Computational modelling of Parkinson's disease: A multiscale approach with deep brain stimulation and stochastic noise.
Computational modelling of Parkinson's disease: A multiscale approach with deep brain stimulation and stochastic noise.
Where did the research take place?
The study site has not been established. Author addresses may differ from where the research occurred.
Winnipeg, CA · Author affiliation
Department of Statistics, University of Manitoba, Winnipeg, Canada. Electronic address: herrera8@myumanitoba.ca.Location evidence
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Original abstract
Multiscale modelling presents a multifaceted perspective into understanding the mechanisms of the brain and how neurodegenerative disorders like Parkinson's disease (PD) manifest and evolve over time. In this study, we propose a novel co-simulation multiscale approach that unifies both micro- and macroscales to more rigorously capture brain dynamics. The presented design considers the electrodiffusive activity across the brain and in the network defined by the cortex, basal ganglia, and thalamus that is implicated in the mechanics of PD, as well as the contribution of presynaptic inputs in the highlighted regions. The application of deep brain stimulation (DBS) and its effects, along with the inclusion of stochastic noise are also examined. We found that the thalamus exhibits large, fluctuating spiking in both the deterministic and stochastic conditions, suggesting that noise contributes primarily to neural variability, rather than driving the overall spiking activity. Ultimately, this work intends to provide greater insights into the dynamics of PD and the brain which can eventually be converted into clinical use.