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

Abstract

Artificial electrical stimulation of peripheral nerves for sensory feedback restoration can greatly benefit from computational models for simulation-based neural implant design in order to reduce the trial-and-error approach usually taken, thus potentially significantly reducing research and development costs and time. To this end, we built a computational model of a peripheral nerve trunk in which the interstitial space between the fibers and the tissues was modelled using a resistor network, thus enabling distance-dependent ephaptic coupling between myelinated axons and between fascicles as well. We used the model to simulate a) the stimulation of a nerve trunk model with a cuff electrode, and b) the propagation of action potentials along the axons. Results were used to investigate the effect of ephaptic interactions on recruitment and selectivity stemming from artificial (i.e., neural implant) stimulation and on the relative timing between action potentials during propagation. Ephaptic coupling was found to increase the number of fibers that are activated by artificial stimulation, thus reducing the artificial currents required for axonal recruitment, and it was found to reduce and shift the range of optimal stimulation amplitudes for maximum inter-fascicular selectivity. During propagation, while fibers of similar diameters tended to lock their action potentials and reduce their conduction velocities, as expected from previous knowledge on bundles of identical axons, the presence of many other fibers of different diameters was found to make their interactions weaker and unstable.

In vivo microstimulation with cathodic and anodic asymmetric waveforms modulates spatiotemporal calcium dynamics in cortical neuropil and pyramidal neurons of male mice

“…Therefore, increased current will likely be required to achieve the same perceptual intensity even if it is localized. Although, activation threshold and level of induced neural activity can depend on cortical layer and may be affected by neighboring activity through ephaptic interactions, which could impact the spatial spread and magnitude of activation (Caplloncha‐Juan & Sepulveda, 2020; Voigt et al., 2017; Voigt et al., 2018). Importantly, however, the effect of different waveforms on perceptual quality may be an important outcome to consider.…”

Bell’s Palsy—Retroauricular Pain Threshold

“…Accordingly, in the cases of a mild form of nerve impairment, responsiveness of nociceptive neurons in the central nervous system is increased due to stimuli transmitted through the NN on the affected FN side, whereas on the opposite side, pain threshold decreases as a possible effect of ephaptic coupling, electrical interactions between functionally independent adjacent unmyelinated axons on the affected and opposite healthy-unaffected side. Based on the obtained results that patients with the more severe damage have shown a higher pain threshold on the side opposite to the injury, we can conclude that the nociceptive component of pain represents a smaller percentage of the origin of pain correlated with a severe grade of IBP due to nociceptive deafferentation, thus preventing ephaptic transmission [ 19 , 41 , 42 , 43 , 44 , 45 , 46 ].…”

Modelling the effect of ephaptic coupling on spike propagation in peripheral nerve fibres

“…Early experiments have demonstrated that in the presence of a highly resistive extra-cellular medium, action potentials travelling in two parallel, closely spaced axons synchronise and travel at a lower velocity than in the isolated case [4][5][6][7]. This has been reproduced in theoretical work using numerical or analytical tools [8][9][10][11][12][13][14][15][16][17][18]. Both theoretical and experimental work, however, has been restricted thus far to a small number of identical axons due to the experimental or computational effort (with the exception of [18]).…”

“…This has been reproduced in theoretical work using numerical or analytical tools [8][9][10][11][12][13][14][15][16][17][18]. Both theoretical and experimental work, however, has been restricted thus far to a small number of identical axons due to the experimental or computational effort (with the exception of [18]). In contrast, peripheral nerve bundles are composed of a relatively large number of nerve fibres, which follow a wide distribution of axonal diameters [19][20][21].…”