, and intratumoral hemorrhage occurred in 3.1% (95% CI 0% to 6.1%), 0.5% (95% CI 0% to 3.2%), and 0.04% (95% CI 0% to 3%), respectively, with no procedure-related deaths reported. Pooled response rates were 10.4% complete (95% CI 0% to 21.5%), 24.2% partial (95% CI 14.3% to 34.1%), 38.2% stable disease (95% CI 21.5% to 54.9%), and 39.3% progression (95% CI 14.8% to 63.8%). Subanalysis of superselective infusions yielded comparable outcomes to the overall cohort. CONCLUSION: The findings indicate that SIACI/SSIACI chemotherapy following BBBd is a feasible and safe approach for glioma treatment, demonstrating a favorable procedural risk profile and preliminary signs of efficacy. Further studies are warranted to validate these results and refine procedural protocols.
18 Apr 2026
Read appraisal →Neuron
Spatial and single-cell transcriptomic atlas of human suprachiasmatic nucleus
The suprachiasmatic nucleus (SCN) is considered the master pacemaker of the circadian clock in mammals, but our current knowledge of the SCN is mostly based on rodent studies. Here, we report a comprehensive molecular and cellular atlas for the adult human SCN by spatial transcriptomics, single-nucleus RNA sequencing, and deep-learning-based histological analysis. We identified seven human SCN neuron subtypes with specific transcriptomes and spatial distributions. Comparison of humans, mice, and non-human primates revealed the conserved functional segregation within the SCN regulated by LIM homeobox 1 (LHX1) and RAR-related orphan receptor B (RORB). Furthermore, our results suggested that the human SCN has undergone marked reorganization of its neuropeptide signaling network. Finally, integrative analysis of human SCN transcriptomes and genome-wide association studies (GWASs) identified arginine vasopressin (AVP)/neuromedin S (NMS) subtype as the potential neuronal correlate for morningness chronotype. Thus, our spatial and single-cell transcriptomic atlas of the human SCN provided a basis for the understanding of neural and molecular mechanisms of the human circadian clock.
18 Apr 2026
Read appraisal →Emerging microbes & infections
Optimizing machine learning-based diagnosis of neurosyphilis in HIV-negative patients: a multicenter, real-world comparison of international diagnostic criteria
Neurosyphilis continues to rise globally, yet diagnosis remains challenging, often requiring multidisciplinary expertise and multiple CSF assays that are difficult to access in resource-limited settings. We developed and compared machine-learning (ML) models tailored to six international diagnostic guidelines and built a free, web-based tool for guideline-adapted decision support. We assembled 1,648 suspected neurosyphilis cases from four centers, using Guangzhou as the training cohort and Beijing, Xiamen (China) and Seattle (USA) as external validation cohorts. Five algorithms (Random Forest, AdaBoost, SVC, NuSVC, XGBoost) were trained with randomized search and three-fold cross-validation; performance was assessed by AUC, PRAUC, calibration, decision-curve net benefit, Brier score, and SHAP for model explainability. Across models and guidelines, neurological symptoms, CSF protein, and CSF white blood cell count consistently ranked as the strongest predictors. All models achieved excellent discrimination (AUC and PRAUC >0.90) with good calibration, reliability, and positive clinical utility, though performance varied modestly by guideline. These findings indicate that the optimal ML approach depends on the diagnostic definition applied. Our freely available online tool operationalizes these models to provide clinicians worldwide with context-adapted support aligned to local criteria.
18 Apr 2026
Read appraisal →Journal of neurointerventional surgery
Explainable machine learning model for predicting functional outcomes in posterior circulation stroke after thrombectomy
BACKGROUND: The early prediction of functional outcomes in patients with posterior circulation stroke (PCS) is crucial for timely interventions and optimizing treatment plans. We have developed and validated a machine learning (ML) model for predicting 3-month functional outcomes in patients with PCS undergoing endovascular thrombectomy (EVT). METHODS: The derivation cohort, consisting of 202 patients with PCS who underwent EVT at four medical centers from January 2020 to December 2023, was separated for training and internal validation, and an external dataset of 54 patients admitted from January 2020 to July 2023 was used for external validation. The target outcome was a good functional outcome, defined as a modified Rankin Scale score of 0-3 at 3 months. Seven ML models were trained using preoperative features, with the primary evaluation metric being the area under the receiver operating characteristic curve (AUC). The top performing model was further trained using intraoperative and postoperative features. Model interpretations were generated using the Shapley additive explanations (SHAP) method. RESULTS: The Random Forest model demonstrated the best discriminative ability among the models considered. After feature selection, the final preoperative model used seven features, achieving an AUC of 0.83 in the test set and 0.81 in the external validation cohort. The inclusion of intraoperative and postoperative features further enhanced the model's performance, resulting in an AUC of 0.84 and 0.90 in the test set and 0.83 and 0.90 in the external validation cohort, respectively. These models have been incorporated into a publicly accessible web-based calculator (https://zhelvyao-123-60-basilarz.streamlit.app). CONCLUSION: The interpretable ML models provide dynamic accurate predictions of functional outcomes in patients with PCS after EVT, offering valuable insights for personalized risk stratification and optimizing perioperative management, with potential for integration into clinical workflows.
18 Apr 2026
Read appraisal →Journal of neurointerventional surgery
Stroke thrombectomy with a novel reperfusion system including a 0.088'' aspiration catheter: the Imperative Trial
BACKGROUND: Mechanical thrombectomy is the standard of care for large vessel occlusion (LVO) stroke, yet most device trials focus on individual technologies rather than complete systems. OBJECTIVE: To evaluate the safety and efficacy of the Zoom Reperfusion System compared with performance goals derived from a meta-analysis of trials using established thrombectomy technologies. METHODS: Prospective, multicenter, non-randomized, investigational device exemption trial conducted across 26 United States centers from October 2021 to March 2024. Adults with anterior circulation LVO stroke within 8 hours of onset were treated using the Zoom System, which includes aspiration catheters ranging from 0.035″ to 0.071″, and a novel 0.088″ intracranial guide/aspiration catheter. The primary efficacy endpoint was successful reperfusion (modified Thrombolysis in Cerebral Infarction (mTICI) 2b-3) within three passes using Zoom-only components, adjudicated by an independent core laboratory. The primary safety endpoint was symptomatic intracranial hemorrhage (sICH). Secondary outcomes included final reperfusion, first-pass success, time to reperfusion, 90-day modified Rankin Scale (mRS), and mortality. RESULTS: Among 260 patients (median age 68 years; median National Institutes of Health Stroke Scale (NIHSS) 15), successful reperfusion within three passes was achieved in 83% (216/259; 95% CI: 78% to 89%), exceeding the performance goal (p<0.001). Final reperfusion was 92%, with 5.5% requiring rescue devices. First-pass mTICI 2b-3 success was 57%. Median puncture-to-reperfusion time was 19 min. At 90 days, 55% achieved mRS 0-2. The sICH rate was 1.9%, and 90-day mortality was 12.7%. CONCLUSIONS: The Zoom Reperfusion System demonstrated a safety and efficacy profile exceeding prespecified contemporary performance metrics with potential to significantly shorten reperfusion times and reduce the need for rescue therapy. TRIAL REGISTRATION NUMBER: ClinicalTrials.gov--Identifier:NCT04129125.
17 Apr 2026
Read appraisal →Journal of neurointerventional surgery
Transverse venous sinus stenting versus cerebrospinal fluid shunting in idiopathic intracranial hypertension: a multi-institutional and multinational database study
BACKGROUND: Cerebrospinal fluid (CSF) shunting and transverse venous sinus (TVS) stenting are promising treatment choices for idiopathic intracranial hypertension (IIH), addressing different aspects of IIH pathophysiology. However, large-scale comparative data remain limited. METHODS: We performed a multinational, multi-institutional, retrospective propensity score-matched analysis using the TriNetX platform. Adult patients diagnosed with IIH who underwent either TVS stenting or CSF shunting were included. The primary outcome was treatment failure, defined as the need for subsequent procedures (repeat TVS stenting, CSF shunt, or optic nerve fenestration). Secondary outcomes included residual symptoms (headache, visual disturbances, dizziness, and pulsatile tinnitus), ongoing use of IIH-related medications (furosemide, acetazolamide, and topiramate), and healthcare utilization, assessed through unplanned hospital readmissions and emergency department (ED) visits at 1 year follow-up. RESULTS: Of 134 530 IIH patients identified, 1362 underwent TVS stenting and 5278 underwent CSF shunting. After propensity score matching, patients who received TVS stenting had significantly lower odds of repeat interventions (9.6% vs 38.6%; OR 0.169, 95% CI 0.136 to 0.212, P<0.001). They also had significantly lower odds of residual headache, visual disturbances, and papilledema. However, stenting was associated with higher odds of persistent pulsatile tinnitus (6.7% vs 2.2%; OR 3.183, P<0.001). TVS stenting was associated with lower odds of unplanned inpatient readmissions (24.1% vs 40.5%; OR 0.466, P<0.001) and ED visits (22.0% vs 34.0%; OR 0.548, P<0.001). CONCLUSION: Both TVS stenting and CSF shunting resulted in symptomatic improvement and reduced medication use in patients with IIH. However, stenting was associated with significantly lower treatment failure rates, fewer residual papilledema, headaches and visual symptoms, and reduced healthcare utilization. Persistent pulsatile tinnitus was more common following stenting.
17 Apr 2026
Read appraisal →Sleep
Mamba-based deep learning approach for sleep staging on a wireless multimodal wearable system without electroencephalography
STUDY OBJECTIVES: We investigate a Mamba-based deep learning approach for sleep staging on signals from ANNE One (Sibel Health, Chicago, IL), a non-intrusive dual-module wireless wearable system measuring chest electrocardiography, triaxial accelerometry, chest temperature, and finger photoplethysmography and finger temperature. METHODS: We obtained wearable sensor recordings from 357 adults undergoing concurrent polysomnography at a tertiary care sleep lab. Each polysomnography recording was manually scored, and these annotations served as ground truth labels for training and evaluation of our models. Polysomnography and wearable sensor data were automatically aligned using their electrocardiography channels with manual confirmation by visual inspection. We trained a Mamba-based recurrent neural network architecture on these recordings. Ensembling of model variants with similar architectures was performed. RESULTS: After ensembling, the model attains a 3-class (wake, non-rapid eye movement sleep, rapid eye movement sleep) balanced accuracy of 84.02 per cent, F1 score of 84.23 per cent, Cohen's κ of 72.89 per cent, and a Matthews correlation coefficient (MCC) score of 73.00 per cent; a 4-class (wake, light NREM [N1/N2], deep NREM [N3], REM) balanced accuracy of 75.30 per cent, F1 score of 74.10 per cent, Cohen's κ of 61.51 per cent, and MCC score of 61.95 per cent; a 5-class (wake, N1, N2, N3, REM) balanced accuracy of 65.11 per cent, F1 score of 66.15 per cent, Cohen's κ of 53.23 per cent, MCC score of 54.38 per cent. CONCLUSIONS: Our Mamba-based deep learning model can successfully infer major sleep stages from the ANNE One, a wearable system without electroencephalography, and can be applied to data from adults attending a tertiary care sleep clinic.
17 Apr 2026
Read appraisal →PloS one
A pilot randomized controlled trial of neuromodulation-augmented balance training in people with multiple sclerosis: STIM-Balance Protocol
BACKGROUND: Impairments in balance control and falls are common problems for people with multiple sclerosis (PwMS), resulting in mobility limitations and reduced participation. Non-invasive neuromodulation techniques such as functional electrical stimulation (FES) and transcutaneous spinal stimulation (TSS) have revealed promising results in improving motor functions in other neurological populations; however, their effects during task-specific balance training have not been investigated in PwMS. OBJECTIVE: To evaluate the feasibility, acceptability, safety, and preliminary clinical efficacy of neuromodulation-augmented balance training programs on balance, mobility, and neuroplasticity in PwMS (ClinicalTrials.gov Identifier: NCT07174973). METHODS: Twenty-four ambulatory PwMS will be randomly assigned into three groups: (1) visual feedback balance training (VFBT) with sham stimulation, (2) VFBT with active (closed-loop) FES for the ankle muscles and sham TSS, and (3) VFBT with active FES and active (open-loop sub-motor-threshold) TSS at the lumbosacral enlargement. Participants in each group will complete 12 training sessions over six weeks. Feasibility, safety, and acceptability will be assessed through recruitment and adherence metrics, adverse-event monitoring, and semi-structured interviews guided by the Technology Acceptance Model questionnaire-2. Performance-based measures of balance, mobility, and walking speed, as well as patient-reported outcomes of balance confidence, walking ability, and fear of falling will be recorded to assess the preliminary efficacy. Modulation in neural pathways excitability will be quantified by recording motor evoked potentials and spinal motor evoked potentials. CONCLUSION: Findings will help to determine whether neuromodulation-augmented balance training is feasible, safe, and acceptable for PwMS and will guide the design of a future fully powered RCT.
17 Apr 2026
Read appraisal →Psychonomic bulletin & review
Speaker effects in language comprehension: An integrative model of language and speaker processing
The identity of a speaker influences language comprehension through modulating perception and expectation. This review explores speaker effects and proposes an integrative model of language and speaker processing that integrates distinct mechanistic perspectives. We argue that speaker effects arise from the interplay between bottom-up perception-based processes, driven by acoustic-episodic memory, and top-down expectation-based processes, driven by a speaker model. We show that language and speaker processing are functionally integrated through multi-level probabilistic processing: prior beliefs about a speaker modulate language processing at the phonetic, lexical, and semantic levels, while the unfolding speech and message continuously update the speaker model, refining broad demographic priors into precise individualized representations. Within this framework, we distinguish between speaker-idiosyncrasy effects arising from familiarity with an individual and speaker-demographics effects arising from social group expectations. We discuss how speaker effects serve as indices for assessing language development and social cognition, and we encourage future research to extend these findings to the emerging domain of artificial intelligence (AI) speakers, as AI agents represent a new class of social interlocutors that are transforming the way we engage in communication.
17 Apr 2026
Read appraisal →Journal of rehabilitation medicine
Does aerobic exercise affect memory, attention, working memory, and fatigue after acquired brain injury? A single-blinded, randomized controlled pilot study.
OBJECTIVE: To explore potential effects of aerobic exercise on cognitive function, fatigue, and neuroplasticity after stroke or traumatic brain injury. DESIGN: A randomized controlled pilot study. SUBJECTS/PATIENTS: Twelve participants, 10 men and 2 women aged 16-65 years diagnosed with acquired brain injury (moderate to severe stroke or traumatic brain injury), enrolled in an outpatient rehabilitation programme at least 3 months post-injury. METHODS: The intervention group (n = 6) participated in 30 min of aerobic exercise 3-4 times/week for 8 weeks during their outpatient rehabilitation. The control group (n = 6) received routine physical therapy. In addition, both groups received rehabilitation according to their rehabilitation plan. Neuropsychological and endurance testing was performed before and after the intervention and at a 3-month follow-up. Functional magnetic resonance imaging was performed before and after the intervention. RESULTS: Preliminary findings show no significant difference between the groups regarding cognitive variables, fatigue, or neuroplasticity. However, working memory/executive demanding processing speed, measured with the Paced Auditory Serial Addition Test, improved significantly (p = 0.042) in the aerobic exercise group and there were trends of improvements on several other executive tests in the aerobic exercise group. Automatic visual search speed improved in the control group (p = 0.027). CONCLUSION: The question of whether aerobic exercise promotes cognition and fatigue after brain injury remains unanswered. This randomized controlled pilot study and its preliminary findings indicate greater improvements in executive processing speed in the aerobic exercise group, while more automatized attention speed improved more in the control group. The experiences from this study might facilitate the design of future studies on this intriguing topic. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT07429526.
17 Apr 2026
Read appraisal →The Cochrane database of systematic reviews
Amyloid-beta-targeting monoclonal antibodies for people with mild cognitive impairment or mild dementia due to Alzheimer's disease
RATIONALE: Alzheimer's disease is a neurodegenerative disorder and the most common cause of dementia. Aggregated amyloid-beta protein deposits are implicated in its pathogenesis. Amyloid-beta-targeting monoclonal antibodies (sometimes represented as Aβ-mAbs) are potentially disease-modifying for Alzheimer's disease: through the clearance of amyloid in the brain, they may slow cognitive and functional decline. OBJECTIVES: To assess the clinical benefits and harms of amyloid-beta-targeting monoclonal antibodies aducanumab, bapineuzumab, crenezumab, donanemab, gantenerumab, lecanemab, ponezumab, remternetug, and solanezumab in people with mild cognitive impairment or mild dementia due to Alzheimer's disease. SEARCH METHODS: We searched CENTRAL, MEDLINE (PubMed), Embase, and two clinical trials registries (Clinicaltrials.gov and WHO International Clinical Trials Registry Platform), and we undertook reference checking and citation research. The most recent search date was 7 August 2025. ELIGIBILITY CRITERIA: We included randomised controlled trials (RCTs) that lasted at least 12 months and compared amyloid-beta-targeting monoclonal antibodies with placebo or no treatment in people with mild cognitive impairment or mild dementia due to Alzheimer's disease. We included both parallel-group and cluster designs. OUTCOMES: Our outcomes of critical importance were: cognitive function; dementia severity; functional ability; any amyloid-related imaging abnormality (ARIA), which includes oedema (E) and haemorrhage (H); any symptomatic ARIA E and H; symptomatic brain haemorrhage; serious adverse events; and any-cause mortality. We analysed data at 12, 18, 24, and over 24 months of treatment. RISK OF BIAS: We used the Cochrane risk of bias tool RoB 2 to assess the risk of bias in outcomes of critical importance. SYNTHESIS METHODS: We meta-analysed results for each outcome within each comparison using the inverse variance method and the random-effects model. We used GRADE to assess the certainty of evidence for each outcome as very low, low, moderate, or high. INCLUDED STUDIES: Overall, we included 17 studies with 20,342 participants. The mean age of participants in the studies ranged from 70 to 74 years. Seven studies enroled only participants with mild dementia, and one study enroled only participants with mild cognitive impairment. The remaining studies included a mixed population. The mean duration of participants' cognitive impairment ranged from 17 to 52 months. The 17 studies assessed seven different amyloid-beta-targeting monoclonal antibodies: aducanumab (n = 3), bapineuzumab (n = 4), crenezumab (n = 2), donanemab (n = 1), gantenerumab (n = 4), lecanemab (n = 1), and solanezumab (n = 2). All used placebo as a comparison. Eleven studies lasted 18 months, four lasted 24 months, and two lasted more than 24 months. All studies were funded by the pharmaceutical industry. SYNTHESIS OF RESULTS: Below, we report the results of the studies at 18 months. Cognitive function Compared to placebo, amyloid-beta-targeting monoclonal antibodies probably result in little to no difference in cognitive function as measured by the ADAS-Cog (Alzheimer's Disease Assessment Scale-Cognitive) scale (standardised mean difference (SMD) -0.11, 95% confidence interval (CI) -0.16 to -0.06; 13 studies, 9895 participants; moderate certainty). Dementia severity Amyloid-beta-targeting monoclonal antibodies may result in little to no difference in dementia severity as measured by the CDR-SB (Clincal Dementia Rating Sum of Boxes) scale (SMD -0.12, 95% CI -0.24 to -0.00; 9 studies, 8053 participants; low certainty). Functional ability Amyloid-beta-targeting monoclonal antibodies probably result in little to no difference in functional ability as measured on the ADCS-ADL (Alzheimer's Disease Cooperative Study - Activities of Daily Living) scale (SMD 0.09, 95% CI 0.03 to 0.16; 3 studies, 3478 participants; moderate certainty) and may result in a small increase in functional ability if measured with the ADCS-iADL (Alzheimer's Disease Cooperative Study-Instrumental Activities of Daily Living) scale (SMD 0.21, 95% CI 0.10 to 0.32; 1 study, 1252 participants; low certainty) or ADCS-ADL-MCI (Alzheimer's Disease Cooperative Study - Activities of Daily Living for Mild Cognitive Impairment) scale (SMD 0.23, 95% CI 0.12 to 0.33; 4 studies, 2802 participants; low certainty). Adverse events Amyloid-beta-targeting monoclonal antibodies probably result in a small increase in the occurrence of any ARIA E (ARD (absolute risk difference) 107 more per 1000, 95% CI 77 more to 148 more; 11 studies, 13,595 participants; moderate certainty) and probably little to no difference in symptomatic ARIA E (ARD 29 more per 1000, 95% CI 22 more to 38 more; 2 studies, 3522 participants; moderate certainty) or symptomatic ARIA H (ARD 4 more per 1000, 95% CI 1 fewer to 31 more; 1 study, 1795 participants; moderate certainty). Three studies assessing any ARIA H showed heterogeneous results (I2 = 81%), which prevented pooled analysis. At 18 months, amyloid-beta-targeting monoclonal antibodies do not increase serious adverse events (ARD 6 more events per 1000, 95% CI 10 fewer to 26 more; 9 studies, 11,904 participants; high certainty) or overall mortality (ARD 2 more events per 1000, 95% CI 3 fewer to 11 more; 7 studies, 9733 participants; high certainty). We judged the overall risk of bias as low for the outcomes of serious adverse events and mortality. We had some concerns about the overall risk of bias for efficacy outcomes, mainly due to the risk of functional unblinding (i.e. participants and investigators correctly guessing whether a participant is receiving the active drug or placebo because of noticeable side effects). AUTHORS' CONCLUSIONS: The effect of amyloid-beta-targeting monoclonal antibodies on cognitive function and dementia severity at 18 months in people with mild cognitive impairment or mild dementia due to Alzheimer's disease is trivial, while on functional ability, it is small at best. Amyloid-beta-targeting monoclonal antibodies increase the risk of amyloid-related imaging abnormalities. Both desirable outcomes and adverse events were inconsistently reported in the studies included in the review. Successful removal of amyloid from the brain does not seem to be associated with clinically meaningful effects in people with mild cognitive impairment or mild dementia due to Alzheimer's disease. Future research on disease-modifying treatments for Alzheimer's disease should focus on other mechanisms of action. FUNDING: This Cochrane review was funded in part by the Drug and Medical Devices Governance Area, Regione Emilia-Romagna, Bologna, Italy. The publication of this article was supported by "Ricerca Corrente" funding from the Italian Ministry of Health. REGISTRATION: Protocol (2025): PROSPERO registration number CRD420251114325.
17 Apr 2026
Read appraisal →PloS one
Exploration of schizophrenia-associated gene modules using graph theory, co-expression networks, and dimensionality reduction.
The PsychENCODE consortium has generated a comprehensive multi-omic dataset from human brain samples, spanning healthy individuals and those with neuropsychiatric disorders. In this study, we focus exclusively on schizophrenia and analyze PsychENCODE transcriptomic data to construct gene regulatory and co-expression networks, aiming to uncover biologically and clinically relevant gene modules. We apply three independent analytical approaches to the same dataset. First, we preprocess the data using Surrogate Variable Analysis (SVA) to correct for latent variation and normalize gene expression. For graph-based analysis, we use Pearson correlation and the igraph package to construct gene co-expression networks and apply Prim's algorithm to generate minimum spanning trees (MST) and compute centrality measures. Next, we implement Weighted Gene Co-expression Network Analysis (WGCNA), assuming a scale-free topology, to identify modules associated with schizophrenia traits. Dimensionality reduction is performed using Principal Component Analysis (PCA), with visualization aided by t-distributed Stochastic Neighbor Embedding (t-SNE) and Multidimensional Scaling (MDS). Functional enrichment is carried out using Gene Ontology (GO) and KEGG pathway databases. Each method reveals distinct yet complementary biological signatures associated with schizophrenia. The igraph-based approach highlights differentially expressed genes (DEGs) with high centrality, uncovering hub genes involved in mRNA export, synaptic signaling, and ion transport. WGCNA identifies co-expression modules strongly correlated with schizophrenia diagnosis, enriched for immune response, histone modification, and mRNA surveillance pathways. PCA isolates key genes contributing to diagnostic variance, with enrichment in neurotransmitter release cycles and cytokine signaling. Collectively, these results underscore the involvement of immune, synaptic, and epigenetic processes in schizophrenia and demonstrate the power of using multiple, orthogonal analytical lenses.
