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NRV: An open framework for in silico evaluation of peripheral nerve electrical stimulation strategies.
- Source :
-
PLoS computational biology [PLoS Comput Biol] 2024 Jul 12; Vol. 20 (7), pp. e1011826. Date of Electronic Publication: 2024 Jul 12 (Print Publication: 2024). - Publication Year :
- 2024
-
Abstract
- Electrical stimulation of peripheral nerves has been used in various pathological contexts for rehabilitation purposes or to alleviate the symptoms of neuropathologies, thus improving the overall quality of life of patients. However, the development of novel therapeutic strategies is still a challenging issue requiring extensive in vivo experimental campaigns and technical development. To facilitate the design of new stimulation strategies, we provide a fully open source and self-contained software framework for the in silico evaluation of peripheral nerve electrical stimulation. Our modeling approach, developed in the popular and well-established Python language, uses an object-oriented paradigm to map the physiological and electrical context. The framework is designed to facilitate multi-scale analysis, from single fiber stimulation to whole multifascicular nerves. It also allows the simulation of complex strategies such as multiple electrode combinations and waveforms ranging from conventional biphasic pulses to more complex modulated kHz stimuli. In addition, we provide automated support for stimulation strategy optimization and handle the computational backend transparently to the user. Our framework has been extensively tested and validated with several existing results in the literature.<br />Competing Interests: The authors have declared that no competing interests exist.<br /> (Copyright: © 2024 Couppey et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)
Details
- Language :
- English
- ISSN :
- 1553-7358
- Volume :
- 20
- Issue :
- 7
- Database :
- MEDLINE
- Journal :
- PLoS computational biology
- Publication Type :
- Academic Journal
- Accession number :
- 38995970
- Full Text :
- https://doi.org/10.1371/journal.pcbi.1011826