title: Using pre-treatment electroencephalography data to predict response to transcranial magnetic stimulation therapy for major depression.
abstract: We investigate the use of machine learning methods based on the pre-treatment electroencephalograph (EEG) to predict response to repetitive transcranial magnetic stimulation (rTMS), which is a non-pharmacological form of therapy for treating major depressive disorder (MDD). The learning procedure involves the extraction of a large number of candidate features from EEG data, from which a very small subset of most statistically relevant features is selected for further processing. A statistical prediction model based on mixture of factor analysis (MFA) model is constructed from a training set that classifies the respective subject into responder and non-responder classes. A leave-2-out (L2O) cross-validation procedure is used to evaluate the prediction performance. This pilot study involves 27 subjects who received either left high-frequency (HF) active rTMS therapy or simultaneous left HF and right low-frequency active rTMS therapy. Our results indicate that it is possible to predict rTMS treatment efficacy of either treatment modality with a specificity of 83% and a sensitivity of 78%, for a combined accuracy of 80%.
authors.affiliation: Electrical and Computer Engineering Department, McMaster University, Hamilton, ON L8S 4K1, Canada. khodaya@mcmaster.ca
authors.forename: Ahmad
authors.initials: A
authors.lastname: Khodayari-Rostamabad
authors.forename: James P
authors.initials: JP
authors.lastname: Reilly
authors.forename: Gary M
authors.initials: GM
authors.lastname: Hasey
authors.forename: Hubert
authors.initials: H
authors.lastname: deBruin
authors.forename: Duncan
authors.initials: D
authors.lastname: MacCrimmon
mesh_terms: D000328:Adult; D000368:Aged; D000465:Algorithms; D001185:Artificial Intelligence; D003865:Major Depressive Disorder / Q000175:diagnosis* / Q000628:therapy*; D004569:Electroencephalography / Q000379:methods*; D004867:Equipment Design; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D010865:Pilot Projects; D012680:Sensitivity and Specificity; D050781:Transcranial Magnetic Stimulation / Q000379:methods*; D016896:Treatment Outcome