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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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Mohammad S .E Sendi
BME PhD Defense Presentation
Date:2022-04-12
Time: 3:30 to 530 pm ET
Location / Meeting Link: https://bluejeans.com/446100081/6680
Committee Members:
Dr. Vince D. Calhoun, PhD (Advisor) Dr. Robert E. Gross, MD, PhD (Co-Advisor) Dr. Svjetlana Miocinovic, MD, PhD Dr. Eva L. Dyer, PhD Dr. Helen S. Mayberg, MD Dr. Jeffrey Herron, PhD
Title: An active learning framework for quantifying the effect of neuromodulation
Abstract: Open-loop neuromodulation is a neurosurgical treatment that modulates the brain's neural functioning by delivering an electrical signal using predefined stimulation parameters to a specific deep anatomical structure of the central nervous system. Despite the clinical success of neuromodulation in treating different neurological and neuropsychiatric disorders, the success rate is still not satisfying, and the result varies from patient to patient. The first step in developing a patient-specific neuromodulation therapy is finding the effect of the stimulation on the neural dynamics and linking the stimulation parameters and neural response for each individual. Sampling stimulation parameters to make this link based on the grid search is time-consuming, expensive, and even impossible when the parameters scale up. Optimal sampling based on active learning is an intelligent solution for designing an experiment in which human decision-making is less than optimal for the task. In more detail, active learning is a paradigm in which machine learning models can direct the learning process by providing dynamic suggestions/queries for the "next-best experiment." The main contribution of this Ph.D. dissertation includes 1) developing an analytic pipeline to find the effect biomarker of stimulation, 2) developing an active learning framework to design optimal experiment integrated with an in silico model, 3) implementing the active learning framework in a neuromodulation device for an in vivo model.