17 Apr 2026
Read appraisal →Journal of neurology, neurosurgery, and psychiatry
Breaking the PROMISE: poor association between brain volume loss and clinical disability worsening over 2 years of follow-up in primary progressive multiple sclerosis.
BACKGROUND: Phase 2 clinical trials in primary progressive multiple sclerosis (PPMS) often use MRI brain volume measures as their primary endpoint. Here, we investigate the longitudinal association between change in MRI outcomes and change in disability over 2 years of follow-up in a large and well-characterised PPMS trial dataset. METHODS: Using the dataset from PROMISE (n=943, mean age of 58.0 years, mean Expanded Disability Status Scale (EDSS) 5.0, and mean disease duration of 10.5 years at baseline), a phase 3 trial of glatiramer acetate in PPMS, we described the change in MRI and disability measures during follow-up and used multivariable logistic regression models to investigate whether change in four MRI outcomes (grey matter volume, white matter volume, brain fraction and total burden of disease) was associated with seven measures of clinical disability worsening (including the EDSS, Timed 25 Foot Walk, Nine-Hole Peg Test and Paced Auditory Serial Addition Test). RESULTS: We found only weak and inconsistent associations between change in MRI and physical and cognitive outcome measures over 2 years of follow-up. Change in MRI measures in year 1 of the trial did not predict change in physical nor cognitive disability in year 2. DISCUSSION: The association of the investigated volumetric MRI measures and clinical disability outcomes was noticeably weak and inconsistent. Our findings question the suitability of brain volume loss and lesion burden accumulation as functionally relevant trial endpoints in PPMS and inform the design and interpretation of clinical trials in PPMS.
17 Apr 2026
Read appraisal →Scientific reports
Predicting post-stroke functional outcome using explainable machine learning and integrated data
Functional outcome after acute ischemic stroke (AIS) varies widely, and existing prognostic scores may not capture complex relationships. We evaluated a diverse set of clinical characteristics and blood biomarkers with multiple machine learning models to predict 3-month functional outcome after AIS, and used explainable artificial intelligence to identify key drivers of performance. Models were trained on 506 patients aged 18-69 years with AIS enrolled at four stroke units in western Sweden. We compared extreme gradient boosting, multilayer perceptron (MLP), and L1- and L2-regularized logistic regression. Feature importance was assessed with Shapley additive global explanations. Of the 506 patients, 105 had an unfavorable outcome (modified Rankin Scale score > 2). All models showed high area under the curve (AUROC, 0.900-0.906). The MLP achieved the highest precision-recall performance (AUPRC, 0.773 ± 0.080) and sensitivity (0.655 ± 0.096), though with lower specificity (0.920 ± 0.035). Stroke severity (NIH Stroke Scale score) was the dominant predictor across models. Among biomarkers, brain-derived tau (BD-tau) was most informative, followed by inflammation-related plasma proteins. In conclusion, machine learning accurately predicted functional outcome after AIS. BD-tau and inflammation-related proteins contributed predictive information above stroke severity, suggesting a potential for blood biomarkers to enhance individualized prognostication after AIS.
17 Apr 2026
Read appraisal →PloS one
Assessing the real-world safety of vutrisiran for transthyretin-mediated amyloidosis with polyneuropathy: Based on WHO-VigiAccess and FAERS databases
BACKGROUND: Vutrisiran is a double-stranded siRNA targeting transthyretin mRNA, primarily for the treatment of transthyretin-mediated amyloidosis (ATTR) with polyneuropathy. Understanding its safety in real-world settings is crucial for achieving optimal treatment outcomes for patients. METHODS: This study assessed the safety of vutrisiran for clinical use by analyzing all adverse drug reaction (ADR) reports in WHO-VigiAccess and U.S. Food and Drug Administration's Adverse Event Reporting System (FAERS) databases with vutrisiran as the primary suspected drug and by using four disproportionality analysis and Bayesian information component. Weibull distribution was also utilized to predict the time of occurrence of ADR. RESULTS: During the research process, WHO-VigiAccess reported 721 adverse reaction reports related to vutrisiran, while FAERS documented 732 ADR reports. The analysis revealed that: this study not only demonstrated known adverse reactions such as injection site reactions, such as redness, pain, or swelling at the injection site; systemic reactions like fatigue and myalgia; pain in extremity; and potential risks associated with reduced vitamin A levels, but also detected potential safety signals related to drug such as night blindness and cardiac pacemaker insertion. Furthermore, it was observed that in both the WHO-VigiAccess and FAERS databases, the highest reporting rates of ADR were found in the category of general disorders and administration site conditions. It also confirms the importance of monitoring the occurrence of adverse reactions within one year after drug treatment. CONCLUSION: The study identified several known ADRs and detected potential positive signals that may be associated with the drug. These research results provide clinicians with more information to consider and monitor adverse reactions in patients with ATTR-related polyneuropathy after treatment with vutrisiran, offering more evidence for its safety in real-world settings.
16 Apr 2026
Read appraisal →Neuroscience
Neural responses to acute hypoxia and hyperoxia
The brain's high metabolic demands make it highly sensitive to oxygen level changes, with both hypoxia and hyperoxia affecting cognitive and neural function. However, the underlying neural mechanisms remain unclear. This study aimed to characterise the effects of acute normobaric hypoxia and hyperoxia on neural function and perceptual thresholds using EEG, visual evoked potentials (VEPs), and critical flicker fusion (CFF) thresholds. In a single-blind, randomised, crossover design, thirty participants inhaled 10.5%, 21%, or 100% oxygen while resting-state EEG, VEPs, and CFF thresholds were recorded. Hypoxia reduced CFF thresholds in an exposure-dependent manner, while hyperoxia produced a smaller, transient reduction. Resting-state EEG revealed significantly increased power across delta, theta, and beta bands in response to hypoxia. Whereas hyperoxia induced smaller increases in theta and alpha and decreases in gamma power and a reduction in beta power over the sensorimotor area. Hypoxia increased spectral entropy, indicating impaired network synchronisation, while hyperoxia increased fuzzy entropy, particularly in eyes-closed states, potentially reflecting enhanced neural complexity. In VEPs, hypoxia reduced P1 amplitude and increased N2 deflections, reflecting disrupted early and late sensory processing, with sustained effects across blocks. Hyperoxia transiently increased P1 amplitude and showed minor effects on other components. These findings demonstrate the impact of altered cerebral oxygen on fundamental neural processing and the associated state- and time-dependent effects on brain function. This provides insight into the mechanisms underlying oxygen-induced neuromodulation and highlights the value of EEG and CFF as biomarkers for tracking neural responses to oxygen variability.
16 Apr 2026
Read appraisal →The Journal of clinical investigation
Disrupted neurovascular-endocrine coupling in type 1 diabetes with impaired awareness of hypoglycemia
BACKGROUNDRecurrent hypoglycemia in type 1 diabetes (T1D) may culminate in impaired awareness of hypoglycemia (IAH). While neuroimaging studies identified affected brain regions, more complex perspectives integrating vascular dynamics with endocrine profile are needed.METHODSHere, 26 healthy adults, 30 T1D patients with normal hypoglycemia awareness (NAH), and 25 T1D patients with IAH underwent a hyperinsulinemic stepped clamp (euglycemia → hypoglycemia 50 mg/dL) combined with pseudo-continuous arterial spin-labeling MRI. Cerebral blood flow (CBF) and sympathetic vasomotor range (0.02-0.05 Hz) CBF oscillations were modeled against serially sampled plasma cortisol, epinephrine, norepinephrine, and glucagon.RESULTSIn healthy individuals treated as controls, hypoglycemia evoked robust thalamo-striatal and salience-interoceptive CBF increases (mean Cohen's d across significant clusters = 0.93) and suppression of vasomotor oscillations (d = 0.71). T1D retained CBF response but failed to attenuate oscillations (dT1D>controls = 0.43). IAH further blunted hypoglycemia-associated CBF increase, especially in thalamus, striatum, and insula (dNAH>IAH = 0.51). Hormone-CBF coupling differed quantitatively: cortisol/epinephrine-CBF correlations were positive in controls (r = 0.37/0.26), negative in NAH (-0.16/-0.40), and strongly positive in IAH (0.42/0.46).CONCLUSIONThus, our findings indicate that T1D disrupts dynamic, sympathetic modulation of CBF, whereas IAH additionally impairs perfusion reserve and shows maladaptive catecholamine-dependent CBF regulation, suggesting a qualitatively distinct neurovascular phenotype.TRIAL REGISTRATIONClinicalTrials.gov: NCT02747680 and NCT02866435.FUNDINGNIH (P41-EB-015894, P30-NS-076408, R01-DK-099137, R56-DK-099137, and DP1 AG093028); National Center for Advancing Translational Sciences of the NIH (KL2-TR-000113 and UL1-TR-000114); DP1 AG093028; Charles University, Czech Republic (Cooperatio Program, research area NEUR), Brain Dynamics (grant number CZ.02.01.01/00/22_008/0004643); General University Hospital in Prague (MH CZ-DRO-VFN64165).
16 Apr 2026
Read appraisal →Science advances
Drawn-on-skin electronic tattoo as a closed-loop sensing-stimulation system for the muscles
Simultaneous electromyography (EMG) sensing and closed-loop electrical stimulation (ES) could enable interactive muscle training, motor rehabilitation, and kinesthetic feedback. However, current systems often suffer from nonconformal device/skin interfaces, poor wearability, and anatomical variability among users, limiting signal fidelity and stimulation precision. Here, we present a fully wireless, skin-integrated electronic tattoo platform that records high-quality EMG and delivers closed-loop ES through custom drawn-on-skin, conformal electrodes with strong adhesion and high spatial accuracy. The system supports closed-loop muscle interactions across body regions and even between individuals. Machine learning models classify EMG patterns from different hand gestures with >90% accuracy and adapt ES parameters to stimulate specific muscle groups. In a heavy object holding task, users reached the required grip behavior substantially faster than unassisted controls, indicating improved neuromuscular efficiency. This closed-loop framework not only supports personalized muscle control but also supports coordinated activation across multiple sites or users, unlocking possibilities in interactive motor training, remote rehabilitation, and virtual reality environments.
16 Apr 2026
Read appraisal →The New England journal of medicine
Asundexian for Secondary Stroke Prevention
BACKGROUND: Patients with noncardioembolic ischemic stroke or transient ischemic attack (TIA) are at risk for recurrent stroke. Low factor XI levels are associated with a reduced risk of ischemic stroke. Asundexian inhibits activated factor XI. Whether the addition of asundexian to antiplatelet therapy would be superior to antiplatelet therapy alone for the secondary prevention of ischemic stroke is unclear. METHODS: In this phase 3, double-blind trial, we randomly assigned patients within 72 hours after the onset of a noncardioembolic ischemic stroke or high-risk TIA to receive asundexian (50 mg once daily) or placebo, in addition to planned dual or single antiplatelet therapy. Patients had at least one of the following: a nonlacunar infarct on imaging, a history of atherosclerosis, or evidence of atherosclerotic plaque at any location on cerebrovascular imaging. The primary efficacy outcome was ischemic stroke. The composite of death from cardiovascular causes, myocardial infarction, or stroke was a key secondary outcome. The primary safety outcome was major bleeding. RESULTS: Among 12,327 patients who underwent randomization (6162 to the asundexian group and 6165 to the placebo group), the incidence of ischemic stroke was lower in the asundexian group than in the placebo group (6.2% vs. 8.4%; cause-specific hazard ratio, 0.74; 95% confidence interval [CI], 0.65 to 0.84; P<0.001). The incidence of the composite of death from cardiovascular causes, myocardial infarction, or stroke was lower in the asundexian group than in the placebo group. The incidence of major bleeding was similar in the asundexian group and the placebo group (1.9% and 1.7%, respectively; cause-specific hazard ratio, 1.10; 95% CI, 0.85 to 1.44). The incidence of adverse events was 69.3% in the asundexian group and 70.1% in the placebo group; the incidence of serious adverse events was 19.2% and 19.5%, respectively. CONCLUSIONS: Among patients with noncardioembolic ischemic stroke or high-risk TIA treated with antiplatelet therapy, asundexian at a daily dose of 50 mg resulted in lower risks of ischemic stroke and major cardiovascular events than placebo, without a higher risk of major bleeding. (Funded by Bayer; OCEANIC-STROKE ClinicalTrials.gov number, NCT05686070.).
16 Apr 2026
Read appraisal →Modern rheumatology
Epidemiological and clinical insights into Behçet's disease in Japan: a 2019 nationwide analysis
OBJECTIVES: To clarify contemporary epidemiological and clinical trends of Behçet's disease in Japan by analysing the 2019 National Database of Designated Intractable Diseases (MHLW), describing patient demographics, organ involvement, impact on daily life, and treatment patterns. METHODS: This study analysed the 2019 data from the National Database of Designated Intractable Diseases of Japan, provided by the Japanese Ministry of Health, Labour and Welfare. We focused on age distribution, symptom frequency, daily life impact, and treatment trends. RESULTS: In the 2019 database, 10 406 patients with BD were registered. The median age was 55 years, with a higher proportion of women affected (53.2%). Serious manifestations such as ocular, vascular, and central nervous system involvement were more frequent in males, whereas females showed more mucocutaneous and articular symptoms, representing mild forms of the disease. Pharmacological treatment was common (93.8%), with corticosteroids (44.3%), colchicine (50.9%), infliximab (17.0%), and adalimumab (9.6%) being frequently used. BD significantly affected daily life, with nearly one-third experiencing impaired mobility or depression. CONCLUSIONS: This nationwide analysis highlighted sex-based differences in clinical features and found that BD continues to substantially affect quality of life. The widespread use of biologics suggests a shift in therapeutic strategies.
16 Apr 2026
Read appraisal →Physiological measurement
Towards real-time sleep stage classification: a deep learning approach leveraging PPG and ECG
Objective.This work aims to enable adaptive consumer sleep technologies (CSTs) for sleep intervention by developing a deep learning model for sleep stage classification using wearable sensor data.Approach.We propose an end-to-end deep learning approach leveraging Photoplethysmography (PPG) signals, commonly available in CSTs. Model performance is improved by pretraining with electrocardiography (ECG) from the large-scale multi-ethnic study of atherosclerosis dataset datasets. Training and evaluation are conducted with the Dataset for Real-time sleep stage EstimAtion using Multisensor wearable Technology and an additional dataset comprising of 13 participants (aged 22-71 years) without prior known sleep disorders. The dataset contains combined synchronized polysomnography (PSG) and Empatica E4 wearable data, annotated with American Academy of Sleep Medicine (AASM) sleep stages.Main results.The proposed method demonstrates sleep stage classification from minimally processed PPG signals for real-time intervention. While ECG-trained models are not directly transferable to PPG, fine-tuning significantly improves performance, achieving up to a 29% increase in multi-stage classification accuracy.Conclusion.Pretraining with ECG and fine-tuning with PPG increases sleep stage classification for end-to-end deep learning models, exceeding previous efforts particularly in 3-stage sleep classification.Significance.This work contributes to sleep health by developing a sleep stage classification model for minimally processed PPG sensor data and takes a step further towards making adaptive CSTs feasible for use with wearable sensors.
16 Apr 2026
Read appraisal →Progress in biomedical engineering (Bristol, England)
Navigation paradigms for non-invasive BCI-controlled wheelchairs: a systematic review
Brain-controlled powered wheelchairs represent a promising advancement for individuals with neurological conditions that significantly impair motor function. Despite substantial progress, brain-controlled wheelchairs have not been adapted for real-world settings. This article systematically reviews recent trends in brain-computer interface (BCI) technology for wheelchair navigation and control, highlighting the contributions and limitations of various navigation paradigms. The review was conducted in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analyses guidelines, sourcing studies from four databases (PubMed, Scopus, IEEE Xplore, Google Scholar) published between 2000 and April 2025. It focused on non-invasive BCI paradigms and real-world navigation experiments. The results were narratively synthesized and classified into two primary categories: BCI-based navigation paradigms and wheelchair-based navigation paradigms, along with intersecting concepts such as single-variant BCI, hybrid BCI, control switches, and proportional control. Of the 149 full-text articles reviewed, 47 were included and categorized by navigation paradigm, comprising 20 BCI-based and 27 wheelchair-based studies, with 6 involving participants with motor disabilities. Quality assessment scores ranged from 40% to 95%, with approximately 40% of the studies demonstrating a low risk of bias. The findings indicate that low-level navigation control was predominant in BCI wheelchair studies, with 31 studies employing minimal or no obstacle avoidance. Most studies (57%) integrated sensors for obstacle avoidance, localization, mapping, and autonomous navigation. Twenty-two studies utilized control switches, and five incorporated proportional control for wheelchair navigation. Additionally, motor imagery and steady-state visually evoked potential paradigms have emerged as the most common approaches for generating control commands, highlighting their potential for effective navigation. Given the potential societal impact on a large number of individuals, future research should prioritize enhancing the reliability and adaptability of BCI wheelchair systems in real-world environments.
16 Apr 2026
Read appraisal →Journal of affective disorders
Comorbid clinical and biomarker outcomes in patients with obsessive-compulsive disorder: An umbrella review and evidence map
BACKGROUND: Obsessive-compulsive disorder (OCD) is a chronic psychiatric condition with diverse comorbidities, yet outcome research remains limited. Thus, we aimed to systematically review existing meta-analyses of OCD-associated comorbid outcomes and assess the strength of evidence. METHODS: We conducted an umbrella review of meta-analyses, searching PubMed/MEDLINE, Embase, CINAHL, and Google Scholar up to July 7, 2025. Although we intended to include all studies related to OCD-associated comorbid outcomes, only observational studies were available. Effect sizes were recalculated using random-effects models, and all estimates were converted to equivalent standardized mean differences (eSMD) with 95 % confidence intervals (CIs). The methodological quality of the included reviews was assessed using the AMSTAR 2 tool, and the class of evidence (CE) for each association was graded according to predefined criteria (Class I-IV). RESULTS: We identified 17 meta-analyses that included 373 primary studies, covering 52 outcomes in 156,161 participants across 40 countries. Patients with OCD were associated with autistic traits outcomes, including restricted and repetitive behaviors (eSMD, 1.66 [95 % CI, 1.25-2.07]; CE = IV), and total autism-spectrum quotient score (1.40 [1.06-1.81]; CE = IV). OCD also showed associations of varying strength with several other outcomes, including suicidal attempt (0.64 [0.47-0.81]; CE = II) and toxoplasmosis infection (0.37 [0.15-0.59]; CE = III). Furthermore, quality of life was decreased across multiple domains in individuals with OCD (emotional, -1.47 [-1.96 to -0.98]; CE = II; global, -0.79 [-1.02 to -0.55]; CE = II; work/social, -1.21 [-1.72 to -0.69]; CE = III). CONCLUSION: Our study highlights the comprehensive impact of OCD, indicating associations with autism, suicidality, social-cognitive deficits, impaired quality of life, and neurobiological markers. STUDY REGISTRATION: PROSPERO CRD420251124380.
16 Apr 2026
Read appraisal →JMIR mHealth and uHealth
Effect of a Comprehensive Mobile-Based Respiratory Training Program on Respiratory Function in Survivors of Acute Stroke: Randomized Controlled Trial
BACKGROUND: Respiratory dysfunction frequently occurs during the acute phase of stroke and is associated with reduced ventilatory capacity, respiratory muscle weakness, and increased pulmonary complications. However, delivering standardized respiratory training during hospitalization is often constrained by staffing and service continuity. OBJECTIVE: This study aimed to evaluate the efficacy, safety, and feasibility of a hospital-based comprehensive mobile-based respiratory training program (CMRTP) added to conventional rehabilitation in people with acute stroke who are inpatients. METHODS: This single-center, assessor-blinded randomized controlled trial enrolled 40 patients within 2 weeks after stroke onset with respiratory dysfunction (forced vital capacity <80% predicted). Participants were randomized (1:1) to CMRTP plus conventional rehabilitation or conventional rehabilitation alone. The CMRTP was delivered via the WeChat-based AIRHUB platform and performed 20 minutes twice daily, 5 days per week for 2 weeks, either independently or with caregiver assistance as needed. The primary outcome was change in forced vital capacity from baseline to week 2. Secondary outcomes included forced expiratory volume in 1 second (FEV₁), peak expiratory flow, maximal inspiratory pressure, maximal expiratory pressure, and modified Barthel index. All outcomes were assessed face-to-face by a blinded senior physician, and all analyses followed an intention-to-treat principle. RESULTS: Of 56 screened patients, 40 were randomized, and 39 completed the study. Adherence to the CMRTP reached 96%, and no serious adverse events occurred; mild, transient events (fatigue, dizziness, and hyperventilation) were recorded. Compared with the control group, the CMRTP group demonstrated greater improvement in forced vital capacity at week 2 (mean difference 0.77 L; 95% CI 0.39-1.16; P<.001; η²=0.32), with additional between-group differences in maximal inspiratory pressure (P=.001; η²=.25), maximal expiratory pressure (P<.001; η²=.08), and modified Barthel index (P=.001; η²=.26). No significant group differences were found for forced expiratory volume in 1 second or peak expiratory flow. CONCLUSIONS: A 2-week hospital-based mobile respiratory training program is feasible and safe in people with acute stroke who are inpatients and yields clinically meaningful improvements in respiratory function and daily functional performance when added to conventional rehabilitation.
15 Apr 2026
Read appraisal →International immunopharmacology
Multicentre development and validation of a risk model integrating immunotherapy and coagulation biomarkers for thrombosis in autoimmune neurological disorders
PURPOSE: Patients with Autoimmune Neurological Disorders (ANDs) require routine immunomodulatory therapy, which inherently increases thrombosis risk. Despite this recognised association, reliable tools for thrombosis prediction remain limited, highlighting the need for accurate risk stratification to inform targeted prevention strategies. PATIENTS AND METHODS: This multicentre retrospective study developed a machine learning-based thrombosis risk prediction model using data from 786 patients with ANDs receiving immunomodulatory therapy. The model was trained on data from three centres and externally validated on an independent fourth centre, evaluating six algorithms based on the area under the receiver operating characteristic curve (AUROC), area under the precision-recall curve (AUPRC), and Brier score. Feature selection and Shapley Additive Explanations (SHAP) analysis were applied to ensure model interpretability. RESULTS: Among all algorithms tested, XGBoost (XGB) demonstrated the best performance. On the external validation set, the XGB model achieved an AUROC of 0.949, AUPRC of 0.544, and Brier score of 0.045, significantly outperforming the traditional Padua score. SHAP analysis identified the top three thrombotic risk factors: elevated D-dimer levels, prolonged immobilisation, and advanced age. The total doses of intravenous immunoglobulin and glucocorticoid were also confirmed as significant contributors. The model further enabled stratification into low-, intermediate-, and high-risk groups, offering a practical tool for individualised thromboprophylaxis. CONCLUSION: This study presents a high-performing, interpretable model for early thrombosis risk prediction in patients with ANDs, which facilitates timely clinical decision-making and targeted preventive strategies to reduce thromboembolic complications.
