Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 12 appraisals
Cell host & microbe
Adjunctive fecal microbiota transplantation for major depressive disorder: A randomized, double-blind, placebo-controlled trial
Gut microbiota may influence antidepressant treatment outcomes, yet whether targeted modulation can enhance efficacy remains unclear. We conducted a randomized, double-blind, placebo-controlled trial in patients with major depressive disorder, administering a 2-week course of fecal microbiota transplantation (FMT) capsules or placebo as an adjunct to escitalopram (ChiCTR2300071421). Remission rates at week 8 did not differ significantly between groups, but FMT produced greater reductions in Hamilton Depression Rating Scale, 17-item version (HAMD-17) scores at weeks 2 and 8. FMT was well-tolerated with a safety profile comparable to placebo. Multi-omics analyses show durable donor microbial engraftment and enrichment of beneficial Lachnospiraceae and Oscillospiraceae taxa. Microbial remodeling is accompanied by an increase in serum bile acids that correlate with the alleviation of depressive symptoms. Mediation analysis supports a bile-acid-mediated suppression of inflammatory pathways linking microbial changes to antidepressant effects. Overall, FMT may provide a safe avenue to enhance escitalopram efficacy through microbiota-directed regulation of bile-acid metabolism and inflammation.
10 July 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 →Science advances
Artificial sparse neuron dendrites for visual information inference
In the human brain, dendrites exhibit nonlinear integration and sparse parallel processing capabilities, which can effectively perform visual tasks by integrating only a small subset of neuronal signals and play a crucial role in high-level information inference. However, conventional neuromorphic devices often ignore these important properties and require all neurons to perceive complete information. This makes it difficult to effectively replicate the efficient spatiotemporal processing capabilities of biological neuron dendrites. In this study, we present an artificial neuron dendrite array that integrates neurons, synapses, and dendrites, emulating the spatiotemporal spike integration properties of biological dendrites for precise parallel computation. Through multigate threshold regulation, the array enables parallel sparse spiking inference with random spatial distribution. This inference process forms a sparse dendritic spiking neural network (SD-SNN) that can perform compression, depth detection, and prediction. As a result, the SD-SNN achieves high-efficiency static and dynamic object processing while using only 0.5% of neuronal activity, slashing the power consumption by 98 and 65%, respectively. Our work reduces neural activity in the perception process by 99.5% while enhancing spatiotemporal computing capabilities and computational efficiency.
21 June 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 →Philosophical transactions. Series A, Mathematical, physical, and engineering sciences
A sentence is worth a thousand pictures: can large language models understand hum4n L4ngu4ge and the W0rld behind W0rds?
The current generation of large language models (LLMs) has been linked to claims about human-like linguistic performance, and their applications are hailed both as a step towards artificial general intelligence and as a major advance in understanding the cognitive and even neural basis of human language. To assess these claims, first, we analysed the contribution of LLMs as theoretically informative representations of a target cognitive system versus atheoretical mechanistic tools. Second, we evaluated the models' ability to see the bigger picture through top-down feedback from higher levels of processing, which requires grounding in previous expectations and past world experience. We hypothesize that since models lack grounded cognition, they cannot take advantage of these features and instead solely rely on fixed associations between represented words and word vectors. To assess this, we ran a novel leet task (l33t t4sk), which requires decoding sentences in which letters are systematically replaced by numbers. In line with our hypothesis, the results suggest that humans excel in this task, whereas models struggle. We interpret these results by identifying the key abilities that are still missing from the current state of development of these models, which require solutions that go beyond increased system scaling. This article is part of the theme issue 'World models in natural and artificial intelligence'.
15 May 2026
Read appraisal →Translational psychiatry
Acute placebo responsiveness predicts longitudinal expectation effects in antidepressant treatment
Positive treatment expectations are known to enhance antidepressant efficacy. Experimental placebo studies aim to provide mechanistic insights into this association, yet their findings have not been clearly linked to clinical expectation effects within individual patients. In a randomized crossover trial, 61 participants with major depressive disorder (31 women, 30 men) completed an emotion classification task and mood assessments after administration of a saline nasal spray labeled as either oxytocin (sham oxytocin treatment, deceptive placebo) or saline (control). Forty-five participants subsequently underwent antidepressant treatment and were monitored weekly for treatment expectations and depressive symptoms, with follow-ups extending to three months. Using linear mixed-effect models, we examined trajectories of clinical expectation effects and their prediction by experimental placebo-induced changes in emotional processing. Sham oxytocin treatment acutely induced positive expectations, improved mood, and shifted emotional processing toward positivity. Longitudinal modeling revealed that weekly reductions in depressive symptoms were predicted by higher treatment expectations reported the preceding week. These clinical expectation effects were most pronounced in individuals exhibiting strong acute placebo responses in emotional processing. Overall, our findings indicate that positive treatment expectations facilitate critical aspects of emotional processing in depression and that individual responsiveness generalizes across different treatments and time windows. Experimental placebo assessments offer an ecologically valid model for elucidating mechanisms and predictors of clinical expectation effects and highlight expectation sensitivity as a promising target for treatment optimization.
