Inter and intra-hemispheric structural imaging markers predict depression relapse after electroconvulsive therapy: a multisite study. Issue 12 (December 2017)

• Record Type: Journal Article
• Authors: Wade, Benjamin; Sui, Jing; Hellemann, Gerhard; Leaver, Amber; Espinoza, Randall; Woods, Roger; Abbott, Christopher; Joshi, Shantanu; Narr, Katherine
• Is Part Of: Translational psychiatry. Volume 7:Issue 12(2017)
• Page Start: 1
• Page End: 12
• Publication Date: 2017-12
• Subjects: Psychiatry -- Research -- Periodicals; Neurosciences -- Research -- Periodicals; Psychiatry -- Periodicals; Neurosciences -- Periodicals; Translational Research -- Periodicals; Health Policy -- Periodicals; Public Health -- Periodicals; 616.89
• DOI: 10.1038/s41398-017-0020-7
• Languages: English
• ISSNs: 2158-3188

Abstract

Abstract Relapse of depression following treatment is high. Biomarkers predictive of an individual's relapse risk could provide earlier opportunities for prevention. Since electroconvulsive therapy (ECT) elicits robust and rapidly acting antidepressant effects, but has a >50% relapse rate, ECT presents a valuable model for determining predictors of relapse-risk. Although previous studies have associated ECT-induced changes in brain morphometry with clinical response, longer-term outcomes have not been addressed. Using structural imaging data from 42 ECT-responsive patients obtained prior to and directly following an ECT treatment index series at two independent sites (UCLA:n = 17, age = 45.41±12.34 years; UNM:n = 25; age = 65.00±8.44), here we test relapse prediction within 6-months post-ECT. Random forests were used to predict subsequent relapse using singular and ratios of intra and inter-hemispheric structural imaging measures and clinical variables from pre-, post-, and pre-to-post ECT. Relapse risk was determined as a function of feature variation. Relapse was well-predicted both within site and when cohorts were pooled where top-performing models yielded balanced accuracies of 71–78%. Top predictors included cingulate isthmus asymmetry, pallidal asymmetry, the ratio of the paracentral to precentral cortical thickness and the ratio of lateral occipital to pericalcarine cortical thickness. Pooling cohorts and predicting relapse from post-treatment measures provided the best classification performances. However, classifiers trained on each age-disparate cohort were less informative for prediction in the held-out cohort. Post-treatment structural neuroimaging measures and the ratios of connected regions commonly implicated in depression pathophysiology are informative of relapse risk. Structural imaging measures may have utility for devising more personalized preventative medicine approaches.