  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.
Methods: An active 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. dZ was 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. Conclusion and 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.
