Enhancing the selective electrical activation of human vagal nerve fibers: a comparative computational modeling study with validation in a rat sciatic model.

Objective.Vagus nerve stimulation (VNS) is an emerging treatment option for a myriad of medical disorders, where the method of delivering electrical pulses can vary depending on the clinical indication. In this study, we investigated the relative effectiveness of electrically activating the cervical vagus nerve among three different approaches: nerve cuff electrode stimulation (NCES), transcutaneous electrical nerve stimulation (TENS), and enhanced TENS (eTENS). The objectives were to characterize factors that influenced nerve activation and to compare the nerve recruitment properties as a function of nerve fiber diameter.Methods.The Finite Element Model, based on data from the Visible Human Project, was implemented in COMSOL. The three simulation types were compared under a range of vertical and horizontal displacements relative to the location of the vagus nerve. Monopolar anodic stimulation was examined, along with latency and activation of different fiber sizes. Nerve activation was determined via the activating function and McIntyre-Richardson-Grill models, and activation thresholds were validated in anin-vivorodent model.Results.While NCES produced the lowest activation thresholds, eTENS generally performed superior to TENS under the range of conditions and fiber diameters, producing activation thresholds up to three times lower than TENS. eTENS also preserved its enhancement when surface electrodes were displaced away from the nerve. Anodic stimulation revealed an inhibitory region that removed eTENS benefits. eTENS also outperformed TENS by up to four times when targeting smaller diameter nerve fibers, scaling similar to a cuff electrode. In latency and activation of smaller diameter nerve fibers, eTENS results resembled those of NCES more than a TENS electrode. Activation threshold ratios were consistent inin-vivovalidation.Significance.Our findings expand upon previously identified mechanisms for eTENS and further demonstrate how eTENS emulates a nerve cuff electrode to achieve lower activation thresholds. This work further characterizes considerations required for VNS under the three stimulation methods.

Automatic three-dimensional reconstruction of fascicles in peripheral nerves from histological images.

Computational studies can be used to support the development of peripheral nerve interfaces, but currently use simplified models of nerve anatomy, which may impact the applicability of simulation results. To better quantify and model neural anatomy across the population, we have developed an algorithm to automatically reconstruct accurate peripheral nerve models from histological cross-sections. We acquired serial median nerve cross-sections from human cadaveric samples, staining one set with hematoxylin and eosin (H&E) and the other using immunohistochemistry (IHC) with anti-neurofilament antibody. We developed a four-step processing pipeline involving registration, fascicle detection, segmentation, and reconstruction. We compared the output of each step to manual ground truths, and additionally compared the final models to commonly used extrusions, via intersection-over-union (IOU). Fascicle detection and segmentation required the use of a neural network and active contours in H&E-stained images, but only simple image processing methods for IHC-stained images. Reconstruction achieved an IOU of 0.42±0.07 for H&E and 0.37±0.16 for IHC images, with errors partially attributable to global misalignment at the registration step, rather than poor reconstruction. This work provides a quantitative baseline for fully automatic construction of peripheral nerve models. Our models provided fascicular shape and branching information that would be lost via extrusion.

Enhanced transcutaneous electrical nerve stimulation achieved by a localized virtual bipole: a computational study of human tibial nerve stimulation.

OBJECTIVE: Electrical neuromodulation is a clinically effective therapeutic instrument, currently expanding into newer indications and larger patient populations. Neuromodulation technologies are also moving towards less invasive approaches to nerve stimulation. In this study, we investigated an enhanced transcutaneous electrical nerve stimulation (eTENS) system that electrically couples a conductive nerve cuff with a conventional TENS electrode. The objectives were to better understand how eTENS achieves lower nerve activation thresholds, and to test the feasibility of applying eTENS in a human model of peripheral nerve stimulation. APPROACH: A finite element model (FEM) of the human lower leg was constructed to simulate electrical stimulation of the tibial nerve, comparing TENS and eTENS. Key variables included surface electrode diameter, nerve cuff properties (conductivity, length, thickness), and cuff location. Enhanced neural excitability was predicted by relative excitability (RE > 1), derived using either the activating function (AF) or the nerve activation threshold (MRG model). MAIN RESULTS: Simulations revealed that a localized 'virtual bipole' was created on the target nerve, where the isopotential surface of the cuff resulted in large potential differences with the surrounding tissue. The cathodic part (nerve depolarization) of the bipole enhanced neural excitability, predicted by RE values of up to 2.2 (MRG) and 5.5 (AF) when compared to TENS. The MRG model confirmed that action potentials were initiated at the cathodic edge of the nerve cuff. Factors contributing to eTENS were larger surface electrodes, longer cuffs, cuff conductivity (>1×103 S m-1), and cuff position relative to the cathodic surface electrode. SIGNIFICANCE: This study provides a theoretical basis for designing and testing eTENS applied to various neural targets and data suggesting function of eTENS in large models of nerve stimulation. Although eTENS carries key advantages over existing technologies, further work is needed to translate this approach into effective clinical applications.