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

OBJECTIVE: Fast neural Electrical Impedance Tomography (FnEIT) 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 EIT 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 FEM-based statistical model of temporal dispersion in porcine subdiaphragmatic nerve was developed and experimentally validated ex-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 (SNR) 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 minutes. 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

Quantification of clinically applicable stimulation parameters for precision near-organ neuromodulation of human splenic nerves

Abstract: Neuromodulation is a new therapeutic pathway to treat inflammatory conditions by modulating the electrical signalling pattern of the autonomic connections to the spleen. However, targeting this sub-division of the nervous system presents specific challenges in translating nerve stimulation parameters. Firstly, autonomic nerves are typically embedded non-uniformly among visceral and connective tissues with complex interfacing requirements. Secondly, these nerves contain axons with populations of varying phenotypes leading to complexities for axon engagement and activation. Thirdly, clinical translational of methodologies attained using preclinical animal models are limited due to heterogeneity of the intra- and inter-species comparative anatomy and physiology. Here we demonstrate how this can be accomplished by the use of in silico modelling of target anatomy, and validation of these estimations through ex vivo human tissue electrophysiology studies. Neuroelectrical models are developed to address the challenges in translation of parameters, which provides strong input criteria for device design and dose selection prior to a first-in-human trial

Model of impedance changes in nerve fibres

Fast neural Electrical Impedance Tomography (EIT) is a method able to image electrical activity in nerves by measuring impedance changes (dZ) which occur as ion channels open. While it can image fast activity in large peripheral nerves, for imaging inside smaller nerves, the signal-to-noise-ratio must be maximized which requires optimization of EIT parameters. If optimized, fast neural EIT could be of benefit in the new field of electrical stimulation of autonomic nerves (“Electroceuticals”) that could allow cross-sectional imaging of the fascicles and precise neuromodulation of internal organs supplied by them to treat associated medical conditions. // The purpose of this thesis work was to develop an accurate model of nerve fibres that could validate experimental data, predict optimal parameters for imaging with EIT and explain the nature of the observed signals. In chapter 2, relevant literature on EIT, membrane biophysics and existing models of nerve fibres is reviewed. Accurate 3D FEM models of unmyelinated fibres bi-directionally coupled with external space, including Hodgkin-Huxley giant axon of the squid (single and multiple) and mammalian C nociceptor are developed in chapter 3. The models explain available experimental data and optimize fast neural EIT in unmyelinated nerves. In chapter 4, an accurate FEM model of a myelinated fibre coupled with extracellular space is developed and utilized for the same purposes. Dispersion in unmyelinated fibres is studied in chapter 5 by development of the accurate FEM models of 50-fibre HH and C nociceptor nerves, followed by extension to the statistical models of realistic nerves with thousands of fibres. The models provide the maximum distances over which EIT may be used for imaging fascicular activity for each kind of nerve and showed that dZ could be seen further then compound action potential if it is biphasic