15 Apr 2026
Read appraisal →Neurology
Outcomes After Minor Ischemic Stroke in Older Patients Treated With IV Thrombolysis vs Standard of Care in the TEMPO-2 Trial
BACKGROUND AND OBJECTIVES: There are few high-quality data on thrombolysis outcomes in the oldest patients, especially for minor stroke. We examined safety and efficacy outcomes of thrombolysis in patients older than 80 years, compared with those ≤80 years, in the Tenecteplase Versus Standard of care for Minor Ischaemic Stroke With Proven Occlusion (TEMPO-2) trial. METHODS: In this post hoc analysis of the TEMPO-2 randomized controlled trial, we compared outcomes and adverse events in patients with minor stroke and symptomatic intracranial occlusion or focal perfusion lesion within 12 hours of symptom onset, assigned to tenecteplase vs nonthrombolytic standard of care, in those >80 years and ≤80 years. The primary outcome was responder analysis of the 90-day modified Rankin Scale (responder was mRS = 0-1 if premorbid mRS = 0-1, 0-2 if premorbid mRS = 2). Secondary outcomes included neurologic recovery according to the NIH Stroke Scale (NIHSS) at 5 days/discharge, vessel recanalization, Lawton Instrumental Activities of Daily Living Scale, EuroQol-5 Dimension (EQ-5D), and adverse events. We used mixed-effects Poisson, ordinal logistic, and quantile regression as appropriate, adjusted for sex, baseline NIHSS, and treatment assignment as fixed-effects and enrolling site as random effects. RESULTS: Among 884 patients in the intention-to-treat analysis (208 [23.5%] >80 years, 365 [41.6%] female). Significant interactions between age and treatment were observed for mRS responder, mRS 0-1, return to baseline function, EQ-5D index, and vessel recanalization. Patients >80 years generally fared worse with tenecteplase for mRS outcomes (mRS 0-1 in 49 [46.2%] vs 61 [59.8%] with control, adjusted risk ratio [aRR] 0.83, 95% CI 0.72-0.97), while among patients ≤80 years, there was no significant difference (249 [76.4%] vs 260 [74.7%], aRR 1.01, 95% CI 0.98-1.03). However, in both age groups, patients were more likely to achieve recanalization of visible occlusions (aRR ≤80 years 2.06, 95% CI 1.60-2.65, >80 years 2.77, 95% CI 2.21-3.47) and NIHSS = 0 at 5 days/discharge when assigned to tenecteplase (aRR ≤80 years 1.14, 95% CI 1.04-1.26, >80 years 1.21, 95% CI 1.10-1.33). Serious adverse events (SAEs) were more frequent with tenecteplase among patients >80 years (risk ratio 2.29, 95% CI 1.27-4.13), particularly hemorrhages and stroke progression/recurrence (any extra/intracranial hemorrhage in 5 [4.7%] vs 0 with standard of care, p = 0.026). DISCUSSION: Older patients (>80 years) with minor stroke and visible occlusion or perfusion lesion assigned to tenecteplase were more likely to achieve recanalization of occlusions and short-term neurologic recovery, as were younger patients, but they experienced worse 90-day outcomes with more frequent hemorrhagic and stroke progression/recurrence-related SAEs. Overall, these results, while post hoc, do not support thrombolysis for minor strokes in older patients. TRIAL REGISTRATION INFORMATION: ClinicalTrials.gov: NCT02398656. CLASSIFICATION OF EVIDENCE: This study provides Class III evidence that in patients >80 years with minor stroke and visible occlusion or perfusion lesion, IV thrombolysis with tenecteplase is associated with worse functional outcomes at 90 days compared with nonthrombolytic standard of care therapies.
15 Apr 2026
Read appraisal →Biomedical physics & engineering express
EEG-CMT: spatial-temporal representation of EEG for emotion recognition using convolutional neural networks and vision transformers
Background. Recent researches on electroencephalogram (EEG) based emotion recognition face challenges in effectively mapping the spatial positional relationships of EEG acquisition electrodes. Additionally, conventional models struggled to simultaneously capture both fine-grained temporal-spatial features and long-range dependencies in EEG signals.New method. To address these limitations, we propose a novel EEG data processing method that incorporates spatial relative position encoding and a hybrid neural architecture integrating convolutional neural networks (CNNs) with self-attention mechanisms. This approach systematically encodes the spatial topology of electrodes to enhance the representation of temporal-spatial information. CNNs are employed to extract localized temporal-spatial micro-patterns, while self-attention modules model global contextual dependencies across extended sequences, thereby enhancing model's representational capacity.Results. The experimental results and feature visualizations demonstrate that our method achieves state-of-the-art (SOTA) subject-dependent performance on two benchmark emotion recognition datasets, reaching an average accuracy of 97.51% on the SEED dataset and 96.13% on the SEED-IV dataset. Moreover, the learned spatial features align well with known neuroscientific patterns of emotional processing. Comprehensive ablation studies further validate the necessity and effectiveness of both the spatial-encoded data processing strategy and the hybrid architecture design.Comparison with Existing Methods. Compared to other hybrid neural network models, our proposed method (EEG-CMT) achieves the highest classification accuracy. Specifically, it outperforms baseline algorithms by margins ranging from 0.86% to 11.43% on the SEED dataset, and from 9.49% to 39.52% on the SEED-IV dataset.Conclusions. The proposed method effectively addresses key limitations in existing EEG-based emotion recognition models by jointly leveraging spatial topology and hybrid modeling techniques. These innovations significantly improve the model's ability to recognize emotions from EEG data and provide neural interpretable insights, offering a promising direction for future research in affective brain-computer interfaces.
15 Apr 2026
Read appraisal →Neurology
Systolic Blood Pressure Trajectory and Outcomes in Acute Intracerebral Hemorrhage: Pooled Analysis of the 4 INTERACT and ATACH-II Clinical Trials
BACKGROUND AND OBJECTIVES: While moderate and rapid systolic blood pressure (SBP) lowering was associated with better functional outcomes after intracerebral hemorrhage (ICH), large reductions in SBP within 1 hour, for example, reductions from >200 to <140 mm Hg, diminished or even reversed these beneficial effects. We aimed to define the optimal trajectory of SBP control in relation to favorable functional outcomes after acute ICH. METHODS: We conducted a pooled analysis of individual patient-level data from all 4 Intensive Blood Pressure Reduction in Acute Cerebral Hemorrhage Trial (INTERACT) and second Antihypertensive Treatment of Acute Cerebral Hemorrhage (ATACH-II) trials, which were international, randomized, open-label, blinded, end point-assessed, controlled trials that determined the effectiveness of early intensive SBP control in acute ICH. Latent class analysis grouped SBP trajectories over the first 24 hours (9 measurements) into defined clusters. The primary outcome was functional recovery at 90 days after randomization, defined as modified Rankin Scale (mRS) scores of 3-6. Logistic regression models with adjustment for baseline covariates and trial were used to determine associations between SBP cluster trajectories and outcomes in INTERACT, with validation in ATACH-II. RESULTS: A total of 11,269 patients (INTERACT n = 10,269; ATACH-II n = 1,000; mean age 62.4 years; female 36.4%) with at least 1 postrandomization SBP reading were included. Six SBP trajectories were identified: low, moderate-to-low, moderate, high, high-to-moderate, and high-to-low. Compared with the low SBP group, associations with poor functional outcome (mRS scores 3-6) increased progressively across other groups in INTERACT (p = 0.04 for trend). Adjusted odds ratios (95% CI) for groups 2 to 6 were 1.16 (0.98-1.37), 1.44 (1.18-1.75), 1.46 (1.15-1.87), 1.90 (1.32-2.73), and 1.28 (1.02-1.60), respectively. A similar albeit nonsignificant trend was observed in ATACH-II due to limited power. DISCUSSION: Distinct SBP trajectories over 24 hours defined prognosis after ICH, with a severe hypertensive group having the highest odds of death or disability, regardless of the BP-lowering strategy used. These findings highlight the importance of well-controlled but tailored SBP management strategies after ICH. CLASSIFICATION OF EVIDENCE: This study provides Class III evidence that distinct SBP trajectories over 24 hours are associated with prognosis after acute ICH, with a severe hypertensive group having the highest odds of poor functional outcome, regardless of the BP-lowering strategy used. TRIAL REGISTRATION INFORMATION: (INTERACT1 NCT00226096; INTERACT2 NCT00716079; INTERACT3 NCT03209258; INTERACT4 NCT03790800; ATACH-2 NCT01176565).
15 Apr 2026
Read appraisal →Food & function
The effect of anthocyanins through diet and supplementation on cognitive function in older adults: a multi-centre randomised controlled clinical trial
Anthocyanins, a flavonoid subclass present in certain blue, purple and red fruits and vegetables, have potential neuroprotective properties due to their anti-inflammatory, antioxidant, and signalling effects. Clinical and epidemiological studies of anthocyanins provide promising evidence for attenuation of memory loss and improved cognitive function. We investigated the effects of anthocyanins, provided through diet or supplementation, on cognitive function and related parameters. A multi-centre, 24-week randomised, parallel 3-arm clinical trial was conducted in participants aged 60-85 years with self-reported memory complaints and scoring ≤13 on the Memory Index Score within the Telephone Montreal Cognitive Assessment (T-MoCA). Participants were randomised to high anthocyanin diet, freeze-dried product derived from blackcurrants (250 mg anthocyanins per day), or control (placebo). The primary outcome was auditory episodic memory functioning. Secondary outcomes were additional cognitive functions, subjective memory complaints and self-reported depression symptoms, blood pressure, inflammatory biomarkers, lipid profile, vascular and microvascular endothelial function tests. Mixed linear modelling evaluated changes over time using both intention-to-treat and completer analyses. Of 110 participants enrolled (mean age 69.2 years SD 6.7; 36 males, 74 females), 94 completed the study. There was no treatment × time effect on any primary or secondary outcomes. We postulate that the potential anti-inflammatory, antioxidant, and signalling effects of anthocyanins were insufficient to significantly modulate any outcomes due to participants' overall good health and lack of relevant cognitive decline. We recommend future interventional studies focus on subjects with diagnosed cognitive impairment, such as MCI and dementia, and those with more severe comorbidities, particularly chronic inflammatory conditions. Trial Sponsor: National Health and Medical Research Centre (NHMRC) Dementia Collaborative Research Centre.
15 Apr 2026
Read appraisal →Child's nervous system : ChNS : official journal of the International Society for Pediatric Neurosurgery
The role of age and Galassi type in management and surgical outcomes of pediatric middle fossa arachnoid cysts: a systematic review and meta-analysis
PURPOSE: Arachnoid cysts are found in approximately 2.6% of the pediatric population, mostly asymptomatic, incidental findings. However, those with mass effect symptoms from cyst growth may require surgery. Because of the variability in progression, we investigated the interaction of patient age with middle fossa arachnoid cyst (MFAC) clinical outcomes. METHODS: A systematic review using PubMed, Google Scholar, and Web of Science electronic databases was completed according to the PRISMA guidelines to identify articles detailing pediatric MFACs. RESULTS: Sixty articles meeting the inclusion criteria, totaling 1024 patients, were included. Signs of elevated ICP (194/873, prevalence: 22.2%, 95% CI: 19.5-25.0%) and primary headaches (263/873, prevalence: 30.1%, 95% CI: 27.1-33.2%) were the most common presenting symptoms. In the evaluation of the hemispheric location of MFACs, younger ages at presentation were more likely to present with bilateral MFACs (OR: 0.71, 95% CI: 0.52-0.98, p = 0.034). Most MFACs in the retrospective studies had left laterality (421/677, prevalence: 62.2%, 95% CI: 58.6-65.1%) and were singular (791/817, prevalence:96.8%, 95% CI: 95.6-98.2%). A higher proportion of younger patients required a second operation (OR: 0.82, 95% CI: 0.72-0.93, p = 0.002) and cerebrospinal fluid shunting (OR: 0.88, 95% CI: 0.79-0.97, p = 0.011) across the case reports. There was no significant difference in complications when comparing older children (5-18 years) to young children (≤ 4 years) (p = 0.737) or Galassi type (p = 0.736), but Galassi type 1 was associated with management through observation (p = 0.019). CONCLUSION: Younger patients presented with a higher rate of bilateral MFACs and required more invasive treatment methods and repeat operations more frequently, suggesting that infants and young children with MFACs may require greater surveillance during and after management to minimize complications.
14 Apr 2026
Read appraisal →Neurology
Wearable Technology and Its Role in Neurologic Care: Emerging Issues in Neurology
Consumer wearable devices are commonly used by patients and consumers for several reasons with increasing application as new technologies are developed. Use of these devices is an emerging issue in Neurology because of increased adoption and the additional data reported to providers by patients. Understanding of possible functions, limitations, and effect on patients of non-US Food and Drug Administration (FDA)-cleared wearable technology to inform neurologic care is needed. A common theme in people with neurologic conditions regarding consumer wearables and associated tracking applications is that there is significant promise in these tools, but adherence (days per use/per week), continued engagement (attrition), and unintended consequences such as heightened anxiety remain important issues. Further understanding and validation of these devices is needed within the field of Neurology before full use and confidence can be achieved. Below, we provide examples of non-FDA-cleared wearable devices used in Neurology in the areas of epilepsy, headache, cardiac monitoring, and sleep.
14 Apr 2026
Read appraisal →Neurology
Type 1 Diabetes and Incident Dementia: An Analysis in the All of Us Cohort
BACKGROUND AND OBJECTIVES: Although diabetes mellitus (DM) is a well-established determinant of dementia risk, most studies have evaluated type 2 DM (T2DM) or any DM. The influence of type 1 DM (T1DM) on dementia risk remains unclear. We evaluated associations of T1DM and T2DM, separately, with incident dementia using linked electronic health records (EHRs). METHODS: This prospective cohort study used previously collected survey and EHR data from the All of Us (AoU) cohort, a convenience sample of US adults. Eligible participants were 50 years or older and completed baseline surveys. Enrollment began in 2017, with data available through October 2023, along with records before enrollment. Mean follow-up from baseline was 2.4 years. We developed an algorithm to distinguish DM type based on count of T1DM encounters. This algorithm was validated against 2 reference measures: self-reported diabetes type and C-peptide values. Using AoU data, we classified participants as having no DM, T1DM, or T2DM. We ascertained incident dementia using ICD-9, ICD-10, and Systematized Nomenclature of Medicine codes in participants' EHRs. We estimated hazard ratios (HRs) and 95% CIs for the association of diabetes type with incident dementia using Cox proportional hazards models. RESULTS: Among 283,772 participants (mean [SD] age 64.62 [8.96] years; 56.7% women), 60.3% identified as non-Hispanic White and 13.3% as Hispanic/Latino. Optimal DM classification algorithm cutoffs varied by reference standard: self-reported diabetes: ≥1 T1DM EHR encounter (sensitivity: 0.59; specificity: 0.90) and C-peptide: ≥3 T1DM EHR encounters (sensitivity: 0.76; specificity: 0.79). Defining T1DM as having ≥1 T1DM encounter, 5,442 participants had T1DM. Compared with those without DM, participants with T1DM had higher dementia incidence (sociodemographic-adjusted HR 2.82; 95% CI 2.28-3.48) and those with T2DM also had elevated risk (sociodemographic-adjusted HR 2.08; 95% CI 2.87-2.31). Results were similar across sex, race, and ethnicity strata. DISCUSSION: In AoU, DM was associated with elevated dementia risk, with the highest risk among those with T1DM. These findings highlight the need to better understand mechanisms tying T1DM to dementia.
14 Apr 2026
Read appraisal →Journal of speech, language, and hearing research : JSLHR
Auditory-Somatosensory Interactions in Speech Perception in Individuals With Hearing Impairment: An Exploratory Case Study
OBJECTIVES: Individuals with hearing impairment can perceive speech sounds with the help of cochlear implants or hearing aids. Recent studies have shown that orofacial somatosensory inputs may modify speech perception. This study explored the potential role of such somatosensory inputs in speech perception in individuals with hearing impairment. DESIGN: Twenty-one native French-speaking participants with various profiles of bilateral hearing impairment and wearing hearing aids and/or cochlear implants were tested to explore the extent to which an orofacial somatosensory stimulation, consisting in facial skin stretch provided by a robotic device, would affect their speech perception performance in a vowel identification task, in comparison with 25 participants with normal hearing. This potential somatosensory effect was evaluated in relation to their hearing ability assessed by both their hearing threshold of digits in acoustic noise and their audiological profile, and with their production ability assessed by variability in a vowel production task. RESULTS: The somatosensory effects varied depending on participants' hearing ability, with three different profiles. A first group of eight participants, who were able to identify the vowels auditorily though with thresholds in noise higher than for individuals with normal hearing, did not show any somatosensory effect, with identical vowel identification scores without and with facial deformation. The other 13 participants were unable to auditorily identify the vowels and had high thresholds in noise. Among them, eight participants who had used a hearing device for a long duration (≥ 18 years) showed a strong somatosensory bias modifying vowel identification when facial deformation was present. These participants also showed greater variability in vowel production. The last five participants in this second group showed no somatosensory effect at all. CONCLUSION: These three profiles were related to the role of auditory experience and hearing ability in the development of auditory-somatosensory integration.
12 Apr 2026
Read appraisal →Molecular biomedicine
Machine learning integrated clinical-proteomics data identifies a 6-protein panel signature for atherosclerotic severity and enhanced patient stratification
Atherosclerosis, a major cause of adverse cardiovascular events and mortality rates worldwide, stems from sustained lipid accumulation and subsequent chronic inflammation within the arterial walls. An early identification of patients at risk is crucial to prevent life-threatening thrombotic events and provide effective and personalized treatments. Leveraging the power of machine learning (ML) to enhance diagnostics and biomarker discovery, we applied a high-throughput approach using five ML classification algorithms (MLCA), integrating clinical and serum proteomic data from patients with carotid atherosclerotic stenosis (AT, n:60), dyslipidemic patients (DLP, n:55), and healthy controls (HC, n:66). As a result, a robust 6-protein panel (B2M, GPV, MMP9, PLF4, TSP1, and FB isoforms) was identified with a ROC-AUC value > 0.9 for all algorithms applied, highly discriminating AT patients compared to DLP or CTRL. The levels of these proteins were further validated in an independent external cohort, including patients presenting with acute atherothrombotic stroke, corroborating the potential of this panel as biomarker for atherosclerosis severity. In addition, the combined clinical-proteomic ML approach provided a more accurate patient stratification than the clinical or proteomic analysis alone. Mechanistically, the identified biomarkers highlight the importance of platelet activation, uncontrolled angiogenesis and intraplaque haemorrhage in the atherosclerotic process, underscoring the need for multipathway therapies to prevent unwanted thrombotic events.
12 Apr 2026
Read appraisal →Journal of neurology
Effect of exercise training on clinical and physiological variables in adults with myotonic dystrophy type 1: a systematic review and meta-analysis
Myotonic dystrophy type 1 (DM1) is a progressive neuromuscular disorder characterized by muscle weakness, fatigue, reduced endurance, and impaired quality of life. This systematic review and meta-analysis aimed to evaluate the effects of exercise training on clinical and physiological outcomes in adults with DM1. Six databases were searched for studies published between 2003 and 2023. Randomized controlled trials, quasi-experimental studies, and longitudinal cohort studies involving adults with DM1 undergoing aerobic or resistance training were included, and risk of bias was assessed. Meta-analyses used standardized mean differences with fixed- or random-effects models based on heterogeneity. Fourteen studies were included. Most studies demonstrated low risk of bias. Only one study reported any adverse events, and the incidence of these events was similar in occurrence between the intervention and standard care only groups. Meta-analysis demonstrated a moderate improvement in strength (p = 0.022). Endurance showed a large but non-significant pooled effect (p = 0.14). Meta-analyses for fatigue (p = 0.49) and sleep (p > 0.05) were inconclusive. Narrative synthesis indicated improvements in most clinical and physiological outcomes, while sleep outcomes showed minimal or inconsistent changes across studies. Exercise training appears safe and provides moderate benefits for strength and selected clinical and physiological outcomes in adults with DM1. Future well-powered trials with standardized protocols are needed to optimize exercise prescription.
12 Apr 2026
Read appraisal →Nature communications
Exposome-wide patterns predict brain health in aging
Promoting brain health is vital for well-being and reducing healthcare burdens. Brain health as measured with the Brain Age Gap (BAG) - the difference between chronological and predicted brain age- relates to many factors. However, a holistic view, integrating the range of factors an individual brain is exposed to, is missing for understanding how the exposome shapes brain health. After computing BAG as an indicator of grey matter (GM) health, we predicted it using machine learning based on 261 exposome variables (spanning biomedical, environmental, lifestyle, socio-affective, and early life domains) in UK Biobank participants. Exposome data can predict GM health with factors pertaining to cardiovascular and bone health, along with alcohol and smoking, nutrition and diabetes showing greater contribution to the prediction. In such domains, life period and duration of exposure appeared crucial. These findings call for early prevention in cardiovascular and metabolic health to promote life-long brain health.
12 Apr 2026
Read appraisal →Journal of speech, language, and hearing research : JSLHR
Information Load Predicts Stuttering Events
PURPOSE: One potential contributor to the variability of stuttering events is information load, quantified by assessing word predictability based on preceding context. This study investigated whether words with greater information load are more likely to be stuttered using word surprisal as an index while controlling for relevant linguistic variables. We also examined whether this relationship varied with stuttering severity and impact. METHOD: We analyzed spontaneous speech samples from 35 adults who stutter, drawn from the FluencyBank corpus. Word surprisal values were computed using the Generative Pretrained Transformer 2 large language model and analyses controlled for six linguistic predictors of stuttering. We also tested whether clinical measures of stuttering severity and overall impact predicted individuals' sensitivity to surprisal. RESULTS: Higher word surprisal significantly increased the likelihood of stuttering, even after controlling for linguistic covariates. However, we found no evidence that stuttering severity or overall impact moderated this relationship. CONCLUSIONS: These findings suggest that words with greater information load are more likely to elicit stuttering during spontaneous speech. This pattern may reflect increased communicative demands associated with high-surprisal words. The absence of a moderating effect of stuttering severity or impact may indicate that individual differences in coping with communicative pressure, rather than clinical severity alone, shape stuttering variability.
11 Apr 2026
Read appraisal →Journal of speech, language, and hearing research : JSLHR
Neuroimaging Advances in Brain Function of Laryngeal Dystonia: A Systematic Review
PURPOSE: This study aims to review neuroimaging research on brain function in laryngeal dystonia (LD), summarize current evidence on its pathophysiological mechanisms, and highlight directions for future investigation. METHOD: PubMed, Scopus, and Web of Science Core Collection were searched from database inception to November 2025. Two reviewers independently screened studies, extracted data, and assessed study quality using the Newcastle-Ottawa Scale. Eligible studies were synthesized through systematic review and visual analysis. RESULTS: A total of 33 studies were included. These studies employed imaging techniques such as functional magnetic resonance imaging and voxel-based morphometry to identify abnormalities in patients with LD. Structurally, key findings include gray matter alterations in sensorimotor areas, reduced thalamic volume, and impaired white matter integrity. Functionally, this manifests as sensorimotor integration dysfunction (characterized by M1/S1 and cerebellar hypoactivation alongside pallidal and thalamic hyperactivation) and network decoupling (e.g., weakened M1-S1 connectivity). CONCLUSIONS: The pathological mechanism of LD is a complex network imbalance involving the core "cortico-basal ganglia-thalamo-cerebellar" loop, driven primarily by sensorimotor integration dysfunction. Current research is limited in quantity and suffers from significant methodological heterogeneity. Future studies should further clarify the functional interactions among brain regions and their relationship with clinical symptoms, thereby providing a theoretical basis for the development of targeted therapies. SUPPLEMENTAL MATERIAL: https://doi.org/10.23641/asha.31329103.
