Psychiatry

AI-guided stimuli discovery and generation to optimize facial emotion perception studies in autism

Understanding perceptual differences between autistic and neurotypical adults requires behavioral assays that are sensitive, reliable, and mechanistically informative. Facial emotion perception is a useful test case because group differences have been reported, but findings vary across studies. Here we show that this variability may reflect image-level sparsity: autistic-neurotypical differences i...

Evaluating the Effect of Frame Rate in Sequence-Based Classification of Autism-Related Self-Stimulatory Hand Idiosyncrasies

Autism spectrum disorder (ASD) affects over 75 million individuals worldwide, yet scalable computational methods for remote behavioral screening remain limited. This study addresses two complementary challenges in automated detection of autism-related self-stimulatory behaviors from video: (1) identifying the optimal sequence-based neural network architecture and temporal sampling rate, and (2) ch...

Data-driven trajectories of atrophy explain clinical heterogeneity across Lewy body diseases

Background: Lewy body diseases (LBD) collectively share alpha-synuclein Lewy pathology, yet present wide clinical heterogeneity, with overlapping moto...

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,...

Accelerometry-Derived REM Sleep Behavior Disorder Predicts Future Parkinson's Disease in the UK Biobank

Estimating Parkinson's disease (PD) risk years before diagnosis remains an unmet need. We applied a validated machine learning classifier for REM slee...

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...

Wavelet Scattering Transform for Interpretable Schizophrenia Biomarker Discovery and Classification from Resting-State EEG

Schizophrenia is a debilitating neuropsychiatric disorder characterized by profound cortical network dysregulation, for which objective, clinically tr...

CALM: Interpretable Cross-Modal Alignment for Biomarker Discovery from Unpaired Data

The interaction between brain structure and genetic influences is key to understanding neuropsychiatric disorders. However, most large-scale datasets ...

SABER: A Semantic-Aligned Brain Network Analysis Framework via Multi-scale Hypergraphs

Effective brain disease diagnosis requires the synergy of brain connectivity patterns and high-level semantic knowledge. Existing methods, however, la...

A 3D Brain Geometry Toolkit for Multisite Neuroimaging Analysis

Compared to traditional gross volumetrics, surface- based models provide greater spatial precision for understanding brain alterations related to deve...

A Local Outpatient Practice-Level Prediction Model for Short-Term Psychiatric Emergency Presentation

Importance: Psychiatric emergency department (ED) presentations are difficult to predict using general medical risk stratification tools. Health infor...

Improving ART Retention Through Machine Learning-Guided Targeting of Interventions: A Monte Carlo Simulation Study in Lilongwe, Malawi

Retention in antiretroviral therapy care remains a major challenge in high-burden settings such as Malawi, where substantial loss to follow up undermi...

Integrating Genetic, Environmental, Cognitive, and Temperament Data for ADHD Prediction in Explainable Deep Learning Models

Objective: Attention-deficit/hyperactivity disorder (ADHD) is clinically and etiologically heterogeneous, and diagnostic decisions may benefit from in...

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...

Progressive Self-Supervised Learning with Individualized Community Assignment for Brain Network Analysis

Brain networks exhibit a modular community structure that varies across individuals and neurological conditions. However, existing self-supervised lea...

Psychosocial Factors Outweigh Short-Term Environmental Exposures in Subjective Cognitive Difficulties: A Causal AI Study

ObjectiveTo evaluate the associations of short-term environmental exposures with subjective cognitive difficulties and attention-related outcomes and ...

Fine-tuning a multimodal large language model for clinician-grade autism behavioral scoring from short home videos

Autism spectrum disorder (ASD) affects 1 in 31 US children, yet median age at diagnosis exceeds four years. Artificial intelligence pipelines that pro...

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 ...

Evaluating Generative Video AI for Standardized Psychiatric Patient Simulation With Graded Hygiene Deterioration.

Abstract Introduction: A clinician's initial assessment during the mental status examination (MSE) places substantial weight on a patient's general ap...