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

Modeling the excitation of nerve axons under transcutaneous stimulation

Computational models enable a safe and convenient way to study the excitation of nerve fibers under external stimulation. Contemporary models calculate the electric field distribution from transcutaneous stimulation and the resulting neuronal response separately. This study uses finite element methods to develop a multi-scale model that couples electric fields within macroscopic tissue layers and microscopic nerve fibers in a single-stage computational framework. The model included a triaxial myelinated nerve fiber bundle embedded within a volume conductor of tissue layers to represent the median nerve innervating the forearm muscles. The model captured the excitability of nerve fibers under transcutaneous stimulation and their nerve-tissue interactions to a transient external stimulus. The determinants of the strength-duration curve, rheobase, and chronaxie for the proposed model had close correlations with in-vivo experimentation on human participants. Additionally, the excitability indices for the triaxial myelinated nerve fiber implemented using the finite element method agreed well with experimental data from the literature. The validity of the proposed model encourages its use for applications involving transcutaneous stimulation. Capable of capturing field distribution across realistic morphologies, the model can serve as a testbed to improve stimulation protocols and electrode designs with subject-level specificity.

Tutorial: A guide to techniques for analysing recordings from the peripheral nervous system

The nervous system, through a combination of conscious and automatic processes, enables the regulation of the body and its interactions with the environment. The peripheral nervous system is an excellent target for technologies that seek to modulate, restore or enhance these abilities as it carries sensory and motor information that most directly relates to a target organ or function. However, many applications require a combination of both an effective peripheral nerve interface and effective signal processing techniques to provide selective and stable recordings. While there are many reviews on the design of peripheral nerve interfaces, reviews of data analysis techniques and translational considerations are limited. Thus, this tutorial aims to support new and existing researchers in the understanding of the general guiding principles, and introduces a taxonomy for electrode configurations, techniques and translational models to consider.

Overcoming temporal dispersion for measurement of activity-related impedance changes in unmyelinated nerves

Objective.Fast neural electrical impedance tomography is an imaging technique that has been successful in visualising electrically evoked activity of myelinated fibres in peripheral nerves by measurement of the impedance changes (dZ) accompanying excitation. However, imaging of unmyelinated fibres is challenging due to temporal dispersion (TP) which occurs due to variability in conduction velocities of the fibres and leads to a decrease of the signal below the noise with distance from the stimulus. To overcome TP and allow electrical impedance tomography imaging in unmyelinated nerves, a new experimental and signal processing paradigm is required allowing dZ measurement further from the site of stimulation than compound neural activity is visible. The development of such a paradigm was the main objective of this study.Approach.A finite element-based statistical model of TP in porcine subdiaphragmatic nerve was developed and experimentally validatedex-vivo. Two paradigms for nerve stimulation and processing of the resulting data-continuous stimulation and trains of stimuli, were implemented; the optimal paradigm for recording dispersed dZ in unmyelinated nerves was determined.Main results.While continuous stimulation and coherent spikes averaging led to higher signal-to-noise ratios (SNRs) at close distances from the stimulus, stimulation by trains was more consistent across distances and allowed dZ measurement at up to 15 cm from the stimulus (SNR = 1.8 ± 0.8) if averaged for 30 min.Significance.The study develops a method that for the first time allows measurement of dZ in unmyelinated nerves in simulation and experiment, at the distances where compound action potentials are fully dispersed.

Translating node of Ranvier currents to extraneural electrical fields: a flexible FEM modeling approach

Simulations of electroneurogram recording could help find the optimal set of electrodes and algorithms for selective neural recording. However, no flexible methods are established for selective neural recording as for neural stimulation. This paper proposes a method to couple a compartmental and a FEM nerve model, implemented in NEURON and COMSOL, respectively, to translate Node of Ranvier currents into extraneural electric fields. The study simulate ex-vivo experimental conditions, and the method allows flexibility in electrode geometries and nerve topologies. This model has been made available in a public repository⁴. So far, the model behavior complies with available experimental results and expectations from literature. There is good agreement in terms of signal amplitude and waveform, and computational times are acceptable, leaving room for flexible simulation studies complementary to animal tests.

