Computational model advance deep brain stimulation for Parkinson's disease

TL;DR

This paper reviews how computational models, including mathematical and predictive types, enhance understanding and advancement of deep brain stimulation for Parkinson's disease, addressing mechanisms, treatment optimization, and future directions.

Contribution

It provides a comprehensive overview of computational modeling approaches and their roles in improving DBS treatment and understanding in Parkinson's disease.

Findings

Mathematical models elucidate DBS mechanisms.

Predictive models assist in personalized treatment planning.

Models guide electrode design and future technology development.

Abstract

Deep brain stimulation(DBS)has become an effective intervention for advanced Parkinson's disease, but the exact mechanism of DBS is still unclear. In this review, we discuss the history of DBS, the anatomy and internal architecture of the basal ganglia(BG), the abnormal pathological changes of the BG in Parkinson's disease, and how computational models can help understand and advance DBS. We also describe two types of models:mathematical theoretical models and clinical predictive models. Mathematical theoretical models simulate neurons or neural networks of BG to shed light on the mechanistic principle underlying DBS, while clinical predictive models focus more on patients' outcomes, helping to adapt treatment plans for each patient and advance novel electrode designs. Finally, we provide insights and an outlook on future technologies.