Machine Learning Identifies Large-Scale Reward-Related Activity Modulated by Dopaminergic Enhancement in Major Depression.

Journal: Biological psychiatry. Cognitive neuroscience and neuroimaging
PMID: 31784354

Abstract

BACKGROUND: Theoretical models have emphasized systems-level abnormalities in major depressive disorder (MDD). For unbiased yet rigorous evaluations of pathophysiological mechanisms underlying MDD, it is critically important to develop data-driven approaches that harness whole-brain data to classify MDD and evaluate possible normalizing effects of targeted interventions. Here, using an experimental therapeutics approach coupled with machine learning, we investigated the effect of a pharmacological challenge aiming to enhance dopaminergic signaling on whole-brain response to reward-related stimuli in MDD.

Authors

  • Yuelu Liu
    BlackThorn Therapeutics, San Francisco, California.
  • Roee Admon
    Department of Psychology, University of Haifa, Haifa, Israel.
  • Monika S Mellem
    BlackThorn Therapeutics, San Francisco, California.
  • Emily L Belleau
    McLean Hospital, Belmont, Massachusetts; Department of Psychiatry, Harvard Medical School, Boston, Massachusetts.
  • Roselinde H Kaiser
    Department of Psychology and Neuroscience, University of Colorado, Boulder, Colorado.
  • Rachel Clegg
    McLean Hospital, Belmont, Massachusetts.
  • Miranda Beltzer
    McLean Hospital, Belmont, Massachusetts.
  • Franziska Goer
    McLean Hospital, Belmont, Massachusetts.
  • Gordana Vitaliano
    McLean Hospital, Belmont, Massachusetts; Department of Psychiatry, Harvard Medical School, Boston, Massachusetts.
  • Parvez Ahammad
    BlackThorn Therapeutics, San Francisco, California.
  • Diego A Pizzagalli
    McLean Hospital, Belmont, Massachusetts; Department of Psychiatry, Harvard Medical School, Boston, Massachusetts. Electronic address: dap@mclean.harvard.edu.

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