Computational phenotyping of brain-behavior dynamics underlying approach-avoidance conflict in major depressive disorder

Adaptive behavior requires balancing approach and avoidance based on the rewarding and aversive consequences of actions. Imbalances in this evaluation are thought to characterize mood disorders such as major depressive disorder (MDD). We present a novel application of the drift diffusion model (DDM) suited to quantify how offers of reward and aversiveness, and neural correlates thereof, are dynamically integrated to form decisions, and how such processes are altered in MDD. Hierarchical parameter estimation from the DDM demonstrated that the MDD group differed in three distinct reward-related parameters driving approach-based decision making. First, MDD was associated with reduced reward sensitivity, measured as the impact of offered reward on evidence accumulation. Notably, this effect was replicated in a follow-up study. Second, the MDD group showed lower starting point bias towards approaching offers. Third, this starting point was influenced in opposite directions by Pavlovian effects and by nucleus accumbens activity across the groups: greater accumbens activity was related to approach bias in controls but avoid bias in MDD. Cross-validation revealed that the combination of these computational biomarkers were diagnostic of patient status, with accumbens influences being particularly diagnostic. Finally, within the MDD group, reward sensitivity and nucleus accumbens parameters were differentially related to symptoms of perceived stress and depression. Collectively, these findings establish the promise of computational psychiatry approaches to dissecting approach-avoidance decision dynamics relevant for affective disorders.Author summary: Many of the decisions we make involve weighing the costs and benefits of options in order to decide whether to approach or avoid an offer, such as deciding whether a new and advanced phone is worth the price. Major depressive disorder is associated with alterations in approach and avoidance behavior, but we know less about how the disorder is associated with solving the conflict of approaching or avoiding options with costs and benefits. Here we apply a computational model to investigate the cognitive mechanisms of solving this conflict, how these mechanisms are affected in depression, and how activity in brain regions involved in this process are informative for identifying the disorder. We found that depressed participants differed from healthy controls in both cognitive processes and in how brain activity was linked to these processes. Specifically, depression was associated with reduced sensitivity to benefits, but not costs (represented in the task by reward points and aversive images, respectively), a lack of bias to approach offers, and alterations in how the mapping of motor responses to approach or avoid offers influenced this bias. Further, we found that activity in nucleus accumbens and the pregenual anterior cingulate were informative in classifying disease status. Altogether, these findings indicate the utility in applying computational models to identify biomarkers of MDD in approach-avoidance conflict.