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.
Highlights
Manipulations of the peripheral nervous system (PNS) by implanted devices might soon serve as a treatment for various medical conditions
Future Bioelectronic Medicines will need more precise stimulation interfaces and the capability to analyse nerve activity to stimulate in an adaptive manner
First advances towards a decoding of information from peripheral nerves have been successfully undertaken (Citi et al 2008; Lubba et al 2017). To both accelerate the design of interfaces and to further develop decoding algorithms, computational peripheral nerve models that integrate physiological insights acquired in experiments at different levels will be of great merit to predict stimulation efficiency and recording selectivity and as a source of surrogate data
Summary
Manipulations of the peripheral nervous system (PNS) by implanted devices might soon serve as a treatment for various medical conditions. To accommodate conductivity inhomogeneities, precomputed membrane currents from compartmental cell simulators can be imported into a finite element model (FEM) solver (as a point source or boundary condition) where the potential over the entire space and time span is computed based on the quasistatic Maxwell equations (cf McIntyre and Grill 2001; Lempka and McIntyre 2013; Ness et al 2015). This costlier method was employed in the recent aforementioned works on peripheral nerves (Grinberg et al 2008; Raspopovic et al 2011). The user can stay in Python to stimulate nerves and record from them in silico
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