In vivo characterization of regenerative peripheral nerve interface function
Open accessAbstract
Objective. Regenerative peripheral nerve interfaces (RPNIs) are neurotized free autologous muscle grafts equipped with electrodes to record myoelectric signals for prosthesis control. Viability of rat RPNI constructs have been demonstrated using evoked responses. In vivo RPNI characterization is the next critical step for assessment as a control modality for prosthetic devices. Approach. Two RPNIs were created in each of two rats by grafting portions of free muscle to the ends of divided peripheral nerves (peroneal in the left and tibial in the right hind limb) and placing bipolar electrodes on the graft surface. After four months, we examined in vivo electromyographic signal activity and compared these signals to muscular electromyographic signals recorded from autologous muscles in two rats serving as controls. An additional group of two rats in which the autologous muscles were denervated served to quantify cross-talk in the electrode recordings. Recordings were made while rats walked on a treadmill and a motion capture system tracked the hind limbs. Amplitude and periodicity of signals relative to gait were quantified, correlation between electromyographic and motion recording were assessed, and a decoder was trained to predict joint motion. Main Results. Raw RPNI signals were active during walking, with amplitudes of 1 mV PP , and quiet during standing, with amplitudes less than 0.1 mV PP . RPNI signals were periodic and entrained with gait. A decoder predicted bilateral ankle motion with greater than 80% reliability. Control group signal activity agreed with literature. Denervated group signals remained quiescent throughout all evaluations. Significance. In vivo myoelectric RPNI activity encodes neural activation patterns associated with gait. Signal contamination from muscles adjacent to the RPNI is minimal, as demonstrated by the low amplitude signals obtained from the Denervated group. The periodicity and entrainment to gait of RPNI recordings suggests the transduced signals were generated via central nervous system control.
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Previous animal work showed that RPNIs need about three months to mature and stabilize.
“results from previous animal work, which demonstrated that it took approximately 3 months for RPNIs to mature and stabilize (12, 15–17).”
From A regenerative peripheral nerve interface allows real-time control of an artificial hand in upper limb amputees (2020) · cites this paper as [16]
The Methods (§2.1; page unavailable) say the rats recuperated for four months to allow healing and graft reinnervation, with evaluation during the fourth month. The paper reports that RPNI signal amplitudes did not vary appreciably over the evaluation period (Figure 4 caption; page unavailable), but it does not demonstrate that maturation and stabilization took approximately three months. (LLM, conf=0.84)
Experimental — accuracy not yet measuredThis process creates a stable peripheral nerve bioamplifier that produces high-amplitude EMG signals that could be used to control a prosthetic device.
“This process creates a stable, peripheral nerve bioamplifier that produces high-amplitude EMG signals, which could be used to control a prosthetic device (14–17).”
From A regenerative peripheral nerve interface allows real-time control of an artificial hand in upper limb amputees (2020) · cites this paper as [16]
Useful results have been obtained using the RPNI technique in experimental models.
“A number of useful results have been obtained using the RPNI technique in experimental models (Ceballos et al., 2002; Ursu et al., 2016).”
