Effort-based decision-making in major depressive disorder: a translational model of motivational anhedonia

Abstract

Anhedonia is a core feature of major depressive disorder (MDD), but the precise nature of anhedonic symptoms is unknown. Whereas anhedonia has traditionally been viewed as a deficit in the experience of pleasure, more recent evidence suggests that reduced anticipation and motivation may also be a core feature of this symptom. Here, we provide data from a study in MDD patients and healthy controls using a translational measure of reward motivation, the Effort Expenditure for Rewards Task (EEfRT or "effort"). This task offers subjects a series of trials where they may choose to expend more or less effort for the opportunity to win varying amounts of monetary rewards. We found that MDD patients were less willing to expend effort for rewards than controls. Additionally, we observed that patients were less able to effectively use information about magnitude and probability of rewards to guide their choice behavior. Finally, within the MDD patient group, duration of the current episode was a significant negative predictor of EEfRT task performance. These findings offer novel support for theoretical models proposing that anhedonia in MDD may reflect specific impairments in motivation and reward-based decision-making.

Figure 1

Schematic diagram of a single trial of the Effort Expenditure for Rewards Task (‘EEfRT’). A) Subjects begin by seeing a 1s fixation cue. B) 5s choice period in which subjects are presented with information regarding the reward magnitude of the hard task for that trial, and the probability of receiving any reward for that trial. C) 1s “ready” screen. D) Subjects make rapid button presses to complete the chosen task for 7s (easy task) or 21s (hard task). E) Subjects receive feedback on whether they have completed the task. F) Subjects receive reward feedback as to whether they received any money for that trial.

Figure 2

Bar graph of mean proportions of HC/HR choices for control subjects and patients with MDD across levels of probability. See results section and table 2 for inferential statistical analysis using GEE models.