Depression

DepressionX: Knowledge Infused Residual Attention for Explainable Depression Severity Assessment

In today's interconnected society, social media platforms have become an important part of our lives, where individuals virtually express their thoughts, emotions, and moods. These expressions offer valuable insights into their mental health. This paper explores the use of platforms like Facebook, $\mathbb{X}$ (formerly Twitter), and Reddit for mental health assessments. We propose a domain know...

U-Fair: Uncertainty-based Multimodal Multitask Learning for Fairer Depression Detection

Machine learning bias in mental health is becoming an increasingly pertinent challenge. Despite promising efforts indicating that multitask approaches often work better than unitask approaches, there is minimal work investigating the impact of multitask learning on performance and fairness in depression detection nor leveraged it to achieve fairer prediction outcomes. In this work, we undertake ...

Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data

Background: Adolescents are particularly vulnerable to mental disorders, with over 75% of cases manifesting before the age of 25. Research indicates...

Enhanced Large Language Models for Effective Screening of Depression and Anxiety

Depressive and anxiety disorders are widespread, necessitating timely identification and management. Recent advances in Large Language Models (LLMs)...

Deep Learning-Based Feature Fusion for Emotion Analysis and Suicide Risk Differentiation in Chinese Psychological Support Hotlines

Mental health is a critical global public health issue, and psychological support hotlines play a pivotal role in providing mental health assistance...

Investigating Large Language Models in Inferring Personality Traits from User Conversations

Large Language Models (LLMs) are demonstrating remarkable human like capabilities across diverse domains, including psychological assessment. This s...

Motif Discovery Framework for Psychiatric EEG Data Classification

In current medical practice, patients undergoing depression treatment must wait four to six weeks before a clinician can assess medication response ...

LlaMADRS: Prompting Large Language Models for Interview-Based Depression Assessment

This study introduces LlaMADRS, a novel framework leveraging open-source Large Language Models (LLMs) to automate depression severity assessment usi...

Large Language Models for Mental Health Diagnostic Assessments: Exploring The Potential of Large Language Models for Assisting with Mental Health Diagnostic Assessments -- The Depression and Anxiety Case

Large language models (LLMs) are increasingly attracting the attention of healthcare professionals for their potential to assist in diagnostic asses...

Deformed Probability Estimation in Goal-Directed reinforcement learning model explains anxious-depression dimensions of psychiatric disorders

Psychiatric disorders are complex, multi-dimensional pathologies rooted in diverse cognitive processes. Computational psychiatry aims to reveal distor...

Neural Timescale of Adolescents Major Depressive Disorder

Adolescent major depressive disorder (MDD) is characterized by heterogeneous symptomatology and complex neurodevelopmental underpinnings. Here, we inv...

Accurate and Interpretable Prediction of Antidepressant Treatment Response from Receptor-informed Neuroimaging

Conventional antidepressants show moderate efficacy in treating major depressive disorder. Psychedelic-assisted therapy holds promise, yet individual ...

Low-dimensional brain-symptom associations delineate depression phenotypes with distinct connectivity biomarkers and symptom profiles

Depression is neurobiologically and clinically heterogeneous. New approaches using resting-state functional MRI (rs-fMRI) functional connectivity (FC)...

Large-Scale Neural Network Compensation Underlying Camouflaging in Trait Autism and Its Potential Mental Health Costs

Social camouflaging refers to strategies to hide or compensate for social difficulties, often at a significant mental health cost, and is particularly...

A theory for self-sustained balanced states in absence of strong external currents

Recurrent neural networks with balanced excitation and inhibition exhibit irregular asynchronous dynamics, which is fundamental for cortical computati...

Comparative Computational Modeling of Approach–Avoidance Biases in Suicidal Populations via Hierarchical Bayesian Inference

Pavlovian “approach or avoid” impulses are critical behavioral biases that, in excess, are linked to multiple psychiatric conditions. To investigate h...

Imaging cellular activity simultaneously across all organs of a vertebrate reveals body-wide circuits

All cells in an animal collectively ensure, moment-to-moment, the survival of the whole organism in the face of environmental stressors1,2. Physiology...

Resolving Heterogeneity in Major Depression: Overcoupling and Undercoupling Subtypes Exhibit Differential Treatment Response and Molecular Pathways

Major depressive disorder (MDD) exhibits significant heterogeneity whose neurobiological mechanisms remain elusive. Alterations in morphological-funct...

Model-based EEG phenotyping uncovers distinct neurocomputational mechanisms underlying learning impairments across psychopathologies

Major depressive disorder (MDD), bipolar disorder (BP), and schizophrenia (SCZ) involve learning impairments with poorly understood mechanisms. Unders...

Predicting Future Development of Stress-Induced Anhedonia From Cortical Dynamics and Facial Expression

The current state of mental health treatment for individuals diagnosed with major depressive disorder leaves billions of individuals with first-line t...