A computational model of cognitive deficits in medicated and unmedicated Parkinson's Disease patients

We present a neural network model of behavioral performance in medicated and unmedicated Parkinson's disease (PD) patients in various behavioral tasks. The model extends existing models of the basal ganglia and PD and further simulates the role of prefrontal dopamine (PFC DA) in behavioral performance, including stimulus-response learning, reversal, and working memory (WM) processes. In this model, PD is associated with decreased DA levels in the basal ganglia and PFC, whereas DA medications increase DA levels in both brain structures. Simulation results show that DA medications impair stimulus-response learning, which is in agreement with experimental data [1, 2]. We also show that decreased DA levels in the PFC in unmedicated patients is associated with impaired WM performance, as found experimentally [3-6]. Increase in tonic DA levels in the PFC, due to DA medications, enhances WM performance, in line with modeling and experimental data [7-9]. Furthermore, as reported in Cools et al. [10], we show that DA medications impair reversal learning. In addition, this model shows that extended training of the reversal phase leads to enhanced reversal performance in medicated PD patients, which is a new prediction of the model. Overall, the model provides a unified account for performance in various behavioral tasks using the same computational principles.