Depression

Multi-Source Multi-View Graph Domain Adaptation with Hyperbolic Residual Encoding for Cross-Site MDD Identification from rs-fMRI

Cross-site identification of major depressive disorder (MDD) from resting-state functional magnetic resonance imaging (rs-fMRI) is hindered by inter-site distribution shifts and heterogeneous functional connectivity (FC) views. These views capture complementary neural relationships but exhibit distinct site biases and graph topologies, complicating alignment without sacrificing disease-relevant in...

Electroconvulsive Therapy Drives Sensorimotor Network Segregation in Depression: A Multiscale Edge-Centric Connectomic Study

Background: Electroconvulsive therapy (ECT) induces widespread brain effects and remains the most effective intervention for severe major depressive disorder (MDD). However, how ECT reshapes the global organization of functional connectomes remains poorly understood. Edge-centric connectomics offers a framework for characterizing large-scale reconfiguration beyond conventional node-based analyses....

A Neuro-Symbolic Knowledge Graph and Large Language Model Hybrid Architecture for Multi-Modality Mental Health Counseling

Background: Depression and anxiety are managed largely between clinical visits, yet outpatient care lacks scalable, accountable mechanisms for between...

Neurai-VN Benchmark: Standardized Machine Learning Models for Multimodal Digital Phenotyping in Mental Health Classification

Digital phenotyping (DP) using smartphones and wearable devices has shown considerable potential for mental health monitoring. However, progress remai...

Context-dependent facial-expression patterns during affective film viewing in patients with bipolar depression

Background: Emotion dysregulation is a core feature of bipolar disorder (BD), yet its behavioral expression during depressive episodes, and potential ...

PocketPPD: Screening for Postpartum Depression Risk Using Passive Smartphone Sensing

Postpartum depression (PPD) is a serious perinatal mental health condition affecting approximately 20% of new mothers worldwide. Common screening appr...

Reconsidering the case against risk prediction in self-harm: routinely collected health data distinguishes groups at higher and lower risk of adverse outcomes following paracetamol overdose

Background. UK clinical guidance recommends that structured risk prediction tools and risk stratification should not be used in self-harm, to predict ...

Conversational trajectory degrades large language model detection of suicidal ideation relative to clinicians: a preregistered study

Background General-purpose large language models increasingly encounter emotional and therapy-like conversation, yet are not developed or evaluated as...

Auditing Construct Overlap in Explainable Machine Learning: Evidence from Burnout-Depression Prediction Across Student Cohorts

Explainable machine learning (XML) pipelines applied to composite mental health outcomes can produce apparently-robust, cross-population-stable risk h...

Navigating Hierarchy: Hyperbolic Learning on Brain Graphs for Disorder Diagnosis

Functional brain networks exhibit a hierarchical organization across ROI, community, and whole-brain levels, supporting local processing, inter-commun...

Reward Valuation in Vision Language Models: Causal Mechanisms Underlying Anhedonia

Recent Vision-Language Models capture increasingly complex aspects of human cognition. Here we ask whether this alignment extends to reward valuation,...

Machine Learning for Depression Screening and Intervention: an Original Circadian Rhythm Score-based Methodology

Depression screening from large-scale behavioral data is challenged by fragmented circadian indicators, limited interpretability, and the lack of inte...

Integrating dynamic nomogram and machine learning for personalized disability prediction in elderly cardiometabolic multimorbidity: routine blood markers and mental health

Abstract Background: Disability prediction in elderly with cardiometabolic multimorbidity (CMM) is limited. We developed a dynamic nomogram and addres...

EEG-Based Identification of Adolescent Non-Suicidal Self-Injury and Neurophysiological Interpretation Using an Explainable Deep Learning Framework

Non-suicidal self-injury (NSSI) among adolescents is a prevalent mental health problem and an important indicator of potential suicide risk. Early obj...

BrainRiem: Riemannian Prototype Learning for Source-Free Cross-Site Brain Network Diagnosis

Multi-site functional MRI (fMRI) studies are essential for robust neuropsychiatric diagnosis yet suffer severe domain shifts from scanner heterogeneit...

Predicting Depression and Anxiety Progression in Multiple Sclerosis from Longitudinal Clinical Data Using Machine Learning

Depression and anxiety are highly prevalent in multiple sclerosis (MS), yet tools for predicting mental health trajectories from clinical data remain ...

Expresso-AI: Explainable Video-Based Deep Learning Models for Depression Diagnosis

Given the widespread prevalence of depression and its consequential impact on individuals and society, it is crucial to obtain objective measures for ...

Evidence-guided AI regularization for suicidal ideation prediction in pediatric bipolar disorder

Background: Suicide prediction models in psychiatry often rely on purely data-driven feature selection, which can produce unstable and clinically opaq...

Personalizing Suicide Risk Assessment: Machine Learning Extraction of Cross-Modal Interactions Between Psychosocial and Demographic Factors in Veterans

Background: Veterans face an elevated risk of suicide compared to the general population, motivating national efforts to develop predictive models tha...

Comparative Evaluation of Pretrained Large Language Models for Suicide Risk Prediction from Clinical Notes in U.S. Veterans

Background: Suicide remains a significant and potentially preventable cause of death among United States veterans. Predictive models based on structur...