Simultaneous decoding of cardiovascular and respiratory functional changes from pig intraneural vagus nerve signals
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Abstract
Objective . Bioelectronic medicine is opening new perspectives for the treatment of some major chronic diseases through the physical modulation of autonomic nervous system activity. Being the main peripheral route for electrical signals between central nervous system and visceral organs, the vagus nerve (VN) is one of the most promising targets. Closed-loop VN stimulation (VNS) would be crucial to increase effectiveness of this approach. Therefore, the extrapolation of useful physiological information from VN electrical activity would represent an invaluable source for single-target applications. Here, we present an advanced decoding algorithm novel to VN studies and properly detecting different functional changes from VN signals. Approach . VN signals were recorded using intraneural electrodes in anaesthetized pigs during cardiovascular and respiratory challenges mimicking increases in arterial blood pressure, tidal volume and respiratory rate. We developed a decoding algorithm that combines discrete wavelet transformation, principal component analysis, and ensemble learning made of classification trees. Main results . The new decoding algorithm robustly achieved high accuracy levels in identifying different functional changes and discriminating among them. Interestingly our findings suggest that electrodes positioning plays an important role on decoding performances. We also introduced a new index for the characterization of recording and decoding performance of neural interfaces. Finally, by combining an anatomically validated hybrid neural model and discrimination analysis, we provided new evidence suggesting a functional topographical organization of VN fascicles. Significance . This study represents an important step towards the comprehension of VN signaling, paving the way for the development of effective closed-loop VNS systems.
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Advanced methods that incorporate biopotential decoding algorithms have enabled a superior understanding of the anatomical organisation of the vagus nerve with respect to cardiac and respiratory functions.
“Advanced methods that incorporate biopotential decoding algorithms have enabled a superior understanding of the anatomical organisation of the vagus nerve with respect to cardiac and respiratory functions [200].”
From Active Neural Interface Circuits and Systems for Selective Control of Peripheral Nerves: A Review (2024) · cites this paper as [200]
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Kumar, A., Ashawat, M. S., Pandit, V. R., & Kumar, P. (2024) · Current Pharmaceutical Design · doi:10.2174/0113816128326489240827100537 · Cited by 2 · see citing works
Pollina, L., Vallone, F., Ottaviani, M. M., Strauss, I., Recchia, F. A., Moccia, S., & Micera, S. (2022) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc48229.2022.9871818 · Cited by 1 · see citing works
Self-citationGu, M., Li, Y., Li, R., Wang, X., Xiang, T., Zhang, Y., Cheng, C., Wang, J., & Sui, X. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2025.108140 · Cited by 1 · see citing works
Borda, E., Gaillet, V., Leccardi, M. J. I. A., Zollinger, E. G., Moreira, R. C., & Ghezzi, D. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.03.21.485100
Bu, Y., Kurniawa, J. F., Prince, J., Nguyen, A. K. L., Ho, B., Sit, N. L. J., Pham, T. V., Wu, V. M., Tjhia, B., Shin, A. J., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.21203/rs.3.rs-1789303/v1
Malpica-Morales, A., Kalliadasis, S., Malliaras, G. G., & Güemes, A. (2025) · Machine Learning Science and Technology · doi:10.1088/2632-2153/ae023f
Spiking Band Power Improves in-Silico Decoding of Vagus Nerve Activity From Intraneural Recordings
Verardo, C., Romeni, S., & Micera, S. (2025) · International IEEE/EMBS Conference on Neural Engineering · doi:10.1109/ner61569.2025.11589026
Self-citation
- Authors
- Vallone, F., Ottaviani, M. M., Dedola, F., Cutrone, A., Romeni, S., Panarese, A. M., Bernini, F., Cracchiolo, M., Strauss, I., Gabisonia, K., Gorgodze, N., Mazzoni, A., Recchia, F. A., & Micera, S.
- Affiliations
- Scuola Superiore Sant'Anna (IT)
- École Polytechnique Fédérale de Lausanne (CH)
- Corresponding author
- Vallone, Fabio
- Bernini, Fabio
- Recchia, Fabio A
- Micera, Silvestro
- ORCID
- Venue
- Journal of Neural Engineering
- Publisher
- IOP Publishing
- Published
- 2021-07-07
- Volume
- 18
- Issue
- 4
- Pages
- 0460a2
- ISSN
- 1741-2552 (ISSN-L)
- 1741-2560
- OpenAlex
- W3176566005
- Cited by
- 35
- References
- 59
- Citations by year
- 2025: 7 · 2024: 8 · 2023: 5 · 2022: 12 · 2021: 2 · 2020: 1
- FWCI
- 2.70
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Journal of Neural Engineering (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nd)
- PubMed (publishedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion, cc-by-nc-nd)
- CINECA IRIS Institutional Research Information System (Sant'Anna School of Advanced Studies) (submittedVersion)
- Funders
- Fondation Bertarelli (824071)
- Horizon 2020 Framework Programme (EC H2020, 824071, H2020-FETPROACT-2018-01)
- Topics
- Life Sciences › Neuroscience › Neurology › Vagus Nerve Stimulation Research
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neural Engineering
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Heart Rate Variability and Autonomic Control
- Keywords
- vagus nerve, neural decoding, bioelectronic medicine, cardiovascular response, respiratory response, discrete wavelet transform, principal component analysis, ensemble learning, classification trees, electrode placement, hybrid neural model, pigs
- MeSH terms
- Animals, Autonomic Nervous System, Electrodes, Nervous System Physiological Phenomena, Swine, Vagus Nerve, Vagus Nerve Stimulation
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