Objective: Apply four differential equations to generate projections considering the effect of dopamine on Parkinson’s disease progression, leading to the development of an initial prototype of a mathematical and computational model.

Background: Parkinson’s disease (PD) is a progressive neurodegenerative disorder affecting more than 10 million individuals worldwide. Despite significant advancements in symptomatic management, no available therapies effectively modify disease progression. Current diagnostics depend on late-stage clinical symptoms, limiting early intervention and personalized treatment. Computational modeling has emerged as a promising tool to enhance the understanding of PD pathophysiology, offering the potential to identify predictive biomarkers and optimize therapeutic interventions.

Method: The prototype is currently being developed to simulate abnormal neural activity. The approach integrates neuronal firing representations to investigate the relationship between dopamine depletion, neuronal dysfunction, and disease evolution. The model employs differential equations to represent neuronal variability, analyze large-scale network oscillations and connectivity patterns, and incorporate hybrid architectures. Preliminary analyses are being conducted to refine an algorithm capable of projecting disease progression.

Results: Preliminary computational tests are being conducted to assess the model’s ability to replicate key features of Parkinson’s disease (PD), including progressive oscillatory dysfunction in dopamine-depleted networks. Early analyses suggest that distinct neurophysiological signatures could potentially serve as biomarkers for disease progression and therapeutic response. Further validation is planned, including the integration of clinical datasets to improve projection accuracy.

Conclusion: This study investigates the potential of mathematical and computational modeling to enhance the understanding of PD progression. The proposed system may contribute to early diagnosis, optimization of neuromodulatory interventions, and personalized treatment strategies. Future work will focus on validating the model using real-world patient data to improve predictive accuracy and clinical applicability.

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MDS Abstracts - https://www.mdsabstracts.org/abstract/mathematical-and-computational-modeling-prototype-to-understand-the-evolution-of-parkinsons-disease-using-four-differential-equations/