Brain functional imaging data, especially functional magnetic resonance imaging (fMRI)
data, have been employed to reflect functional integration of the brain. Alteration in brain …

Neuroimaging‐based approaches have been extensively applied to study mental illness in
recent years and have deepened our understanding of both cognitively healthy and …

Neurobiological heterogeneity in schizophrenia is poorly understood and confounds current
analyses. We investigated neuroanatomical subtypes in a multi-institutional multi-ethnic …

With the increasing prevalence of autism spectrum disorder (ASD), it is important to identify
ASD patients for effective treatment and intervention, especially in early childhood …

Abstract Since Emil Kraepelin's conceptualization of endogenous psychoses as dementia
praecox and manic depression, the separation between primary psychotic disorders and …

Machine learning approaches for clinical psychology and psychiatry explicitly focus on
learning statistical functions from multidimensional data sets to make generalizable …

Background Current fMRI-based classification approaches mostly use functional
connectivity or spatial maps as input, instead of exploring the dynamic time courses directly …

Default mode network (DMN) has been reported altered in schizophrenia (SZ) using static
connectivity analysis. However, the studies on dynamic characteristics of DMN in SZ are still …

Abstract Machine learning techniques provide new methods to predict diagnosis and clinical
outcomes at an individual level. We aim to review the existing literature on the use of …

Convolutional neural networks (CNNs) have shown promising results in classifying
individuals with mental disorders such as schizophrenia using resting-state fMRI data …