Abstract
Impulsivity involves irresistibility in execution of actions and is prominent in medication condition of Parkinson’s disease (PD) patients. In this chapter, we model a probabilistic reversal learning task in PD patients with and without impulse control disorder (ICD) to understand the basis of their neural circuitry responsible for displaying ICD in PD condition. The proposed model is of the basal ganglia (BG) action selection dynamics, and it predicts the dysfunction of both dopaminergic (DA) and serotonergic (5HT) neuromodulator systems to account for the experimental results. Furthermore, the BG is modeled after utility function framework with DA controlling reward prediction and 5HT controlling the loss and risk prediction, respectively. The striatal model has three pools of medium spiny neurons (MSNs) including those with D1 receptor (R) alone, D2R alone, and co-expressing D1R–D2R neurons. Some significant results modeled are increased reward sensitivity during ON medication and an increased punishment sensitivity during OFF medication in patients. The lower reaction times (RT) in ICD subjects compared to that of the non-ICD category of the PD ON patients are also explained. Other modeling predictions include a significant decrease in the sensitivity to loss and risk in the ICD patients.
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Balasubramani, P.P., Srinivasa Chakravarthy, V., Ravindran, B., Moustafa, A.A. (2018). Modeling Serotonin’s Contributions to Basal Ganglia Dynamics in Parkinson’s Disease with Impulse Control Disorders. In: Computational Neuroscience Models of the Basal Ganglia. Cognitive Science and Technology. Springer, Singapore. https://doi.org/10.1007/978-981-10-8494-2_13
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DOI: https://doi.org/10.1007/978-981-10-8494-2_13
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