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Lubba CH, Le Guen Y, Jarvis S, Jones NS, Cork SC, Eftekhar A, Schultz SR. PyPNS: Multiscale Simulation of a Peripheral Nerve in Python. Neuroinformatics 2019; 17:63-81. [PMID: 29948844 PMCID: PMC6394768 DOI: 10.1007/s12021-018-9383-z] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
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.
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Affiliation(s)
- Carl H Lubba
- Department of Bioengineering, Imperial College London, South Kensington, London, SW7 2AZ, UK.
| | - Yann Le Guen
- Department of Bioengineering, Imperial College London, South Kensington, London, SW7 2AZ, UK
| | - Sarah Jarvis
- Department of Bioengineering, Imperial College London, South Kensington, London, SW7 2AZ, UK
| | - Nick S Jones
- Department of Mathematics, Imperial College London, South Kensington, London, SW7 2AZ, UK
| | - Simon C Cork
- Department of Medicine, Imperial College London, South Kensington, London, SW7 2AZ, UK
| | - Amir Eftekhar
- Department of Electrical and Electronic Engineering, Imperial College London, South Kensington, London, SW7 2AZ, UK
| | - Simon R Schultz
- Department of Bioengineering, Imperial College London, South Kensington, London, SW7 2AZ, UK.
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