Machine learning identifies large-scale reward-related activity modulated by dopaminergic enhancement in major depression

Abstract

BackgroundTheoretical 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-brains response to reward-related stimuli in MDD.\n\nMethodsUsing a double-blind placebo-controlled design, functional magnetic resonance imaging (fMRI) data from 31 unmedicated MDD participants receiving a single dose of 50 mg amisulpride (MDDAmisulpride), 26 MDD participants receiving placebo (MDDPlacebo), and 28 healthy controls receiving placebo (HCPlacebo) were analyzed. An importance-guided machine learning technique for model selection was used on whole-brain fMRI data probing reward anticipation and consumption to identify features linked to MDD (MDDPlacebo vs. HCPlacebo) and dopaminergic enhancement (MDDAmisulpride vs. MDDPlacebo).\n\nResultsHighly predictive classification models emerged that distinguished MDDPlacebo from HCPlacebo (AUC=0.87) and MDDPlacebo from MDDAmisulpride (AUC=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.\n\nConclusionsResults 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.\n\nClinicalTrials.gov identifierNCT01253421 and NCT01701258