A computational basal ganglia model to assess the role of STN-DBS on Impulsivity in Parkinson's disease

Abstract

Deep Brain Stimulation (DBS) of Sub Thalamic Nucleus (STN) is the most sought out therapeutic technique for the treatment of motor symptoms in advanced parkinsonian conditions. But the effect of STN-DBS on cognition was observed to be contrary with Impulsivity being observed as the most common side effect. Among the numerous behavioral tasks, Iowa Gambling Task (IGT) captures one of the impulsivity features (premeditation) and the task resembles real life decision making scenario. A 2D spiking network of basal ganglia (BG) was modeled to study the cognitive aspects of Parkinson's disease (PD) during medication and DBS. The model consist of key BG nuclei such as the Globus Pallidus externus (GPe) and Globus Pallidus internus (GPi) and STN modeled as Izhikevich 2D spiking neurons and striatal output as Poisson process. The concept of dopamine being the reward prediction error was utilized to update the cortico-striatal weights. The model was then tested on 3 conditions i.e., healthy controls, PD ‘ON’ and STN-DBS. The effect of DBS on decision making (in terms of IGT score) was studied by changing the electrode position (in STN) in the model. Our results indicate that changing the electrode's position and current spread independently leads to a critical change in performance levels. The model also shows that simulated PD ‘ON’ medication performed poorly compared to healthy controls as observed in experiments. The simulated results suggest that electrode position or current spread might be the probable reason for the observed controversial outcomes in STN-DBS patients. This is one of the first models to use spiking neurons to test the effects of dopamine medications and STN DBS on complex decision making.