A computational model of cognitive deficits in medicated and unmedicated persons with parkinson’s disease

Ahmed A. Moustafa*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterResearchpeer-review


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. We also show that decreased DA levels in the PFC in unmedicated patients is associated with impaired WM performance, as found experimentally. Increase in tonic DA levels in the PFC, due to DA medications, enhances WM performance, in line with modeling and experimental data. Furthermore, 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.

Original languageEnglish
Title of host publicationNeuroplasticity in Learning and Rehabilitation
EditorsGerry Leisman, Joav Merrick
PublisherNova Science Publishers
Number of pages21
ISBN (Electronic)9781634843065
ISBN (Print)9781634843058
Publication statusPublished - 1 Jan 2016
Externally publishedYes

Publication series

NameFunctional Neurology


Dive into the research topics of 'A computational model of cognitive deficits in medicated and unmedicated persons with parkinson’s disease'. Together they form a unique fingerprint.

Cite this