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ASCENT (Automated Simulations to Characterize Electrical Nerve Thresholds): A pipeline for sample-specific computational modeling of electrical stimulation of peripheral nerves

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  • Eric D Musselman
  • Jake E Cariello
  • Warren M Grill
  • Nicole A Pelot

Abstract

Electrical stimulation and block of peripheral nerves hold great promise for treatment of a range of disease and disorders, but promising results from preclinical studies often fail to translate to successful clinical therapies. Differences in neural anatomy across species require different electrodes and stimulation parameters to achieve equivalent nerve responses, and accounting for the consequences of these factors is difficult. We describe the implementation, validation, and application of a standardized, modular, and scalable computational modeling pipeline for biophysical simulations of electrical activation and block of nerve fibers within peripheral nerves. The ASCENT (Automated Simulations to Characterize Electrical Nerve Thresholds) pipeline provides a suite of built-in capabilities for user control over the entire workflow, including libraries for parts to assemble electrodes, electrical properties of biological materials, previously published fiber models, and common stimulation waveforms. We validated the accuracy of ASCENT calculations, verified usability in beta release, and provide several compelling examples of ASCENT-implemented models. ASCENT will enable the reproducibility of simulation data, and it will be used as a component of integrated simulations with other models (e.g., organ system models), to interpret experimental results, and to design experimental and clinical interventions for the advancement of peripheral nerve stimulation therapies.Author summary: Despite promising results from preclinical studies, novel therapies using electrical stimulation of peripheral nerves often fail to produce successful clinical outcomes due to differences in neural anatomy across species. These differences often require different electrodes to interface with the nerves and/or different stimulation parameters to achieve equivalent nerve responses. Further, differences in nerve anatomy across a population contribute to differences in nerve responses to stimulation. These inter-species and inter-individual differences can be studied using computational modeling of individual-specific peripheral nerve morphology and biophysical properties. To accelerate the process of computational modeling of individual nerve anatomy, we developed ASCENT, a software platform for simulating the responses of sample-specific nerves to electrical stimulation with custom electrodes and stimulation parameters. ASCENT automates the complex, multi-step process required to build computational models of preclinical and clinical studies and to design novel stimulation protocols using biophysically realistic simulations. The ASCENT pipeline will be used to develop technologies that increase the selectivity and efficiency of stimulation and to accelerate the translation of novel peripheral nerve stimulation therapies to the clinic.

Suggested Citation

  • Eric D Musselman & Jake E Cariello & Warren M Grill & Nicole A Pelot, 2021. "ASCENT (Automated Simulations to Characterize Electrical Nerve Thresholds): A pipeline for sample-specific computational modeling of electrical stimulation of peripheral nerves," PLOS Computational Biology, Public Library of Science, vol. 17(9), pages 1-19, September.
  • Handle: RePEc:plo:pcbi00:1009285
    DOI: 10.1371/journal.pcbi.1009285
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    Cited by:

    1. Minhaj A. Hussain & Warren M. Grill & Nicole A. Pelot, 2024. "Highly efficient modeling and optimization of neural fiber responses to electrical stimulation," Nature Communications, Nature, vol. 15(1), pages 1-19, December.
    2. Federico Ciotti & Robert John & Natalija Katic Secerovic & Noemi Gozzi & Andrea Cimolato & Naveen Jayaprakash & Weiguo Song & Viktor Toth & Theodoros Zanos & Stavros Zanos & Stanisa Raspopovic, 2024. "Towards enhanced functionality of vagus neuroprostheses through in silico optimized stimulation," Nature Communications, Nature, vol. 15(1), pages 1-20, December.

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