eMB: Multiscale Analysis of Neural Dynamical Differences Between Parkinsonian and Healthy Animals

Parkinson's disease affects millions of people, impairing their ability to move and severely affecting their quality of life. While current treatments can reduce symptoms, they are only temporarily effective and are more invasive as the disease progresses. This project will help us better understand how Parkinson's disease changes the way different parts of the brain work together by developing new theories informed by appropriate experimental data. The novel theories will specifically dissect how motor commands are disrupted and mis-interpreted in Parkinson’s disease. By learning more about the impact of these changes, the results of the project will inform future developments of smarter treatments that are tailored to each person's brain activity in real time, making some invasive therapies more effective. The project will also help students develop interdisciplinary skills by performing impactful research at the intersection of math, engineering, and neuroscience. A long-term goal of this research is to improve the lives of people living with Parkinson's disease by helping them maintain greater mobility and independence. The knowledge gained may also support new approaches for treating other related brain disorders. The complexity of neural activity in Parkinson’s disease requires mathematical theory and computational tools to better clarify how motor commands are disrupted. This project is on the development of data-driven realistic computational models and mathematical theory to describe information propagation in crucial neural circuits that are severely impaired in this disease: the basal ganglia thalamo-coritical network. Despite well-established markers of Parkinson’s disease in neural activity, i.e., increases in oscillatory power in the beta frequency band, the precise details of successful (or unsuccessful) motor information transmission are unknown. This theoretical approach combined with contemporary data from animals and human intracranial recordings spanning a large range of spatial scales will uncover the underlying mechanisms of when, where and how information is disrupted, and also how a network can change to facilitate the recovery of information in specific parts of this neural circuit. Students will be positively impacted from this project in several ways: the results will be incorporated in graduate courses and enhance their education, and trainees will acquire technical skills in mathematics and engineering to conduct fruitful research. Finally, this framework will be used to discover optimal protocols for adaptive Deep Brain Stimulation, where the electrical stimulation to treat symptoms can be adapted to individual patients. Adaptive Deep Brain Stimulation is a promising treatment, but current protocols are relatively crude and thus could immensely improve in efficacy with the proposed research. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.