Brain regulation of emotional conflict predicts antidepressant treatment response for depression

Gregory A. Fonzo, Amit Etkin (), Yu Zhang, Wei Wu, Crystal Cooper, Cherise Chin-Fatt, Manish K. Jha, Joseph Trombello, Thilo Deckersbach, Phil Adams, Melvin McInnis, Patrick J. McGrath, Myrna M. Weissman, Maurizio Fava and Madhukar H. Trivedi
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Gregory A. Fonzo: The University of Texas at Austin
Amit Etkin: Stanford University
Yu Zhang: Stanford University
Wei Wu: Stanford University
Crystal Cooper: University of Texas Southwestern Medical Center
Cherise Chin-Fatt: University of Texas Southwestern Medical Center
Manish K. Jha: University of Texas Southwestern Medical Center
Joseph Trombello: University of Texas Southwestern Medical Center
Thilo Deckersbach: Massachusetts General Hospital
Phil Adams: College of Physicians and Surgeons of Columbia University
Melvin McInnis: University of Michigan
Patrick J. McGrath: College of Physicians and Surgeons of Columbia University
Myrna M. Weissman: College of Physicians and Surgeons of Columbia University
Maurizio Fava: Massachusetts General Hospital
Madhukar H. Trivedi: University of Texas Southwestern Medical Center

Nature Human Behaviour, 2019, vol. 3, issue 12, 1319-1331

Abstract: Abstract The efficacy of antidepressant treatment for depression is controversial due to the only modest superiority demonstrated over placebo. However, neurobiological heterogeneity within depression may limit overall antidepressant efficacy. We sought to identify a neurobiological phenotype responsive to antidepressant treatment by testing pretreatment brain activation during response to, and regulation of, emotional conflict as a moderator of the clinical benefit of the antidepressant sertraline versus placebo. Using neuroimaging data from a large randomized controlled trial, we found widespread moderation of clinical benefits by brain activity during regulation of emotional conflict, in which greater downregulation of conflict-responsive regions predicted better sertraline outcomes. Treatment-predictive machine learning using brain metrics outperformed a model trained on clinical and demographic variables. Our findings demonstrate that antidepressant response is predicted by brain activity underlying a key self-regulatory emotional capacity. Leveraging brain-based measures in psychiatry will forge a path toward better treatment personalization, refined mechanistic insights and improved outcomes.

Date: 2019
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DOI: 10.1038/s41562-019-0732-1

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