Review on EEG and ERP predictive biomarkers for major depressive disorder

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.

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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.

Item Type: Article
Impact Factor: cited By 35
Uncontrolled 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
Depositing User: Ms Sharifah Fahimah Saiyed Yeop
Date Deposited: 26 Mar 2022 03:20
Last Modified: 26 Mar 2022 03:20
URI: http://scholars.utp.edu.my/id/eprint/31486

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