Identifying the Basal Ganglia Network Model Markers for Medication-Induced Impulsivity in Parkinson's Disease Patients
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 impulsivi...
Autores principales: | , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2015
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4456385/ https://www.ncbi.nlm.nih.gov/pubmed/26042675 http://dx.doi.org/10.1371/journal.pone.0127542 |
_version_ | 1782374831741730816 |
---|---|
author | Balasubramani, Pragathi Priyadharsini Chakravarthy, V. Srinivasa Ali, Manal Ravindran, Balaraman Moustafa, Ahmed A. |
author_facet | Balasubramani, Pragathi Priyadharsini Chakravarthy, V. Srinivasa Ali, Manal Ravindran, Balaraman Moustafa, Ahmed A. |
author_sort | Balasubramani, Pragathi Priyadharsini |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-4456385 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-44563852015-06-09 Identifying the Basal Ganglia Network Model Markers for Medication-Induced Impulsivity in Parkinson's Disease Patients Balasubramani, Pragathi Priyadharsini Chakravarthy, V. Srinivasa Ali, Manal Ravindran, Balaraman Moustafa, Ahmed A. PLoS One Research Article 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. Public Library of Science 2015-06-04 /pmc/articles/PMC4456385/ /pubmed/26042675 http://dx.doi.org/10.1371/journal.pone.0127542 Text en © 2015 Balasubramani et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
spellingShingle | Research Article Balasubramani, Pragathi Priyadharsini Chakravarthy, V. Srinivasa Ali, Manal Ravindran, Balaraman Moustafa, Ahmed A. Identifying the Basal Ganglia Network Model Markers for Medication-Induced Impulsivity in Parkinson's Disease Patients |
title | Identifying the Basal Ganglia Network Model Markers for Medication-Induced Impulsivity in Parkinson's Disease Patients |
title_full | Identifying the Basal Ganglia Network Model Markers for Medication-Induced Impulsivity in Parkinson's Disease Patients |
title_fullStr | Identifying the Basal Ganglia Network Model Markers for Medication-Induced Impulsivity in Parkinson's Disease Patients |
title_full_unstemmed | Identifying the Basal Ganglia Network Model Markers for Medication-Induced Impulsivity in Parkinson's Disease Patients |
title_short | Identifying the Basal Ganglia Network Model Markers for Medication-Induced Impulsivity in Parkinson's Disease Patients |
title_sort | identifying the basal ganglia network model markers for medication-induced impulsivity in parkinson's disease patients |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4456385/ https://www.ncbi.nlm.nih.gov/pubmed/26042675 http://dx.doi.org/10.1371/journal.pone.0127542 |
work_keys_str_mv | AT balasubramanipragathipriyadharsini identifyingthebasalganglianetworkmodelmarkersformedicationinducedimpulsivityinparkinsonsdiseasepatients AT chakravarthyvsrinivasa identifyingthebasalganglianetworkmodelmarkersformedicationinducedimpulsivityinparkinsonsdiseasepatients AT alimanal identifyingthebasalganglianetworkmodelmarkersformedicationinducedimpulsivityinparkinsonsdiseasepatients AT ravindranbalaraman identifyingthebasalganglianetworkmodelmarkersformedicationinducedimpulsivityinparkinsonsdiseasepatients AT moustafaahmeda identifyingthebasalganglianetworkmodelmarkersformedicationinducedimpulsivityinparkinsonsdiseasepatients |