11 Apr 2026
Read appraisal →Cell genomics
Variant-resolved prediction of context-specific isoform variation with a graph-based attention model
In eukaryotes, most genes produce multiple transcript isoforms that diversify the transcriptome and proteome, serving as a key mechanism of functional regulation. Genetic variation can disrupt the RNA processing signals that shape isoform structure and abundance, yet modeling these effects at full-length isoform resolution remains challenging due to the complexity of transcript regulation. Here, we introduce Otari, an attention-based graph neural network framework trained on the human genomic sequence and long-read transcriptomes across 30 tissue types and brain regions. Otari predicts tissue-specific differential isoform abundance by integrating sequence-derived epigenetic and post-transcriptional signals, enabling isoform-resolved variant effect interpretation. Applied to large-scale variant datasets, including an autism cohort, Otari uncovers patterns of isoform dysregulation undetectable at the gene level, such as variant-driven perturbations in isoform abundance and microexon usage implicated in autism pathophysiology. We provide Otari as a resource for powering isoform-level analyses across tissues at scale.
11 Apr 2026
Read appraisal →European journal of trauma and emergency surgery : official publication of the European Trauma Society
A machine-learning-based tool for stroke risk prediction in blunt cerebrovascular injury: development and preliminary evaluation
PURPOSE: Stroke risk correlates with the Biffl grading system in blunt cerebrovascular injury (BCVI). Although anti-thrombotic therapy is the mainstay of stroke prevention, no point-of-care clinical decision-support tool exists to guide timing for therapy. We sought to develop an interactive online calculator that incorporates patient-specific demographic and injury characteristics to estimate stroke risk and risk reduction with anti-thrombotic (AT) administration. METHODS: Data from BCVI patients (n = 1,197) at a Level I Trauma Center were retrospectively collected. Six machine learning methods were employed to predict stroke risk with and without AT therapy. Class imbalance was addressed using downsampling and/or class weighting. Model performance was assessed using 10-fold cross-validation. The model was implemented as an R-based Shiny online application. RESULTS: Stroke rate among the population was 4%, and the strongest predictors for stroke were the greatest Biffl grade of carotid (aOR [95%CI] = 2.02 [1.62-2.53]) and vertebral injuries (1.44 [1.18-1.77]). The least absolute shrinkage and selection operator (LASSO) model outperformed all others, achieving 66% [33%-100%] sensitivity and 74% [62%-82%] specificity for stroke prediction, with an area under the receiver operating characteristic curve of 0.79 [0.57-0.95]. This model was integrated into an interactive online tool ( https://grady-bcvi-calc.shinyapps.io/calculator/ ), where patient demographic and injury characteristics can be used to compute baseline stroke risk and estimate stroke risk with AT. CONCLUSION: We developed and evaluated a preliminary predictive model for personalized stroke risk assessment in patients with BCVI using key risk factors. The integration of patient-specific risk-benefit assessments into clinical decision-making could optimize and reduce variability in AT therapy. External validation is warranted to prepare this tool for broad clinical applicability.
11 Apr 2026
Read appraisal →Cerebellum (London, England)
Clinical Spectrum of SEZ6L2 Autoimmunity: A Case Report and Systematic Review
Since the initial report of seizure‑related 6 homolog-like 2 (SEZ6L2) autoimmunity in a patient with cerebellar ataxia and retinopathy, subsequent reports have expanded the phenotype. Although rare, timely recognition is essential because this condition is immunotherapy-responsive. To describe a case of SEZ6L2 autoimmunity and conduct a systematic review (2014-2025) to characterize this rare disease. An 87-year-old man presented with subacute gait disturbance and cognitive decline, progressing to wheelchair dependence within 4 months. Examination showed dysarthria, appendicular and axial ataxia, dysmetria, tremor, and postural instability. Brief Ataxia Rating Scale (BARS) was 19.5. CSF studies and brain MRI were unremarkable, whereas FDG-PET demonstrated diffuse cerebellar hypometabolism. Malignancy screening was negative. After empiric intravenous immunoglobulin (IVIg) 2 g/kg, SEZ6L2 antibody assays returned positive in serum and CSF. Monthly IVIg was continued with mild improvement (BARS 18 at 7 months). Systematic review identified 12 articles (18 patients). Including our case, 19 patients (median age 60 years, 63% female) were analyzed. All had subacute cerebellar ataxia. Cognitive dysfunction and Parkinsonism were seen in 78.9% and 42.1%, respectively. Other symptoms included depression, pyramidal signs, nausea, tremor, sleep disturbance, and dysautonomia. Treatment was heterogeneous, with 47.4% showing partial improvement. SEZ6L2 autoimmunity is a rare, immunotherapy-responsive cause of subacute cerebellar ataxia, often with parkinsonism and cognitive changes. Presentations may extend beyond these manifestations.
11 Apr 2026
Read appraisal →Scientific data
Continuous observation of Parkinsonian symptoms using symptom diaries & wearable accelerometry
Treatment adjustments in Parkinson's Disease (PD) are often based on clinical evaluations at single time points which are insufficient to adequately assess real-life motor fluctuations. Patient-written symptom diaries on the other hand are highly subjective and require well-educated and adherent patients to provide reliable results. Wearable accelerometry might provide a reliable, objective, and continuous diagnostic method to assess PD motor symptoms & fluctuations. However, large datasets of simultaneous sensor data and symptom diaries are needed for such method development and validation. We here provide a well-described, open-science dataset of simultaneous, bilateral, wrist-worn accelerometry and symptom diary data from 66 participants (41 male, 25 female) with PD. On average, participants provided data for 6.0 consecutive days resulting in a total of 393.8 days for the dataset as a whole. Symptom diaries include data on kinetic state, tremor, freezing of gait, falls, and PD-related medication intake. Further demographic information is also provided. This dataset will support the development and validation of accelerometry-based approaches to assessing motor symptoms and fluctuations in PD.
11 Apr 2026
Read appraisal →Neurology
Neurovascular Unit Dysfunction and Cerebral Small Vessel Disease in Parkinson Disease: A Systematic Review and a Meta Analysis
BACKGROUND AND OBJECTIVES: Parkinson disease (PD) pathogenesis remains incompletely understood; beyond nigrostriatal loss, nondopaminergic mechanisms including neurovascular unit dysfunction may contribute to disability. Cerebral small vessel disease (CSVD) burden reflects neurovascular dysfunction in the form of white matter hyperintensities (WMH), lacunes, cerebral microbleeds (CMB), and enlarged perivascular spaces (ePVS). In PD cohorts, CSVD burden correlates with worse motor and gait scores. The aim of the study was to explore whether patients with PD exhibit greater CSVD burden than healthy controls (HC). METHODS: We conducted a PRISMA-conformant systematic review and meta-analysis of studies including adults with idiopathic PD and a HC group that presented data comparing CSVD burden in these 2 groups. Six databases (MEDLINE, Embase, CINAHL Plus, CENTRAL, Scopus, and Web of Science) were searched on May 14 and 15, 2024. Two reviewers independently screened records, extracted data, and assessed risk of bias, with discrepancies resolved by consensus. Continuous outcomes were pooled as standardized mean differences (SMD); dichotomous outcomes as odds ratios (ORs). We evaluated small-study effects for pooled analyses with 10 or more studies using funnel plots and Egger regression test. RESULTS: We examined 13,403 records. Forty-six studies (45 cross-sectional) met inclusion criteria, totaling 3,817 PD and 2,593 HC (mean ages: 66.9 and 66.5, respectively). WMH volume (k = 21) was higher in PD (SMD 0.36, 95% CI 0.11-0.62). Visual ratings also indicated higher WMH in PD: global (k = 14) SMD 0.27 (95% CI 0.08-0.46); periventricular (k = 11) SMD 0.32 (95% CI 0.12-0.51); deep (k = 8) SMD 0.20 (95% CI 0.09-0.31). Differences in CMB (k = 6; OR 1.18, 95% CI 0.57-2.42) and lacunes (k = 4; OR 1.48, 95% CI 0.58-3.78) were not significant. ePVS results were heterogeneous but trended toward greater burden in PD, most notably in the midbrain (k = 3; SMD 1.80, 0.15-3.44). Overall evidence quality was rated as low, reflecting the observational nature of the included studies. DISCUSSION: Our analysis showed PD to be associated with greater WMH burden and increased midbrain ePVS. Pooled differences in CMB and lacunes were not significant. Substantial heterogeneity and cross-sectional designs limit certainty; standardized imaging and prospective cohorts are needed to define mechanisms and clinical implications.
10 Apr 2026
Read appraisal →ACS applied materials & interfaces
NeuroTex: Functionalized Hybrid Polymer-Metal Textile for Soft, Robust, and Dry Auricular Neural Interfaces
Wearable bioelectronics would benefit from dry, easy-to-apply electrodes for sensing physiological signals. Bioelectronic interfaces must maintain high conductivity, low electrode-skin impedance, and reliable coupling between the electrode and skin under sweat, motion, and prolonged use. Metallic textiles offer good conductivity and stable connections but are hydrophobic, prone to cracking and oxidation, and dependent on conductive gels to achieve the low impedance required for low-amplitude electrophysiological signals. Here, we introduce NeuroTex, a commercially available silver nanowire textile that is PEDOT:PSS-functionalized to improve the electrode-skin-electronics interface. The PEDOT:PSS coating reduced the area-normalized resistance of the textile by almost 50%, suppressed silver oxidation in phosphate-buffered saline, and lowered the contact impedance by more than 85%. Mechanical evaluations under torsion, stretching, bending, and low-force fatigue confirmed the preserved fiber integrity and intact electronic connectivity. Furthermore, the soft and hydrophilic properties of the coating enhanced the conformal skin interfacing. Integrated into an in-ear monitoring platform, NeuroTex enabled multimodal biosignal recordings of in-ear electroencephalography (ieEEG), electro-oculography (ieEOG), electromyography (ieEMG), and electrocardiography (ieECG). Reliable acquisition of the technically demanding ieEEG signal was achieved, demonstrating alpha-modulation responses comparable to those of gel-based electrodes. ieEEG recordings recorded over several hours captured distinct sleep patterns to further confirm stable performance during natural sleep. These results establish NeuroTex as a gel-free, mechanically resilient, and electrochemically stable interface suitable for wearable physiological monitoring.
10 Apr 2026
Read appraisal →Scientific reports
Association between estimated plasma volume status and all-cause mortality in critically ill patients with non-traumatic subarachnoid hemorrhage: analysis of the MIMIC-IV database
BACKGROUND: The estimated plasma volume status (ePVS) is an independent risk factor for poor prognosis in certain cardiovascular disease populations. However, the relationship between ePVS and prognosis in critically ill patients with non-traumatic subarachnoid hemorrhage (NSAH) remains unclear. This study aims to explore the association between ePVS and all-cause mortality (ACM) in NSAH patients. METHODS: A total of 750 NSAH patients were included in this retrospective study. The primary outcome was 1-month ACM. A Cox proportional hazards model was constructed to examine the relationship between ePVS and ACM in critically ill NSAH patients. A restricted cubic spline model was used to assess the relationship between ePVS and the outcome. RESULTS: In this patient cohort, 1-month, 3-month, and 1-year ACM rates were 21%, 25%, and 29%, respectively. After adjusting for potential confounders, we found a U-shaped relationship between ePVS and 1-month ACM, with an inflection point at 3.94. Specifically, when ePVS is below the inflection point, each unit increase in ePVS reduces the mortality risk by 56% [HR, 0.44 (95% CI 0.25, 0.76)]. However, once the inflection point is surpassed, each unit increase in ePVS increases the mortality risk by 1.8 times [HR, 2.80 (95% CI 1.58, 4.97)]. CONCLUSION: We identified a U-shaped relationship between ePVS and ACM in critically ill patients with NSAH, suggesting both low and high ePVS levels may portend adverse outcomes. Prospective multicenter studies are warranted to validate these findings and elucidate underlying mechanisms.
10 Apr 2026
Read appraisal →Scientific reports
SPG601-associated modulation of resting-state EEG and improvement in executive function in a fragile X syndrome randomized controlled crossover study
Fragile X syndrome (FXS) is the most common inherited cause of intellectual disability and autism spectrum disorder. Despite extensive research, no targeted treatments exist for the core symptoms of FXS. SPG601 represents the first BK channel activator to enter clinical testing for FXS, designed to restore synaptic function by correcting specific ion channel dysfunction downstream of fragile X messenger ribonucleoprotein protein (FMRP) loss. We conducted a randomized, double-blind, placebo-controlled, 2-period balanced crossover study in 10 adult men with genetically confirmed full-mutation fragile X syndrome. Participants received single doses of SPG601 800 mg and placebo separated by a 1-week washout period. Given the first in FXS nature of the study, the safety and tolerability of SPG601 were evaluated as a primary outcome. Additional endpoints included resting-state EEG power spectral density analysis across predefined frequency bands and auditory-evoked gamma oscillations combined with cognitive assessments using validated instruments and clinical global impressions scales. Among EEG measurements, excessive high frequency gamma band activity has been most extensively validated as a biomarker across species in FXS and has demonstrated correlation with severity of the human FXS phenotype and therefore served as the primary neurophysiologic endpoint. SPG601 was well tolerated with a favorable safety profile. SPG601 demonstrated significant modulation of resting-state EEG power spectral density compared to placebo. Significant treatment effects were observed for gamma power (F = 5.20, p = 0.023), alpha2 power (F = 17.43, p < 0.001), and theta power (F = 7.06, p = 0.008). SPG601 also significantly modulated aperiodic EEG slope (F = 5.28, p = 0.022), indicating alterations in the broadband 1/f component reflecting excitation-inhibition balance. Cognitive improvement was observed in the NIH Toolbox Flanker Inhibitory Control and Attention Test across multiple scoring metrics (p = 0.027). No significant effects were observed on auditory-evoked gamma measures. This study provides the first clinical evidence that SPG601 produces significant neurophysiological changes in FXS, accompanied by a cognitive enhancement in executive function.
10 Apr 2026
Read appraisal →Lupus science & medicine
Unsupervised clustering of clinical and laboratory features of systemic lupus erythematosus: insights from a multicentre cohort
OBJECTIVE: This study explored the relationship between clinical phenotypes and immuno-molecular features of systemic lupus erythematosus (SLE) using unsupervised machine learning and multi-omics integration. METHODS: A multicentre cohort of 1065 SLE patients from five teaching hospitals was studied. Unsupervised clustering was performed using integrated clinical and laboratory data to identify distinct phenotypic clusters. Multi-omics profiling, including proteomics and transcriptomics, was conducted for each cluster, comparing with healthy controls. In addition, 899 patients were prospectively followed for over 1 year to assess therapeutic responses and remission outcomes. RESULTS: Unsupervised clustering identified five distinct clusters of SLE based on clinical phenotypes and immunological signatures, and an intrinsic association was found between each cluster and immune-inflammatory features. Cluster 1 (renal; 14.27%) had predominant renal involvement (87.5%) and high disease activity. Cluster 2 (mucocutaneous; 39.53%) exhibited mainly cutaneous manifestations (72.45%) with mild visceral involvement and the highest rates of achieving low disease activity state (46.42%). Cluster 3 (neuropsychiatric; 14.74%) had a high frequency of neuropsychiatric manifestations (96.18%) and anti-U1 ribonucleoprotein antibodies positivity (42.04%). Cluster 4 (haematologic; 22.63%) was characterised by haematologic involvement, predominantly cytopenia (95.85%), with frequent antiphospholipid antibody positivity and direct Coombs positivity (87.55%). Cluster 5 (cardiopulmonary; 8.83%) exhibited prominent cardiopulmonary involvement (97.87%) and serositis, with enrichment of inflammatory CD161+ regulatory T cells. Transcriptomic and proteomic analyses confirmed distinct molecular signatures and cluster-specific enrichment of biological pathways. Key molecule apolipoprotein A4 was validated using enzyme-linked immunosorbent assay. Regarding therapeutic outcomes at 1 year follow-up, Cluster 2 had the highest proportions achieving lupus low disease activity state (46.42%) and tapering glucocorticoids. Cyclophosphamide use was associated with greater clinical improvement (r=0.284; p=0.023) in Cluster 3, while in Cluster 5, rituximab use was associated with favourable responses (r=0.286; p=0.019). CONCLUSIONS: We identified five SLE clusters with distinct clinical, immunological features and treatment outcomes, demonstrating an intrinsic link between clinical involvement and immune-inflammatory alterations.
9 Apr 2026
Read appraisal →Science advances
Filament sensing tension: A bionic artificial tendon for self-force-regulated artificial muscle-driven wearable robotics
Artificial muscles have been in development for decades and play a crucial role in the field of wearable robotics. However, a universal device for both sensing and regulating forces in artificial muscles is still absent, rendering scenario-required force control unfeasible. Inspired by Golgi tendon organs, this work proposes bionic ExoTendon for force sensing and regulating in artificial muscles as a general solution. Leveraging the principles of triboelectrification and electrostatic induction, the ExoTendon offers high linearity, minimal hysteresis, and adjustable sensitivity and range, making it suitable as the tendon of artificial muscles. Specifically, the ExoTendon effectively enable adjustment to the pretension of the artificial muscles, yielding various assistive forces under the same input. With the optimized pretension level and closed-loop force control, the artificial muscle-based exosuit achieves self-force regulation, substantially improving walking balance and speed in stroke subjects under low input, demonstrating its promise in wearable robotics and rehabilitation medicine.
9 Apr 2026
Read appraisal →Journal of neurology
Systematic review of movement disorders mislabeled as functional: when incongruence misleads
BACKGROUND: Misdiagnosis of functional movement disorders (FMD) remains a concern for clinicians. We sought to review the phenomenology and clinical features associated with FMD misdiagnosis. METHODS: We conducted a systematic review of case reports, case series, and observational studies of adults diagnosed with FMD and subsequently reclassified as having another neurological or medical condition. PubMed was searched from inception to September 13, 2025, focusing on reported features of inconsistency, incongruence with known diseases, and low-validity features. Isolated (iMD) and mixed (mMD) movement disorders were analyzed separately. RESULTS: Forty-two included studies comprised 150 patients, of which 73 cases underwent detailed analysis. Gait disturbance (35%) and dystonia (27.5%) were the most common iMD phenomenologies (n = 40), while jerks and rigidity (24%) were the most common mMD phenomenology (n = 33). Incongruence with known diseases (50.7%), psychological factors (34.2%), and symptom variability (31.5%) were the main factors associated with misdiagnosis. Application of incongruence alone was associated with the highest rate of FMD misdiagnosis, similar to that of applying neither incongruence nor inconsistency (49% in both cases). Conversely, reliance on inconsistency alone was least associated with an FMD misdiagnosis (13.7%). Misdiagnoses of iMD were more likely in the absence of documented inconsistency (OR 3.94, 95% CI 1.32-12.82). CONCLUSIONS: FMD misdiagnoses, particularly of gait disorders and dystonia, were most commonly associated with the application of incongruence as diagnostic criterion. The lowest rate of FMD misdiagnosis was associated with ascertaining at least two positive neurological signs of inconsistency.
9 Apr 2026
Read appraisal →PloS one
The Relationships between physical activity, sedentary behaviour, sleep, and dementia: A systematic review and meta-analysis of cohort studies
OBJECTIVE: This study aimed to summarize the observational evidence from prospective cohort studies examining the associations of regular physical activity, sedentary behaviour, and sleep duration with incident dementia among community-dwelling adults aged 35 years and older. METHODS: Systematic literature searches (1946 to August 2025) of CINAHL, EMBASE, MEDLINE, PSYCINFO, and SPORTDISCUS were performed. Eligible studies included community-dwelling adults aged 35 + years with at least one year of follow-up and valid measures of movement behaviours and dementia outcomes. Studies were excluded if they included participants with baseline dementia, lacked risk estimates for all-cause dementia. Grey literature was excluded. Random effects meta-analysis generated pooled risk ratio (RR) and 95% confidence intervals (CI). Primary exposures were defined using national thresholds for physical activity, sedentary time, and sleep duration. Subgroup analyses were performed by age and follow-up duration. RESULTS: Forty-nine studies with physical activity (n = 2,855,529), 17 studies on sleep duration (n = 1,344,170), and three studies on sedentary duration (n = 295,809) were included. Regular physical activity significantly reduced the risk of incident dementia (pooled RR = 0.75, 95% CI = 0.68 to 0.82), though heterogeneity was substantial and partially explained by subgroup analyses. Prolonged sedentary behaviour (8 + hours/day sitting) increased dementia risk (RR = 1.27, 95% CI = 1.17 to 1.39) with low heterogeneity. Moreover, both short (<7 hours; RR = 1.18, 95% CI = 1.09 to 1.28) and long (>8 hours; RR = 1.28, 95% CI = 1.15 to 1.43) sleep were linked to higher dementia risk compared with 7-8 hours. Heterogeneity was moderate to substantial. CONCLUSION: Regular physical activity, less sedentary time and appropriate nightly sleep (7-8 h) may be associated with reduced risk of dementia and are potentially modifiable factors in the prevention or delay of dementia. Future studies with middle-aged adults and longer-term follow-up including changes in movement behaviours over time are needed to better understand the relationship between physical activity, sedentary behaviour, and sleep for dementia risk. PROSPERO REGISTRATION: CRD42021272054.
9 Apr 2026
Read appraisal →PloS one
Dizziness in the emergency department and risk of stroke: A systematic review and meta-analysis
BACKGROUND: Dizziness is a frequent emergency department (ED) presentation, and a subset of patients, especially those with isolated dizziness without focal neurological deficits, have stroke but are prone to misdiagnosis and adverse outcomes. Reported stroke prevalence in ED dizziness cohorts is highly heterogeneous, and comprehensive assessments of isolated dizziness remain limited. METHODS: Following PRISMA guidelines, we systematically searched PubMed, Web of Science, Embase, and the Cochrane Library for relevant studies. We included cross-sectional studies reporting stroke prevalence among all ED patients with dizziness or isolated dizziness. A random-effects model was used for meta-analysis to calculate pooled prevalence. Subgroup analyses and Egger's test were employed to explore heterogeneity and publication bias. The diagnostic accuracy of bedside diagnostic tools was also systematically reviewed. RESULTS: Twenty-nine studies involving 161,013 ED patients presenting with dizziness were included. The pooled stroke prevalence among all ED dizziness patients (n = 158,583) was 5.5% (95% CI: 4.1-7.1). Among patients with isolated dizziness (n = 2,559), the pooled prevalence was 13.9% (95% CI: 8.2-20.9), substantially higher than in the overall dizziness cohort. Subgroup analyses indicated diagnostic methods and hospital level as major contributors to heterogeneity. Summary analysis of bedside diagnostic tools showed that HINTS and STANDING examinations have high diagnostic accuracy overall, while the TriAGe+ score can be applied flexibly for screening or confirmation based on different cut-off points. CONCLUSIONS: ED patients with dizziness carry a meaningful, setting-dependent stroke risk. Standardized bedside exams (HINTS, STANDING) improve early triage, and selectively deploying neuroimaging helps prevent missed strokes when risk is high.
