An electroencephalographic signature predicts antidepressant response in major depression

Wei Wu, Yu Zhang, Jing Jiang, Molly 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*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

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
Pages (from-to)439-447
Number of pages15
JournalNature Biotechnology
Volume38
Issue number4
Early online date10 Feb 2020
DOIs
Publication statusPublished - Apr 2020

Keywords

  • TRANSCRANIAL MAGNETIC STIMULATION
  • SINGLE-TRIAL EEG
  • BRAIN-FUNCTION
  • OSCILLATIONS
  • MECHANISMS
  • MODERATORS
  • BIOMARKERS
  • CONFLICT
  • NETWORKS
  • OUTCOMES

Cite this

Wu, W., Zhang, Y., Jiang, J., Lucas, M., 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