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There is now a CONTENT FREEZE for Mercury while we switch to a new platform. It began on Friday, March 10 at 6pm and will end on Wednesday, March 15 at noon. No new content can be created during this time, but all material in the system as of the beginning of the freeze will be migrated to the new platform, including users and groups. Functionally the new site is identical to the old one. webteam@gatech.edu
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Advisor:
Robert E. Gross, M.D. Ph.D. (Georgia Institute of Technology/Emory University)
Committee:
Babak Mahmoudi, Ph.D. (Georgia Institute of Technology/Emory University)
Christopher J. Rozell, Ph.D. (Georgia Institute of Technology)
John T. Gale, Ph.D. (Emory University)
Joseph R. Manns, Ph.D. (Emory University)
Optimizing neuromodulation for temporal lobe epilepsy treatment based on a surrogate neural state model
The conventional approach for preclinical studies requires a large amount of time and resources to find effective stimulation parameters and often fails due to the inter-subject variability in stimulation effect. As an alternative, I presented a novel data-driven approach which can optimize the neuromodulation more effectively and efficiently by investigating the stimulation effect on the surrogate neural state model. For the new approach, I implemented and demonstrated a variety of machine learning techniques to explore the stimulation effect, to describe the pathological neural states and to optimize the stimulation parameters. Specifically, first, I built a data-driven neural state model to estimate a seizure susceptibility based on electrophysiological recordings. The output of the model played a surrogate role by providing a metric which was regulated via the MS optogenetic stimulation. Second, I further increased the effectiveness of the stimulation by implementing in vivo Bayesian optimization which quickly finds the subject-specific optimal stimulation parameters. Finally, I tested whether modulating the surrogate neural state model affected the symptom of epilepsy (i.e. seizure). The treatment efficacy of the data-driven surrogate approach was compared to the stimulation with an empirically selected parameter set. The stimulation parameters to maximize the hippocampal theta (4-10Hz) power, which was a surrogate of the epileptic symptom, was more effective than the empirically selected parameter (7Hz) for the seizure suppression.
