Volume 7, Number 2 (Spring 2016 -- 2016)                   BCN 2016, 7(2): 107-114 | Back to browse issues page




DOI: 10.15412/J.BCN.03070204
PMID: 27303605
PMCID: PMC4892315

Cited 0 times in PubMed Central

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Pourhedayat A, Sarbaz Y. A Grey Box Neural Network Model of Basal Ganglia for Gait Signal of Patients with Huntington Disease. BCN. 2016; 7 (2) :107-114
URL: http://bcn.iums.ac.ir/article-1-519-en.html

1- B.Sc. Department of Mechatronics Engineering, School of Engineering-Emerging Technologies, University of Tabriz, Tabriz, Iran.
2- Ph.D. Department of Mechatronics Engineering, School of Engineering-Emerging Technologies, University of Tabriz, Tabriz, Iran.
Abstract:  

Introduction: Huntington disease (HD) is a progressive neurodegenerative disease which affects movement control system of the brain. HD symptoms lead to patient’s gait change and influence stride time intervals. In this study, we present a grey box mathematical model to simulate HDdisorders. This model contains main physiological findings about BG.
Methods: We used artificial neural networks (ANN) and predetermined data to model healthy state behavior, and then we trained patients with HD with this model. All blocks and relations between them were designed based on physiological findings.
Results: According to the physiological findings, increasing or decreasing model connection weights are indicative of change in secretion of respective neurotransmitters. Our results show the simulating ability of the model in normal condition and diferent disease stages.
Conclusion: Fine similarity between the presented model and BG physiological structure with its high ability in simulating HD disorders, introduces this model as a powerful tool to analyze HD behavior.

Type of Study: Original | Subject: Computational Neuroscience
Received: 2015/04/6 | Accepted: 2015/07/23 | Published: 2016/04/1

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