Computational tools for highly efficient modeling of vagus nerve stimulation using multi-modal imaging

Computational modeling of neural stimulation therapies 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.However, many modeling tools are computationally demanding and lack important anatomical realism.To build an unprecedented population of models of human vagus nerve stimulation (VNS), we are conducting the most comprehensive mapping of the vagus nerve (VN) to date, from the perspectives of sample size (n50 subjects, left and right), nerve length (brainstem to abdomen), imaging modalities, and image resolution.We are quantifying the gross anatomy, three-dimensional nerve morphology, and microanatomy.We are advancing computational tools to enable modeling of this foundational dataset.We developed methods to model the true-3D structure of the fascicular morphology, rather than using an extrusion approach with a constant cross section of the nerve.We developed a surrogate nerve fiber model that enables simulation of activation thresholds orders of magnitude faster than the industry-standard approach.We developed a NEURON-based Python library to standardize simulations of extracellular stimulation.Our advances in computational modeling of nerve stimulation will accelerate the design process in preclinical stages and will be invaluable in the clinical programming of nerve stimulation devices.Efficient approaches are essential to tractable modeling of multimodal imaging data being collected across numerous individuals and nerve locations.