Neural reinforcement learning signals predict recovery from impulse control disorder symptoms in Parkinson's disease.

BACKGROUND: Impulse control disorders (ICD) in Parkinson's disease (PD) are associated with a heavy burden on patients and caretakers. While recovery can occur, ICD persists in many patients despite optimal management. The basis for this inter-individual variability in recovery is unclear and poses a major challenge to personalized health care. METHODS: We adopt a computational psychiatry approach and leverage the longitudinal, prospective Personalized Parkinson Project (N=136 persons with PD, within 5 years of diagnosis) to combine dopaminergic learning theory-informed fMRI with machine learning (at baseline) to predict ICD symptom recovery after two years of follow-up. We focused on a change in QUIP-rs across the entire cohort, regardless of an ICD diagnosis. RESULTS: Greater reinforcement learning signals during gain trials but not loss trials at baseline, including those in the ventral striatum, medial prefrontal cortex and the behavioral accuracy score measured while ON medication were associated with greater recovery from impulse control symptoms two 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. CONCLUSIONS: 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. Hence, we showed that RL modelling parameters predict recovery from ICD symptoms in PD.

Impulse control disorder in Parkinson's disease is associated with abnormal frontal value signalling.

Dopaminergic medication is well established to boost reward- versus punishment-based learning in Parkinson's disease. However, there is tremendous variability in dopaminergic medication effects across different individuals, with some patients exhibiting much greater cognitive sensitivity to medication than others. We aimed to unravel the mechanisms underlying this individual variability in a large heterogeneous sample of early-stage patients with Parkinson's disease as a function of comorbid neuropsychiatric symptomatology, in particular impulse control disorders and depression. One hundred and ninety-nine patients with Parkinson's disease (138 ON medication and 61 OFF medication) and 59 healthy controls were scanned with functional MRI while they performed an established probabilistic instrumental learning task. Reinforcement learning model-based analyses revealed medication group differences in learning from gains versus losses, but only in patients with impulse control disorders. Furthermore, expected-value related brain signalling in the ventromedial prefrontal cortex was increased in patients with impulse control disorders ON medication compared with those OFF medication, while striatal reward prediction error signalling remained unaltered. These data substantiate the hypothesis that dopamine's effects on reinforcement learning in Parkinson's disease vary with individual differences in comorbid impulse control disorder and suggest they reflect deficient computation of value in medial frontal cortex, rather than deficient reward prediction error signalling in striatum. See Michael Browning (https://doi.org/10.1093/brain/awad248) for a scientific commentary on this article.

Role of dopamine and clinical heterogeneity in cognitive dysfunction in Parkinson's disease.

Parkinson's disease (PD) is commonly treated with dopaminergic medication, which enhances some, while impairing other cognitive functions. It can even contribute to impulse control disorder and addiction. We describe the history of research supporting the dopamine overdose hypothesis, which accounts for the large within-patient variability in dopaminergic medication effects across different tasks by referring to the spatially non-uniform pattern of dopamine depletion in dorsal versus ventral striatum. However, there is tremendous variability in dopaminergic medication effects not just within patients across distinct tasks, but also across different patients. In the second part of this chapter we review recent studies addressing the large individual variability in the negative side effects of dopaminergic medication on functions that implicate dopamine, such as value-based learning and choice. These studies begin to unravel the mechanisms of dopamine overdosing, thus revising the strict version of the overdose hypothesis. For example, the work shows that the canonical boosting of reward-versus punishment-based choice by medication is greater in patients with depression and a non-tremor phenotype, which both implicate, among other pathology, more rather than less severe dysregulation of the mesolimbic dopamine system. Future longitudinal cohort studies are needed to identify how to optimally combine different clinical, personality, cognitive, neural, genetic and molecular predictors of detrimental medication effects in order to account for as much of the relevant variability as possible. This will provide a useful tool for precision neurology, allowing individual and contextual tailoring of (the dose of) dopaminergic medication in order to maximize its cognitive benefits, yet minimize its side effects.