A multivariate neuroimaging biomarker of individual outcome to transcranial magnetic stimulation in depression. Issue 16 (22nd July 2019)

• Record Type: Journal Article
• Authors: Cash, Robin F. H.; Cocchi, Luca; Anderson, Rodney; Rogachov, Anton; Kucyi, Aaron; Barnett, Alexander J.; Zalesky, Andrew; Fitzgerald, Paul B.
• Is Part Of: Human brain mapping. Volume 40:Issue 16(2019)
• Page Start: 4618
• Page End: 4629
• Publication Date: 2019-07-22
• Subjects: BOLD power -- depression -- functional connectivity -- machine learning -- magnetic resonance imaging -- transcranial magnetic stimulation; Brain mapping -- Periodicals; 611.81
• DOI: 10.1002/hbm.24725
• Languages: English
• ISSNs: 1065-9471

Abstract

Abstract: The neurobiology of major depressive disorder (MDD) remains incompletely understood, and many individuals fail to respond to standard treatments. Repetitive transcranial magnetic stimulation (rTMS) of the dorsolateral prefrontal cortex (DLPFC) has emerged as a promising antidepressant therapy. However, the heterogeneity of response underscores a pressing need for biomarkers of treatment outcome. We acquired resting state functional magnetic resonance imaging (rsfMRI) data in 47 MDD individuals prior to 5–8 weeks of rTMS treatment targeted using the F3 beam approach and in 29 healthy comparison subjects. The caudate, prefrontal cortex, and thalamus showed significantly lower blood oxygenation level‐dependent (BOLD) signal power in MDD individuals at baseline. Critically, individuals who responded best to treatment were associated with lower pre‐treatment BOLD power in these regions. Additionally, functional connectivity (FC) in the default mode and affective networks was associated with treatment response. We leveraged these findings to train support vector machines (SVMs) to predict individual treatment responses, based on learned patterns of baseline FC, BOLD signal power and clinical features. Treatment response (responder vs. nonresponder) was predicted with 85–95% accuracy. Reduction in symptoms was predicted to within a mean error of ±16% ( r = .68, p < .001). These preliminary findings suggest that therapeutic outcome to DLPFC‐rTMS could be predicted at a clinically meaningful level using only a small number of core neurobiological features of MDD, warranting prospective testing to ascertain generalizability. This provides a novel, transparent and physiologically plausible multivariate approach for classification of individual response to what has become the most commonly employed rTMS treatment worldwide. This study utilizes data from a larger clinical study (Australian New Zealand Clinical Trials Registry: Investigating Predictors of Response to Transcranial Magnetic Stimulation for the Treatment of Depression; ACTRN12610001071011; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=336262 ).