Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 5 appraisals
Cognitive, affective & behavioral neuroscience
Emotion detection unveiled: A cognitive-computational synthesis of physiological models, machine learning, and datasets.
This comprehensive survey synthesizes state-of-the-art advancements in emotion recognition based on physiological signals, specifically focusing on the paradigm shift occurring between 2021 and 2025. Crucially, we move beyond a technical review by establishing a novel Cognitive-Computational Synthesis Framework (CCSF). This framework explicitly maps multimodal physiological manifestations (e.g., electroencephalogram (EEG), electrocardiogram (ECG), and galvanic skin response (GSR)) to underlying cognitive processes, such as attentional allocation, arousal regulation, and perceptual bias, providing a theoretical foundation for explainable AI (XAI) in affective computing. We meticulously examine the transition from traditional machine learning to advanced deep learning architectures, highlighting how recent innovations in Transformers, self-supervised learning, and diffusion models have shattered previous performance plateaus. While earlier dimensional models were often limited to 70-75% accuracy, this survey details how modern architectures now achieve benchmarks exceeding 95% on seminal datasets like SEED and DREAMER. Furthermore, the survey provides a rigorous analysis of 40 key studies (identified via PRISMA protocols), evaluating them based on their validation strategies, cross-subject generalizability, and adversarial robustness. By bridging the gap between raw physiological data and cognitive theory, this work offers a strategic roadmap for the next generation of robust, interpretable, and real-time emotion recognition systems.
3 Aug 2026
Read appraisal →Cell reports methods
AmygdalaGo-BOLT for boundary-aware segmentation of the human amygdala
Tracing the boundaries of the amygdala from brain images remains a major challenge in human neuroscience. Although large-scale neuroimaging studies increasingly collect thousands of scans to investigate structural development in children and adolescents, reliable segmentation of the amygdala is difficult due to its small size and complex morphology-particularly in pediatric populations. To address this, we developed AmygdalaGo-BOLT, a boundary-aware deep learning model specifically designed for amygdala segmentation. The model was trained and validated on 1,086 manually labeled pediatric MRI scans, with independent datasets used to assess generalizability. It integrates multiscale feature extraction, spatial priors, and self-attention mechanisms within a compact encoder-decoder architecture to enhance boundary detection. Across imaging centers and age groups, AmygdalaGo-BOLT demonstrates strong agreement with expert manual annotations, while substantially improving efficiency and accuracy relative to existing tools. This enables robust and scalable analysis of amygdala morphology in population neuroscience studies where manual tracing is impractical.
22 July 2026
Read appraisal →NeuroImage
Editorial for the Special Issue on Harmonization Techniques for MRI
This editorial introduces the special issue on neuroimaging harmonization and situates its contributions within the broader methodological landscape of multi-site MRI analysis. As large-scale neuroimaging studies continue to aggregate data across scanners, protocols, and institutions, harmonization has become essential for reducing non-biological variability while preserving meaningful biological signals. We review the major classes of harmonization approaches, including statistical methods based on the ComBat family and deep learning methods that operate at the voxel level. We also review domain generalization strategies designed for previously unseen sites, and network-aware harmonization techniques that go beyond the voxel for connectivity and connectome data. Across these developments, several cross-cutting challenges emerge, including modality-specific performance, preservation of biological information, validation using traveling-subjects and large observational datasets, and the need for scalable, standardized, and privacy-preserving frameworks. Collectively, the articles in this special issue illustrate the rapid progress of the field and highlight that robust harmonization will be critical for enabling reproducible and generalizable discoveries in multi-site neuroimaging.
