title: Using pre-treatment EEG data to predict response to SSRI treatment for MDD.
abstract: The problem of identifying in advance the most effective treatment agent for various psychiatric conditions remains an elusive goal. To address this challenge, we propose a machine learning (ML) methodology to predict the response to a selective serotonin reuptake inhibitor (SSRI) medication in subjects suffering from major depressive disorder (MDD), using pre-treatment electroencephalograph (EEG) measurements. The proposed feature selection technique is a modification of the method of Peng et al [10] that is based on a Kullback-Leibler (KL) distance measure. The classifier was realized as a kernelized partial least squares regression procedure, whose output is the predicted response. A low-dimensional kernelized principal component representation of the feature space was used for the purposes of visualization and clustering analysis. The overall method was evaluated using an 11-fold nested cross-validation procedure for which over 85% average prediction performance is obtained. The results indicate that ML methods hold considerable promise in predicting the efficacy of SSRI antidepressant therapy for major depression.
authors.affiliation: Electrical and Computer Engineering Department, McMaster University, Hamilton, ON, L8S 4K1, Canada. akhodayari@ieee.org
authors.forename: Ahmad
authors.initials: A
authors.lastname: Khodayari-Rostamabad
authors.forename: James P
authors.initials: JP
authors.lastname: Reilly
authors.forename: Gary
authors.initials: G
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; D003865:Major Depressive Disorder / Q000188:drug therapy* / Q000503:physiopathology*; D004569:Electroencephalography / Q000379:methods*; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D025341:Principal Component Analysis; D017367:Selective Serotonin Reuptake Inhibitors / Q000627:therapeutic use*; D016896:Treatment Outcome; D055815:Young Adult
chemical_list: D017367:Selective Serotonin Reuptake Inhibitors