eprintid: 5826
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/00/58/26
datestamp: 2023-11-09 16:17:34
lastmod: 2023-11-09 16:17:34
status_changed: 2023-11-09 16:03:59
type: article
metadata_visibility: show
creators_name: Mumtaz, W.
creators_name: Malik, A.S.
creators_name: Yasin, M.A.M.
creators_name: Xia, L.
title: Review on EEG and ERP predictive biomarkers for major depressive disorder
ispublished: pub
keywords: Bioelectric phenomena; Biomarkers; Clustering algorithms; Electrophysiology; Magnetic resonance imaging; Neuroimaging; Patient treatment, Antidepressants; Event related potentials; Major depressive disorder; Nonresponse; Response; Treatment efficacy; Treatment outcomes, Electroencephalography, amfebutamone; antidepressant agent; dopamine uptake inhibitor; escitalopram; paroxetine; serotonin noradrenalin reuptake inhibitor; serotonin uptake inhibitor, alpha rhythm; anterior cingulate; Article; classification algorithm; data analysis; disease marker; EEG abnormality; electroencephalogram; episodic memory; event related potential; experimental design; frontal cortex; functional magnetic resonance imaging; human; hypothalamus hypophysis adrenal system; information processing; low resolution brain electromagnetic tomography; machine learning; major depression; outcome assessment; pathophysiology; prediction; predictive value; priority journal; recognition; therapy effect; theta rhythm; treatment outcome; treatment response
note: cited By 49
abstract: 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.
date: 2015
publisher: Elsevier Ltd
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84937690679&doi=10.1016%2fj.bspc.2015.07.003&partnerID=40&md5=5f828a3fd9d98222092cd0121da59257
id_number: 10.1016/j.bspc.2015.07.003
full_text_status: none
publication: Biomedical Signal Processing and Control
volume: 22
pagerange: 85-98
refereed: TRUE
issn: 17468094
citation:   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