Identifying the Basal Ganglia Network Model Markers for Medication-Induced Impulsivity in Parkinson's Disease Patients

Abstract

Impulsivity, i.e. irresistibility in the execution of actions, may be prominent in Parkinson's disease (PD) patients who are treated with dopamine precursors or dopamine receptor agonists. In this study, we combine clinical investigations with computational modeling to explore whether impulsivity in PD patients on medication may arise as a result of abnormalities in risk, reward and punishment learning. In order to empirically assess learning outcomes involving risk, reward and punishment, four subject groups were examined: healthy controls, ON medication PD patients with impulse control disorder (PD-ON ICD) or without ICD (PD-ON non-ICD), and OFF medication PD patients (PD-OFF). A neural network model of the Basal Ganglia (BG) that has the capacity to predict the dysfunction of both the dopaminergic (DA) and the serotonergic (5HT) neuromodulator systems was developed and used to facilitate the interpretation of experimental results. In the model, the BG action selection dynamics were mimicked using a utility function based decision making framework, with DA controlling reward prediction and 5HT controlling punishment and risk predictions. The striatal model included three pools of Medium Spiny Neurons (MSNs), with D1 receptor (R) alone, D2R alone and co-expressing D1R-D2R. Empirical studies showed that reward optimality was increased in PD-ON ICD patients while punishment optimality was increased in PD-OFF patients. Empirical studies also revealed that PD-ON ICD subjects had lower reaction times (RT) compared to that of the PD-ON non-ICD patients. Computational modeling suggested that PD-OFF patients have higher punishment sensitivity, while healthy controls showed comparatively higher risk sensitivity. A significant decrease in sensitivity to punishment and risk was crucial for explaining behavioral changes observed in PD-ON ICD patients. Our results highlight the power of computational modelling for identifying neuronal circuitry implicated in learning, and its impairment in PD. The results presented here not only show that computational modelling can be used as a valuable tool for understanding and interpreting clinical data, but they also show that computational modeling has the potential to become an invaluable tool to predict the onset of behavioral changes during disease progression.

The Subthalamic Nucleus, Limbic Function, and Impulse Control

“…PD patients with pathological gambling have likewise shown reward hypersensitivity compared to disease-matched controls, with antiglutamatergic therapy (amantadine) associated with decreases in reward hypersensitivity at least in a few patients (Cera et al ., 2014). Furthermore, network modeling studies of PD patients with ICDs on dopaminergic medication suggest that these patients are hypersensitive to reward compared to loss, while PD patients with ICDs not on dopaminergic medication were hypersensitive to loss but not to reward (Balasubramani et al ., 2015). Meanwhile, PD patients without ICDs showed no difference between reward and punishment learning (Balasubramani et al ., 2015).…”

“…Furthermore, network modeling studies of PD patients with ICDs on dopaminergic medication suggest that these patients are hypersensitive to reward compared to loss, while PD patients with ICDs not on dopaminergic medication were hypersensitive to loss but not to reward (Balasubramani et al ., 2015). Meanwhile, PD patients without ICDs showed no difference between reward and punishment learning (Balasubramani et al ., 2015). In another study, PD patients with ICDs were shown to exhibit hyposensitivity to loss (Piray et al ., 2014).…”

A computational model of loss of dopaminergic cells in Parkinson’s disease due to glutamate-induced excitotoxicity

“…However, recent years see the emergence of two novel lines of investigation into PD pathogenesis. These approaches, that aim to understand the PD pathology at cellular and network level, mark a significant deviation from the traditional approaches (Rodriguez et al, 1998;Bolam and Pissadaki, 2012;Pissadaki and Bolam, 2013;Balasubramani et al, 2015aBalasubramani et al, , 2015bPacelli et al, 2015;Chakravarthy and Moustafa, 2018).…”

Bipolar oscillations between positive and negative mood states in a computational model of Basal Ganglia

“…This lumped model of the previous subsection has been extended to the BG network model with the value and the risk functions computed by the medium spiny neurons (MSNs) in the striatum (Balasubramani et al, 2015a; Balasubramani et al, 2015b). Our earlier studies proposed that striatal DA1 receptor (D1R) expressing MSNs code for value function, while the MSNs coexpressing D1R-D2R code for the risk function (Balasubramani et al, 2015b).…”

“…This lumped model of the previous subsection has been extended to the BG network model with the value and the risk functions computed by the medium spiny neurons (MSNs) in the striatum (Balasubramani et al, 2015a; Balasubramani et al, 2015b). Our earlier studies proposed that striatal DA1 receptor (D1R) expressing MSNs code for value function, while the MSNs coexpressing D1R-D2R code for the risk function (Balasubramani et al, 2015b). Whereas the D1R MSNs project via the direct pathway (DP) to GPi, the D2R and the D1R-D2R co-expressing MSNs project to the GPe in the indirect pathway (IP) (Albin et al, 1989; Hasbi et al, 2011; Perreault et al, 2011).…”