a grey box neural network model of basal ganglia for gait signal of patients with huntington disease

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.