9 Apr 2026
Read appraisal →Microbiology spectrum
Evaluation of contezolid's cerebrospinal fluid concentration and safety in tuberculous meningitis patients
Tuberculous meningitis (TBM) is the most severe form of tuberculosis (TB) with high mortality. This study evaluated the cerebrospinal fluid (CSF) concentration and safety of the novel oxazolidinone contezolid compared to linezolid in TBM patients. In this randomized prospective study, 10 TBM patients received either a linezolid-containing (n = 5) or contezolid-containing (n = 5) anti-TB regimen. CSF and blood concentrations were measured at 2 and 6 h post-dose. Peak (2 h) and trough (6 h) concentrations, area under the concentration-time curve (AUC), and safety were assessed. Contezolid CSF concentrations exceeded the Mycobacterium tuberculosis (Mtb) MIC (0.5 μg/mL) at 2 h (median: 1.0806 μg/mL, range: 0.9295-1.3165 μg/mL) but declined by 6 h (median: 0.7920 μg/mL, range: 0.1867-1.0194 μg/mL). Linezolid CSF concentrations were significantly higher than contezolid at both 2 h (median: 3.251 μg/mL, range: 1.9545-4.9636 μg/mL; P = 0.008) and 6 h (median: 1.623 μg/mL, range: 0.941-1.765 μg/mL; P = 0.016), remaining above MIC in all samples at 6 h. The mean CSF AUC for contezolid (4.637 μg·h/mL, 95% CI: 3.599-5.675) was significantly lower than that for linezolid (12.537 μg·h/mL, 95% CI: 7.8797-17.277; P = 0.008). Blood concentrations were higher than CSF for both drugs at all time points. No serious drug-related adverse events occurred. Contezolid effectively penetrates the blood-CSF barrier in TBM patients, achieving CSF concentrations above the MIC for Mtb. Although its CSF exposure was significantly lower than linezolid, its demonstrated penetration and safety profile suggest contezolid warrants further investigation as a potential treatment option for drug-resistant TBM.IMPORTANCETuberculous meningitis (TBM) is the deadliest form of tuberculosis, especially difficult-to-treat drug-resistant TBM. Finding new, effective, and safe medicines is critical. This study provides evidence in TBM patients that a newer antibiotic, contezolid, successfully reaches the infection site in cerebrospinal fluid (CSF) at levels expected to kill Mycobacterium tuberculosis. While linezolid achieved higher levels in CSF, contezolid still reached concentrations predicted to be effective and caused no serious side effects in our study. This is important because contezolid might offer a safer alternative to linezolid, which can have significant long-term toxicity. These promising results suggest contezolid could become a valuable new weapon against refractory drug-resistant TBM, potentially saving lives where current options are limited or too toxic.
9 Apr 2026
Read appraisal →Brain : a journal of neurology
Leveraging machine learning to predict Parkinson's disease using pre-symptomatic proteomics data
In Parkinson's disease, non-motor symptoms precede the characteristic motor manifestations by up to 20 years. However, predicting the disease risk remains challenging. We applied machine learning to 2937 plasma proteins assayed in UK Biobank participants with Parkinson's disease to predict disease outcome up to 14 years before diagnosis. Of the 446 proteins found to be dysregulated in incident cases compared with controls, a subset of 23 predicted disease with an area under the curve (AUC) value of 0.78 in incident and 0.795 in prevalent cases. The utility of the identified proteomic signatures was validated in an independent cohort, based on 16 proteins shared across datasets, leading to a validation AUC of up to 0.76. Parkinson's disease-related pathways, including neuron death and amyloid-β clearance, were enriched up to 9 years before diagnosis. Furthermore, a co-expression network analysis revealed protein modules associated with disease risk and time-to-diagnosis. Our findings underscore the potential of applying machine learning to large-scale proteomics data from pre-symptomatic patients for early disease prediction.
9 Apr 2026
Read appraisal →Brain : a journal of neurology
Multimodal multicentre investigation of diagnostic and prognostic markers in disorders of consciousness
Severely brain-injured patients may enter a spectrum of conditions collectively known as disorders of consciousness. This spectrum includes clinical conditions such as unresponsive wakefulness syndrome or minimally conscious state, where the behavioural assessment of consciousness can often be deceptive. To bridge this dissociation, neuroimaging techniques are employed to identify the residual brain functions. Each neuroimaging modality imperfectly captures distinct aspects of brain preservation-functional, anatomical, or both. In this study, we adopt a comprehensive approach by integrating the neurophysiology and neuroimaging modalities available from the standard and advanced clinical assessments through interpretable machine learning. The electrophysiological modalities included high-density EEG (resting state and task), whereas neuroimaging modalities included anatomical and resting-state functional MRI, diffusion MRI and 18F-fluorodeoxyglucose PET. Our investigation reveals that specific modalities, such as functional assessments, provide comprehensive insights into the currently evaluated state of consciousness, the diagnosis of the patients. Conversely, structural modalities offer valuable information about the patient's evolution within the consciousness spectrum. We validate the proposed analysis with data coming from other centres with different acquisition parameters. Importantly, we demonstrate that model performance improves with an increase in the number of modalities. We observe a higher inter-modality disagreement for minimally conscious state patients and those patients who improve. Lastly, we observe a difference in feature importances between diagnosis and prognosis, with an interaction between modality and anatomical structures: some subcortical markers tend to contribute more to prognosis, while other cortical markers are more informative for diagnosis. This integrative multimodal and machine learning methodology presents a promising avenue for a more nuanced understanding of disorders of consciousness, contributing to enhanced diagnostic precision, prognostic capabilities and the personalization of rehabilitative strategies in clinical practice.
9 Apr 2026
Read appraisal →Journal of the American Heart Association
Effects of Computed Tomography Hyperdense Middle Cerebral Artery Sign on the Efficacy of Endovascular Therapy in Acute Ischemic Stroke With Large Infarcts: A Subgroup Analysis of the ANGEL-ASPECT Trial
BACKGROUND: The hyperdense middle cerebral artery sign (HMCAS) observed on noncontrast computed tomography is associated with thrombus composition and thrombectomy outcomes. The impact of HMCAS on the efficacy of endovascular therapy (EVT) in patients with large core infarcts remains unclear. METHODS: This analysis uses data from the ANGEL-ASPECT (Endovascular Therapy in Acute Anterior Circulation Large Vessel Occlusive Patients With a Large Infarct Core) trial, a multicenter randomized controlled trial conducted in China. Patients with acute ischemic stroke and anterior-circulation large-vessel occlusion were categorized according to whether HMCAS was present on baseline noncontrast computed tomography. The primary outcome was the 90-day modified Rankin Scale score. RESULTS: Of the 432 patients included in this analysis, 33% were HMCAS positive on baseline noncontrast computed tomography. In the EVT-treated patients, patients with HMCAS had worse functional outcomes than those without HMCAS (adjusted relative risk, 0.44 [95% CI, 0.26-0.73]; P=0.002). Patients with HMCAS required a greater number of thrombectomy passes (P<0.001). Among patients without HMCAS, EVT was associated with better functional outcomes than medical management (generalized odds ratio [OR], 2.78 [95% CI, 1.80-4.27]; P<0.001), whereas this benefit was not statistically significant among patients with HMCAS (adjusted OR, 1.69 [95% CI, 0.93-3.08]; P=0.09). There was no significant interaction between HMCAS status and treatment assignment (P=0.19). CONCLUSIONS: In this subgroup analysis comparing EVT with medical management, we found no statistically significant treatment-by-HMCAS status interaction, indicating that the benefits of EVT extend to patients with large core infarcts irrespective of HMCAS status. However, the magnitude of benefit appears greater in patients without HMCAS, whereas those with HMCAS tend to have a poorer overall prognosis. REGISTRATION: URL: https://www.clinicaltrials.gov; Unique Identifier: NCT04551664.
9 Apr 2026
Read appraisal →Neurology
Efficacy of Pyridostigmine in Myasthenia Gravis: A Randomized, Double-Blind, Placebo-Controlled Crossover Trial
BACKGROUND AND OBJECTIVES: Pyridostigmine, an acetylcholinesterase inhibitor, is a symptomatic drug approved for the treatment of myasthenia gravis (MG), but no randomized controlled trials substantiate its widespread use. We aimed to assess the efficacy and cost utility of pyridostigmine in patients with anti-acetylcholine receptor-positive MG (AChR MG). METHODS: A randomized, double-blind, placebo-controlled crossover trial was conducted at Leiden University Medical Center, a tertiary center for the treatment of MG in the Netherlands. Main eligibility criteria were current use of pyridostigmine and a stable dose of other MG treatments. Participants were assigned to a sequence of 2 treatment periods for 5 days separated by a 2-day washout, in which patients either first received placebo and then pyridostigmine, or vice versa. Pyridostigmine dosing matched each participant's prestudy regimen. The primary outcome was change in the Myasthenia Gravis Impairment Index (MGII) score. Secondary efficacy outcome measures included the Myasthenia Gravis Activities of Daily Living (MG-ADL) scale, the Quantitative Myasthenia Gravis (QMG) score, and the revised 15-item Myasthenia Gravis Quality of Life (MG-QOL15r) questionnaire. For the post hoc cost-utility analysis, a mathematical model was developed to translate the observed study results into long-term annual effect on societal costs and quality-adjusted life years (QALYs). RESULTS: A total of 19 patients (median age 59 years, 58% female) were recruited between March 23, 2023, and February 21, 2024. The estimated mean difference in the MGII score between pyridostigmine and placebo interventions was 5.3 (95% CI 1.9-8.7, p = 0.004). Secondary efficacy outcome measures showed estimated mean differences of 1.4 (95% CI 0.5-2.3) for the QMG score, 1.2 (95% CI 0.5-1.8) for the MG-ADL score, and 2.0 (95% CI 0.03-3.91) for the MG-QoL15r score. The post hoc cost-utility analysis showed lower annual societal costs (€6,565, 95% CI €328-€15,945) and annual improved QALYs (0.106, 95% CI 0.019-0.210) for patients using pyridostigmine. DISCUSSION: This trial showed that, in patients with AChR MG chronically treated with pyridostigmine, pyridostigmine demonstrated benefit over placebo across all efficacy outcome measures and substantially reduced societal costs. TRIAL REGISTRATION INFORMATION: The trial was registered at EudraCT (2021-004110-20, registration date: July 12, 2022) and ClinicalTrials.gov (NCT05919407, registration date: June 16, 2023). First patient enrolled: March 23, 2023. CLASSIFICATION OF EVIDENCE: This study provides Class I evidence that, in adults with AChR ab-positive ocular or generalized MG on stable standard-of-care therapy and currently using pyridostigmine, pyridostigmine improves MG symptoms compared with placebo.
9 Apr 2026
Read appraisal →PloS one
Hybrid deep learning and feature selection approach for autism detection from rs-fMRI data
Autism Spectrum Disorder (ASD) is a neurodevelopmental condition that is primarily characterized by deficits in social communication and restricted or repetitive behavioral patterns. Although psychologists contribute significantly to the understanding of ASD, offering insights into its cognitive, emotional, and behavioral dimensions through assessments, diagnoses, therapeutic approaches, and family support, the diagnostic process remains complex. This complexity arises from the diverse manifestations of the disorder and the challenges associated with data sharing. In addition, conventional machine learning approaches for ASD detection may struggle with high-dimensional neuroimaging data and may require careful feature engineering. Consequently, this motivated us to enhance ASD diagnosis by incorporating deep learning (DL) techniques for feature extraction alongside a modified exponential-trigonometric optimization (ETO) algorithm as a feature selection (FS) technique. The modified ETO integrates the Arithmetic Optimization Algorithm (AOA) and the Guided Learning Strategy (GLS) to improve diagnostic performance. To evaluate the effectiveness of the proposed model, we utilized resting-state functional MRI (rs-fMRI) data from the Autism Brain Imaging Data Exchange (ABIDE I). Furthermore, the performance of the proposed model was compared with that of established models. The results indicate that the proposed model achieves competitive and, in most cases, superior performance compared with the benchmark methods, demonstrating superior accuracy, sensitivity, and AUC in diagnosing ASD. On average across the three atlas-based feature sets, the proposed model has an accuracy, sensitivity, and AUC of 73%, 78%, and 79%, respectively.
9 Apr 2026
Read appraisal →Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine
Subjective and objective measures of sleep-related function from the Cardiovascular Endpoints For Obstructive Sleep Apnea with Twelfth Cranial Nerve Stimulation (CARDIOSA-12) trial : Sleep-related functional outcomes in CARDIOSA-12.
PURPOSE: The effect of therapeutic versus partially therapeutic levels of hypoglossal nerve stimulation (HGNS) therapy on cognitive measures and patient-reported outcomes (PROs) was evaluated in patients with moderate-severe obstructive sleep apnea (OSA). METHODS: In a 10-week, double-blind, randomized crossover therapy trial, subjects optimized on HGNS therapy underwent 4 weeks each of active and control (partially therapeutic) HGNS therapy. Cognitive measures (psychomotor vigilance test [PVT], digit symbol substitution test [DSST]) and sleep-related PROs were assessed after each 4-week treatment period. RESULTS: Sixty subjects were randomized and completed the primary study protocol, including PROs. A subset of randomized subjects (n = 43) underwent cognitive testing. This subset was, on average, older (65.7 ± 10.2 years), overweight/obese (body mass index [BMI] 29.7 ± 4.6 kg/m2), and had severe OSA at baseline (apnea-hypopnea index [AHI] 34.2 ± 15.0 events/h). There were no differences in cognitive performance measures between active and control (partially therapeutic) HGNS conditions in all subjects; in the per-protocol analysis, however, improvement in DSST reaction time with active HGNS was demonstrated (mean [95% CI] change on active therapy = - 153.6 [- 285.3, - 22.0] ms; p = 0.025). Significant improvements in all PROs (Epworth Sleepiness Scale, Insomnia Severity Index, Functional Outcomes of Sleep Questionnaire, and Snoring Visual Analog Scale) were observed in the active HGNS condition. CONCLUSIONS: In this secondary analysis of the CARDIOSA-12 randomized crossover trial of subjects using HGNS, subjective PROs, but not objective cognitive measures, were improved with active HGNS compared to control (partially therapeutic) HGNS therapy. These findings warrant additional investigation examining the relationship between subjective and objective neurocognitive outcomes in OSA. The effect of hypoglossal nerve stimulation (HGNS), a novel and promising therapy for obstructive sleep apnea (OSA), on neurocognitive deficits remains underexplored. This study evaluated whether HGNS therapy improves cognitive performance using the psychomotor vigilance test (PVT) and digit symbol substitution test (DSST), along with patient-reported outcomes (PROs) related to sleepiness, snoring, insomnia, and sleep-related function. In the modified intention-to-treat analysis, no significant differences in PVT or DSST outcomes were observed between active and control (partially therapeutic) HGNS therapy; however, in a subset of participants with ≥ 50% reduction in AHI with active HGNS, improvement in DSST reaction time was noted. All PROs significantly improved with active HGNS, suggesting benefits in subjective measures without substantial changes in objective cognitive measures.
9 Apr 2026
Read appraisal →Genome research
spRefine denoises and imputes spatial transcriptomic data with a reference-free framework powered by genomic language model
The analysis of spatial transcriptomic data is hindered by high noise levels and missing gene measurements, challenges that are further compounded by the higher cost of spatial data compared to traditional single-cell data. To overcome this challenge, we introduce spRefine, a deep learning framework that leverages genomic language models to jointly denoise and impute spatial transcriptomic data. Our results demonstrate that spRefine yields more robust cell- and spot-level representations after denoising and imputation, substantially improving data integration. In addition, spRefine serves as a strong framework for model pretraining and the discovery of novel biological signals, as highlighted by multiple downstream applications across data sets of varying scales. Notably, spRefine enhances the accuracy of spatial aging clock estimations and uncovers new aging-related relationships associated with key biological processes, such as neuronal function loss, which offers new insights for analyzing aging effect with spatial transcriptomics.
8 Apr 2026
Read appraisal →Neurology
Efficacy and Safety of Amifampridine in Myasthenia Gravis: A Randomized, Double-Blind, Placebo-Controlled Crossover Trial
BACKGROUND AND OBJECTIVES: Amifampridine, a short-acting potassium-channel blocker, is the first-line treatment for Lambert-Eaton myasthenic syndrome. Its benefit as add-on to pyridostigmine in acetylcholine receptor (AChR)-positive myasthenia gravis (MG) has been proposed, but no randomized controlled trials have been conducted. We aimed to evaluate the efficacy and safety of amifampridine in AChR-MG with insufficient symptom control on pyridostigmine. METHODS: A randomized, double-blind, placebo-controlled crossover trial was conducted at Leiden University Medical Center, a tertiary center for the treatment of MG in the Netherlands, as part 2 of the IMPACT-MG trial. Eligible patients had either completed part 1 (pyridostigmine vs placebo) or failed the initial 2-day pyridostigmine washout, and had an Myasthenia Gravis Impairment Index (MGII) score >10. Participants were randomized (1:1:1) to 1 of 3 treatment sequences. Each treatment period lasted 5 days, with a 2-day washout between treatments, during which participants received either 30 mg or 60 mg of amifampridine modified release, or placebo. The primary outcome was the difference in MGII score between the 2 amifampridine dosages and placebo. Pharmacokinetics, safety, and tolerability were assessed throughout the trial. RESULTS: Between March 2023 and April 2024, 20 patients were enrolled (median age 60 years, 55% female). For the MGII score, the estimated mean differences were -1.0 (95% CI -4.1 to 2.0, p = 0.681) for amifampridine 30 mg vs placebo and -1.7 (95% CI -4.7 to 1.4, p = 0.379) for amifampridine 60 mg vs placebo. No statistically significant association was observed between pharmacokinetic parameters and the MGII. No serious adverse events were reported. Adverse events were more common with amifampridine 30 mg (12 patients [60%]) and 60 mg (15 patients [75%]), compared with placebo (6 patients [30%]), with paresthesia, fatigue, numbness, dizziness, and sleep disturbances most frequently reported. Three patients discontinued amifampridine early due to adverse events. DISCUSSION: The addition of amifampridine to pyridostigmine was not superior to treatment with pyridostigmine alone and was associated with a higher incidence of adverse events. TRIAL REGISTRATION INFORMATION: The trial was registered at EudraCT (2021-004110-20, registration date: July 12, 2022) and ClinicalTrials.gov (NCT05919407, registration date: June 16, 2023). First patient enrolled: March 23, 2023. CLASSIFICATION OF EVIDENCE: This study provides Class I evidence that in patients with AChRAb+ MG, the addition of amifampridine to pyridostigmine was not superior to treatment with pyridostigmine alone and was associated with a higher incidence of adverse events.
8 Apr 2026
Read appraisal →JAMA
Tenecteplase for Acute Non-Large Vessel Occlusion 4.5 to 24 Hours After Ischemic Stroke: The OPTION Randomized Clinical Trial
IMPORTANCE: The efficacy and safety of intravenous tenecteplase in non-large vessel occlusion acute ischemic stroke beyond 4.5 hours after symptom onset remain uncertain. OBJECTIVE: To assess the efficacy and safety of intravenous tenecteplase administered 4.5 to 24 hours after stroke onset in patients with non-large vessel occlusion and salvageable brain tissue. DESIGN, SETTING, AND PARTICIPANTS: This randomized, open-label, blinded end-point clinical trial was conducted at 48 centers in China. A total of 566 patients with non-large vessel occlusion stroke and evidence of potentially salvageable tissue determined on perfusion imaging presenting within 4.5 to 24 hours of the time last seen well were recruited between June 2, 2023, and August 4, 2025 (final follow-up, October 28, 2025). INTERVENTIONS: Patients were randomly assigned 1:1 using a minimization algorithm to receive intravenous tenecteplase (0.25 mg/kg; maximum dose, 25 mg; n = 282) or standard medical treatment (n = 284). MAIN OUTCOMES AND MEASURES: The primary efficacy outcome was an excellent functional outcome, defined as a score of 0 or 1 on the modified Rankin Scale at 90 days. Safety outcomes included symptomatic intracranial hemorrhage within 36 hours and mortality within 90 days. RESULTS: Among the 570 patients randomized, 566 were included in the primary analysis (median age, 68 [IQR, 59-75] years; 196 female [34.6%]). An excellent functional outcome was observed in 123 of 282 patients (43.6%) in the tenecteplase group and 97 of 284 (34.2%) in the control group (risk ratio, 1.28 [95% CI, 1.04-1.57]; P = .02). The incidence of symptomatic intracranial hemorrhage at 2.8% was higher with tenecteplase than with standard medical treatment at 0% (risk difference, 2.85% [95% CI, 1.16%-5.54%]; P = .004), and the mortality at 90 days was 5.0% and 3.2%, respectively (risk ratio, 1.57 [95% CI, 0.69-3.57]; P = .28). CONCLUSIONS AND RELEVANCE: Among patients with non-large vessel occlusion acute ischemic stroke and salvageable brain tissue, intravenous tenecteplase administered 4.5 to 24 hours after onset resulted in a greater likelihood of an excellent functional outcome at 90 days than standard care but had an increased risk of symptomatic intracranial hemorrhage. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT05752916.
8 Apr 2026
Read appraisal →JMIR mHealth and uHealth
A Mobile App-Based Individualized Nonpharmacological Intervention for Behavioral and Psychological Symptoms in Dementia: Pilot Randomized Controlled Trial
BACKGROUND: Behavioral and psychological symptoms of dementia (BPSD) are common and negatively impact both individuals with dementia and their families. Although nonpharmacological interventions are recommended as the first-line treatments, their use in community settings is limited by access and caregiver resources. Existing approaches are often not individualized and rely on institutional or clinician-led delivery. OBJECTIVE: We developed a caregiver-initiated and individualized multimodal mobile app. The app delivers tailored nonpharmacological interventions-such as music therapy, exercise, and reminiscence therapy-based on each user's preferences and functional abilities. This study aimed to evaluate the effectiveness of this mobile app-based intervention in reducing BPSD in community-dwelling persons living with dementia. METHODS: This study used a single-blinded randomized controlled trial design. Participants were recruited from an outpatient clinic of a tertiary hospital, a dementia care center, and 5 home care service centers. A total of 36 dyads participated, each comprising a community-dwelling person living with dementia aged 60 years or older and their primary family caregiver. The dyads were randomly allocated to either the intervention or control group. The intervention group received a caregiver-initiated, multimodal, mobile app-based individualized intervention for 4 weeks, whereas the control group continued with usual care. The primary outcomes were overall BPSD, agitated behavior, and depression. The secondary outcomes were nighttime sleep efficiency and caregiver competency in managing BPSD. Assessments were conducted at baseline, immediately after the intervention, and at a 2-week follow-up. RESULTS: Of the 36 randomized dyads, 33 were included in the final analysis. Although the intervention group showed greater reductions in overall BPSD, agitated behavior, and depression after the intervention, no significant group-by-time interaction effects were observed in the total sample. In the subgroup analysis of participants with clinically significant baseline BPSD, a statistically significant improvement in overall BPSD was found in favor of the intervention group (β=-12.885, 95% CI -24.530 to -1.240; P=.03). No significant effects were observed for either nighttime sleep efficiency or competence in the management of BPSD. CONCLUSIONS: A mobile app-based individualized intervention may offer a flexible, caregiver-initiated approach to managing BPSD in home-care settings. While overall effects were limited, exploratory subgroup findings provided meaningful insights, indicating potential benefits for those with higher baseline symptom severity. The results highlight the need for further research on adaptive personalization and optimized intervention delivery to enhance the clinical effectiveness of digital dementia care.
