Reward and Punishment Learning as Predictors of Cognitive Behavioral Therapy Response in Parkinson's Disease Comorbid with Clinical Depression.

Depression is highly comorbid among individuals with Parkinson's Disease (PD), who often experience unique challenges to accessing and benefitting from empirically supported interventions like Cognitive Behavioral Therapy (CBT). Given the role of reward processing in both depression and PD, this study analyzed a subset (N = 25) of participants who participated in a pilot telemedicine intervention of PD-informed CBT, and also completed a Reward- and Punishment-Learning Task (RPLT) at baseline. At the conclusion of CBT, participants were categorized into treatment responders (n = 14) and non-responders (n = 11). Responders learned more optimally from negative rather than positive feedback on the RPLT, while this pattern was reversed in non-responders. Computational modeling suggested group differences in learning rate to negative feedback may drive the observed differences. Overall, the results suggest that a within-subject bias for punishment-based learning might help to predict response to CBT intervention for depression in those with PD. Plain Language Summary: Performance on a Computerized Task may predict which Parkinson's Disease Patients benefit from Cognitive Behavioral Treatment of Clinical Depression.