15 July 2026
Read appraisal →European journal of psychotraumatology
Attenuating trauma- and cocaine-related intrusions by blocking memory reconsolidation with minocycline: protocol for a transdiagnostic randomized controlled trial
Background: Post-traumatic stress disorder (PTSD) and substance use disorders frequently co-occur, with their comorbidity linked to poorer treatment outcomes. Beyond this epidemiological overlap, PTSD and, in particular, cocaine use disorder (CUD) have also been conceptually linked, as maladaptive memory processes are thought to play a central role in both of their etiopathogeneses. PTSD and CUD are each characterized by intrusive memories - either trauma or cocaine related - that sustain distress and craving, respectively. These parallels have motivated transdiagnostic mechanistic research targeting memory reconsolidation. Matrix metalloproteinase-9 (MMP-9) is critically involved in synaptic plasticity and memory updating, and has been suggested as a pharmacological entry point for modulating reconsolidation-related processes.Objective: This study examines whether pharmacological interference with reconsolidation via MMP-9 inhibition by minocycline modulates trauma- and cocaine-related intrusive memories and their neurobiological correlates in PTSD and CUD.Method: In this monocentric, randomized, double-blind, placebo-controlled trial (NCT05902819), individuals with PTSD (n = 60) or CUD (n = 60) receive a single dose of minocycline - a tetracycline inhibiting MMP-9 - or placebo prior to an individualized, imagery-based reactivation of their most salient intrusive memory. Intrusive memories in day-to-day life are assessed using event-based ecological momentary assessment and pre-post intrusion questionnaires. Neurobiological correlates of maladaptive memory processing are indexed through functional magnetic resonance imaging and magnetic resonance spectroscopy, and peripheral biomarkers are obtained from blood, urine, and hair samples. Diagnostic status and comorbid symptoms are characterized using clinical interviews and standardized self-report measures.Conclusions: This trial adopts a mechanistic framework to investigate whether minocycline-augmented memory reactivation modulates intrusions and associated neurobiological markers in PTSD and CUD. By directly targeting candidate memory mechanisms shared by both disorders, it evaluates a transdiagnostic reconsolidation-based approach and is expected to inform models of intrusive memory persistence and updating. The findings may guide future development of memory-focused interventions in trauma- and addiction-related disorders. This randomized controlled trial evaluates a transdiagnostic, mechanism-focused intervention targeting maladaptive memory processes in PTSD and cocaine use disorder.Minocycline is used as an MMP-9 inhibitor to modulate intrusive trauma- and cocaine-related memories following brief imagery-based memory reactivation.The study integrates multimodal neurobiological assessments (fMRI, MRS, peripheral biomarkers) with ecological momentary intrusion measures to characterize changes in memory-related mechanisms.
14 May 2026
Read appraisal →Journal of neurology
Systematic review of movement disorders mislabeled as functional: when incongruence misleads
BACKGROUND: Misdiagnosis of functional movement disorders (FMD) remains a concern for clinicians. We sought to review the phenomenology and clinical features associated with FMD misdiagnosis. METHODS: We conducted a systematic review of case reports, case series, and observational studies of adults diagnosed with FMD and subsequently reclassified as having another neurological or medical condition. PubMed was searched from inception to September 13, 2025, focusing on reported features of inconsistency, incongruence with known diseases, and low-validity features. Isolated (iMD) and mixed (mMD) movement disorders were analyzed separately. RESULTS: Forty-two included studies comprised 150 patients, of which 73 cases underwent detailed analysis. Gait disturbance (35%) and dystonia (27.5%) were the most common iMD phenomenologies (n = 40), while jerks and rigidity (24%) were the most common mMD phenomenology (n = 33). Incongruence with known diseases (50.7%), psychological factors (34.2%), and symptom variability (31.5%) were the main factors associated with misdiagnosis. Application of incongruence alone was associated with the highest rate of FMD misdiagnosis, similar to that of applying neither incongruence nor inconsistency (49% in both cases). Conversely, reliance on inconsistency alone was least associated with an FMD misdiagnosis (13.7%). Misdiagnoses of iMD were more likely in the absence of documented inconsistency (OR 3.94, 95% CI 1.32-12.82). CONCLUSIONS: FMD misdiagnoses, particularly of gait disorders and dystonia, were most commonly associated with the application of incongruence as diagnostic criterion. The lowest rate of FMD misdiagnosis was associated with ascertaining at least two positive neurological signs of inconsistency.
9 Apr 2026
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