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.

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 crosssections 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.

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