ASCENT (Automated Simulations to Characterize Electrical Nerve Thresholds): A pipeline for sample-specific computational modeling of electrical stimulation of peripheral nerves
Fig 4
Modeling a multifascicular nerve with different cuff rotations to illustrate key ASCENT processes in context of configuration data hierarchy.
(A) Binary images of the fascicle boundaries (“inner” perineurium: i.tif) and nerve boundary (n.tif) to serve as inputs to the pipeline (left) and the resulting deformed traces for placement in a circular cuff electrode (right) following physics-based Python operations. The sample JSON configuration used is provided in examples/results/cuff_rotation/mock.json along with the binary images shown. (B) COMSOL FEM solved for the potentials generated by 1 mA delivered through the monopolar Enteromedics cuff. A contact impedance describes the perineurium ensheathing each fascicle (not shown). Given that it is a monopolar cuff, there is only one solution basis for a given cuff placement; a given Model will result in a basis solution for each contact, i.e., a basis *.mph file for each contact delivering 1 mA. (C) Inputs to NEURON simulations for activation and block: extracellular potentials (i.e., potentials/ sampled from bases/ corresponding to coordinates defined by fibersets/) and waveforms. The green arrow in the fibersets/ panel points to the (x,y,0)-location for the fiber potentials plotted along the length of the nerve in the potentials/ panel. (D) Heatmaps of activation and block thresholds for 10 μm diameter MRG fibers for four different cuff rotations on a multifascicular nerve. The black arc around the nerve represents the exposed contact length delivering current on the inside surface of the Enteromedics cuff. The heatmaps were generated using Query’s heatmaps() method.