Neural Reinforcement Learning Signals Predict Recovery From Impulse Control Disorder Symptoms in Parkinson's Disease

Impulse control disorders (ICDs) in Parkinson's disease are associated with a heavy burden on patients and caretakers. While recovery can occur, ICDs persist in many patients despite optimal management. The basis for this interindividual variability in recovery is unclear and poses a major challenge to personalized health care.

We adopted a computational psychiatry approach and leveraged the longitudinal, prospective Personalized Parkinson Project (136 people with Parkinson's disease, within 5 years of diagnosis) to combine dopaminergic learning theory-informed functional magnetic resonance imaging with machine learning (at baseline) to predict ICD symptom recovery after 2 years of follow-up. We focused on change in Questionnaire for Impulsive-Compulsive Disorders in Parkinson's Disease Rating Scale scores in the entire sample regardless of an ICD diagnosis.

Greater reinforcement learning signals during gain trials but not loss trials at baseline, including those in the ventral striatum and medial prefrontal cortex, and the behavioral accuracy score measured while on medication were associated with greater recovery from impulse control symptoms 2 years later. These signals accounted for a unique proportion of the relevant variability over and above that explained by other known factors, such as decreases in dopamine agonist use.

Our results provide a proof of principle for combining generative model-based inference of latent learning processes with machine learning-based predictive modeling of variability in clinical symptom recovery trajectories. We showed that reinforcement learning modeling parameters predicted recovery from ICD symptoms in Parkinson's disease.