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 …
Applications of artificial intelligence− machine learning for detection of stress: a critical overview
Psychological distress is a major contributor to human physiology and pathophysiology, and
it has been linked to several conditions, such as auto-immune diseases, metabolic …
it has been linked to several conditions, such as auto-immune diseases, metabolic …
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 …
A Bayesian model of category-specific emotional brain responses
Understanding emotion is critical for a science of healthy and disordered brain function, but
the neurophysiological basis of emotional experience is still poorly understood. We …
the neurophysiological basis of emotional experience is still poorly understood. We …
Effects of home-based telerehabilitation in patients with stroke: a randomized controlled trial
J Chen, D Sun, S Zhang, Y Shi, F Qiao, Y Zhou, J Liu… - Neurology, 2020 - AAN Enterprises
Objective To determine the effects of a 12-week home-based motor training
telerehabilitation program in patients with subcortical stroke by combining motor function …
telerehabilitation program in patients with subcortical stroke by combining motor function …
Combining complex networks and data mining: why and how
The increasing power of computer technology does not dispense with the need to extract
meaningful information out of data sets of ever growing size, and indeed typically …
meaningful information out of data sets of ever growing size, and indeed typically …
The use of machine learning techniques in trauma-related disorders: a systematic review
LF Ramos-Lima, V Waikamp… - Journal of psychiatric …, 2020 - Elsevier
Establishing the diagnosis of trauma-related disorders such as Acute Stress Disorder (ASD)
and Posttraumatic Stress Disorder (PTSD) have always been a challenge in clinical practice …
and Posttraumatic Stress Disorder (PTSD) have always been a challenge in clinical practice …
Supervised machine learning for diagnostic classification from large-scale neuroimaging datasets
There are growing concerns about the generalizability of machine learning classifiers in
neuroimaging. In order to evaluate this aspect across relatively large heterogeneous …
neuroimaging. In order to evaluate this aspect across relatively large heterogeneous …
Differential patterns of dynamic functional connectivity variability of striato–cortical circuitry in children with benign epilepsy with centrotemporal spikes
Benign epilepsy with centrotemporal spikes (BECTS) is characterized by abnormal (static)
functional interactions among cortical and subcortical regions, regardless of the active or …
functional interactions among cortical and subcortical regions, regardless of the active or …
Disrupted cortical hubs in functional brain networks in social anxiety disorder
Objective The network hubs, characterized by the large number of connections to other
regions, play important roles in the proper and effective transfer of information. Previous …
regions, play important roles in the proper and effective transfer of information. Previous …