PyPNS: Multiscale Simulation of a Peripheral Nerve in Python

Abstract

Bioelectronic Medicines that modulate the activity patterns on peripheral nerves have promise as a new way of treating diverse medical conditions from epilepsy to rheumatism. Progress in the field builds upon time consuming and expensive experiments in living organisms. To reduce experimentation load and allow for a faster, more detailed analysis of peripheral nerve stimulation and recording, computational models incorporating experimental insights will be of great help. We present a peripheral nerve simulator that combines biophysical axon models and numerically solved and idealised extracellular space models in one environment. We modelled the extracellular space as a three-dimensional resistive continuum governed by the electro-quasistatic approximation of the Maxwell equations. Potential distributions were precomputed in finite element models for different media (homogeneous, nerve in saline, nerve in cuff) and imported into our simulator. Axons, on the other hand, were modelled more abstractly as one-dimensional chains of compartments. Unmyelinated fibres were based on the Hodgkin-Huxley model; for myelinated fibres, we adapted the model proposed by McIntyre et al. in 2002 to smaller diameters. To obtain realistic axon shapes, an iterative algorithm positioned fibres along the nerve with a variable tortuosity fit to imaged trajectories. We validated our model with data from the stimulated rat vagus nerve. Simulation results predicted that tortuosity alters recorded signal shapes and increases stimulation thresholds. The model we developed can easily be adapted to different nerves, and may be of use for Bioelectronic Medicine research in the future.

Mechanistic modeling suggests that low-intensity focused ultrasound can selectively recruit myelinated or unmyelinated nerve fibers

“…With electrical stimulation, threshold peak extracellular voltages required to elicit a travelling AP decreased with increasing pulse duration, and then reached an asymptotical (so-called “rheobase”) regime for long enough pulses ( Figure 4A ). In line with previous modeling studies (Lubba et al, 2019; Tarnaud et al, 2018b), excitation thresholds for the myelinated axon were lower than those of the unmyelinated axon over the entire range of pulse durations. This result can be explained by two main factors.…”

Peripheral Nerve Magnetoneurography with Optically Pumped Magnetometers

“…All subjects underwent left arm antecubital fossa median nerve stimulation with the DS7AH constant current stimulator (Digitimer Ltd, UK) using a 1 Hz, 200 μs pulse-width, monopolar square-wave [26]. The protocol for Aδ fiber activation was derived from Mackenzie et al (1975), in which 1 Hz upper extremity peripheral nerve stimulation intensity was slowly increased to produce a sharp pinprick sensation resulting in a reproducible Aδ fiber nerve conduction velocity of 12-15 m/s [27,28]. The antecubital fossa median nerve was visualized with B-mode ultrasound imaging and marked for electrical stimulator placement.…”

Modelling the effects of ephaptic coupling on selectivity and response patterns during artificial stimulation of peripheral nerves

“…The different properties of the fiber morphology that depend on the diameter-internodal length, morphology of the myelin attachment (MYSA) and paranodal (FLUT) regions and number of myelin layers, were fitted to a linear regression each, using the values from [35]. Variables whose linear regressions yielded negative values were fitted to a quadratic curve, as done in [40].…”