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


There was recurring interest in using magnetic neural stimulation for implantable localized stimulation. But, the big 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 flexibility to research novel coil designs which will lead to lower stimulation threshold voltages and energies, there's a want for a model that accurately predicts the magnetic field–tissue interaction that leads to neuronal stimulation. During this study, we offer a computational framework to accurately estimate the stimulation threshold and have validated the model with in vivo magnetic stimulation experiments. To create 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 technique, we have a tendency to computed the spatial-temporal distribution of the induced electrical 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 styles with different geometrical parameters inside the ninety five% confidence interval (experiments count = 4) of measured in vivo stimulation thresholds for the rat sciatic nerve.

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