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

Yuelu Liu, Roee Admon, Monika S. Mellem, Emily L. Belleau, Roselinde H. Kaiser, Rachel Clegg, Miranda Beltzer, Franziska Goer, Gordana Vitaliano, Parvez Ahammad, Diego A. Pizzagalli

Research output: Contribution to journalArticlepeer-review

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. Methods: Using a double-blind, placebo-controlled design, we analyzed functional magnetic resonance imaging data from 31 unmedicated MDD participants receiving a single dose of 50 mg amisulpride (MDDAmisulpride), 26 MDD participants receiving placebo (MDDPlacebo), and 28 healthy control subjects receiving placebo (HCPlacebo) recruited through two independent studies. An importance-guided machine learning technique for model selection was used on whole-brain functional magnetic resonance imaging data probing reward anticipation and consumption to identify features linked to MDD (MDDPlacebo vs. HCPlacebo) and dopaminergic enhancement (MDDAmisulpride vs. MDDPlacebo). Results: Highly predictive classification models emerged that distinguished MDDPlacebo from HCPlacebo (area under the curve = 0.87) and MDDPlacebo from MDDAmisulpride (area under the curve = 0.89). Although reward-related striatal activation and connectivity were among the most predictive features, the best truncated models based on whole-brain features were significantly better relative to models trained using striatal features only. Conclusions: Results indicate that in MDD, enhanced dopaminergic signaling restores abnormal activation and connectivity in a widespread network of regions. These findings provide new insights into the pathophysiology of MDD and pharmacological mechanism of antidepressants at the system level in addressing reward processing deficits among depressed individuals.

Original languageEnglish
Pages (from-to)163-172
Number of pages10
JournalBiological Psychiatry: Cognitive Neuroscience and Neuroimaging
Volume5
Issue number2
DOIs
StatePublished - Feb 2020

Bibliographical note

Publisher Copyright:
© 2019 Society of Biological Psychiatry

Keywords

  • Biomarker
  • Biotypes
  • Depression
  • Dopamine
  • Machine learning
  • fMRI

ASJC Scopus subject areas

  • Clinical Neurology
  • Biological Psychiatry
  • Cognitive Neuroscience
  • Radiology Nuclear Medicine and imaging

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