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A multivariate neuroimaging biomarker of individual outcome to transcranial magnetic stimulation in depression
Journal article   Open access   Peer reviewed

A multivariate neuroimaging biomarker of individual outcome to transcranial magnetic stimulation in depression

Robin F. H. Cash, Luca Cocchi, Rodney Anderson, Anton Rogachov, Aaron Kucyi, Alexander J. Barnett, Andrew Zalesky and Paul B. Fitzgerald
Human brain mapping, v 40(16), pp 4618-4629
01 Nov 2019
DOI: https://doi.org/10.1002/hbm.24725
PMID: 31332903
Featured in Collection :   UN Sustainable Development Goals @ Drexel
url
https://doi.org/10.1002/hbm.24725View
Published, Version of Record (VoR) Open

Abstract

Life Sciences & Biomedicine Neuroimaging Neurosciences Neurosciences & Neurology Radiology, Nuclear Medicine & Medical Imaging Science & Technology
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; ).

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55 citations in Web of Science
56 citations in Scopus

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UN Sustainable Development Goals (SDGs)

This publication has contributed to the advancement of the following goals:

#3 Good Health and Well-Being

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Collaboration types
Domestic collaboration
International collaboration
Web of Science research areas
Neuroimaging
Neurosciences
Radiology, Nuclear Medicine & Medical Imaging
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