Speaker: Nicole Pelot, Ph.D. – Duke University

Bio: Dr. Nicole (“Nikki”) Pelot is a biomedical engineer at Duke University, conducting neural engineering research. Nikki leads a team working to advance neuromodulation of the autonomic nervous system, including anatomical & imaging studies, extensive development of computational methods for anatomically realistic models of nerve stimulation, optimization methods to develop improved therapies, and in vivo electrophysiology. In 2022, she won the Gold Electrode Award from Neurotech Reports, and in 2023, she won the inaugural Rising Star Award from the North American Neuromodulation Society (NANS). In addition to scientific excellence, she continually works to improve her academic project management and mentorship skills.

Abstract: Vagus nerve stimulation (VNS) is used clinically to treat epilepsy, depression, and stroke sequelae, and it is under investigation for many other applications. Computational modeling of neural stimulation, block, and recording enables analysis of mechanisms of action; design of electrode geometries and stimulation parameters to improve therapeutic responses and reduce side effects; translation of therapies from preclinical studies to clinical application; and systematic clinical programming of devices to account for inter-individual variability. Many modeling tools are computationally demanding and lack important anatomical realism. To address these challenges, we are conducting the most comprehensive mapping of the vagus nerve to date, from the perspectives of sample size, nerve length, imaging modalities, and image resolution. In parallel, we are building novel tools to model VNS that can leverage these multi-modal, multi-scale imaging data with improved computational efficiency. We are developing software for simulating three-dimensional nerve morphology and GPU-based fiber models. We are quantifying the effects of inter-individual anatomical variability and uncertainty in anatomical quantification from imaging data on activation thresholds, and we are using global optimization to design selective VNS for improved therapeutic efficacy and reduced side effects.

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