Simulation of impedance changes with a FEM model of a myelinated nerve fibre.

Objective: Fast neural electrical impedance tomography (EIT) is a method which permits imaging of neuronal activity in nerves by measuring the associated impedance changes (dZ). Due to the small magnitudes of dZ signals, EIT parameters require optimization, which can be done using in silico modelling: apart from predicting the best parameters for imaging, it can also help to validate experimental data and explain the nature of the observed dZ. This has previously been completed for unmyelinated fibres, but an extension to myelinated fibres is required for the development of a full nerve model which could aid imaging neuronal traffic at the fascicular level and optimise neuromodulation of the supplied internal organs to treat various diseases.

Approach: An active finite element method (FEM) model of a myelinated fibre coupled with external space was developed. A spatial dimension was added to the experimentally validated space-clamped model of a human sensory fibre using the double cable paradigm. Electrical parameters of the model were changed so that nodal and internodal membrane potential as well as propagation velocity agreed with experimental values. Impedance changes were simulated during activity under various conditions and the optimal parameters for imaging were determined.

Main Results: When using AC, dZ could be recorded only at frequencies above 4 kHz, which is supported by experimental data. Optimal bandwidths for dZ measurement were found to increase with AC frequency.

Significance: The novel fully bi-directionally coupled FEM model of a myelinated fibre was able to optimize EIT for myelinated fibres and explain the biophysical basis of the measured signals.