From Selectivity and Longevity of Peripheral-Nerve and Machine Interfaces: A Review. (2017) · cites this paper as Ursu et al., 2016
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Cites
Learning to control a brain–machine interface for reaching and grasping by primates
Carmena, J. M., Lebedev, M. A., Crist, R. E., O'Doherty, J. E., Santucci, D. M., Dimitrov, D. F., Patil, P. G., Henriquez, C. S., & Nicolelis, M. A. L. (2003) · PLoS Biology · doi:10.1371/journal.pbio.0000042 · Cited by 1,761 · see citing works
Upper limb prosthesis use and abandonment
Biddiss, E. A., & Chau, T. T. (2007) · Prosthetics and Orthotics International · doi:10.1080/03093640600994581 · Cited by 1,123 · see citing works
Targeted Muscle Reinnervation for Real-time Myoelectric Control of Multifunction Artificial Arms
Kuiken, T. A. (2009) · JAMA · doi:10.1001/jama.2009.116 · Cited by 1,089 · see citing works
Navarro, X., Krueger, T. B., Lago, N., Micera, S., Stieglitz, T., & Dario, P. (2005) · Journal of the Peripheral Nervous System · doi:10.1111/j.1085-9489.2005.10303.x · Cited by 845 · see citing works
Implanted Neural Interfaces: Biochallenges and Engineered Solutions
Grill, W. M., Norman, S. E., & Bellamkonda, R. V. (2009) · Annual Review of Biomedical Engineering · doi:10.1146/annurev-bioeng-061008-124927 · Cited by 488 · see citing works
Resnik, L. J., Klinger, S. L., & Etter, K. (2013) · Prosthetics and Orthotics International · doi:10.1177/0309364613506913 · Cited by 214 · see citing works
Kung, T. A., Langhals, N. B., Martin, D. C., Johnson, P. J., Cederna, P. S., & Urbanchek, M. G. (2014) · Plastic & Reconstructive Surgery · doi:10.1097/prs.0000000000000168 · Cited by 181 · see citing works
Self-citationSingle-Neuron Stability during Repeated Reaching in Macaque Premotor Cortex
Chestek, C. A., Batista, A. P., Santhanam, G., Yu, B. M., Afshar, A., Cunningham, J. P., Gilja, V., Ryu, S. I., Churchland, M. M., & Shenoy, K. V. (2007) · Journal of Neuroscience · doi:10.1523/jneurosci.0959-07.2007 · Cited by 174 · see citing works
Gramsbergen, A., Ijkema‐Paassen, J., & Meek, M. F. (2000) · Experimental Neurology · doi:10.1006/exnr.1999.7233 · Cited by 104 · see citing works
Neural machine interfaces for controlling multifunctional powered upper-limb prostheses
Ohnishi, K., Weir, R. F. F., & Kuiken, T. (2006) · Expert Review of Medical Devices · doi:10.1586/17434440.4.1.43 · Cited by 103 · see citing works
Neuromechanical Control of Locomotion in the Rat
Thota, A. K., Carlson, S. L., Knapp, E., Thompson, B., & Jung, R. (2005) · Journal of Neurotrauma · doi:10.1089/neu.2005.22.442 · Cited by 96 · see citing works
Real-Time, Simultaneous Myoelectric Control Using Force and Position-Based Training Paradigms
Ameri, A., Scheme, E., Kamavuako, E., Englehart, K., & Parker, P. (2014) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2013.2281595 · Cited by 87 · see citing works
Fifer, M. S., Acharya, S., Benz, H. L., Mollazadeh, M., Crone, N. E., & Thakor, N. V. (2012) · IEEE Pulse · doi:10.1109/mpul.2011.2175636 · Cited by 74 · see citing works
FitzGerald, J. J., Lago, N., Benmerah, S., Serra, J., Watling, C. P., Cameron, R. E., Tarte, E., Lacour, S. P., McMahon, S. B., & Fawcett, J. W. (2012) · Journal of Neural Engineering · doi:10.1088/1741-2560/9/1/016010 · Cited by 73 · see citing works
Electrically stimulated signals from a long-term Regenerative Peripheral Nerve Interface
Langhals, N. B., Woo, S. L., Moon, J. D., Larson, J. V., Leach, M. K., Cederna, P. S., & Urbanchek, M. G. (2014) · 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society · doi:10.1109/embc.2014.6944004 · Cited by 30 · see citing works
Self-citationGore, R. K., Choi, Y., Bellamkonda, R., & English, A. (2015) · Journal of Neural Engineering · doi:10.1088/1741-2560/12/1/016017 · Cited by 26 · see citing works
(2013) · CRC Press eBooks · doi:10.1201/b15079 · Cited by 24 · see citing works
Microscale Electrode Implantation during Nerve Repair
Urbanchek, M. G., Wei, B. P. C., Egeland, B. M., Abidian, M. R., Kipke, D. R., & Cederna, P. S. (2011) · Plastic & Reconstructive Surgery · doi:10.1097/prs.0b013e3182268ac8 · Cited by 19 · see citing works
