Computational Phenotyping of Effort-Based Decision Making in Unmedicated Adults with Remitted Depression.

Abstract

Background: Reduced motivation is an core feature of major depressive disorder (MDD). Yet, the extent to which this deficit persists in remitted MDD (rMDD) remains unclear. Here, we examined effort-based decision-making as one aspect of amotivation in rMDD using computational phenotyping to characterize decision-making processes and strategies.

Methods: Unmedicated adults with rMDD (N=40) and healthy controls (HCs, N=68) completed the Effort Expenditure for Rewards Task (EEfRT). Repeated-measures ANOVA and computational modeling-including hierarchical drift diffusion modeling (DDM) and subjective value modeling (SVM)]-were applied to quantify decision-making dynamics in effort allocation across different reward magnitudes and probabilities.

Results: Relative to HCs, rMDD individuals made overall fewer hard task choices, with an attenuated effect when accounting for anhedonia. However, specific to high reward, high probability conditions, rMDD individuals chose to expend effort more often than HCs. This was supported by the DDM results revealing that rMDD individuals showed a drift rate biased toward selecting the easy task, counteracted by heightened influence of reward probability and magnitude. Probed with SVM, this was not driven by group differences in decision strategies with respect to magnitude and probability information utilization.

Conclusions: Collectively, these findings suggest that while individuals with rMDD exhibit persistent motivational deficits, they retain a heightened sensitivity to high-value rewards, requiring more substantial or certain rewards to engage in effortful tasks. This pattern may reflect impairments in reward processing and effort-cost computations, contributing to motivational dysfunction. Targeting reward sensitivity and effort allocation could be valuable for interventions aimed at preventing MDD relapse.