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Imaging-based Programming of DBS in Parkinson's Disease
This lecture covers computational modeling for deep brain stimulation (DBS) programming in Parkinson's disease, focusing on the registration of brain atlases, detection of lead positions, and statistical analysis. It discusses the optimal stimulation settings for subthalamic nucleus (STN) DBS in Parkinson's disease, emphasizing the clinical improvement and percentage of hotspot stimulated. The lecture also explores the use of imaging-based programming techniques to improve DBS outcomes, including probabilistic mapping and neuronal network modeling. Additionally, it delves into the future of DBS programming, such as closed-loop and adaptive stimulation strategies.