Psychiatry

Learning Image Derived PDE-Phenotypes from fMRI Data

Partial Differential Equations (PDEs) model various physical phenomena, such as electromagnetic fields and fluid mechanics. Methods like Sparse Identification of Nonlinear Dynamics (SINDy) and PDE-Net 2.0 have been developed to identify and model PDEs based on data using sparse optimization and deep neural networks, respectively. While PDE models are less commonly applied to fMRI data, they hold...

AI-Driven Early Mental Health Screening: Analyzing Selfies of Pregnant Women

Major Depressive Disorder and anxiety disorders affect millions globally, contributing significantly to the burden of mental health issues. Early screening is crucial for effective intervention, as timely identification of mental health issues can significantly improve treatment outcomes. Artificial intelligence (AI) can be valuable for improving the screening of mental disorders, enabling early...

Multi-Stage Graph Learning for fMRI Analysis to Diagnose Neuro-Developmental Disorders

The insufficient supervision limit the performance of the deep supervised models for brain disease diagnosis. It is important to develop a learning ...

The Effect of Acute Stress on the Interpretability and Generalization of Schizophrenia Predictive Machine Learning Models

Introduction Schizophrenia is a severe mental disorder, and early diagnosis is key to improving outcomes. Its complexity makes predicting onset and ...

ScriptViz: A Visualization Tool to Aid Scriptwriting based on a Large Movie Database

Scriptwriters usually rely on their mental visualization to create a vivid story by using their imagination to see, feel, and experience the scenes ...

HyperBrain: Anomaly Detection for Temporal Hypergraph Brain Networks

Identifying unusual brain activity is a crucial task in neuroscience research, as it aids in the early detection of brain disorders. It is common to...

GENEVIC: GENetic data Exploration and Visualization via Intelligent interactive Console.

SUMMARY: The vast generation of genetic data poses a significant challenge in efficiently uncovering valuable knowledge. Introducing GENEVIC, an AI-dr...

Feature-Prescribed Iterative Learning Control of Waggle Dance Movement for Social Motor Coordination in Joint Actions

Extensive experiments suggest that motor coordination among human participants may contribute to social affinity and emotional attachment, which has...

Suicide Phenotyping from Clinical Notes in Safety-Net Psychiatric Hospital Using Multi-Label Classification with Pre-Trained Language Models

Accurate identification and categorization of suicidal events can yield better suicide precautions, reducing operational burden, and improving care ...

Interpretation of SNP combination effects on schizophrenia etiology based on stepwise deep learning with multi-precision data.

Schizophrenia genome-wide association studies (GWAS) have reported many genomic risk loci, but it is unclear how they affect schizophrenia susceptibil...

Spiders Based on Anxiety: How Reinforcement Learning Can Deliver Desired User Experience in Virtual Reality Personalized Arachnophobia Treatment

The need to generate a spider to provoke a desired anxiety response arises in the context of personalized virtual reality exposure therapy (VRET), a...

Large-scale digital phenotyping: identifying depression and anxiety indicators in a general UK population with over 10,000 participants

Digital phenotyping offers a novel and cost-efficient approach for managing depression and anxiety. Previous studies, often limited to small-to-medi...

Analysis of In-Home Movement Patterns for Depression Assessment in Older Adults - A Feasibility Study.

Depression significantly impacts the wellbeing of older Australians, posing considerable challenges to their overall quality of life. This study aimed...

Unveiling Functional Biomarkers in Schizophrenia: Insights from Region of Interest Analysis Using Machine Learning.

BACKGROUND: Schizophrenia is a complex and disabling mental disorder that represents one of the most important challenges for neuroimaging research. T...

MicroHDF: predicting host phenotypes with metagenomic data using a deep forest-based framework.

The gut microbiota plays a vital role in human health, and significant effort has been made to predict human phenotypes, especially diseases, with the...

Diagnosis and Pathogenic Analysis of Autism Spectrum Disorder Using Fused Brain Connection Graph

We propose a model for diagnosing Autism spectrum disorder (ASD) using multimodal magnetic resonance imaging (MRI) data. Our approach integrates bra...

Explainable AI for Autism Diagnosis: Identifying Critical Brain Regions Using fMRI Data

Early diagnosis and intervention for Autism Spectrum Disorder (ASD) has been shown to significantly improve the quality of life of autistic individu...

Exploring Gaze Pattern Differences Between Autistic and Neurotypical Children: Clustering, Visualisation, and Prediction

Autism Spectrum Disorder (ASD) affects children's social and communication abilities, with eye-tracking widely used to identify atypical gaze patter...

Brain Network Diffusion-Driven fMRI Connectivity Augmentation for Enhanced Autism Spectrum Disorder Diagnosis

Functional magnetic resonance imaging (fMRI) is an emerging neuroimaging modality that is commonly modeled as networks of Regions of Interest (ROIs)...

Introducing ELLIPS: An Ethics-Centered Approach to Research on LLM-Based Inference of Psychiatric Conditions

As mental health care systems worldwide struggle to meet demand, there is increasing focus on using language models to infer neuropsychiatric condit...