8 Apr 2026
Read appraisal →Journal of the American Heart Association
Blood Pressure Parameters and Risk of Cognitive Decline and Dementia in Type 2 Diabetes: Results From the ADVANCE Trial
BACKGROUND: Blood pressure (BP) variability and cumulative BP load are significantly associated with cardiovascular disease risk beyond mean systolic BP, but less is known regarding their associations with cognitive decline/dementia and whether these associations differ by cognitive function at baseline or by sex. The aims of this study were to determine associations of different BP parameters with cognitive decline/dementia in patients with type 2 diabetes and explore differences by mild cognitive impairment at baseline and sex. METHODS: Using data from the ADVANCE (Action in Diabetes and Vascular Disease: Preterax and Diamicron Modified Release Controlled Evaluation) study, BP parameters were calculated from an 18-month exposure window comprising measurements at 3, 4, 6, 12, and 18 months, after randomization. Logistic regression was used to estimate the odds ratio (OR) per SD higher and 95% CI for the associations of BP parameters with the composite outcome of cognitive decline (≥3 points from baseline on the Mini-Mental State Examination) or clinical diagnosis of dementia. RESULTS: Of the 11 140 ADVANCE participants, 9586 patients had 5 complete BP measurements within the 18-month exposure window. After a mean follow-up of 3.5 years, 1674 (17.5%) participants were diagnosed with cognitive decline and/or dementia. Overall, variability and baseline pulse pressure (PP), but not BP load, were associated with higher odds of cognitive decline/dementia (OR: variability in systolic BP, 1.11 [95% CI, 1.05-1.17]; diastolic BP, 1.11 [95% CI, 1.05-1.17]; PP, 1.05 [95% CI, 1.00-1.11]; mean arterial pressure, 1.13 [95% CI, 1.07-1.19]; and baseline PP, 1.19 [95% CI, 1.13-1.25]). There were no differences by mild cognitive impairment at baseline or sex. CONCLUSIONS: Higher BP variability and baseline PP, but not mean BP or BP load, were associated with higher odds of cognitive decline/dementia in patients with type 2 diabetes. BP variability and PP may be important therapeutic markers for the preservation of brain health. REGISTRATION: URL: https://clinicaltrials.gov; Unique Identifier: NCT00145925.
8 Apr 2026
Read appraisal →Journal of the American Heart Association
Development and Validation of a Machine Learning Model to Predict Oral Anticoagulant Use in Stroke From Prothrombin Time-International Normalized Ratio and Activated Partial Thromboplastin Time
BACKGROUND: Timely recognition of oral anticoagulant use is critical in acute stroke but is often hampered by impaired consciousness and unavailable medication history. We investigated whether routinely available coagulation tests, prothrombin time-international normalized ratio and activated partial thromboplastin time, paired with machine learning can identify anticoagulant exposure at presentation. METHODS: We performed a multicenter diagnostic modeling study using the Japan Stroke Data Bank. Adults with ischemic or hemorrhagic stroke admitted in 2016 to 2021 comprised the derivation cohort; those in 2022 formed a temporally distinct validation cohort. Three models (random forest, extreme gradient boosting, and neural network) used prothrombin time-international normalized ratio and activated partial thromboplastin time as predictors to classify anticoagulant status: vitamin K antagonist, direct oral anticoagulant, or none. Discrimination (area under the receiver operating characteristic curve and area under the precision-recall curve), calibration, and decision curve analysis were assessed. Model outputs were implemented as bedside probability heat maps. RESULTS: Among 30 767 patients (derivation n=24 857; validation n=5910), vitamin K antagonist/direct oral anticoagulant use was 4.5%/8.2% in derivation and 2.8%/9.5% in validation. In the validation cohort, the neural network yielded the best performance: area under the receiver operating characteristic curve, 0.933 (95% CI, 0.908-0.955) and area under the precision-recall curve, 0.52 (95% CI, 0.44-0.61) for vitamin K antagonist; area under the receiver operating characteristic curve, 0.841 (95% CI, 0.822-0.858) and area under the precision-recall curve, 0.39 (95% CI, 0.35-0.43) for direct oral anticoagulant, with preserved calibration and highest net benefit across clinically relevant thresholds. Heat maps summarized class probabilities over the prothrombin time-international normalized ratio×activated partial thromboplastin time space to support rapid inference. CONCLUSIONS: A simple machine learning model using only prothrombin time-international normalized ratio and activated partial thromboplastin time classified vitamin K antagonist and direct oral anticoagulant exposure with high accuracy and practical clinical utility. This tool assists emergency decision-making when medication history is unknown, supporting the safety and timeliness of acute stroke care.
8 Apr 2026
Read appraisal →Physiological measurement
NeuroSleep: neuromorphic event-driven single-channel EEG sleep staging for edge-efficient sensing
Objective. Reliable, continuous neural sensing on wearable edge platforms is fundamental to long-term health monitoring; however, for electroencephalography (EEG)-based sleep monitoring, dense high-frequency processing is often computationally prohibitive under tight energy budgets.Approach. To address this bottleneck, this paper proposes NeuroSleep, an integrated event-driven sensing and inference system for energy-efficient sleep staging. NeuroSleep first converts raw EEG into complementary multi-scale bipolar event streams using Residual Adaptive Multi-Scale Delta Modulation, enabling an explicit fidelity-sparsity trade-off at the sensing front end. Furthermore, NeuroSleep adopts a hierarchical inference architecture that comprises an Event-based Adaptive Multi-scale Response module for local feature extraction, a Local Temporal-Attention Module for context aggregation, and an epoch-leaky integrate-and-fire module to capture long-term state persistence.Main results. Experimental results using subject-independent 5-fold cross-validation on the sleep-EDF expanded sleep-cassette subset (78 subjects, 153 overnight recordings) with single-channel EEG demonstrate that NeuroSleep achieves a mean accuracy of 74.2% with only 0.932 M parameters while reducing sparsity-adjusted effective operations by approximately 53.6% relative to dense processing. Compared to the representative dense Transformer baseline, NeuroSleep improves accuracy by 7.5% with a 45.8% reduction in computational load.Significance. By coupling neuromorphic event encoding with state-aware context modeling, NeuroSleep offers a deployment-oriented framework for single-channel sleep staging that reduces redundant high-rate processing and improves energy scalability for wearable and edge platforms.
8 Apr 2026
Read appraisal →Journal of neural engineering
Large-scale training data enhances silent speech decoding with around-ear EEG.
Objective. Silent speech decoding (SSD) offers a potential communication alternative for individuals with impaired vocalization. However, conventional multi-electrode electroencephalography (EEG) or facial electromyography (EMG) systems require cumbersome preparation and are unsuitable for daily use. This study evaluates the practicality of SSD using a wearable around-ear EEG device, focusing on data scaling, cross-subject transfer, vocabulary extensibility, and online decoding performance.Approach. We collected 72 h of around-ear EEG from 24 healthy participants and one individual with incomplete locked-in syndrome (LIS) during silent, vocalized, and attempted speech, and integrated these around-ear EEG recordings with prior EMG + high-density EEG datasets, yielding 282.4 total h of training data. Using a 64-word classification task as the evaluation metric, we assessed: (1) whether larger datasets improve around-ear EEG-based SSD, (2) whether healthy-participant data supplement limited LIS-participant data despite articulatory differences, (3) transferability to unseen vocabulary, and (4) online user-interface performance.Main results. Large-scale EEG/EMG data improved SSD accuracy in both healthy participants and the LIS participant. Training on the heterogeneous dataset achieved 56.6% accuracy for healthy users and 47.3% for the LIS participant. Fine-tuning this decoder for new vocabulary increased the accuracy by 22 percentage points relative to training from scratch. Regression analysis showed that, for decoding in the LIS participant, data from the LIS participant contributed approximately four times the weight of healthy-participant data, quantifying data strategies for SSD. Online experiments achieved top-1/top-5 accuracies of 47.2%/76.0% for healthy users and 26.5%/49.1% for the LIS participant.Significance. The results indicate that lightweight, commercially feasible around-ear EEG can enable practical SSD when combined with large-scale healthy-participant data, supporting online operation. Moreover, models trained on a 64-word vocabulary facilitate decoding of a new vocabulary, providing a path toward SSD systems requiring minimal LIS-participant data. This study advances non-invasive SSD systems suitable for everyday communication.
8 Apr 2026
Read appraisal →BMJ open
Adverse childhood experiences and cognitive outcomes among older adults in low-income and middle-income countries: a systematic review
OBJECTIVES: While adverse childhood experiences (ACEs) have been consistently linked to poorer cognitive outcomes in later life, far less is known about ACEs' contribution to dementia and cognitive ageing risk in low-income and middle-income countries (LMICs), despite their growing and disproportionate share of global Alzheimer's disease and related dementias (ADRD) burden. This study aimed to systematically review existing evidence on the association between individual ACEs and cognitive outcomes among adults aged 40 years and older in LMIC settings. DESIGN: Systematic review and narrative synthesis. DATA SOURCE: We searched Medline, Embase, PsycINFO and CINAHL from the inception of each database to January 2026. ELIGIBILITY CRITERIA: Observational studies examining the association between individual ACEs and cognitive outcomes among adults aged 40 and older in LMICs. DATA EXTRACTION AND SYNTHESIS: Two reviewers independently screened each record, assessed risk of bias using the Joanna Briggs Institute critical appraisal tool and extracted data. Results were illustrated using descriptive forest plots and a narrative synthesis. RESULTS: Our systematic review included 14 studies, primarily from upper-middle-income countries, that assessed the relationship between individual ACEs and cognitive impairment (n=4) and function (n=10). 10 studies reported significant associations between ACEs and poorer cognitive function and increased risk of cognitive impairment. No study assessed the association between ACEs and ADRD. Parental death, neglect and mental health issues in the family showed consistent associations with cognitive outcomes, whereas experiencing hunger and poor health in childhood showed inconsistent associations. CONCLUSIONS: Our review corroborated evidence from high-income countries that exposure to ACEs is associated with a long-term risk of poorer cognitive outcomes among older populations in LMICs. However, the available literature remains limited, with most studies originating from upper-middle-income countries, few examining cognitive impairment and none assessing ADRD. These findings underscore substantial gaps in knowledge and highlight the need for research across diverse LMIC settings to clarify the role of broader psychosocial and contextual childhood experiences in shaping cognitive ageing and dementia risk. PROSPERO REGISTRATION NUMBER: CRD42024501816.
8 Apr 2026
Read appraisal →Journal of neural engineering
EDAPT: towards calibration-free BCIs with continual online adaptation
Objective.Brain-computer interfaces (BCIs) suffer from accuracy degradation as neural signals drift over time and vary across users, requiring frequent recalibration that limits practical deployment. Our goal is to develop a framework that eliminates the need for separate calibration phases by enabling continual, real-time model adaptation to new users and changing signal characteristics.Approach.We propose EDAPT, a task- and model-agnostic framework for continual online learning. EDAPT first establishes a robust baseline decoder through population-level pretraining (PRE) on data from multiple users. It then personalizes this model during deployment using supervised continual finetuning on a trial-by-trial basis. Due to its modular design, EDAPT can be composed with unsupervised domain adaptation (UDA) techniques to further address distribution shifts.Main results.We validate EDAPT across nine datasets, three BCI paradigms, and four deep learning architectures. EDAPT consistently improves decoding accuracy over static models for nearly all subjects and datasets, raising mean balanced accuracy from 0.80 to 0.87 on representative datasets (table3). Ablation studies confirm that the combination of PRE and online finetuning is the primary driver of this performance gain, with further improvements on some datasets when using UDA techniques. We demonstrate real-time feasibility of the framework, with adaptation latencies under 200 ms on consumer-grade hardware. Our scaling analysis further reveals that decoding accuracy is primarily determined by the total pretraining data budget, rather than its specific allocation between subjects and trials.Significance.These findings demonstrate that continual online learning is a practical and effective strategy for creating high-performance, user-adaptive BCIs. By systematically addressing the bottleneck of model recalibration, EDAPT reduces a major barrier to the widespread adoption of BCI technology and helps advance neurotechnology toward robust, user-friendly, real-world applications.
8 Apr 2026
Read appraisal →Proceedings of the National Academy of Sciences of the United States of America
AI-derived therapeutic development of a serotonin receptor-targeting drug for the treatment of opioid use disorder
The opioid epidemic has led to a devastating loss of life nationwide. Of those dependent on opioids, many individuals desire to quit or reduce use, but their efforts are often unsuccessful given the powerful reinforcing properties associated with opioid drugs, including fentanyl. Here, we developed a therapeutic drug based on an AI-based platform, which was rationally designed to identify markers of dysregulation from human drug user postmortem brain tissue. Two top candidate compounds, GATC-021 and GATC-1021, were synthesized and validated with in vitro screening for target specificity. Thereafter, the drug candidates were examined for their effectiveness in modulating opioid reinforcement and intake. GATC-1021 emerged as the most efficacious compound as it significantly decreased fentanyl intake in both male and female rats, without any notable side effects. GATC-1021 was further found to modulate gene expression patterns in addiction-relevant brain regions following fentanyl-induced alterations. These findings validate GATC's AI-based platform with its polypharmacy-focused drug development approach and further support the clinical potential of GATC-1021 as a promising therapeutic for those suffering from opioid use disorder.
7 Apr 2026
Read appraisal →PloS one
A multi-modal data fusion and real-time monitoring on stroke risk prediction using federated learning
Predicting the risk of stroke is one of the critical problems in healthcare, which necessitates efficient solutions for providing accurate and prompt risk assessments while preserving data confidentiality. This work proposes a new framework using Federated Learning (FL) to combine Multi-Layer Perceptron (MLP) and Gated Recurrent Unit (GRU) models that are essential in analyzing multimodal data. Implemented in Python, the approach incorporates two datasets: Dataset 1, which consists of Demographic data medical history, and lifestyle data, and the second dataset, which includes the normal condition and the affected stroke condition CT scan images. Imputation of missing values, feature normalization by Min-Max scaling, and handling of imbalanced classes with SMOTE make the data pre-processing procedures exhaustive. In FL architecture three clients -Client A, Client B, and Client C - process a split multimodal dataset containing static and sequential information. Each client independently trains an MLP-GRU model. Each is applied with MLP handling static features from Dataset 1 and GRU handling sequential features from Dataset 2. To update models, Federated Averaging is used on a central server, to create a global model that is then returned to the clients for further refinement. The accuracy of the proposed method averages 99.00% and surpasses other models by 2.5% including CNN, LSTM, Random Forest, and SVM. By enhancing MLP with GRU and applying them to a privacy-preserving FL framework,The study addresses the fragmented use of multimodal medical data, where clinical records and imaging are generally evaluated separately, resulting in inadequate diagnostic support. The strategy integrates complementary modalities to create a more comprehensive perspective of patient health, enhancing healthcare predictive accuracy and decision-making. This incentive is essential for improving computational methods and linking technical advancement with medical objectives like fast diagnosis and therapy planning. The introduction emphasises the therapeutic necessity of harmonising organized and unstructured data to reduce diagnostic ambiguity. A translational approach is used to discuss how multimodal integration might improve clinical workflows, develop collaborative healthcare systems, and support sustainable medical practices. This repeated emphasis links methodological advances to real-world healthcare issues, boosting the study's academic relevance sets.
7 Apr 2026
Read appraisal →JMIR medical informatics
Deep Learning for Content-Based Medical Image Retrieval in Picture Archiving and Communication Systems for Brain Tumor Detection: Algorithm Development and Validation
BACKGROUND: Advances in medical imaging have led to massive archives, yet navigating these datasets remains challenging due to the limitations of traditional text-based search engines. While content-based medical image retrieval (CBMIR) offers a visual feature-based solution to enhance clinical workflows and research, its operational integration into picture archiving and communication systems (PACS) remains a significant bottleneck. Despite the progress in deep learning for feature extraction, CBMIR tools are rarely integrated and effectively implemented within existing radiology information systems due to complex protocol barriers. OBJECTIVE: To address these challenges, this study develops a CBMIR system meticulously designed to cater to 7 distinct types of brain tumors as seen in brain magnetic resonance images. Our system is tailored to assist radiologists and health care professionals in efficiently retrieving pertinent historical medical images, thereby providing quantitative decision support for radiologists and facilitating evidence-based case comparison, with the potential to improve retrieval efficiency and clinical workflow, rather than directly enhancing diagnostic accuracy. METHODS: The dataset used in this study was collected from a single medical center and is not publicly available. The core innovation is a state-of-the-art deep learning-based feature extraction algorithm specifically engineered for the CBMIR system. We use GoogLeNet as the primary architecture, incorporating generalized mean pooling to capture nuanced local features and an embedding layer for dimension reduction. Crucially, we address the integration gap by harmonizing 2 open-source projects to successfully embed the CBMIR system into a functional PACS environment via standard protocols. RESULTS: The image dataset contains 658 participants with 15,873 images collected from 2000 to 2017. The empirical findings of our research demonstrate the performance and robustness of the proposed CBMIR system. Our system achieves a remarkable mean average precision score of 89.16% and an equally impressive Precision@10 score of 94.08%. These metrics affirm the system's efficacy in retrieving relevant medical images. Furthermore, we successfully integrate the CBMIR system into a PACS by successfully harmonizing 2 open-source projects. CONCLUSIONS: This study presents the design and implementation of a PACS-integrated CBMIR system for brain magnetic resonance imaging, and the experimental results demonstrate that the system can achieve efficient and accurate image retrieval within a clinical workflow.
7 Apr 2026
Read appraisal →Translational vision science & technology
Diagnosis of High Intracranial Pressure by Non-Optic Nerve Retinal Image Features
PURPOSE: To assess the performance of an artificial intelligence deep learning (DL) model compared with neuro-ophthalmologists for the classification of subjects with elevated intracranial pressure (ICP) and papilledema versus control subjects, based on retinal images with and without masking the optic nerve heads (ONHs). METHODS: Widefield retinal images were obtained in 32 subjects (70 images) with elevated ICP and papilledema and 31 control subjects (62 images). ONH-unmasked images were generated by cropping the image to a 30° circular region. ONH-masked images were generated by masking the ONH and peripapillary region. Classification was performed using a convolutional neural network model and by two neuro-ophthalmologists (graders). RESULTS: For the ONH-unmasked images, the classification accuracies of the model, grader 1, and grader 2 were 83% (area under the receiver operating characteristic curve [AUC] = 0.94), 93%, and 86%, respectively. For the ONH-masked images, the classification accuracies of the model, grader 1, and grader 2 were 79% (AUC = 0.90), 66%, and 76%, respectively. The classification performance of the DL model and graders did not significantly differ for both datasets (P ≥ 0.38). There was no significant effect of ONH masking on the performance of the DL model (P = 1.0) and grader 2 (P = 0.38), whereas the performance of grader 1 was significantly reduced (P = 0.02). CONCLUSIONS: Both expert graders and the DL models demonstrated excellent performance for classifying retinal images of subjects with elevated and normal ICP using images with or without masking the ONH. TRANSLATIONAL RELEVANCE: Methods for assessment of non-optic nerve retinal image features have the potential to improve diagnosis and monitoring the progression and response to treatment of elevated ICP.
7 Apr 2026
Read appraisal →Journal of neurology
Accuracy of muscle ultrasonography in detecting fasciculations for the diagnosis of amyotrophic lateral sclerosis: a systematic review and meta-analysis
PURPOSE: This systematic review and meta-analysis aims to evaluate the diagnostic accuracy of muscle ultrasonography in detecting fasciculations for the diagnosis of amyotrophic lateral sclerosis (ALS). METHODS: Following PRISMA-DTA guidelines, we systematically searched PubMed, Embase, Cochrane Library, Ovid Medline, Sinomed, Web of Science, CNKI and VIP for studies published up to July 8, 2025 that evaluated muscle ultrasonography to detect fasciculations for ALS diagnosis. The study protocol was registered in PROSPERO (CRD420251057866). Studies were screened using predefined inclusion and exclusion criteria and data were extracted. Risk of bias was assessed with QUADAS-2. Statistical analyses (Stata 16.0 and R 4.5.1 with the "midas," "metandi," and "mada" packages) were used to calculate pooled sensitivity (Sen), specificity (Spe), positive likelihood ratio (LR+), negative likelihood ratio (LR-), and diagnostic odds ratio (DOR). We constructed forest plots, hierarchical summary receiver operating characteristic (HSROC) curves, summary ROC (SROC) curves and calculated the area under the SROC curve (AUC). Univariate meta-regression and subgroup analyses explored sources of heterogeneity. Publication bias was assessed using Deeks' funnel plot asymmetry test. Fagan nomograms were also used to illustrate the changes from pre-test to post-test probability and to enhance clinical interpretability. RESULTS: Thirteen studies involving 1176 participants met the inclusion criteria. Muscle ultrasonography for fasciculation detection in ALS yielded a pooled sensitivity of 0.87 (95% CI 0.83-0.91) and specificity of 0.91 (95% CI 0.86-0.94). The pooled LR+ was 9.81 (95% CI 6.25-15.40) and LR- was 0.14 (95% CI 0.10-0.19), with a DOR of 70.03 (95% CI 41.72-117.56). The area under the SROC curve was 0.94 (95% CI 0.91-0.95). Meta-regression identified scan duration as a primary factor influencing diagnostic accuracy, with scan durations ≥ 30 s associated with higher sensitivity but relatively lower specificity. Deeks' funnel plot showed no significant asymmetry (p = 0.61), indicating no notable publication bias. Fagan nomograms showed that, at a pre-test probability of 30%, the post-test probability increased to 81% after a positive MUS result and decreased to 6% after a negative result. CONCLUSION: Muscle ultrasonography demonstrates good pooled diagnostic accuracy for detecting fasciculations in ALS and may serve as a useful adjunct to electrodiagnostic evaluation. Scan duration appears to significantly affect the diagnostic performance, with longer scanning improving sensitivity at the cost of reduced specificity. We speculate that prolonged scanning may be more useful in clinical scenarios where fasciculations are subtle or atypical, whereas shorter scanning may be sufficient when fasciculations are already readily apparent. Nevertheless, further large-scale prospective studies are needed to validate standardized scanning protocols and to better define the clinical role of MUS in ALS diagnostic pathways.
7 Apr 2026
Read appraisal →Neuroscience
Comparative analysis of key phagocytic genes in humans and mice using machine learning integrated with single-cell RNA sequencing
Microglial phagocytosis is essential for neurological recovery after intracerebral hemorrhage (ICH). Using single-cell RNA sequencing, we compared microglial responses in murine and human ICH and identified striking species-specific temporal patterns. Murine microglia exhibited a sustained enhancement of phagocytic activity, whereas human microglia showed only a transient increase followed by a decline and persistent inflammation. To identify genes associated with phagocytic differences, we evaluated five machine learning models and selected XGBoost as the best-performing model. This analysis identified Tlr2 in mice and CLEC7A in humans as genes associated with microglial phagocytic status. Inferred transcription factor activity analysis further revealed stronger phagocytosis- and inflammation-associated transcriptional activity in murine phagocytic microglial subclusters, whereas human microglia were predominantly characterized by inflammation-associated transcription factors. Consistent with these results, Tlr2 expression was markedly increased at day 14 in single-cell data, and immunostaining confirmed its colocalization with IBA1+ microglia and upregulation at days 3 and 7 after ICH. Together, our findings demonstrate that integrating single-cell RNA sequencing with machine learning facilitates the identification of phagocytosis-associated genes and reveals both conserved and divergent patterns of microglial phagocytosis, providing new insights into species-specific responses to ICH.
6 Apr 2026
Read appraisal →Neuroscience
Effects of single/dual-target transcranial alternating current stimulation on working memory in healthy individuals
This study compared the effects of single- and dual-target high-definition transcranial alternating current stimulation(tACS) on working memory and explored the associated neural mechanisms using functional near-infrared spectroscopy(fNIRS). In a randomized, single-blind, sham-controlled design, 45 healthy participants received either dual-target stimulation, single-target stimulation, or sham stimulation, before and after which working memory performance was performed using n-back tasks with simultaneous fNIRS recording. Behavioral results showed that dual-target stimulation improved working memory performance, particularly under higher cognitive load, and yielded a greater proportion of responders when compared with sham stimulation, whereas single-target stimulation induced relatively limited effects. fNIRS findings indicated that dual-target stimulation modulated task-related cortical activation in prefrontal and temporal regions and enhanced both intra- and inter-hemispheric functional connectivity. Moreover, improvements in task accuracy were associated with strengthened functional connectivity within the frontoparietal and default mode networks. These findings suggest that dual-target tACS may be a promising strategy for enhancing working memory and highlight the importance of network-level modulation in cognitive enhancement.
