Framework for In-Silico Neuromodulatory Peripheral Nerve Electrode Experiments to Inform Design and Visualize Mechanisms

 The nervous system exists as our interface to the world, both integrating and interpreting sensory information and coordinating voluntary and involuntary movements. Given its importance, it has become a target for neuromodulatory therapies. The research to develop these therapies cannot be done purely on living tissues - animals, manpower, and equipment make that cost prohibitive and, given the cost of life required, it would be unethical to not search for alternatives. Computation modeling, the use of mathematics and modern computational power to simulate phenomena, has sought to provide such an alternative since the work of Hodgkin and Huxley in 1952. These models, though they cannot yet replace in-vivo and in-vitro experiments, can ease the burden on living tissues and provide details difficult or impossible to ascertain from them. This thesis iterates on previous frameworks for performing in-silico experiments for the purposes of mechanistic exploration and threshold prediction. To do so, an existing volume conductor model and validated nerve-fiber model were joined and a series of programs were developed around them to perform a set of in-silico experiments. The experiments are designed to predict changes in thresholds of behaviors elicited by bioelectric neuromodulation to parametric changes in experimental setup and to explore the mechanisms behind bioelectric neuromodulation, particularly surrounding the recently discovered Low Frequency Alternating Current (LFAC) waveform. This framework improved upon its predecessors through efficiency-oriented design and modularity, allowing for rapid simulation on consumer-grade computers. Results show a high degree of convergence with in-vivo experimental results, such as mechanistic alignment with LFAC and being within an order of magnitude of in-vivo pulse-stimulation threshold results for equivalent in-vivo and in-silico experimental designs.