title: 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.
authors.affiliation: Department of Biotechnology, Indian Institute of Technology, Madras, Chennai, India.
authors.forename: Pragathi Priyadharsini
authors.initials: PP
authors.lastname: Balasubramani
authors.affiliation: Department of Biotechnology, Indian Institute of Technology, Madras, Chennai, India.
authors.forename: V Srinivasa
authors.initials: VS
authors.lastname: Chakravarthy
authors.affiliation: School of Medicine, Ain Shams University, Cairo, Egypt.
authors.forename: Manal
authors.initials: M
authors.lastname: Ali
authors.affiliation: Department of Computer Science and Engineering, Indian Institute of Technology, Madras, Chennai, India.
authors.forename: Balaraman
authors.initials: B
authors.lastname: Ravindran
authors.affiliation: Marcs Institute for Brain and Behaviour & School of Social Sciences and Psychology, University of Western Sydney, Penrith, Australia.
authors.forename: Ahmed A
authors.initials: AA
authors.lastname: Moustafa
mesh_terms: D000704:Analysis of Variance; D001479:Basal Ganglia; D015415:Biomarkers / Q000378:metabolism*; D004298:Dopamine / Q000378:metabolism; D005260:Female; D006801:Humans; D007175:Impulsive Behavior; D008297:Male; D016571:Neural Networks, Computer; D010300:Parkinson Disease / Q000150:complications* / Q000188:drug therapy*; D012701:Serotonin / Q000378:metabolism; D013647:Task Performance and Analysis
chemical_list: D015415:Biomarkers; D012701:Serotonin; D004298:Dopamine