Computational modelling of nerve stimulation and recording with peripheral visceral neural interfaces

Objective.Neuromodulation of visceral nerves is being intensively studied for treating a wide range of conditions, but effective translation requires increasing the efficacy and predictability of neural interface performance. Here we use computational models of rat visceral nerve to predict how neuroanatomical variability could affect both electrical stimulation and recording with an experimental planar neural interface.Approach.We developed a hybrid computational pipeline,VisceralNerveEnsembleRecording andStimulation (ViNERS), to couple finite-element modelling of extracellular electrical fields with biophysical simulations of individual axons. Anatomical properties of fascicles and axons in rat pelvic and vagus nerves were measured or obtained from public datasets. To validate ViNERS, we simulated pelvic nerve stimulation and recording with an experimental four-electrode planar array.Main results.Axon diameters measured from pelvic nerve were used to model a population of myelinated and unmyelinated axons and simulate recordings of electrically evoked single-unit field potentials (SUFPs). Across visceral nerve fascicles of increasing size, our simulations predicted an increase in stimulation threshold and a decrease in SUFP amplitude. Simulated threshold changes were dominated by changes in perineurium thickness, which correlates with fascicle diameter. We also demonstrated that ViNERS could simulate recordings of electrically-evoked compound action potentials (ECAPs) that were qualitatively similar to pelvic nerve recording made with the array used for simulation.Significance.We introduce ViNERS as a new open-source computational tool for modelling large-scale stimulation and recording from visceral nerves. ViNERS predicts how neuroanatomical variation in rat pelvic nerve affects stimulation and recording with an experimental planar electrode array. We show ViNERS can simulate ECAPS that capture features of our recordings, but our results suggest the underlying NEURON models need to be further refined and specifically adapted to accurately simulate visceral nerve axons.

An implantable ENG detector with in-system velocity selective recording (VSR) capability

Detection and classification of electroneurogram (ENG) signals in the peripheral nervous system can be achieved by velocity selective recording (VSR) using multi-electrode arrays. This paper describes an implantable VSR-based ENG recording system representing a significant development in the field since it is the first system of its type that can record naturally evoked ENG and be interfaced wirelessly using a low data rate transcutaneous link. The system consists of two CMOS ASICs one of which is placed close to the multi-electrode cuff array (MEC), whilst the other is mounted close to the wireless link. The digital ASIC provides the signal processing required to detect selectively ENG signals based on velocity. The design makes use of an original architecture that is suitable for implantation and reduces the required data rate for transmission to units placed outside the body. Complete measured electrical data from samples of the ASICs are presented that show that the system has the capability to record signals of amplitude as low as 0.5 μV, which is adequate for the recording of naturally evoked ENG. In addition, measurements of electrically evoked ENG from the explanted sciatic nerves of Xenopus Laevis frogs are presented.