A $\mu$m-Scale Computational Model of Magnetic Neural Stimulation in Multifascicular Peripheral Nerves

There has been recurring interest in using magnetic neural stimulation for implantable localized stimulation. However, the large stimulation voltages and energies necessary to evoke neuronal activity have tempered this interest. To investigate the potential of magnetic stimulation as a viable methodology and to provide the ability to investigate novel coil designs that can result in lower stimulation threshold voltages and energies, there is a need for a model that accurately predicts the magnetic field-tissue interaction that results in neuronal stimulation. In this study, we provide a computational framework to accurately estimate the stimulation threshold and have validated the model with in vivo magnetic stimulation experiments. To make such predictions, we developed a micrometer-resolution anatomically driven computational model of rat sciatic nerve and quantified the effect of tissue heterogeneity (i.e., fascicular organization, axon distribution, and density) and axonal membrane capacitance on the resulting threshold. Using the multiresolution impedance method, we computed the spatial-temporal distribution of the induced electric field in the nerve and applied this field to a Frankenhaeuser-Huxley axon model in NEURON to simulate the nonlinear mechanisms of the membrane channels. The computational model developed predicts the stimulation thresholds for four magnetic coil designs with different geometrical parameters within the 95% confidence interval (experiments count = 4) of measured in vivo stimulation thresholds for the rat sciatic nerve.