Machine learning in major depression: From classification to treatment outcome prediction

S Gao, VD Calhoun, J Sui - CNS neuroscience & therapeutics, 2018‏ - Wiley Online Library
Aims Major depression disorder (MDD) is the single greatest cause of disability and
morbidity, and affects about 10% of the population worldwide. Currently, there are no …

Towards a brain‐based predictome of mental illness

B Rashid, V Calhoun - Human brain map**, 2020‏ - Wiley Online Library
Neuroimaging‐based approaches have been extensively applied to study mental illness in
recent years and have deepened our understanding of both cognitively healthy and …

Support vector machine

DA Pisner, DM Schnyer - Machine learning, 2020‏ - Elsevier
In this chapter, we explore Support Vector Machine (SVM)—a machine learning method that
has become exceedingly popular for neuroimaging analysis in recent years. Because of …

Unsupervised cross-domain functional MRI adaptation for automated major depressive disorder identification

Y Fang, M Wang, GG Potter, M Liu - Medical image analysis, 2023‏ - Elsevier
Resting-state functional magnetic resonance imaging (rs-fMRI) data have been widely used
for automated diagnosis of brain disorders such as major depressive disorder (MDD) to …

An insight into diagnosis of depression using machine learning techniques: a systematic review

S Bhadra, CJ Kumar - Current medical research and opinion, 2022‏ - Taylor & Francis
Background In this modern era, depression is one of the most prevalent mental disorders
from which millions of individuals are affected today. The symptoms of depression are …

Review of EEG, ERP, and brain connectivity estimators as predictive biomarkers of social anxiety disorder

A Al-Ezzi, N Kamel, I Faye, E Gunaseli - Frontiers in psychology, 2020‏ - frontiersin.org
Social anxiety disorder (SAD) is characterized by a fear of negative evaluation, negative self-
belief and extreme avoidance of social situations. These recurrent symptoms are thought to …

Machine learning studies on major brain diseases: 5-year trends of 2014–2018

K Sakai, K Yamada - Japanese journal of radiology, 2019‏ - Springer
Abstract In the recent 5 years (2014–2018), there has been growing interest in the use of
machine learning (ML) techniques to explore image diagnosis and prognosis of therapeutic …