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Basal ganglia circuits changes in Parkinson's disease patients.

ABSTRACT: Functional changes in basal ganglia circuitry are responsible for the major clinical features of Parkinson's disease (PD). Current models of basal ganglia circuitry can only partially explain the cardinal symptoms in PD. We used functional MRI to investigate the causal connectivity of basal ganglia networks from the substantia nigra pars compacta (SNc) in PD in the movement and resting state. In controls, SNc activity predicted increased activity in the supplementary motor area, the default mode network, and dorsolateral prefrontal cortex, but, in patients, activity predicted decreases in the same structures. The SNc had decreased connectivity with the striatum, globus pallidus, subthalamic nucleus, thalamus, supplementary motor area, dorsolateral prefrontal cortex, insula, default mode network, temporal lobe, cerebellum, and pons in patients compared to controls. Levodopa administration partially normalized the pattern of connectivity. Our findings show how the dopaminergic system exerts influences on widespread brain networks, including motor and cognitive networks. The pattern of basal ganglia network connectivity is abnormal in PD secondary to dopamine depletion, and is more deviant in more severe disease. Use of functional MRI with network analysis appears to be a useful method to demonstrate basal ganglia pathways in vivo in human subjects.

SUBMITTER: Wu T

PROVIDER: S-EPMC4163196 | biostudies-literature | 2012 Aug

REPOSITORIES: biostudies-literature

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Basal ganglia circuits changes in Parkinson's disease patients.

Wu Tao T Wang Jue J Wang Chaodong C Hallett Mark M Zang Yufeng Y Wu Xiaoli X Chan Piu P

Neuroscience letters 20120717 1

Functional changes in basal ganglia circuitry are responsible for the major clinical features of Parkinson's disease (PD). Current models of basal ganglia circuitry can only partially explain the cardinal symptoms in PD. We used functional MRI to investigate the causal connectivity of basal ganglia networks from the substantia nigra pars compacta (SNc) in PD in the movement and resting state. In controls, SNc activity predicted increased activity in the supplementary motor area, the default mode ...[more]

PMID: 22813979

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Project 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.

Dataset Information

Basal ganglia circuits changes in Parkinson's disease patients.

Publications

Basal ganglia circuits changes in Parkinson's disease patients.

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