Abstract
Patients with Parkinson’s disease (PD) are often treated with dopaminergic medication. Dopaminergic medication is known to improve both motor and certain nonmotor symptoms, such as depression. However, it can contribute to behavioral impairment, for example, by enhancing risky choice. Here we characterize the computational mechanisms that contribute to dopamine-induced changes in risky choice in PD patients with and without a depression (history). We adopt a clinical–neuroeconomic approach to investigate the effects of dopaminergic medication on specific components of risky choice in PD. Twenty-three healthy controls, 21 PD patients with a depression (history), and 22 nondepressed PD patients were assessed using a well-established risky choice paradigm. Patients were tested twice: once after taking their normal dopaminergic medication and once after withdrawal of their medication. Dopaminergic medication increased a value-independent gambling propensity in nondepressed PD patients, while leaving loss aversion unaffected. By contrast, dopaminergic medication effects on loss aversion were associated with current depression severity and with drug effects on depression scores. The present findings demonstrate that dopaminergic medication increases a value-independent gambling bias in nondepressed PD patients. Moreover, the current study raises the hypothesis that dopamine-induced reductions in loss aversion might underlie previously observed comorbidity between depression and medication-related side effects in PD, such as impulse control disorder.
Highlights
Parkinson’s disease (PD) is a neurodegenerative disorder characterized by, among other things, degeneration of dopaminergic neurons leading to striatal dopamine depletion
The data demonstrate that dopaminergic medication increases a value-independent gambling bias in nondepressed PD patients, as it does in healthy controls (Rigoli et al, 2016; Rutledge et al, 2015)
Dopamine-induced increases in a valueindependent gambling bias have been shown using computational model-based analyses grounded in reinforcement learning theory (Rutledge et al, 2015), mean-variance theory (Rigoli et al, 2016), and prospect theory (Kahneman & Tversky, 1979)
Summary
Parkinson’s disease (PD) is a neurodegenerative disorder characterized by, among other things, degeneration of dopaminergic neurons leading to striatal dopamine depletion. All patients receive dopaminergic medication, impulse control disorder (ICD) occurs only in a subset (∼ 10% − 15%; Weintraub et al, 2010). In keeping with this clinical variability, we know that there is large individual variability in the nature and extent of dopaminergic drug effects on cognitive and decision functions (e.g., Cools & D’Esposito, 2011). We aimed to characterize the mechanisms that contribute to individual variability in medication-induced changes in risky choice in PD.
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