**PROJECT DESCRIPTION**
*Introduction and Objectives*
Deep brain stimulation (DBS) represents one of the major clinical breakthroughs in the age of translational neuroscience. In 1987, Benabid and colleagues demonstrated that high frequency stimulation can mimic the effects of ablative neurosurgery in Parkinson’s disease (PD), while offering two key advantages to previous procedures: adjustability and reversibility. This project will employ artificial intelligence strategies, which have allowed for unprecedented innovations in translational, personalized, high-definition medicine, to further elevate the therapeutic potential of DBS. By developing a computational framework for decoding behavioral and disease states from combined subthalamic and cortical population recordings, this work will inform future adaptive stimulation paradigms for PD and other movement disorders. The central aim is to develop a computational framework for deep learning-based multi-feature decoding of behavioral and disease states from electrocorticography (ECoG), in order to advance the evolution of aDBS.
*Proposed Research*
The concurrent use of research ECoG during DBS surgery recently has enabled basic neuroscience investigation of human cortical-subcortical network dynamics. The overall goal of this project is to establish intelligent algorithms to identify physiological and pathophysiological states in ECoG data that predict epochs during which stimulation would facilitate movement or reduce symptoms based on multisite and multispectral oscillatory circuit activity combined with deep learning.
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[1]: https://media.springernature.com/original/springer-static/image/art:10.1007/s13311-018-00705-0/MediaObjects/13311_2018_705_Fig1_HTML.png
