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

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 (MDD_Amisulpride), 26 MDD participants receiving placebo (MDD_Placebo), and 28 healthy control subjects receiving placebo (HC_Placebo) 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 (MDD_Placebo vs. HC_Placebo) and dopaminergic enhancement (MDD_Amisulpride vs. MDD_Placebo).

Results: Highly predictive classification models emerged that distinguished MDD_Placebo from HC_Placebo (area under the curve = 0.87) and MDD_Placebo from MDD_Amisulpride (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.

Trial registration: ClinicalTrials.gov NCT01253421 NCT01701258.

Keywords: Biomarker; Biotypes; Depression; Dopamine; Machine learning; fMRI.