Psychiatry

Generalizability of machine learning for classification of schizophrenia based on resting-state functional MRI data.

Machine learning has increasingly been applied to classification of schizophrenia in neuroimaging research. However, direct replication studies and studies seeking to investigate generalizability are scarce. To address these issues, we assessed within-site and between-site generalizability of a machine learning classification framework which achieved excellent performance in a previous study using...

Multimodal fusion of structural and functional brain imaging in depression using linked independent component analysis.

Previous structural and functional neuroimaging studies have implicated distributed brain regions and networks in depression. However, there are no robust imaging biomarkers that are specific to depression, which may be due to clinical heterogeneity and neurobiological complexity. A dimensional approach and fusion of imaging modalities may yield a more coherent view of the neuronal correlates of d...

The Bot Will See You Now: A History and Review of Interactive Computerized Mental Health Programs.

The goal of automating complex human activities dates to antiquity. The mental health field has also made use of advances in technology to assist pati...

Phasic dopamine release identification using convolutional neural network.

Dopamine has a major behavioral impact related to drug dependence, learning and memory functions, as well as pathologies such as schizophrenia and Par...

A comparison of machine learning algorithms for the surveillance of autism spectrum disorder.

OBJECTIVE: The Centers for Disease Control and Prevention (CDC) coordinates a labor-intensive process to measure the prevalence of autism spectrum dis...

Individualized prediction of depressive disorder in the elderly: A multitask deep learning approach.

INTRODUCTION: Depressive disorder is one of the major public health problems among the elderly. An effective depression risk prediction model can prov...

Machine learning discovery of longitudinal patterns of depression and suicidal ideation.

BACKGROUND AND AIM: Depression is often accompanied by thoughts of self-harm, which are a strong predictor of subsequent suicide attempt and suicide d...

Machine learning and big data analytics in bipolar disorder: A position paper from the International Society for Bipolar Disorders Big Data Task Force.

OBJECTIVES: The International Society for Bipolar Disorders Big Data Task Force assembled leading researchers in the field of bipolar disorder (BD), m...

A Multi-Domain Connectome Convolutional Neural Network for Identifying Schizophrenia From EEG Connectivity Patterns.

OBJECTIVE: We exploit altered patterns in brain functional connectivity as features for automatic discriminative analysis of neuropsychiatric patients...

Development and validation of multivariable prediction models of remission, recovery, and quality of life outcomes in people with first episode psychosis: a machine learning approach.

BACKGROUND: Outcomes for people with first-episode psychosis are highly heterogeneous. Few reliable validated methods are available to predict the out...

PTSD and its dissociative subtype through the lens of the insula: Anterior and posterior insula resting-state functional connectivity and its predictive validity using machine learning.

Individuals with post-traumatic stress disorder (PTSD) typically experience states of reliving and hypervigilance; however, the dissociative subtype o...

A LightGBM-Based EEG Analysis Method for Driver Mental States Classification.

Fatigue driving can easily lead to road traffic accidents and bring great harm to individuals and families. Recently, electroencephalography- (EEG-) b...

Digital Innovations for Global Mental Health: Opportunities for Data Science, Task Sharing, and Early Intervention.

PURPOSE: Globally, individuals living with mental disorders are more likely to have access to a mobile phone than mental health care. In this commenta...

Clustering suicides: A data-driven, exploratory machine learning approach.

Methods of suicide have received considerable attention in suicide research. The common approach to differentiate methods of suicide is the classifica...

Combining mobile-health (mHealth) and artificial intelligence (AI) methods to avoid suicide attempts: the Smartcrises study protocol.

BACKGROUND: The screening of digital footprint for clinical purposes relies on the capacity of wearable technologies to collect data and extract relev...

Using machine learning to explain the heterogeneity of schizophrenia. Realizing the promise and avoiding the hype.

Despite extensive research and prodigious advances in neuroscience, our comprehension of the nature of schizophrenia remains rudimentary. Our failure ...

Machine-learning-based classification between post-traumatic stress disorder and major depressive disorder using P300 features.

BACKGROUND: The development of optimal classification criteria for specific mental disorders which share similar symptoms is an important issue for pr...

Symptomatology differences of major depression in psychiatric versus general hospitals: A machine learning approach.

BACKGROUND: Symptomatology differences of major depressive disorder (MDD) in psychiatric and general hospitals in China leads to possible misdiagnosis...

Machine Learning to Understand the Immune-Inflammatory Pathways in Fibromyalgia.

Fibromyalgia (FM) is a chronic syndrome characterized by widespread musculoskeletal pain, and physical and emotional symptoms. Although its pathophysi...

Identifying schizophrenia subgroups using clustering and supervised learning.

Schizophrenia has a 1% incidence rate world-wide and those diagnosed present with positive (e.g. hallucinations, delusions), negative (e.g. apathy, as...