6 Apr 2026
Read appraisal →Neuroscience
Limitations and opportunities in multi-omics integration for neurodevelopmental, neurodegenerative and psychiatric disorders: A systematic review
Recent advances in high-throughput technologies have led to an increased generation of biological data across genomics, transcriptomics, proteomics, epigenomics, and metabolomics. However, a major challenge remains: effectively integrating these multi-omics datasets to allow a more holistic understanding of the complex, interconnected mechanisms underlying human diseases. Neurodevelopmental, neurodegenerative, and psychiatric disorders are particularly multifactorial and heterogeneous, making them candidates for multi-omics approaches. In this context, this systematic review assesses the current state of multi-omics integration in neurological research. Records retrieved from five major databases were processed, and 156 studies were included for further analysis. The most frequently studied conditions were Alzheimer's Disease, Depressive Disorder and Parkinson's Disease, with epigenomics-transcriptomics and metagenomics-metabolomics emerging as the most common omics pairings. The field remains dominated by studies integrating pairs of omics layers. Only a limited number of computational tools are currently being applied to the integration of more than two omics layers, highlighting a gap in comprehensive multi-omics modeling. Despite progress, key challenges persist, including data accessibility and the need for standardized frameworks to allow cross-study comparisons. Moreover, most computational findings lack experimental validation in wet-laboratory settings. Future research should address these challenges, develop scalable algorithms for integrating multi-omics data, and leverage large, open-access datasets. Integrating computational predictions with experimental validation could help researchers prioritize high-confidence biomarkers relevant to clinical applications. Collaborative efforts among bioinformaticians, clinicians, and experimentalists will be essential to translating these advances into clinically actionable solutions.
6 Apr 2026
Read appraisal →PloS one
Vision Transformer attention alignment with human visual perception in aesthetic object evaluation
Visual attention mechanisms play a crucial role in human perception and aesthetic evaluation. Recent advances in Vision Transformers (ViTs) have demonstrated remarkable capabilities in computer vision tasks, yet their alignment with human visual attention patterns remains underexplored, particularly in aesthetic contexts. This study investigates the correlation between human visual attention and ViT attention mechanisms when evaluating handcrafted objects. We conducted an eye-tracking experiment with 30 participants (9 female, 21 male, mean age 24.6 years) who viewed 20 artisanal objects comprising basketry bags and ginger jars. Using a Pupil Labs eye-tracker, we recorded gaze patterns and generated heatmaps representing human visual attention. Simultaneously, we analyzed the same objects using a pre-trained ViT model with DINO (Self-DIstillation with NO Labels), extracting attention maps from each of the 12 attention heads. We compared human and ViT attention distributions using four complementary metrics-Kullback-Leibler divergence, Structural Similarity Index (SSIM), Pearson's Correlation Coefficient (CC), and Similarity (SIM)-across varying Gaussian parameters ([Formula: see text]), yielding 1,152,000 distance evaluations. Additionally, we performed Areas of Interest (AOI) analysis to quantify ViT attention concentration within object regions. Statistical analysis revealed optimal correlation at [Formula: see text], with attention head #12 showing the strongest alignment with human visual patterns across all metrics. Significant differences were found between attention heads, with heads #7 and #9 demonstrating the greatest divergence from human attention ([Formula: see text]), Tukey HSD test). AOI analysis confirmed that all ViT heads concentrated attention significantly more within object regions than background areas ([Formula: see text]), with heads #12, #1, and #3 achieving lift values of +30 to +40 percentage points. Results indicate that while ViTs exhibit more global attention patterns compared to human focal attention, certain attention heads can approximate human visual behavior, particularly for specific object features like buckles in basketry items. These findings suggest potential applications of ViT attention mechanisms in product design and aesthetic evaluation, while highlighting fundamental differences in attention strategies between human perception and current AI models.
6 Apr 2026
Read appraisal →Cardiovascular toxicology
Association Between Environmental Chemicals Phthalates and Stroke Among the US Adults: A Cross-Sectional Study
Stroke has a multifactorial etiology, and phthalates, as widely used environmental chemicals, may play an underexplored role in cerebrovascular health. This study aimed to evaluate the association between urinary phthalate metabolites and stroke risk using data from the National Health and Nutrition Examination Survey (NHANES), complemented by network toxicology, molecular docking, and machine learning approaches. We analyzed data from 10,771 adult participants in NHANES (2003-2018) who had complete records of stroke status, urinary phthalates, and clinical covariates. Multivariable logistic regression and restricted cubic spline (RCS) models were used to assess associations. We constructed protein-protein interaction networks and performed molecular docking to identify potential toxicological targets. Five machine learning algorithms were employed to develop predictive models for phthalate-related stroke risk. Higher levels of MECPP, MCPP, MNBP, and MEOHP were significantly associated with increased stroke prevalence. Network toxicology identified SRC as a key target, and molecular docking confirmed strong binding affinity between MBZP and SRC. Among the machine learning methods, Random Forest achieved the highest predictive performance (AUC = 0.833). Notably, moderate coffee consumption (≤ 170 g/day) was significantly associated with a reduced risk of stroke (OR = 0.63, 95%CI: 0.51-0.77), suggesting potential dietary mitigation against phthalate-related stroke risk. Our findings demonstrate that certain phthalates may increase stroke risk via molecular pathways involving SRC. Furthermore, moderate coffee consumption may mitigate the adverse cerebrovascular effects of these environmental pollutants.
5 Apr 2026
Read appraisal →Medicine
Prediction model for frailty risk in ischemic stroke patients: Application and validation of support vector machines and nomograms
This study aimed to develop a prediction model based on nomograms and support vector machines (SVM) to assess frailty risk in ischemic stroke patients. Clinical information of ischemic stroke patients admitted to our hospital from January 2023 to December 2024 was retrospectively collected. First, independent risk factors associated with frailty in ischemic stroke patients were identified through univariate and multivariate logistic regression analyses. Subsequently, a nomogram was constructed based on regression analysis results and validated using 10-fold cross-validation. Data were divided into training (70%) and validation (30%) sets. A SVM predictive model was constructed on the training set. The predictive performance of both models was compared using receiver operating characteristic curves. This study included 867 ischemic stroke patients, among whom 296 (34.14%) were classified as frail. The entire cohort was randomly divided into a training set (n = 607) and a validation set (n = 260) at a 7:3 ratio. Logistic regression analysis identified hypertension, impaired self-care ability, physical inactivity, reduced prognosis nutrition index, and depressive status as independent risk factors for frailty in ischemic stroke patients (P < .05). The nomogram demonstrated an area under the receiver operating characteristic curve (AUC) of 0.814 for frailty prediction, while the SVM model achieved an AUC of 0.842, indicating superior predictive capability. Further comparison revealed that while the nomogram offers greater intuitiveness and operational convenience in clinical practice, the SVM model demonstrated superior predictive accuracy. Hypertension, impaired self-care ability, physical inactivity, reduced prognosis nutrition index, and depressive states are independent risk factors for frailty in ischemic stroke patients. Both predictive models (nomogram and SVM) demonstrated high predictive accuracy; however, the SVM model outperformed the nomogram in predictive capability, while the latter retains unique advantages due to its simplicity and clinical applicability.
5 Apr 2026
Read appraisal →Neurology
Long-Term Exposure to Ambient Air Pollution and Incident Amyotrophic Lateral Sclerosis: A Prospective Cohort Analysis of the UK Biobank
BACKGROUND AND OBJECTIVES: Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease with a complex etiology. Although a range of genetic and lifestyle factors have been implicated, the potential role of environmental airborne pollution exposure is uncertain. This study examined the association between long-term ambient exposure to air pollutants and the incidence of ALS in UK Biobank participants. METHODS: This prospective cohort study was based on the UK Biobank participants aged 40-69 years. The analytical sample comprised participants free of ALS at baseline and had complete data on air pollution exposure. Long-term exposure (2006-2021) to nitrogen dioxide (NO2), nitrogen oxides (NOX), fine particulate matter (PM2.5; <2.5 µm), and coarse particulate matter (PM10; <10 µm) was assessed using data from the UK Department for Environment, Food and Rural Affairs at a spatial resolution of 1 × 1 km. To evaluate the association between these pollutants and ALS risk, we used multivariable time-varying Cox proportional hazards models. Several sensitivity analyses were conducted to assess the robustness of the results. We also examined for gene-environment interaction stratified by C9orf72 status and UNC13A genotype. RESULTS: Among the 501,308 participants with a mean age of 56.5 (SD 8.1) years at baseline, 272,764 (54.4%) were female. Over a median follow-up of 8.4 years, 687 individuals developed ALS. We did not observe any associations for any of the examined pollutants and ALS risk. Specifically, the hazard ratios per SD increment for PM10, PM2.5, NOX, and NO2 were 1.03 (95% CI 0.92-1.15), 1.00 (95% CI 0.88-1.14), 1.01 (95% CI 0.90-1.13), and 1.00 (95% CI 0.89-1.12), respectively. Individuals living in areas with the highest tertile of air pollutant exposure, compared with those in the lowest tertile, did not show a higher risk of ALS across any of the pollutants examined (p for trend >0.05). Restricted cubic spline analyses revealed no nonlinear associations between air pollution and ALS risk (all p for nonlinearity >0.05). These results remained robust in various subgroup and sensitivity analyses. No evidence of gene-environment interaction was found. DISCUSSION: In this large population-based study with high statistical power, ambient air pollution was not a risk factor for the development of ALS.
5 Apr 2026
Read appraisal →AJNR. American journal of neuroradiology
Delphi Consensus on Early Neurologic Improvement after Mechanical Thrombectomy
BACKGROUND AND PURPOSE: Early neurologic improvement (ENI) following mechanical thrombectomy (MT) has been a topic of substantial interest. The literature has more than 40 ENI definitions, yet no consensus has been reached. In this Delphi study, we sought to investigate experts' perspectives on the available ENI definitions and determine whether a consensus can be achieved. MATERIALS AND METHODS: The Delphi approach assessed experts' perspectives on available ENI definitions. Following a systematic literature review, the first survey round was prepared and sent to 20 experts using a Web-based anonymous survey platform (qualtrics.com). The first round of questions was publicly shared on the letsgetproof.com platform to gather perspectives of neurointerventionalists worldwide on ENI definitions. Expert and volunteer answers formed the basis of subsequent rounds, in which closed-ended questions were sent only to experts. Consensus was defined as an agreement of ≥70% for binary closed-ended questions and ≥50% for closed-ended questions with >2 response options. RESULTS: Experts completed 3 survey rounds. They recognized that the heterogeneity in ENI definitions is the most critical factor limiting the use of ENI in clinical practice and stroke research. All experts acknowledged that stroke physicians need a standardized universal ENI definition. Consensus was reached on the notion that a unified ENI definition should focus on predicting functional independence at 3 months. They agreed that NIHSS improvement of ≥8 points at 24 hours could serve as the unified ENI definition. They acknowledged that incorporating prestroke comorbidities, occlusion location, baseline NIHSS scores, and procedure-related parameters would complicate the ideal ENI definition and limit its use in clinical settings. The experts could not reach a consensus on the potential utility of ENI as a primary end point in MT trials. CONCLUSIONS: In our Delphi analysis, experts agreed on an ENI definition. They did not endorse using different ENI criteria for patients with comorbidities, posterior circulation occlusions, or high baseline NIHSS scores. Further prospective studies are needed to validate or refine ENI definitions and to address the paucity of data on the prognostic utility of ENI in specific clinical scenarios.
5 Apr 2026
Read appraisal →AJNR. American journal of neuroradiology
Reliability of Pseudocontinuous Arterial Spin-Labeling in Ischemic Stroke and Comparison with Other Perfusion Techniques: Systematic Review and Meta-Analysis
BACKGROUND: Pseudocontinuous arterial spin-labeling (pCASL) is a noninvasive, gadolinium-free MRI technique for assessing brain perfusion. PURPOSE: The test-retest reliability of pCASL-derived CBF measurements in ischemic stroke (IS) were evaluated and pCASL metrics were compared with those from DSC, CTP, and PET. DATA SOURCES: Systematic searches were conducted in MEDLINE, EMBASE, and LILACS. Risk of bias and methodologic quality were evaluated using QUADAS-2 and Quality Appraisal for Reliability Studies tools. STUDY SELECTION: Eleven studies, encompassing 383 participants with IS were included. DATA ANALYSIS: Random-effects meta-analysis with inverse variance method and heterogeneity assessed using I 2 statistics were used. DATA SYNTHESIS: No PET studies were identified. Only 1 low-quality, high-risk-of-bias study assessed test-retest reliability, finding no significant differences in repeated absolute CBF measurements over time and excellent reliability across regions. Most studies comparing pCASL with DSC, and all comparing pCASL with CTP, had a low risk of bias. In the hyperacute phase of IS, pCASL and DSC showed no significant differences in infarct core relative CBF (95% CI, -0.11 to 0.13; I 2 = 0%) or hypoperfusion volumes (95% CI, -0.28 to 0.68; I 2 = 69%). Evidence on the comparability of pCASL with other DSC or CTP metrics was limited. LIMITATIONS: The small number of included studies prevented a meta-analysis for the primary outcome and limited conclusions for secondary outcomes. CONCLUSIONS: Robust conclusions about the test-retest reliability of pCASL or the comparability of metrics obtained with this technique and DSC, CTP, or PET in different phases after IS could not be drawn.
5 Apr 2026
Read appraisal →BMJ open
Development and acceptability of a patient decision aid for people with degenerative cervical myelopathy: an international mixed-methods study
OBJECTIVES: To develop and user-test a patient decision aid for people diagnosed with degenerative cervical myelopathy and who are considering surgery. DESIGN: Mixed-methods study describing the development of a patient decision aid. SETTING: A draft decision aid was developed by a multidisciplinary steering group (including study authors with degenerative cervical myelopathy, health professionals and researchers) informed by the best available evidence, authorship consensus and existing patient decision aids. PARTICIPANTS: Patient-participants and health professional-participants who manage people with degenerative cervical myelopathy were recruited through social media and the steering group's research and practice network. Quantitative questionnaires were used to gather baseline data, descriptive feedback, refine the decision aid and assess its acceptability. Qualitative semi-structured interviews were conducted online to gather feedback on the decision aid and were analysed using reflexive thematic analysis. RESULTS: We conducted 32 interviews: 19 patient-participants and 13 health professional-participants who manage people with degenerative cervical myelopathy (neurosurgeons, neurologists, physiotherapists, orthopaedic surgeons, general practitioners, rehabilitation and pain specialists and consultant occupational physicians and chiropractors). Participants were from 10 countries (Australia, Canada, Cyprus, Germany, Ireland, New Zealand, Sweden, Switzerland, United Kingdom and USA). Most participants rated the decision aid's acceptability as good-to-excellent and agreed with most aspects of the decision aid (eg, defining degenerative cervical myelopathy, management recommendations, potential benefits and harms, questions to consider asking a health professional). CONCLUSION: Our patient decision aid was rated as an acceptable tool by both health professional-participants who treat degenerative cervical myelopathy and patient-participants with lived experience of degenerative cervical myelopathy. This decision aid can be used by clinicians and people with degenerative cervical myelopathy to help with shared decision making following a diagnosis of degenerative cervical myelopathy. A study testing the potential benefits of this decision aid in a clinical setting is recommended.
5 Apr 2026
Read appraisal →Quintessence international (Berlin, Germany : 1985)
Intentional partial odontectomy and effect on inferior alveolar nerve injuries: a systematic review
OBJECTIVES: To synthesize current evidence comparing coronectomy (intentional partial odontectomy) and complete extraction of mandibular third molars in close radiographic proximity to the inferior alveolar nerve, focusing on neurosensory complications and other clinically relevant postoperative outcomes. DATA SOURCES: PubMed, Scopus, and Google Scholar databases were manually searched using strings combining terms such as 'coronectomy,' 'partial odontectomy,' 'root retention,' 'mandibular third molar,' 'extraction,' and 'inferior alveolar nerve injury.' Searches were conducted in accordance with PRISMA 2020 guidelines. CONCLUSION: Coronectomy significantly reduces the risk of inferior alveolar nerve injury in mandibular third molar surgery compared to extraction in cases with inferior alveolar nerve proximity. The main drawback to this technique is subsequent root migration, which is common but rarely clinically problematic. Careful patient selection and follow-up are essential in selecting this procedure.
4 Apr 2026
Read appraisal →BMJ open
Utilisation of brain MRI for the diagnosis of dementia in low and middle-income countries: systematic review and meta-analysis
OBJECTIVES: To synthesise the prevalence and patterns of dementia-relevant structural brain MRI abnormalities in adults with suspected or confirmed dementia in low- and middle-income countries (LMICs), and to summarise MRI protocols and the incremental diagnostic contribution of MRI beyond cognitive screening. DESIGN: Systematic review and meta-analysis. DATA SOURCES: PubMed, EMBASE, Web of Science and PsycINFO (January 1990-27 January 2025), plus reference list screening and targeted manual searches. ELIGIBILITY CRITERIA: Observational or diagnostic-accuracy studies from World Bank-defined LMICs including adults (≥50 years) with suspected or confirmed dementia who underwent brain MRI as part of diagnostic evaluation. DATA EXTRACTION AND SYNTHESIS: Two reviewers independently screened, extracted data and assessed risk of bias using ROBINS-I. Random-effects models pooled prevalence of dementia-relevant MRI abnormalities; diagnostic-accuracy outcomes were synthesised narratively due to heterogeneous reference standards and incomplete reporting. RESULTS: 39 LMIC studies were included; 23 studies (2513 participants) contributed to the meta-analysis. Dementia-relevant MRI abnormalities (defined as ≥1 clinically relevant structural abnormality per study definition) were present in 1248/2513 participants. The pooled prevalence of dementia-relevant MRI abnormalities was 58% (95% CI 43% to 72%), with substantial heterogeneity (I²=95%) and a wide prediction interval (8-96%), indicating marked between-study variability; this estimate should be interpreted as a descriptive summary of study-level proportions rather than a precise population parameter. CONCLUSIONS: Brain MRI frequently demonstrates dementia-relevant pathology in LMIC clinical cohorts, usually with mixed neurodegenerative-vascular patterns. Structured visual ratings may add aetiologic specificity beyond cognitive screening, but pooled estimates should be interpreted as summaries of heterogeneous study-level findings rather than precise population parameters, given high heterogeneity and risk of bias. PROSPERO REGISTRATION NUMBER: CRD42024510241.
4 Apr 2026
Read appraisal →Journal of neurology
Sonographic optic nerve sheath diameter measurement for detecting intracranial hypertension in a suspected population: a systematic review and meta-analysis of diagnostic thresholds and influencing factors
OBJECTIVES: In this study, we aimed to assess the clinical efficacy of sonographic optic nerve sheath diameter (ONSD) measurements for detecting intracranial hypertension (ICH). Our objectives included evaluating its diagnostic precision compared with invasive intracranial pressure (ICP) monitoring; exploring potential sources of heterogeneity, such as measurement technique (multi vs. single-planar), population demographics (Asian vs. non-Asian), and major etiologies; and determining validated thresholds for prompt bedside ICP assessment in acute neurological crises. DATA SOURCES: We comprehensively searched eight databases, including four in English (PubMed, Web of Science, MEDLINE®, and Embase) and four in Chinese (China National Knowledge Infrastructure [CNKI], Wanfang, VIP, and SinoMed). The search covered literature published from the inception of the databases up to May 31, 2025. STUDY SELECTION: This review includes prospective, retrospective, and case-control studies that assess the efficacy of sonographic ONSD measurement for detecting ICH, defined as an ICP exceeding 20 mm Hg, 20 cmH2O, 25 cmH2O with invasive ICP measurement as the gold standard. DATA EXTRACTION: X.T. and Q.T. independently screened the studies and assessed the risk of bias using the QUADAS-2 tool. STATISTICAL ANALYSIS: Diagnostic accuracy was pooled using a bivariate random-effects model to generate summary receiver operating characteristic (SROC) curves. Subgroup analyses of the measurement technique, study population, and etiology were conducted. Publication bias was assessed using Deeks' test. DATA SYNTHESIS: The decision tree analysis revealed that an ONSD ≥ 5.1 mm is the critical diagnostic threshold for detecting intracranial hypertension. The regression tree model indicated a range of CDEI values from 0.64 to 0.95, with a root node value of 0.82 (95% CI: 0.79-0.85). The incorporation of a multiplanar assessment significantly enhanced the prediction of favorable outcomes (CDEI > 0.85; p < 0.001). Moreover, compared with a 20 mmHg threshold, a 25 cmH₂O threshold resulted in greater specificity (88.4% vs. 82.9%) and a higher positive likelihood ratio (LR+ 6.5 vs. 5.0). The utilization of multiplane techniques and experienced operators notably improved diagnostic accuracy, leading to a 50% reduction in missed diagnoses (LR- 0.1). Nontraumatic brain injury cases exhibited superior sensitivity (91.9% vs. 85.2%) and a 107% higher diagnostic odds ratio (63.3 vs. 30.5) than traumatic brain injury cases did. CONCLUSIONS: Findings from this study suggest that an ONSD threshold ≥ 5.1 mm is a promising diagnostic marker for elevated ICP (> 20 cmH₂O/mmHg or acute etiology), although external validation is needed. A multiplane evaluation improves its predictive accuracy for favorable outcomes. The developed decision model supports the use of sonographic ONSD measurement as a supplementary screening tool in emergency and critical care, particularly when invasive monitoring is unavailable. Additionally, an ONSD of approximately 6.0 mm is a potential prognostic indicator for unfavorable outcomes, highlighting a key area for future research. These findings can only be applied after their validation through multicenter studies and the establishment of standardized measurement protocols.
4 Apr 2026
Read appraisal →AJNR. American journal of neuroradiology
Multimodal CT Perfusion-Based Deep Learning for Predicting Stroke Lesion Outcomes in Complete and No Recanalization Scenarios
BACKGROUND AND PURPOSE: Predicting the final location and volume of lesions in acute ischemic stroke is crucial for clinical management. While CTP is routinely used for estimating lesion outcomes, conventional threshold-based methods have limitations. We developed specialized outcome-prediction deep learning models that predict infarct core in successful reperfusion cases and the combined core-penumbra region in unsuccessful reperfusion cases. MATERIALS AND METHODS: We developed single-modal and multimodal deep learning models using CTP parameter maps to predict the final infarct lesion on follow-up DWI. Using a multicenter data set from multiple sites, we developed deep learning models and evaluated them separately for patients with complete recanalization (successful reperfusion [CR], n = 350) and no recanalization (unsuccessful reperfusion [NR], n = 138) after treatment. The CR model was designed to predict the infarct core region, while the NR model predicted the expanded, hypoperfused tissue encompassing both the core and penumbra regions. Five-fold cross-validation was performed for robust evaluation. RESULTS: The multimodal 3D nnU-Net model demonstrated superior performance, achieving mean Dice scores of 35.36% in patients with CR and 50.22% in those with NR. This model substantially outperformed the current clinically used method, providing more accurate outcome estimates than the conventional single-technique threshold-based measures, which yielded Dice scores of 15.73% and 39.71% for CR and NR groups, respectively. CONCLUSIONS: Our approach offered both successful reperfusion and unsuccessful reperfusion estimations for potential treatment outcomes, enabling clinicians to better evaluate treatment eligibility for reperfusion therapies and assess potential treatment benefits. This advancement facilitates more personalized treatment recommendations and has the potential to substantially enhance clinical decision-making in acute ischemic stroke management by providing more accurate tissue outcome predictions than conventional single-technique threshold-based approaches.