Compliant peripheral nerve interfaces

Peripheral nerve interfaces (PNIs) record and/or modulate neural activity of nerves, which are responsible for conducting sensory-motor information to and from the central nervous system, and for regulating the activity of inner organs. PNIs are used both in neuroscience research and in therapeutical applications such as precise closed-loop control of neuroprosthetic limbs, treatment of neuropathic pain and restoration of vital functions (e.g. breathing and bladder management). Implantable interfaces represent an attractive solution to directly access peripheral nerves and provide enhanced selectivity both in recording and in stimulation, compared to their non-invasive counterparts. Nevertheless, the long-term functionality of implantable PNIs is limited by tissue damage, which occurs at the implant-tissue interface, and is thus highly dependent on material properties, biocompatibility and implant design. Current research focuses on the development of mechanically compliant PNIs, which adapt to the anatomy and dynamic movements of nerves in the body thereby limiting foreign body response. In this paper, we review recent progress in the development of flexible and implantable PNIs, highlighting promising solutions related to materials selection and their associated fabrication methods, and integrated functions. We report on the variety of available interface designs (intraneural, extraneural and regenerative) and different modulation techniques (electrical, optical, chemical) emphasizing the main challenges associated with integrating such systems on compliant substrates.

Imaging fascicular organization of rat sciatic nerves with fast neural electrical impedance tomography

Imaging compound action potentials (CAPs) in peripheral nerves could help avoid side effects in neuromodulation by selective stimulation of identified fascicles. Existing methods have low resolution, limited imaging depth, or are invasive. Fast neural electrical impedance tomography (EIT) allows fascicular CAP imaging with a resolution of <200 µm, <1 ms using a non-penetrating flexible nerve cuff electrode array. Here, we validate EIT imaging in rat sciatic nerve by comparison to micro-computed tomography (microCT) and histology with fluorescent dextran tracers. With EIT, there are reproducible localized changes in tissue impedance in response to stimulation of individual fascicles (tibial, peroneal and sural). The reconstructed EIT images correspond to microCT scans and histology, with significant separation between the fascicles (p < 0.01). The mean fascicle position is identified with an accuracy of 6% of nerve diameter. This suggests fast neural EIT can reliably image the functional fascicular anatomy of the nerves and so aid selective neuromodulation.

Increasing signal amplitude in electrical impedance tomography of neural activity using a parallel resistor inductor capacitor (RLC) circuit

OBJECTIVE

To increase the impedance signal amplitude produced during neural activity using a novel approach of implementing a parallel resistor inductor capacitor (RLC) circuit across the current source used in electrical impedance tomography (EIT) of peripheral nerve.

APPROACH

The frequency response of the impedance signal was characterized in the range 4-18 kHz, then a frequency range with significant capacitive charge transfer was selected for experiment with the RLC circuit. Design of the RLC circuit was aided by in vitro impedance measurements on nerve and nerve cuff in the range 5 Hz to 50 kHz.

MAIN RESULTS

The frequency response of the impedance signal across 4-18 kHz showed maximum amplitude at 6-8 kHz, and steady decline in amplitude between 8 and 18 kHz with  -6 dB reduction at 14 kHz. The frequency range 17  ±  1 kHz was selected for the RLC experiment. The RLC experiment was performed on four subjects using an RLC circuit designed to produce a resonant frequency of 17 kHz with a bandwidth of 3.6 kHz, and containing a 22 mH inductive element and a 3.45 nF capacitive element with  +0.8/-  3.45 nF manual tuning range. With the RLC circuit connected, relative increases in the impedance signal (±3σ noise) of 44% (±15%), 33% (±30%), 37% (±8.6%), and 16% (±19%) were produced.

SIGNIFICANCE

The increase in impedance signal amplitude at high frequencies, generated by the novel implementation of a parallel RLC circuit across the drive current, improves spatial resolution by increasing the number of parallel drive currents which can be implemented in a frequency division multiplexed (FDM) EIT system, and aids the long term goal of a real-time FDM EIT system by reducing the need for ensemble averaging.