Predicting tDCS treatment outcomes of patients with major depressive disorder using automated EEG classification

Al-Kaysi, AM; Al-Ani, A; Loo, CK; Powell, TY; Martin, DM; Breakspear, M; Boonstra, TW

Predicting tDCS treatment outcomes of patients with major depressive disorder using automated EEG classification

Al-Kaysi, AM Al-Ani, A

Loo, CK Powell, TY Martin, DM Breakspear, M Boonstra, TW

Permalink

Publication Type:: Journal Article
Citation:: Journal of Affective Disorders, 2017, 208 pp. 597 - 603
Issue Date:: 2017-01-15

Closed Access

	Filename	Description	Size
	Predicting tDCS treatment outcomes.pdf	Published Version	762.04 kB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Al-Kaysi, AM	en_US
dc.contributor.author	Al-Ani, A https://orcid.org/0000-0002-8092-8954	en_US
dc.contributor.author	Loo, CK	en_US
dc.contributor.author	Powell, TY	en_US
dc.contributor.author	Martin, DM	en_US
dc.contributor.author	Breakspear, M	en_US
dc.contributor.author	Boonstra, TW	en_US
dc.date.available	2016-10-21	en_US
dc.date.issued	2017-01-15	en_US
dc.identifier.citation	Journal of Affective Disorders, 2017, 208 pp. 597 - 603	en_US
dc.identifier.issn	0165-0327	en_US
dc.identifier.uri	http://hdl.handle.net/10453/125224
dc.description.abstract	© 2016 Elsevier B.V. Background Transcranial direct current stimulation (tDCS) is a promising treatment for major depressive disorder (MDD). Standard tDCS treatment involves numerous sessions running over a few weeks. However, not all participants respond to this type of treatment. This study aims to investigate the feasibility of identifying MDD patients that respond to tDCS treatment based on resting-state electroencephalography (EEG) recorded prior to treatment commencing. Methods We used machine learning to predict improvement in mood and cognition during tDCS treatment from baseline EEG power spectra. Ten participants with a current diagnosis of MDD were included. Power spectral density was assessed in five frequency bands: delta (0.5–4 Hz), theta (4–8 Hz), alpha (8–12 Hz), beta (13–30 Hz) and gamma (30–100 Hz). Improvements in mood and cognition were assessed using the Montgomery-Åsberg Depression Rating Scale and Symbol Digit Modalities Test, respectively. We trained the classifiers using three algorithms (support vector machine, extreme learning machine and linear discriminant analysis) and a leave-one-out cross-validation approach. Results Mood labels were accurately predicted in 8 out of 10 participants using EEG channels FC4-AF8 (accuracy=76%, p=0.034). Cognition labels were accurately predicted in 10 out of 10 participants using channels pair CPz-CP2 (accuracy=92%, p=0.004). Limitations Due to the limited number of participants (n=10), the presented results mainly aim to serve as a proof of concept. Conclusions These finding demonstrate the feasibility of using machine learning to identify patients that will respond to tDCS treatment. These promising results warrant a larger study to determine the clinical utility of this approach.	en_US
dc.relation.ispartof	Journal of Affective Disorders	en_US
dc.relation.isbasedon	10.1016/j.jad.2016.10.021	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.classification	Psychiatry	en_US
dc.subject.mesh	Humans	en_US
dc.subject.mesh	Electroencephalography	en_US
dc.subject.mesh	Treatment Outcome	en_US
dc.subject.mesh	Cognition	en_US
dc.subject.mesh	Psychomotor Performance	en_US
dc.subject.mesh	Depressive Disorder, Major	en_US
dc.subject.mesh	Adult	en_US
dc.subject.mesh	Middle Aged	en_US
dc.subject.mesh	Female	en_US
dc.subject.mesh	Male	en_US
dc.subject.mesh	Transcranial Direct Current Stimulation	en_US
dc.subject.mesh	Support Vector Machine	en_US
dc.title	Predicting tDCS treatment outcomes of patients with major depressive disorder using automated EEG classification	en_US
dc.type	Journal Article
utslib.citation.volume	208	en_US
utslib.for	1702 Cognitive Sciences	en_US
utslib.for	0806 Information Systems	en_US
utslib.for	11 Medical and Health Sciences	en_US
utslib.for	17 Psychology and Cognitive Sciences	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Biomedical Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
utslib.copyright.status	closed_access	*
pubs.publication-status	Published	en_US
pubs.volume	208	en_US

Abstract:

© 2016 Elsevier B.V. Background Transcranial direct current stimulation (tDCS) is a promising treatment for major depressive disorder (MDD). Standard tDCS treatment involves numerous sessions running over a few weeks. However, not all participants respond to this type of treatment. This study aims to investigate the feasibility of identifying MDD patients that respond to tDCS treatment based on resting-state electroencephalography (EEG) recorded prior to treatment commencing. Methods We used machine learning to predict improvement in mood and cognition during tDCS treatment from baseline EEG power spectra. Ten participants with a current diagnosis of MDD were included. Power spectral density was assessed in five frequency bands: delta (0.5–4 Hz), theta (4–8 Hz), alpha (8–12 Hz), beta (13–30 Hz) and gamma (30–100 Hz). Improvements in mood and cognition were assessed using the Montgomery-Åsberg Depression Rating Scale and Symbol Digit Modalities Test, respectively. We trained the classifiers using three algorithms (support vector machine, extreme learning machine and linear discriminant analysis) and a leave-one-out cross-validation approach. Results Mood labels were accurately predicted in 8 out of 10 participants using EEG channels FC4-AF8 (accuracy=76%, p=0.034). Cognition labels were accurately predicted in 10 out of 10 participants using channels pair CPz-CP2 (accuracy=92%, p=0.004). Limitations Due to the limited number of participants (n=10), the presented results mainly aim to serve as a proof of concept. Conclusions These finding demonstrate the feasibility of using machine learning to identify patients that will respond to tDCS treatment. These promising results warrant a larger study to determine the clinical utility of this approach.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/125224