Machine learning in mental health: a sco** review of methods and applications
BackgroundThis paper aims to synthesise the literature on machine learning (ML) and big
data applications for mental health, highlighting current research and applications in …
data applications for mental health, highlighting current research and applications in …
Classification and prediction of brain disorders using functional connectivity: promising but challenging
Brain functional imaging data, especially functional magnetic resonance imaging (fMRI)
data, have been employed to reflect functional integration of the brain. Alteration in brain …
data, have been employed to reflect functional integration of the brain. Alteration in brain …
Machine learning in resting-state fMRI analysis
Abstract Machine learning techniques have gained prominence for the analysis of resting-
state functional Magnetic Resonance Imaging (rs-fMRI) data. Here, we present an overview …
state functional Magnetic Resonance Imaging (rs-fMRI) data. Here, we present an overview …
Dynamic functional network connectivity in idiopathic generalized epilepsy with generalized tonic–clonic seizure
Idiopathic generalized epilepsy (IGE) has been linked with disrupted intra‐network
connectivity of multiple resting‐state networks (RSNs); however, whether impairment is …
connectivity of multiple resting‐state networks (RSNs); however, whether impairment is …
Neuroimaging in social anxiety disorder—a meta-analytic review resulting in a new neurofunctional model
AB Bruehl, A Delsignore, K Komossa… - … & Biobehavioral Reviews, 2014 - Elsevier
Social anxiety disorder (SAD) is one of the most frequent anxiety disorders. The landmark
meta-analysis of functional neuroimaging studies by Etkin and Wager (2007) revealed …
meta-analysis of functional neuroimaging studies by Etkin and Wager (2007) revealed …
Towards a brain‐based predictome of mental illness
Neuroimaging‐based approaches have been extensively applied to study mental illness in
recent years and have deepened our understanding of both cognitively healthy and …
recent years and have deepened our understanding of both cognitively healthy and …
[HTML][HTML] From estimating activation locality to predicting disorder: a review of pattern recognition for neuroimaging-based psychiatric diagnostics
Psychiatric disorders are increasingly being recognised as having a biological basis, but
their diagnosis is made exclusively behaviourally. A promising approach for …
their diagnosis is made exclusively behaviourally. A promising approach for …
Identifying patients with Alzheimer's disease using resting-state fMRI and graph theory
Objective Study of brain network on the basis of resting-state functional magnetic resonance
imaging (fMRI) has provided promising results to investigate changes in connectivity among …
imaging (fMRI) has provided promising results to investigate changes in connectivity among …
Classification of Alzheimer's disease using whole brain hierarchical network
Regions of interest (ROIs) based classification has been widely investigated for analysis of
brain magnetic resonance imaging (MRI) images to assist the diagnosis of Alzheimer's …
brain magnetic resonance imaging (MRI) images to assist the diagnosis of Alzheimer's …
Multivariate classification of autism spectrum disorder using frequency-specific resting-state functional connectivity—a multi-center study
Background Resting-state functional magnetic resonance imaging studies examining low
frequency fluctuations (0.01–0.08 Hz) have revealed atypical whole brain functional …
frequency fluctuations (0.01–0.08 Hz) have revealed atypical whole brain functional …