Modeling long-term dynamic effects of brain injury on biological mechanisms of potential Parkinson's disease progression

Parkinson's disease (PD), the second most common neurodegenerative disorder affecting over ten million people worldwide, is a multifactorial disease influenced by several biological and environmental factors. Complex interactions controlled by feedback relationships between neuroinflammation, oxidative damage, mitochondria, protein accumulation, and neuron sub-systems are at the center of the brain. While some lifestyle elements reduce vulnerability, head trauma raises the risk for PD. Trauma-induced neuroinflammation is the most prominent short-term consequence, and due to the intricate structure of the brain, multiple variables are affected in the long-term. To study those impacts on potential PD progression, we constructed an individual-level system dynamics model of a specific brain region where dopamine-producing neurons reside. After obtaining the normal aging dynamics, various brain injury scenarios are investigated to see whether healthy individuals would exhibit PD-like behaviors. Then, possible genetic variation and/or lifestyle factors such as healthy diet and exercise are tested on both healthy and PD-prone people. The difficulties in monitoring and quantifying brain-related variables are the primary challenges for this research because only post-mortem analysis allows for neuropathological diagnosis. The model is structurally and behaviorally validated using the qualitative and quantitative knowledge of autopsy reports and animal experiments. In scenario runs, we observed the impact of several external and internal factors. The research aims to provide a comprehensive understanding of relevant brain dynamics in interaction with external factors and identify effective mechanisms for treatment and prevention strategies for PD. This work is open to development by including discoveries from field data and empirical studies.