An electroencephalographic signature predicts antidepressant response in major depression

Wei Wu, Yu Zhang, Jing Jiang, Molly V. Lucas, Gregory A. Fonzo, Camarin E. Rolle, Crystal Cooper, Cherise Chin-Fatt, Noralie Krepel, Carena A. Cornelssen, Rachael Wright, Russell T. Toll, Hersh M. Trivedi, Karen Monuszko, Trevor L. Caudle, Kamron Sarhadi, Manish K Jha, Joseph M. Trombello, Thilo Deckersbach, Phil AdamsPatrick J. McGrath, Myrna M. Weissman, Maurizio Fava, Diego A. Pizzagalli, Martijn Arns, Madhukar H. Trivedi, Amit Etkin

Research output: Contribution to journalArticle

3 Scopus citations

Abstract

Antidepressants are widely prescribed, but their efficacy relative to placebo is modest, in part because the clinical diagnosis of major depression encompasses biologically heterogeneous conditions. Here, we sought to identify a neurobiological signature of response to antidepressant treatment as compared to placebo. We designed a latent-space machine-learning algorithm tailored for resting-state electroencephalography (EEG) and applied it to data from the largest imaging-coupled, placebo-controlled antidepressant study (n = 309). Symptom improvement was robustly predicted in a manner both specific for the antidepressant sertraline (versus placebo) and generalizable across different study sites and EEG equipment. This sertraline-predictive EEG signature generalized to two depression samples, wherein it reflected general antidepressant medication responsivity and related differentially to a repetitive transcranial magnetic stimulation treatment outcome. Furthermore, we found that the sertraline resting-state EEG signature indexed prefrontal neural responsivity, as measured by concurrent transcranial magnetic stimulation and EEG. Our findings advance the neurobiological understanding of antidepressant treatment through an EEG-tailored computational model and provide a clinical avenue for personalized treatment of depression.

Original languageEnglish (US)
Pages (from-to)439-447
Number of pages9
JournalNature biotechnology
Volume38
Issue number4
DOIs
StatePublished - Apr 1 2020

ASJC Scopus subject areas

  • Biotechnology
  • Bioengineering
  • Applied Microbiology and Biotechnology
  • Molecular Medicine
  • Biomedical Engineering

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    Wu, W., Zhang, Y., Jiang, J., Lucas, M. V., Fonzo, G. A., Rolle, C. E., Cooper, C., Chin-Fatt, C., Krepel, N., Cornelssen, C. A., Wright, R., Toll, R. T., Trivedi, H. M., Monuszko, K., Caudle, T. L., Sarhadi, K., Jha, M. K., Trombello, J. M., Deckersbach, T., ... Etkin, A. (2020). An electroencephalographic signature predicts antidepressant response in major depression. Nature biotechnology, 38(4), 439-447. https://doi.org/10.1038/s41587-019-0397-3