Distinct predictive patterns of antidepressant response to sertraline, placebo and bupropion using pre-treatment reward processing as examined through functional MRI and deep learning: Key results from the EMBARC study

Kevin P. Nguyen, Cherise Chin Fatt, Alex Treacher, Cooper Mellema, Crystal Cooper, Manish K Jha, Benji Kurian, Maurizio Fava, Patrick J. McGrath, Phil Adams, Myrna Weissman, Mary L. Phillips, Madhukar H. Trivedi, Albert Montillo

Research output: Contribution to journalArticlepeer-review

Abstract

Uncertainty in selecting the appropriate antidepressant for each patient is a major challenge in treatment of major depressive disorder (MDD). No biologically driven markers are currently available to improve precision in treatment selection, thus leading to a trial-and-error process and prolonged morbidity for most patients. This study developed deep learning models that accurately predict treatment outcomes for sertraline, bupropion and placebo. Models were trained on data from the EMBARC study, in which 223 un-medicated subjects with MDD underwent pre-treatment reward task fMRI and received 8 weeks of treatment with sertraline, bupropion, or placebo. These models integrate fMRI and clinical measures and they explain up to 37% of the variance in ΔHAMD, classify remitters with NNT of 2.3-4.3, and classify responders with NNT of 3.2-4.9. Findings reveal new regions predictive of treatment outcome such as the hippocampus and paracentral lobule, while additional regions implicated in existing research are corroborated. Distinct models were identified for each treatment and provide substantial evidence of their potential to improve precision in treatment selection for MDD.

Original languageEnglish (US)
JournalUnknown Journal
DOIs
StatePublished - Jan 29 2020

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)
  • Agricultural and Biological Sciences(all)
  • Immunology and Microbiology(all)
  • Neuroscience(all)
  • Pharmacology, Toxicology and Pharmaceutics(all)

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