4 Apr 2026
Read appraisal →Advanced science (Weinheim, Baden-Wurttemberg, Germany)
A Wireless, Battery-Free Artificial Throat Patch with Deep Learning for Emotional Speech Recognition
Technologies for sensing and translating throat signals into speech have advanced the communication for people with vocal impairments. However, the practical deployment of these technologies is currently hindered by tethered system, bulky components and the lack of emotional recognition capability. Here, we demonstrate a wireless, battery-free artificial throat patch system (ATPS) coupling with deep learning method to enable simultaneous recognition of speech and emotion. The sensing module of the ATPS integrates a carbon nanotube-based thin-film strain sensor and a miniaturized flexible printed circuit board (FPCB). Real-time sensing and transmission of throat signals is facilitated by a smartphone-linked system incorporating an FPCB with an embedded near-field communication antenna and low-power electronic components. Furthermore, the information-rich sensor readouts are further extracted and successfully demonstrated to be sufficient to emotional recognition of speech by implementing a hybrid deep learning architecture. This proposed system expands the arsenal of tools available for mute patients in speech recognition and communication and further realizes personalized clinical implementations.
4 Apr 2026
Read appraisal →JMIR research protocols
Development and Validation of Machine Learning Models for Predicting Early Cognitive Decline Using Home Sensor-Derived Behavioral Data: Sensors in-Home for Elder Wellbeing (SINEW) Cohort Study
BACKGROUND: As the global population continues to age, the prevalence of geriatric conditions, including dementia and frailty, is also increasing. Early identification of individuals at an elevated risk of these conditions, such as those presenting with mild cognitive impairment (MCI) or prefrailty, can provide a critical window for prompt intervention aimed at preventing or reversing disease progression. To promote such early identification, there is a burgeoning interest in the use of digital sensor technology and predictive modeling. OBJECTIVE: This study aimed to use a continuous, home-based monitoring sensor system for older adults to distinguish those exhibiting normal aging from those with MCI, early dementia, prefrailty, or frailty, and to predict their transition from normal aging to one of these conditions. METHODS: This longitudinal cohort study will recruit 200 community-dwelling adults aged ≥65 years with normal cognition or MCI at baseline. A multi-sensor system will be installed in participants' homes, including passive infrared motion sensors, door contact sensors, bed sensors, medication box sensors, wearable activity bands, and Bluetooth proximity beacons. These devices will continuously capture spatiotemporal activity patterns, mobility indicators, sleep behaviors, and medication-taking routines. Annual assessments will include standardized cognitive tests (eg, Montreal Cognitive Assessment, Mini-Mental State Examination, Rey Auditory-Verbal Learning Test, digit span, Color Trails Test, semantic fluency, Stroop), frailty measures (modified Fried phenotype, gait speed, grip strength), mental health scales, sleep quality, and psychosocial indicators. Sensor-derived features-such as gait variability, activity regularity, sleep fragmentation, and medication adherence patterns-will be integrated with clinical data to develop supervised machine learning models. Planned approaches include logistic regression, random forests, gradient boosting, and deep learning. Model performance will be evaluated using cross-validation and independent test sets. Primary metrics will include area under the receiver operating characteristic curve, sensitivity, specificity, precision, recall, and F1-score. Models will be benchmarked against gold-standard clinical diagnoses and validated using temporal subsets of the dataset. RESULTS: Enrollment for this study started in November 2019 and will continue until March 2030. As of June 2025, we have enrolled 138 participants. Full data analysis has yet to begin. CONCLUSIONS: We aim to develop a reliable and effective sensor system for in-home use that will facilitate the early detection of cognitive and physical decline. In so doing, it will add to our current understanding of digital biomarkers. It is common for older adults to seek clinical intervention only when their cognitive impairment has already reached an advanced stage. The implementation of readily deployable sensor systems within community settings presents us with opportunities for prompt intervention, which holds the potential for delaying or reversing disease progression and allowing for a greater number of functional and meaningful years.
4 Apr 2026
Read appraisal →Advanced science (Weinheim, Baden-Wurttemberg, Germany)
A Wearable Brain-Computer Interface for Mitigating Car Sickness via Attention Shifting
Car sickness, an enormous vehicular travel challenge, affects a significant proportion of the population. Pharmacological interventions are limited by adverse side effects, and effective nonpharmacological alternatives remain to be identified. Here, we introduce a novel attention-shifting method based on a closed-loop, artificial intelligence (AI)-driven, wearable mindfulness brain-computer interface (BCI) to alleviate car sickness. As the user performs an attentional task, i.e., focusing on breathing as in mindfulness, with a wearable headband, the BCI collects and analyzes electroencephalography (EEG) data via a convolutional neural network to assess the user's mindfulness state and provide real-time audiovisual feedback. This approach might sustainedly shift the user's attention from physiological discomfort toward the BCI-based mindfulness practices, thereby mitigating car sickness symptoms. The efficacy of the proposed method was rigorously evaluated in two real-world experiments, namely, short and long car rides, with a large cohort of more than 100 participants susceptible to car sickness. Remarkably, over 83% of the participants rated the BCI-based attention shifting as effective, with significant reductions in car sickness severity, particularly in individuals with severe symptoms. Furthermore, EEG data analysis revealed a neurobiological signature of car sickness, which provided mechanistic insights into the efficacy of the BCI-based attention shifting for alleviating car sickness. This study proposes a wearable, nonpharmacological intervention for car sickness, validated in a relatively large-scale study involving over 100 participants in real-world car riding. These findings, derived from a between-cohort comparison, support the potential of this approach to improve the travel experience for car sickness sufferers and represent a novel practical application of BCI technology.
4 Apr 2026
Read appraisal →Regional anesthesia and pain medicine
Deciphering risk factors for severe postherpetic neuralgia in patients with herpes zoster: an interpretable machine learning approach
INTRODUCTION: Postherpetic neuralgia (PHN) is a common complication of herpes zoster (HZ). This study aimed to use a large real-world electronic medical records database to determine the optimal machine learning model for predicting the progression to severe PHN and to identify the associated risk factors. METHODS: We analyzed the electronic medical records of 23,326 patients diagnosed with HZ from January 2010 to June 2020. PHN was defined as pain persisting for ≥90 days post-HZ, based on diagnostic and prescription codes. Five machine learning algorithms were compared with select the optimal predictive model and a subsequent risk factor analysis was conducted. RESULTS: Of the 23,326 patients reviewed, 8,878 met the eligibility criteria for the HZ cohort. Among these, 801 patients (9.0%) progressed to severe PHN. Among the various machine learning approaches, XGBoost-an approach that combines multiple decision trees to improve predictive accuracy-performed the best in predicting outcomes (F1 score, 0.351; accuracy, 0.900; area under the receiver operating characteristic curve, 0.787). Using this model, we revealed eight major risk factors: older age, female sex, history of shingles and cancer, use of immunosuppressants and antidepressants, intensive initial pain, and the neutrophil-to-lymphocyte ratio. When patients were categorized into low-risk and high-risk groups based on the predictive model, PHN was seven times more likely to occur in the high-risk group (p<0.001). CONCLUSIONS: Leveraging machine learning analysis, this study identifies an optimal model for predicting severe PHN and highlights key associated risk factors. This model will enable the establishment of more proactive treatments for high-risk patients, potentially mitigating the progression to severe PHN.
4 Apr 2026
Read appraisal →Diagnostic and interventional imaging
Deep learning-based image reconstruction significantly improves image quality of MRI examinations of the orbit at 3 Tesla.
PURPOSE: The purpose of this study was to assess the benefit of a deep learning-based image reconstruction (DLBIR) for improving image quality in orbital magnetic resonance imaging (MRI) at 3 Tesla (T). MATERIALS AND METHODS: Seventy-one patients (48 women and 23 men) with a mean age of 52 ± 19.5 (standard deviation [SD]) years (age range: 7-90 years) who underwent MRI examination of the orbit at 3 T between January and June of 2024, were included in the study. Coronal T2-weighted MR images obtained in 70 patients and post-contrast fat-saturated (FS) coronal T1-weighted MR images obtained in 25 patients, were reconstructed with and without DLBIR, resulting in four imaging sets. Two radiologists independently and blindly measured the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of the optic nerves on the four imaging sets. Image quality and orbital abnormalities were assessed using a standardized 5-point Likert scale. Comparisons between MR images obtained with and without DLBIR were performed using Wilcoxon test for ordinal and quantitative variables and McNemar test for paired binary data. RESULTS: SNR and CNR of coronal T2-weighted MR images were significantly greater using DLBIR (26.67 ± 9.03 [SD], and 14.87 ± 10.31 [SD], respectively) than without DLBIR (18.91 ± 7.28 [SD], and 9.78 ± 8.47 [SD], respectively) (P < 0.001). There were no differences in SNR and CNR between post-contrast FS T1-weighted images obtained with DLBIR (85.56 ± 63.13 [SD], and 64 ± 41.38 [SD], respectively) and those obtained without DLBIR (91.36 ± 48.49 [SD], and 43.25 ± 20.4 [SD], respectively) (P = 0.35, and P = 0.14, respectively). Qualitatively, good-to-excellent image quality was obtained more frequently with DLBIR than without DLBIR for T2-weighted and post-contrast FS T1-weighted images with respect to optic nerve sharpness (67 % vs. 16 %, and 8 % vs. 0 %, respectively), brain sharpness (90 % vs. 6 %, and 68 % vs. 4 %, respectively), and overall image quality (73 % vs. 1 % and 36 % vs. 0 %, respectively) (all P ≤ 0.001). No significant differences in the detection rates of orbital abnormalities were found between MR images obtained with and without DLBIR, including optic nerve hyperintensity (34 % vs. 31 %, respectively; P = 0.16) and optic nerve atrophy (33 % for both) on T2-weighted images, and optic nerve enhancement on post-contrast FS T1-weighted images (16 % for both). CONCLUSION: DLBIR significantly improves image quality of MRI examinations of the orbit at 3 T, without losing clinically relevant information.
4 Apr 2026
Read appraisal →Journal of medical Internet research
Predictive Value of Machine Learning for Poststroke Mortality Risk: Systematic Review and Meta-Analysis
BACKGROUND: People with stroke face a high mortality risk, and an accurate prediction model is essential to the guidance of clinical decision-making in this population. Recently, with growing attention paid to machine learning (ML) in stroke care, some researchers have investigated the effectiveness of ML in predicting the mortality risk in stroke. However, systematic evidence is still lacking for its effectiveness. OBJECTIVE: This systematic review aims to evaluate the value of ML in predicting the stroke mortality risk. The findings are expected to offer an evidence-based basis for developing and assessing clinical risk prediction tools. METHODS: A search was made in Cochrane Library, PubMed, Embase, and Web of Science up to June 23, 2025, and studies that reported a complete performance of ML in predicting stroke mortality were included. Studies with only risk factors analyzed were excluded. The risk of bias of the included studies was assessed using PROBAST (Prediction model Risk of Bias Assessment Tool). Pooled risk ratios with 95% CIs and prediction intervals (PIs) were derived using the Hartung-Knapp-Sidik-Jonkman method under a random-effects model. Subgroup analyses were also conducted by model type, stroke type, patient source, and treatment background. Moreover, a metaregression was conducted on the C-index for out-of-hospital mortality at different time points to explore the influence of time factors on the model's predictive performance. RESULTS: Sixty-eight studies were included (23 predicting in-hospital mortality and 45 predicting out-of-hospital mortality), describing the development of 75 prediction models and 43 external validations. The follow-up period was 1 month to 15 years. For predicting in-hospital mortality, the external validation set had a pooled C-index of 0.727 (95% CI 0.677-0.781, 95% PI 0.521-1.000), with sensitivity and specificity of 0.64 (95% CI 0.57-0.70) and 0.74 (95% CI 0.70-0.77), respectively. For predicting out-of-hospital mortality, the pooled C-index was 0.847 (95% CI 0.808-0.887, 95% PI 0.750-0.956) in the external validation set, with sensitivity and specificity of 0.71 (95% CI 0.55-0.82) and 0.76 (95% CI 0.74-0.78), respectively. Comparatively, the overall pooled C-indexes were 0.788 (95% CI 0.766-0.810, 95% PI 0.621-0.999) and 0.812 (95% CI 0.798-0.826, 95% PI 0.693-0.952), respectively. The metaregression revealed a gradual decline in the predictive performance of the overall model and logistic regression model alone, whereas a random forest model maintained sustained performance. Age, National Institutes of Health Stroke Scale score, and stroke-related complications were the most frequently used variables for modeling. CONCLUSIONS: This is the first meta-analysis to demonstrate that ML-based prediction of stroke mortality is feasible. The performance of ML supports its role as an auxiliary tool for identifying high-risk populations, thereby optimizing clinical monitoring and resource allocation. However, due to substantial heterogeneity and a relatively high risk of bias in available studies, caution is warranted in real-world application. The effectiveness of ML may vary across settings, and external validation is recommended before broader implementation. TRIAL REGISTRATION: PROSPERO CRD420251086321; https://www.crd.york.ac.uk/PROSPERO/view/CRD420251086321.
4 Apr 2026
Read appraisal →Stroke
Balancing Innovation and Responsibility: Ethical and Privacy Challenges in Stroke Digital Health
Digital health solutions are being increasingly used to support care delivery across various medical fields. In stroke care, technology has contributed to enhancing prehospital workflows, facilitating communication among health care professionals, and enabling fast-track systems for hyperacute stroke management. Furthermore, digital tools are used to improve etiological diagnosis and allow for more tailored long-term follow-up of stroke patient survivors. Digital technologies have become an integral part of stroke management, and the rapid evolution of artificial intelligence continues to accelerate and drive this transformation. However, ethical and privacy considerations are central and represent a major challenge for the design and implementation of digital solutions in health care. Health care professionals involved in stroke care require a clear understanding of the digital tools they use, including their potential, limitations, and biases, and must be aware of the responsibilities inherent in relying on such systems for treatment decisions and outcome prediction. This topical review aims to examine current digital innovations in stroke care, with a particular focus on the key ethical and privacy challenges they raise.
4 Apr 2026
Read appraisal →Current opinion in neurology
AI in epilepsy neuroimaging
PURPOSE OF REVIEW: Recent advances in the capabilities and usability of artificial intelligence (AI) architectures coupled with increased availability of neuroimaging datasets has fuelled a rapid expansion in AI applications to epilepsy neuroimaging. This review summarizes the main applications of AI in epilepsy neuroimaging and suggests future directions for the field. RECENT FINDINGS: A range of different machine learning approaches, from multi-layer perceptrons to volumetric and graph-based convolutional neural networks, have been utilized for prediction of whether people will have epilepsy, detection of structural epilepsy lesions, localization of seizure onset zones, segmentation of resection cavities after epilepsy surgery as well as for image enhancement. SUMMARY: AI in epilepsy neuroimaging research has primarily focussed on lesion detection and localization, with a number of open and validated tools now available for evaluation across diverse settings. Additional applications of AI in epilepsy neuroimaging are either at earlier stages of development or emerging as new challenges. As these tools and their supporting evidence mature, further work addressing the hurdles of clinical integration is required.
4 Apr 2026
Read appraisal →GeroScience
Integrative machine learning approach to risk prediction for dementia and Alzheimer's disease
Dementia, particularly Alzheimer's disease (AD), presents a growing global health challenge characterized by cognitive decline, behavioral changes, and loss of independence. With increasing life expectancy, early diagnosis and improved clinical strategies are urgently needed. This study developed and evaluated machine learning (ML) models to predict AD risk using UK Biobank data, integrating health, genetic, and lifestyle factors. The cohort included 2878 AD cases and 72,366 controls. Among several algorithms, CatBoost performed best (ROC-AUC = 0.773), especially in females. Inputs included ICD-10 codes from 5 years pre-diagnosis, ApoE-ε4 genotype, and large collection of modifiable risk factors. Despite fewer cases, the risk predictive models for vascular dementia (VaD) outperformed the unique AD models. ApoE-ε4 was the most predictive genetic marker, while other common variants had limited utility. Key non-genetic predictors included comorbidities (e.g., diabetes, hypertension), education, physical activity, and diet. These findings highlight the value of integrating diverse data sources for dementia risk prediction and emphasize the role of sex-specific modeling and modifiable factors in early, personalized intervention strategies.
2 Apr 2026
Read appraisal →Expert review of medical devices
Modalities and algorithms for generalized motor seizure detection and prediction: a scoping review
INTRODUCTION: Generalized tonic - clonic seizures (GTCS) is a major cause of sudden unexpected death in epilepsy (SUDEP); thus, continuous monitoring is essential. Electroencephalography (EEG) is the diagnostic gold standard; however, non-EEG wearables have emerged as promising, user-friendly alternatives enhancing comfort, mobility, and social acceptance. AREAS COVERED: This scoping review compared sensing modalities and computational methods used for seizure detection and prediction, identified effective sensor combinations, and highlighted current research gaps. Following PRISMA-ScR guidelines, Scopus, IEEE Xplore, and PubMed databases were searched up to 22 April 2025. Twenty-nine studies met inclusion criteria. For seizure detection, combining electrodermal activity (EDA) with motion sensors - accelerometers (ACC) and gyroscopes (GYR) - and surface electromyography (sEMG) achieved high reliability, 98.6% precision, while using ACC and EDA alone yielded superior sensitivities up to 97.2% and a lower false alarm rate (FAR) of 0.53/24 h. Regarding seizure prediction, combining EDA, blood volume pulse (BVP), ACC, and temperature showed the highest sensitivity of 75.6%. EXPERT OPINION: Wearable multimodal non-EEG seizure detection and prediction systems offer personalized care but face validation, hardware, and privacy hurdles. Future success depends on efficient AI, IoT integration, patient-centric design, and clear regulations ensuring accessible and trustworthy clinical tools. People with epilepsy experience sudden episodes of abnormal brain activity called seizures, or fits. One serious type, the generalized tonic – clonic seizure (GTCS), causes loss of consciousness, muscle stiffening, and body jerking, which can lead to injury or even sudden unexpected death in epilepsy (SUDEP).Traditionally, seizures are monitored in hospitals using electroencephalography (EEG), which records brain signals. However, EEG systems are uncomfortable for daily use and limit continuous monitoring. Recent innovations in wearable sensor technology – such as motion sensors (accelerometers), skin conductance sensors (electrodermal activity), and heart or blood flow sensors (photoplethysmography) – offer a more practical, noninvasive alternative. These devices can be worn as smartwatches or bracelets, fitting naturally into everyday life.Artificial intelligence (AI) and machine learning play an increasingly important role in these systems. By recognizing complex patterns in motion and physiological signals, AI algorithms can automatically detect seizures and, in early research, may even predict when a seizure is likely to occur. While results so far are promising, more clinical studies and larger datasets are needed to confirm accuracy and reliability across different patient groups.Advances in wearable sensors and artificial intelligence could revolutionize epilepsy management by making continuous seizure monitoring and prediction possible in daily life. Although more evidence is required before widespread clinical adoption, these technologies hold strong potential to improve safety, autonomy, and quality of life for people living with epilepsy.
2 Apr 2026
Read appraisal →Curr Opin Neurol
Neuro-vestibular rehab: new developments.
PURPOSE OF REVIEW: This review highlights recent advances in neuro-vestibular rehabilitation, with emphasis on vestibular adaptation and emerging mobile technologies. It summarizes developments in promoting vestibular plasticity and discusses novel tools such as virtual reality, wearable sensors, and telehealth platforms that enhance access, engagement, and outcomes. The scope is broad, focusing on general principles rather than specific populations. RECENT FINDINGS: New methods to enhance vestibulo-ocular reflex (VOR) adaptation include incremental adaptation devices and gamified exercises. Inducing VOR gain-down adaptation temporarily increases postural sway, which normalizes via sensory reweighting, demonstrating central compensation. Portable tools like StableEyes show promise in boosting VOR gain with brief sessions. Concurrently, technology-driven approaches are gaining traction. Gamified mobile applications and wearable sensors allow home-based rehabilitation with remote supervision and monitoring, showing promising results in conditions like multiple sclerosis. Virtual reality interventions and telehealth models accelerated during the COVID-19 era, expanding therapy delivery to underserved populations. Adjunctive methods such as vibrotactile feedback and galvanic vestibular stimulation are emerging as complementary therapies. SUMMARY: Recent developments are advancing vestibular rehabilitation by refining adaptive training techniques and leveraging digital tools to overcome barriers in access and adherence. These innovations point to a more personalized, technology-enabled approach to optimizing neuro-vestibular recovery.
1 Feb 2026
Read appraisal →Neuro Oncol
EANS-EANO guidelines on the extent of resection in gliomas
Intense efforts are employed to optimize neurosurgical resection of gliomas in different clinical scenarios. However, the level of evidence employed toward personalized treatment decisions is still moderate. Therefore, a joint task force was created by the European Association for Neurosurgical Societies (EANS) and the European Association of Neuro-Oncology (EANO) in order to provide interdisciplinary guidance on the extent of resection (EOR) in glioma surgery. This task force assessed the data on the EOR in gliomas, evaluated the level of evidence, and composed recommendations. The EOR is determined routinely by postoperative magnetic resonance imaging. Since EOR may be associated with survival and may have an impact on epilepsy, neurocognition, quality of life, neurological status, and also on planning of radiation and pharmacotherapy (evidence classes I-IV), recommendations of different levels can be made for the resection of newly diagnosed glioblastomas (level A), recurrent glioblastomas (level C), newly diagnosed (level B) and recurrent (good practice point) IDH mutant gliomas, ependymomas in children (level B) and adults (good practice point) as well as World Health Organization CNS grade 1 tumors (good practice point). We hereby report the outcome of this collaborative effort by the joint task force between EANS and EANO.
1 Jan 2026
Read appraisal →Am Surg
Neurogenic Thoracic Outlet Syndrome: A Current Literature Review
Neurogenic Thoracic Outlet Syndrome (nTOS), the most prevalent type of thoracic outlet syndrome, results from compression of the compression of the brachial plexus as it traverses the scalene triangle and rectopectoralis minor space. It typically presents with upper extremity pain, hand weakness, paresthesia, and muscle atrophy that is often exacerbated by arm elevation or overhead activity. Etiology commonly includes congenital anatomical variants, trauma, and repetitive overhead activity. Diagnosis remains challenging as a result of significant symptom overlap with cervical radiculopathy, brachial plexopathies, and entrapment neuropathies. Work up involves a combination of thorough history and physical with electrophysiologic testing, diagnostic injections and imaging such as MRI or duplex ultrasonography. Initial therapy indicates physical therapy with a focus on decompression and postural correction. In patients with persistent symptoms, motor deficits or failed conservative management, surgical decompression using transaxillary, supraclavicular or minimally invasive techniques is indicated. Rib-sparing surgical decompression is increasingly favored given lower complication rates. Postoperative rehabilitation focuses on restoring range of motion and shoulder girdle strength. This review serves to outline clinical presentation, diagnostic strategies and operative options for nTOS while emphasizing an individualized, stepwise approach to optimize outcomes and minimize risk.
1 Dec 2025
Read appraisal →Nat Hum Behav
Machine learning-assisted optimization of dietary intervention against dementia risk
A healthy diet has been associated with a reduced risk of dementia. Here we devised a Machine learning-assisted Optimizing Dietary intERvention against demeNtia risk (MODERN) diet based on data from 185,012 UK Biobank participants, 1,987 of whom developed all-cause dementia over 10 years. We first identified 25 food groups associated with dementia in a food-wide association analysis. Second, we ranked their importance using machine learning and prioritized eight groups (for example, green leafy vegetables, berries and citrus fruits). Finally, we established and externally validated a MODERN score (0-7), which showed stronger associations with lower risk of dementia-related outcomes (hazard ratio comparing highest versus lowest tertiles: 0.64, 95% CI: 0.43-0.93) than the a priori-defined MIND diet (0.75, 0.61-0.92). Across 63 health-related outcomes, the MODERN diet showed particularly significant associations with mental/behavioural disorders. Multimodal neuroimaging, metabolomics, inflammation and proteomics analyses revealed potential pathways and further support the potential of MODERN diet for dementia prevention.
1 Nov 2025
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