relation: https://khub.utp.edu.my/scholars/5826/
title: Review on EEG and ERP predictive biomarkers for major depressive disorder
creator: Mumtaz, W.
creator: Malik, A.S.
creator: Yasin, M.A.M.
creator: Xia, L.
description: Abstract The selection of suitable antidepressants for Major Depressive Disorder (MDD) has been challenging and is mainly based on subjective assessments that include minimal scientific evidence. Objective measures that are extracted from neuroimaging modalities such as electroencephalograms (EEGs) could be a potential solution to this problem. This approach is achieved by the successful prediction of antidepressant treatment efficacy early in the patient's care. EEG-based relevant research studies have shown promising results. These studies are based on derived measures from EEG and event-related potentials (ERPs), which are called neurophysiological predictive biomarkers for MDD. This paper seeks to provide a detailed review on such research studies along with their possible limitations. In addition, this paper provides a comparison of these methods based on EEG/ERP common datasets from MDD and healthy controls. This paper also proposes recommendations to improve these methods, e.g., EEG integration with other modalities such as functional magnetic resonance imaging (fMRI) and magnetoencephalograms (MEG), to achieve better evidence of the efficacy than EEG alone, to eventually improve the treatment selection process. © 2015 Elsevier Ltd.
publisher: Elsevier Ltd
date: 2015
type: Article
type: PeerReviewed
identifier:   Mumtaz, W. and Malik, A.S. and Yasin, M.A.M. and Xia, L.  (2015) Review on EEG and ERP predictive biomarkers for major depressive disorder.  Biomedical Signal Processing and Control, 22.  pp. 85-98.  ISSN 17468094     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84937690679&doi=10.1016%2fj.bspc.2015.07.003&partnerID=40&md5=5f828a3fd9d98222092cd0121da59257
relation: 10.1016/j.bspc.2015.07.003
identifier: 10.1016/j.bspc.2015.07.003