Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 37 appraisals
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
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 →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 →Journal of neuro-oncology
Predicting progression-free survival in glioblastoma with neuroimaging and machine learning
PURPOSE: Glioblastoma (GBM) is the most prevalent and aggressive form of malignant glioma. Reliable estimation of progression-free survival (PFS) prior to medical intervention could strengthen clinical decision-making and improve patient care. Here, we utilize machine learning (ML) to predict PFS in GBM patients using resting state network (RSN) connectivity before medical intervention. METHODS: GBM patients (N = 45, mean age 62.1 ± 10.3 years, mean PFS 9.5 ± 5.6 months, 62.2% male) were retrospectively recruited from Washington University Medical Center. All patients completed structural neuroimaging and resting-state functional MRI before surgery. Deep neural networks were trained on resting-state functional connectivity to predict PFS. Feature selection identified the 15 strongest predictive features prior to training. RESULTS: Sex (p = 0.0037), overall survival (p = 0.0003), MGMT promoter methylation status (p = 0.0064), presentation of weakness (p = 0.0037), and presentation of memory impairment (p = 0.045) were significantly associated with PFS. Tumor frequency and spatial correlation analyses associated dorsal attention, visual, frontal-parietal, and default mode networks with shorter PFS. Conversely, right-temporal lobe tumors were associated with better outcomes. RSN spatial maps revealed widespread alterations in association networks in GBM patients relative to controls. MRMR feature selection identified thalamic and association network connectivity, including somatomotor, ventral and dorsal attention, and default mode/parietal memory as the strongest predictors of PFS. Using leave-one-out validation, the model predicted PFS with an RMSE of 1.26 months, MAE of 1.08 months, and R² of 0.96 (p < 0.001). CONCLUSIONS: Our findings indicate that GBM alters functional brain organization on a widespread scale, and these global effects are informative of patient outcomes.
31 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 →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 →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 →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 →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 →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 →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 →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 →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 →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 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 →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 →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 →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 →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 →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 →Brain imaging and behavior
Meta-analysis and meta-regression of diagnostic test accuracy of connectome-based predictive modeling in OCD
Functional magnetic resonance imaging studies have reported disruptions in functional connectivity within brain networks, known as connectomes. Researchers have tested connectomes to see whether they serve as biomarkers for various psychiatric conditions. This meta-analysis aims to evaluate the diagnostic test accuracy of predictive models of connectomes derived from resting-state fMRI in diagnosing obsessive-compulsive disorder. A systematic review and meta-analysis were conducted on previous studies assessing the sensitivity, specificity, and accuracy of connectome-based diagnostic models in obsessive-compulsive disorder and healthy controls. Eight studies were identified, comprising 563 individuals with obsessive-compulsive disorder and 564 healthy controls. The results revealed robust diagnostic performance with a pooled sensitivity of 0.827 (95% CI: 0.779-0.867) and specificity of 0.794 (95% CI: 0.759-0.826). Connectome-based diagnostic models demonstrated excellent clinical utility, with an area under the curve of 87% (95% CI: 84%-90%), and significant predictive power as indicated by positive (4.15) and negative (0.21) likelihood ratios, as well as strong diagnostic odds ratio of 18.69 (95% CI: 11.84-29.49). The results highlight the potential of functional connectome-based predictive modeling as a robust tool for accurately diagnosing obsessive-compulsive disorder, with possibility of future implications for early diagnosis, monitoring treatment-related changes, involving in decision modeling, and understanding the biological mechanisms underlying obsessive-compulsive disorder.
20 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 →NeuroImage
Convergent neural signatures of optimal creative performance: A systematic review and meta-analysis
The neural architecture that supports optimal creative performance across diverse contexts remains unclear. To address this question, we conducted a quantitative meta-analysis to identify convergent patterns of brain activation associated with enhanced creativity and to examine their relationships with behavioral improvements. A total of 33 neuroimaging studies involving 965 participants were included, encompassing four enhancement contexts: creativity training, domain-specific expertise, trait-level creativity, and positive stimulus conditioning. Across these contexts, superior creative performance (Hedges' g = 0.668) was consistently characterized by increased activation in the left middle frontal gyrus (LMFG), left superior frontal gyrus (LSFG), and left inferior parietal gyrus (LIPG), together with decreased activation in the right precuneus (95 % CI [0.508, 0.828]). Meta-regression analyses revealed that longer intervention duration and greater professional experience predicted stronger LSFG and LIPG activation, whereas age and sex ratio showed no significant effects. LSFG activation was attenuated during convergent thinking tasks, suggesting task-specific modulation of creative neural circuits. These findings provide systematic evidence for a convergent neural signature of superior creative performance across multiple enhancement pathways, offering empirical targets for future training and neuromodulation-based interventions to optimize creative potential.
