Computational modelling of Parkinson's disease: A multiscale approach with deep brain stimulation and stochastic...

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. Computational modelling has proved to be an invaluable tool in attempts to discern the intricacies of the brain. The results of research in this field can have many different implications, including explanations to the underlying mechanisms of cognition or the origin and development of neurodegenerative diseases (NDDs). This study focuses on Parkinson's disease (PD), a type of NDD that can degrade the quality of life by inducing movement disorders and other non-motor symptoms. We strive to clarify the dynamics of PD and see how deep brain stimulation - a common treatment given to those with PD and other NDDs - affects the brain when in the Parkinsonian state. This study accomplishes this by incorporating a model that links the large-scale behaviour of diffusion across the brain, as well as a microscale environment that simulates the neuron-level functioning for regions in the basal ganglia and thalamus which are significant constituents in PD development. The brain is also characterized by randomness (i.e. noise), an innate feature in its processes. Hence, our design also involves a noise term. In general, we found that this model reflects the inherent unpredictability of neural firing within the brain.