Computation of the electrical potential inside the nerve induced by an electrical stimulus

Damage-free peripheral nerve stimulation by 12-ns pulsed electric field

Modern technologies enable deep tissue focusing of nanosecond pulsed electric field (nsPEF) for non-invasive nerve and muscle stimulation. However, it is not known if PEF orders of magnitude shorter than the activation time of voltage-gated sodium channels (VGSC) would evoke action potentials (APs). One plausible scenario requires the loss of membrane integrity (electroporation) and resulting depolarization as an intermediate step. We report, for the first time, that the excitation of a peripheral nerve can be accomplished by 12-ns PEF without electroporation. 12-ns stimuli at 4.1-11 kV (3.3-8.8 kV/cm) evoked APs similarly to conventional stimuli (100-250 μs, 1-5 V, 103-515 V/m), except for having higher selectivity for the faster nerve fibers. Nerves sustained repeated tetanic stimulations (50 Hz or 100 Hz for 1 min) alternately by 12-ns PEF and by conventional pulses. Such tetani caused a modest AP decrease, to a similar extent for both types of stimuli. Nerve refractory properties were not affected. The lack of cumulative damages even from tens of thousands of 12-ns stimuli and the similarities with the conventional stimulation prove VGSC activation by nsPEF without nerve membrane damage.

Fast Simulation and Optimization Tool to Explore Selective Neural Stimulation

In functional electrical stimulation, selective stimulation of axons is desirable to activate a specific target, in particular muscular function. This implies to simulate a fascicule without activating neighboring ones i.e. to be spatially selective. Spatial selectivity is achieved by the use of multicontact cuff electrodes over which the stimulation current is distributed. Because of the large number of parameters involved, numerical simulations provide a way to find and optimize electrode configuration. The present work offers a computation effective scheme and associated tool chain capable of simulating electrode-nerve interface and find the best spread of current to achieve spatial selectivity.

Forward and inverse electroencephalographic modeling in health and in acute traumatic brain injury

OBJECTIVE

EEG source localization is demonstrated in three cases of acute traumatic brain injury (TBI) with progressive lesion loads using anatomically faithful models of the head which account for pathology.

METHODS

Multimodal magnetic resonance imaging (MRI) volumes were used to generate head models via the finite element method (FEM). A total of 25 tissue types-including 6 types accounting for pathology-were included. To determine the effects of TBI upon source localization accuracy, a minimum-norm operator was used to perform inverse localization and to determine the accuracy of the latter.

RESULTS

The importance of using a more comprehensive number of tissue types is confirmed in both health and in TBI. Pathology omission is found to cause substantial inaccuracies in EEG forward matrix calculations, with lead field sensitivity being underestimated by as much as ≈ 200% in (peri-) contusional regions when TBI-related changes are ignored. Failing to account for such conductivity changes is found to misestimate substantial localization error by up to 35 mm.

CONCLUSIONS

Changes in head conductivity profiles should be accounted for when performing EEG modeling in acute TBI.

SIGNIFICANCE

Given the challenges of inverse localization in TBI, this framework can benefit neurotrauma patients by providing useful insights on pathophysiology.

A simulation study of the combined thermoelectric extracellular stimulation of the sciatic nerve of the Xenopus laevis: the localized transient heat block

The electrical behavior of the Xenopus laevis nerve fibers was studied when combined electrical (cuff electrodes) and optical (infrared laser, low power sub-5 mW) stimulations are applied. Assuming that the main effect of the laser irradiation on the nerve tissue is the localized temperature increase, this paper analyzes and gives new insights into the function of the combined thermoelectric stimulation on both excitation and blocking of the nerve action potentials (AP). The calculations involve a finite-element model (COMSOL) to represent the electrical properties of the nerve and cuff. Electric-field distribution along the nerve was computed for the given stimulation current profile and imported into a NEURON model, which was built to simulate the electrical behavior of myelinated nerve fiber under extracellular stimulation. The main result of this study of combined thermoelectric stimulation showed that local temperature increase, for the given electric field, can create a transient block of both the generation and propagation of the APs. Some preliminary experimental data in support of this conclusion are also shown.

OpenMEEG: opensource software for quasistatic bioelectromagnetics

Background

Interpreting and controlling bioelectromagnetic phenomena require realistic physiological models and accurate numerical solvers. A semi-realistic model often used in practise is the piecewise constant conductivity model, for which only the interfaces have to be meshed. This simplified model makes it possible to use Boundary Element Methods. Unfortunately, most Boundary Element solutions are confronted with accuracy issues when the conductivity ratio between neighboring tissues is high, as for instance the scalp/skull conductivity ratio in electro-encephalography. To overcome this difficulty, we proposed a new method called the symmetric BEM, which is implemented in the OpenMEEG software. The aim of this paper is to present OpenMEEG, both from the theoretical and the practical point of view, and to compare its performances with other competing software packages.

Methods

We have run a benchmark study in the field of electro- and magneto-encephalography, in order to compare the accuracy of OpenMEEG with other freely distributed forward solvers. We considered spherical models, for which analytical solutions exist, and we designed randomized meshes to assess the variability of the accuracy. Two measures were used to characterize the accuracy. the Relative Difference Measure and the Magnitude ratio. The comparisons were run, either with a constant number of mesh nodes, or a constant number of unknowns across methods. Computing times were also compared.

Results

We observed more pronounced differences in accuracy in electroencephalography than in magnetoencephalography. The methods could be classified in three categories: the linear collocation methods, that run very fast but with low accuracy, the linear collocation methods with isolated skull approach for which the accuracy is improved, and OpenMEEG that clearly outperforms the others. As far as speed is concerned, OpenMEEG is on par with the other methods for a constant number of unknowns, and is hence faster for a prescribed accuracy level.

Conclusions

This study clearly shows that OpenMEEG represents the state of the art for forward computations. Moreover, our software development strategies have made it handy to use and to integrate with other packages. The bioelectromagnetic research community should therefore be able to benefit from OpenMEEG with a limited development effort.

A toolchain to simulate and investigate selective stimulation strategies for FES

When contracting a muscle using NFES (Neural Functional Electrical Stimulation), the stimulus always activates the axons of greater diameter first. Also selective activation of given fascicle inside a nerve is not possible with classical cuff electrode as the recruitment is performed uniformly around the nerve. These limits lead to poorly selective muscle recruitment, inducing fatigue and possible pain. To overcome this, selective stimulation strategies can be used. We propose a toolchain to investigate, simulate and tune selective stimulation strategies. It consists of a conduction volume model to compute the electric field generated in the nerve by a cuff electrode surrounding it; an axon model to predict the effect of the field on the nerve fibre - the generation, propagation and possible block of action potentials; and an interface script that links the two models and generates the code of the input function for the nerve fibre model. We present some simulation results to illustrate the possibilities of the toolchain to simulate such strategies. Ongoing experimental validations are also discussed. They will enable us to tune the model and may lead to further improvements.