15 May 2026
Read appraisal →Neuroscience
Recent trends in electrodermal activity signal processing and deep learning methods for emotion recognition
Electrodermal activity (EDA) has emerged as one of the most informative physiological signals for emotion recognition due to its close association with sympathetic nervous system activation. Although research on affective computing has expanded rapidly in recent years, systematic investigations of signal-processing techniques specifically tailored to EDA remain limited. This gap is significant because the selection of decomposition and signal processing strategies directly influences the accuracy, robustness, and interpretability of emotion recognition systems. Furthermore, defining emotion itself and tracing its physiological manifestations through EDA, from raw measurements to recognized affective states, remains an open challenge that highlights the need for methodological rigour. In this review, we synthesise developments published between 2018 and 2025, with a dual focus on advanced EDA signal processing methods and deep learning (DL) approaches for emotion recognition. We review common EDA acquisition sites, discuss challenges arising from non-stationarity and inter-subject variability. The review systematically compares time-domain, frequency-domain, time-frequency, and advanced time-series analysis techniques, alongside emerging end-to-end DL architectures for affective modelling. Unlike prior surveys that emphasise system-level design, this work adopts a signal-processing-centric and physiology-informed perspective. Additionally, we provide a structured comparative evaluation of widely used EDA decomposition methods using key performance metrics, offering a unified framework for method selection and interpretation. Overall, this review serves as a comprehensive resource for researchers and practitioners while advocating hybrid approaches that integrate the interpretability of classical signal processing with the predictive power of predictive methods.
15 May 2026
Read appraisal →Neuroinformatics
Instance-Aware Pseudo-Labeling and Class-Focused Contrastive Learning for Weakly Supervised Domain Adaptive Segmentation of Electron Microscopy
Annotation-efficient segmentation of the numerous mitochondria instances from various electron microscopy (EM) images is highly valuable for biological and neuroscience research. Although unsupervised domain adaptation (UDA) methods can help mitigate domain shifts and reduce the high costs of annotating each domain, they typically have relatively low performance in practical applications. Thus, we investigate weakly supervised domain adaptation (WDA) that utilizes additional sparse point labels on the target domain, which require minimal annotation effort and minimal expert knowledge. To take full use of the incomplete and imprecise point annotations, we introduce a multitask learning framework that jointly conducts segmentation and center detection with a novel cross-teaching mechanism and class-focused cross-domain contrastive learning. While leveraging unlabeled image regions is essential, we introduce segmentation self-training with a novel instance-aware pseudo-label (IPL) selection strategy. Unlike existing methods that typically rely on pixel-wise pseudo-label filtering, the IPL semantically selects reliable and diverse pseudo-labels with the help of the detection task. Comprehensive validations and comparisons on challenging datasets demonstrate that our method outperforms existing UDA and WDA methods, significantly narrowing the performance gap with the supervised upper bound. Furthermore, under the UDA setting, our method also achieves substantial improvements over other UDA techniques.
13 May 2026
Read appraisal →Neuroscience
In silico neuronal morphology classification: A systematic review
Advances in connectomics and the characterization of neuronal diversity have been fundamental to understanding how the brain works. Defining a taxonomy is still challenging and requires complex computational methods. In this paper, we present a systematic review of the state-of-the-art methods for neuronal morphology classification, shedding light on trends and future directions. We address three core research questions: (1) What computational methods are used to classify neuronal morphologies? (2) What kind of representation do the classification models work on? (3) How are the classification models evaluated? We applied a structured protocol including peer-reviewed studies published since 2018. We collected 840 papers from five databases, published between 2018 and 2024. We assessed risk of bias using predefined quality criteria on study design, methodology, and reporting; 35 studies met these standards and were included to extract information and consolidate the results. The results show that Artificial Neural Networks are currently the main method for classifying neurons based on morphometric features (21 papers). Representations based on microscopy images for extracting morphometric features (30) are commonly used. Accuracy (29) is the predominant evaluation measure. Our research findings contribute to the body of knowledge by providing an up-to-date perspective on neuronal classification, which can guide future research in neuroscience and related fields.
11 May 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 →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 →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
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