Computational models of compound nerve action potentials: Efficient filter-based methods to quantify effects of tissue conductivities, conduction distance, and nerve fiber parameters

Abstract

Background Peripheral nerve recordings can enhance the efficacy of neurostimulation therapies by providing a feedback signal to adjust stimulation settings for greater efficacy or reduced side effects. Computational models can accelerate the development of interfaces with high signal-to-noise ratio and selective recording. However, validation and tuning of model outputs against in vivo recordings remains computationally prohibitive due to the large number of fibers in a nerve. Methods We designed and implemented highly efficient modeling methods for simulating electrically evoked compound nerve action potential (CNAP) signals. The method simulated a subset of fiber diameters present in the nerve using NEURON, interpolated action potential templates across fiber diameters, and filtered the templates with a weighting function derived from fiber-specific conduction velocity and electromagnetic reciprocity outputs of a volume conductor model. We applied the methods to simulate CNAPs from rat cervical vagus nerve. Results Brute force simulation of a rat vagal CNAP with all 1,759 myelinated and 13,283 unmyelinated fibers in NEURON required 286 and 15,860 CPU hours, respectively, while filtering interpolated templates required 30 and 38 seconds on a desktop computer while maintaining accuracy. Modeled CNAP amplitude could vary by over two orders of magnitude depending on tissue conductivities and cuff opening within experimentally relevant ranges. Conduction distance and fiber diameter distribution also strongly influenced the modeled CNAP amplitude, shape, and latency. Modeled and in vivo signals had comparable shape, amplitude, and latency for myelinated fibers but not for unmyelinated fibers. Conclusions Highly efficient methods of modeling neural recordings quantified the large impact that tissue properties, conduction distance, and nerve fiber parameters have on CNAPs. These methods expand the computational accessibility of neural recording models, enable efficient model tuning for validation, and facilitate the design of novel recording interfaces for neurostimulation feedback and understanding physiological systems.

NRV: An open framework for in silico evaluation of peripheral nerve electrical stimulation strategies

“… 2 ASCENT provides highly realistic cuff electrode models based on commercially available products. 3 SCENT provides filter-based extracellular recording since version 1.3.0 [ 51 ]. 4 MATLAB, The MathWorks Inc, Massachusetts, USA.…”

“…The MRG model [ 43 ] is used to simulate myelinated fibers and the Rattay and Aberham, Sundt et al and Tigerholm et al models [ 44 , 48 , 50 ] are available for unmyelinated fibers. The capabilities for simulated extracellular recordings using a computationally efficient and accurate filtering method have been implemented since a recent update of the pipeline (ASCENT 1.3.0) [ 51 ]. The pipeline also provides tools for analysis and visualization such as heatmaps of thresholds or video generators for plotting state variables variation over time and space.…”