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 →Biological psychiatry
From Snapshots to Stable Outcomes: Resting-State Functional Magnetic Resonance Imaging-Based Prognosis of Functioning in Patients With Psychosis Risk or Recent-Onset Depression
BACKGROUND: Early recovery of functioning is critical for favorable outcomes in psychotic and affective disorders. Transdiagnostic brain activity patterns may capture pathways for poor outcomes before clinical manifestation, thereby supporting timely prevention and intervention. METHODS: Using machine learning, we evaluated the transdiagnostic prognostic value of resting-state functional magnetic resonance imaging fractional amplitude of low-frequency fluctuations (fALFF) (slow-5 and slow-4 sub-bands) for functional outcomes in patients at clinical high risk for psychosis (n = 217) or with recent-onset depression (n = 198) from the multisite PRONIA (Prognostic Tools for Early Psychosis Management) study. Leave-site-out cross-validation assessed the geographic generalizability of models across disability and symptom domains, with outcomes defined as snapshots at 9- or 18-month follow-up or across both time points. We examined diagnosis-specific performance, generalization to recent-onset psychosis (n = 140), and negative symptoms and the added value of fALFF over clinical prognostication. RESULTS: Transdiagnostic models predicting stable good functioning across follow-ups showed up to 10% higher balanced accuracy (BAC) than snapshot models. Decreased slow-5 fALFFs in the default mode network, executive control network (ECN), and dorsal attentional network (DAN) and increased fALFF in the salience network, ECN, and DAN predicted impairment with BAC = 67% (sensitivity = 65%, specificity = 70%, p < .001). This model generalized to recent-onset psychosis (BAC = 62%, sensitivity = 64%, specificity = 59%, p < .001) and predicted (BAC = 65%, sensitivity = 66%, specificity = 65%, p < .001) and was mediated by negative symptoms. Slow-5-based models improved prognostic accuracy over expert ratings in disability (BACraters = 66%, BACraters+slow-5 = 75%, W = 1680, p < .001) and symptom (BACraters = 61%, BACraters+slow-5 = 71%, W = 1444, p < .001) domains. CONCLUSIONS: We highlighted the prognostic value of fALFF for functional impairment in psychosis risk and early depression. Leveraging trajectorial information, we identified candidate imaging biomarkers to improve prognostication, thereby supporting personalized prevention and recovery strategies.
16 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 →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 →Brain : a journal of neurology
Distinct cerebellar networks underpin clinical improvement in adolescent Tourette disorder.
Adolescence is frequently called the second period of brain maturation. In Tourette disorder (TD), the clinical trajectory of tics and associated psychiatric co-morbidities varies significantly across individuals during the transition from adolescence to adulthood. In this study, we aimed to identify patterns of resting-state functional connectivity that differentiate adolescents with Tourette disorder from their neurotypical peers, and to monitor symptom-specific functional changes over time. We employed a multivariate analysis based on a support vector machine (SVM) to predict patterns of resting-state functional connectivity in 64 adolescents with Tourette disorder and 61 sex- and age-matched healthy controls. Fifty-seven patients with Tourette disorder participated twice, with an interval of 15 months. Based on the results of the multivariate analysis, we implemented a general linear model to test within-group differences in resting-state functional connectivity over time, as well as their correlation with changes in severity of tics, depressive, obsessive-compulsive (OCD) and attention deficit and hyperactivity (ADHD) symptoms. The SVM significantly discriminated Tourette disorder from healthy controls with above-chance accuracy, specificity and sensitivity. The most discriminative connections were prefrontal, striatal and cerebellar networks. Between the visits, adolescents with Tourette disorder showed decreased functional connectivity between vermis 3 and the bilateral supplementary motor area and between vermis 4, 5 and the occipital cortex. The correlation analysis revealed that (i) an improvement between the visits in the severity of tics was associated with increased connectivity between the pre-supplementary motor cortex, anterior cingulate and inferior frontal gyrus; (ii) an improvement of ADHD was associated with decreased connectivity of the anterior cingulate cortex with the inferior temporal cortex; (iii) an improvement of OCD correlated with decreased cerebellar (lobule 8 and vermis 8, 9) connectivity with fronto-polar, superior temporal and superior frontal cortices, and increased cerebello-parietal connectivity, as well as fronto-parietal and fronto-frontal connectivity; and (iv) an improvement of depressive symptoms correlated with decreased connectivity of the cerebellum (lobules 4, 5 and vermis 6) with the precuneus. Our findings support pathophysiological models of Tourette disorder, in which aberrant patterns of functional connectivity are associated with specific comorbidities that may evolve differently throughout adolescence. Resting-state functional connectivity may provide unique endophenotypes, with developmental changes potentially linked to modifications in the severity of comorbid conditions. Distinct regions of the cerebellum and their connectivity with various frontal cortical regions emerge as candidate biomarkers to monitor, and possibly predict, the clinical trajectory of Tourette disorder symptoms, offering insights into disorder pathogenesis and likely guiding clinical decision-making.
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 →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 →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
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 →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
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