Psychiatry

A multimodal vision transformer for interpretable fusion of functional and structural neuroimaging data.

Multimodal neuroimaging is an emerging field that leverages multiple sources of information to diagnose specific brain disorders, especially when deep learning-based AI algorithms are applied. The successful combination of different brain imaging modalities using deep learning remains a challenging yet crucial research topic. The integration of structural and functional modalities is particularly ...

Multi-target neural network model of anxiolytic activity of chemical compounds using correlation convolution of multiple docking energy spectra.

Anxiety disorders are one of the most common mental health pathologies in the world. They require searc h and development of novel effective pharmacologically active substances. Thus, the development of new approaches to the search for anxiolytic substances by artificial intelligence methods is an important area of modern bioinformatics and pharmacology. In this work, a multi-target model of the d...

AI-assisted summary of suicide risk Formulation

Background: Formulation, associated with suicide risk assessment, is an individualised process that seeks to understand the idiosyncratic nature and...

AI Foundation Models for Wearable Movement Data in Mental Health Research

Pretrained foundation models and transformer architectures have driven the success of large language models (LLMs) and other modern AI breakthroughs...

Toward molecular diagnosis of major depressive disorder by plasma peptides using a deep learning approach.

Major depressive disorder (MDD) is a severe psychiatric disorder that currently lacks any objective diagnostic markers. Here, we develop a deep learni...

Primary care research on hypertension: A bibliometric analysis using machine-learning.

Hypertension is one of the most important chronic diseases worldwide. Hypertension is a critical condition encountered frequently in daily life, formi...

Cyborg Insect Factory: Automatic Assembly System to Build up Insect-computer Hybrid Robot Based on Vision-guided Robotic Arm Manipulation of Custom Bipolar Electrodes

The advancement of insect-computer hybrid robots holds significant promise for navigating complex terrains and enhancing robotics applications. This...

A Multi-Label EEG Dataset for Mental Attention State Classification in Online Learning

Attention is a vital cognitive process in the learning and memory environment, particularly in the context of online learning. Traditional methods f...

scMEDAL for the interpretable analysis of single-cell transcriptomics data with batch effect visualization using a deep mixed effects autoencoder

scRNA-seq data has the potential to provide new insights into cellular heterogeneity and data acquisition; however, a major challenge is unraveling ...

Evaluating the Economic Implications of Using Machine Learning in Clinical Psychiatry

With the growing interest in using AI and machine learning (ML) in medicine, there is an increasing number of literature covering the application an...

PhDGPT: Introducing a psychometric and linguistic dataset about how large language models perceive graduate students and professors in psychology

Machine psychology aims to reconstruct the mindset of Large Language Models (LLMs), i.e. how these artificial intelligences perceive and associate i...

Large language models for mental health

Digital technologies have long been explored as a complement to standard procedure in mental health research and practice, ranging from the manageme...

Using machine learning to derive neurobiological subtypes of general psychopathology in late childhood.

Traditional mental health diagnoses rely on symptom-based classifications. Yet this approach can oversimplify clinical presentations as diagnoses ofte...

Leveraging normative personality data and machine learning to examine the brain structure correlates of obsessive-compulsive personality disorder traits.

Brain structure correlates of obsessive-compulsive personality disorder (OCPD) remain poorly understood as limited OCPD assessment has precluded well-...

Making the most of errors: Utilizing erroneous classifications generated by machine-learning models of neuroimaging data to capture disorder heterogeneity.

Within-disorder heterogeneity complicates mapping the neurobiological features of psychopathology to Diagnostic and Statistical Manual of Mental Disor...

Topological and Graph Theoretical Analysis of Dynamic Functional Connectivity for Autism Spectrum Disorder

Autism Spectrum Disorder (ASD) is a prevalent neurological disorder. However, the multi-faceted symptoms and large individual differences among ASD ...

Parsing altered brain connectivity in neurodevelopmental disorders by integrating graph-based normative modeling and deep generative networks

Divergent brain connectivity is thought to underlie the behavioral and cognitive symptoms observed in many neurodevelopmental disorders. Quantifying...

SimBrainNet: Evaluating Brain Network Similarity for Attention Disorders

Electroencephalography (EEG)-based attention disorder research seeks to understand brain activity patterns associated with attention. Previous studi...

Advanced Gesture Recognition in Autism: Integrating YOLOv7, Video Augmentation and VideoMAE for Video Analysis

Deep learning and advancements in contactless sensors have significantly enhanced our ability to understand complex human activities in healthcare s...

EEG-estimated functional connectivity, and not behavior, differentiates Parkinson's patients from health controls during the Simon conflict task

Neural biomarkers that can classify or predict disease are of broad interest to the neurological and psychiatric communities. Such biomarkers can be...