Psychiatry

Generating Medically-Informed Explanations for Depression Detection using LLMs

Early detection of depression from social media data offers a valuable opportunity for timely intervention. However, this task poses significant challenges, requiring both professional medical knowledge and the development of accurate and explainable models. In this paper, we propose LLM-MTD (Large Language Model for Multi-Task Depression Detection), a novel approach that leverages a pre-trained...

Neural Constraints on Cognitive Experience and Mental Health

Understanding how neural dynamics shape cognitive experiences remains a central challenge in neuroscience and psychiatry. Here, we present a novel framework leveraging state-to-output controllability from dynamical systems theory to model the interplay between cognitive perturbations, neural activity, and subjective experience. We demonstrate that large-scale fMRI signals are constrained to low-...

ASD Classification on Dynamic Brain Connectome using Temporal Random Walk with Transformer-based Dynamic Network Embedding

Autism Spectrum Disorder (ASD) is a complex neurological condition characterized by varied developmental impairments, especially in communication an...

When neural implant meets multimodal LLM: A dual-loop system for neuromodulation and naturalistic neuralbehavioral research

We propose a novel dual-loop system that synergistically combines responsive neurostimulation (RNS) implants with artificial intelligence-driven wea...

Optimizing Large Language Models for Detecting Symptoms of Comorbid Depression or Anxiety in Chronic Diseases: Insights from Patient Messages

Patients with diabetes are at increased risk of comorbid depression or anxiety, complicating their management. This study evaluated the performance ...

Eye Movement Characteristics for Predicting a Transition to Psychosis: Longitudinal Changes and Implications.

BACKGROUND AND HYPOTHESIS: Substantive inquiry into the predictive power of eye movement (EM) features for clinical high-risk (CHR) conversion and the...

Neuroimage Analysis Methods and Artificial Intelligence Techniques for Reliable Biomarkers and Accurate Diagnosis of Schizophrenia: Achievements Made by Chinese Scholars Around the Past Decade.

BACKGROUND AND HYPOTHESIS: Schizophrenia (SZ) is characterized by significant cognitive and behavioral disruptions. Neuroimaging techniques, particula...

MentalChat16K: A Benchmark Dataset for Conversational Mental Health Assistance

We introduce MentalChat16K, an English benchmark dataset combining a synthetic mental health counseling dataset and a dataset of anonymized transcri...

Predicting Treatment Response in Body Dysmorphic Disorder with Interpretable Machine Learning

Body Dysmorphic Disorder (BDD) is a highly prevalent and frequently underdiagnosed condition characterized by persistent, intrusive preoccupations w...

Cognitive-Mental-LLM: Evaluating Reasoning in Large Language Models for Mental Health Prediction via Online Text

Large Language Models (LLMs) have demonstrated potential in predicting mental health outcomes from online text, yet traditional classification metho...

Key Predictors of Generativity in Adulthood: A Machine Learning Analysis.

OBJECTIVES: This study aimed to explore a broad range of predictors of generativity in older adults. The study included over 60 predictors across mult...

Exploring Socio-Cultural Challenges and Opportunities in Designing Mental Health Chatbots for Adolescents in India

Mental health challenges among Indian adolescents are shaped by unique cultural and systemic barriers, including high social stigma and limited prof...

Visual Attention Graph

Visual attention plays a critical role when our visual system executes active visual tasks by interacting with the physical scene. However, how to e...

Predicting and Understanding College Student Mental Health with Interpretable Machine Learning

Mental health issues among college students have reached critical levels, significantly impacting academic performance and overall wellbeing. Predic...

Accodemy: AI Powered Code Learning Platform to Assist Novice Programmers in Overcoming the Fear of Coding

Computer programming represents a rapidly evolving and sought-after career path in the 21st century. Nevertheless, novice learners may find the proc...

Learning 3D Medical Image Models From Brain Functional Connectivity Network Supervision For Mental Disorder Diagnosis

In MRI-based mental disorder diagnosis, most previous studies focus on functional connectivity network (FCN) derived from functional MRI (fMRI). How...

A deep learning model for characterizing altered gyro-sulcal functional connectivity in abstinent males with methamphetamine use disorder and associated emotional symptoms.

Failure to manage emotional withdrawal symptoms can exacerbate relapse to methamphetamine use. Understanding the neuro-mechanisms underlying methamphe...

MUTATE: a human genetic atlas of multiorgan artificial intelligence endophenotypes using genome-wide association summary statistics.

Artificial intelligence (AI) has been increasingly integrated into imaging genetics to provide intermediate phenotypes (i.e. endophenotypes) that brid...

The order in speech disorder: a scoping review of state of the art machine learning methods for clinical speech classification

Background:Speech patterns have emerged as potential diagnostic markers for conditions with varying etiologies. Machine learning (ML) presents an op...

Leveraging LLMs for Mental Health: Detection and Recommendations from Social Discussions

Textual data from social platforms captures various aspects of mental health through discussions around and across issues, while users reach out for...