Self-citationMuscle–nerve–muscle neurotization for the reinnervation of denervated somatic muscle
Urbanchek, M. G., Ganz, D. E., Aydın, M. A., van der Meulen, J. H., & Kuzon, W. M. (2004) · Neurological Research · doi:10.1179/016164104225013824 · Cited by 14 · see citing works
Self-citationRegenerative peripheral nerve interface for prostheses control: electrode comparison
Sando, I., Leach, M., Woo, S., Moon, J., Cederna, P., Langhals, N., & Urbanchek, M. (2015) · Journal of Reconstructive Microsurgery · doi:10.1055/s-0035-1565248 · Cited by 14 · see citing works
Self-citation(2001) · PubMed · Cited by 12 · see citing works
Baghmanli, Z., Sugg, K. B., Wei, B. P. C., Shim, B. S., Martin, D. C., Cederna, P. S., & Urbanchek, M. G. (2013) · Plastic & Reconstructive Surgery · doi:10.1097/prs.0b013e3182959f63 · Cited by 11 · see citing works
Self-citationWoo, S. L., Urbanchek, M. G., Leach, M. K., et al. (2014) · doi:10.1109/embc.2014.6944595 · Cited by 10 · see citing works
Self-citationHu, Y., Sando, I. C., Cederna, P. S., et al. (2015) · Plastic & Reconstructive Surgery · doi:10.1097/01.prs.0000472323.43630.7f · Cited by 10 · see citing works
Self-citationFrench, Z. P., Carrothers, N. S., Hassett, C. A., et al. (2014) · Plastic & Reconstructive Surgery · doi:10.1097/01.prs.0000445094.07094.de · Cited by 3 · see citing works
Nedic, A., Ursu, D. C., Sando, I. C., et al. (2015) · Plastic & Reconstructive Surgery · doi:10.1097/01.prs.0000465464.24256.1e · Cited by 1 · see citing works
Self-citation(1994) · Building Research & Information · doi:10.1080/09613219408727368 · Cited by 1 · see citing works
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Farina, D., Vujaklija, I., Sartori, M., Kapelner, T., Negro, F., Jiang, N., Bergmeister, K., Andalib, A., Principe, J., & Aszmann, O. C. (2017) · Nature Biomedical Engineering · doi:10.1038/s41551-016-0025 · Cited by 359 · see citing works
Vu, P. P., Vaskov, A. K., Irwin, Z. T., Henning, P. T., Lueders, D. R., Laidlaw, A. T., Davis, A. J., Nu, C. S., Gates, D. H., Gillespie, R. B., et al. (2020) · Science Translational Medicine · doi:10.1126/scitranslmed.aay2857 · Cited by 291 · see citing works
Self-citationWoo, S. L., Kung, T. A., Brown, D. L., Leonard, J. A., Kelly, B. M., & Cederna, P. S. (2016) · Plastic and Reconstructive Surgery - Global Open · doi:10.1097/gox.0000000000001038 · Cited by 230 · see citing works
Self-citationSelectivity and Longevity of Peripheral-Nerve and Machine Interfaces: A Review.
Ghafoor, U., Kim, S., & Hong, K.-S. (2017) · Frontiers in Neurorobotics · doi:10.3389/fnbot.2017.00059 · Cited by 88 · see citing works
“A number of useful results have been obtained using the RPNI technique in experimental models (Ceballos et al., 2002; Ursu et al., 2016).”
MethodIrwin, Z. T., Schroeder, K. E., Vu, P. P., Tat, D. M., Bullard, A. J., Woo, S. L., Sando, I. C., Urbanchek, M. G., Cederna, P. S., & Chestek, C. A. (2016) · Journal of Neural Engineering · doi:10.1088/1741-2560/13/4/046007 · Cited by 78 · see citing works
Self-citationVu, P. P., Irwin, Z. T., Bullard, A. J., Ambani, S. W., Sando, I. C., Urbanchek, M. G., Cederna, P. S., & Chestek, C. A. (2018) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2017.2772961 · Cited by 61 · see citing works
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- Authors
- Ursu, D. C., Urbanchek, M. G., Nedic, A., Cederna, P. S., & Gillespie, R. B.
- Affiliations
- University of Michigan (US)
- Michigan Medicine (US)
- Corresponding author
- Gillespie, R Brent
- Venue
- Journal of Neural Engineering
- Publisher
- IOP Publishing
- Published
- 2016-02-09
- Volume
- 13
- Issue
- 2
- Pages
- 026012
- ISSN
- 1741-2552 (ISSN-L)
- 1741-2560
- OpenAlex
- W2499561050
- Cited by
- 46
- References
- 27
- Citations by year
- 2026: 1 · 2025: 5 · 2024: 6 · 2023: 7 · 2022: 7 · 2021: 4 · 2020: 6 · 2019: 3 · 2017: 4 · 2016: 3
- FWCI
- 1.87
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Journal of Neural Engineering (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Defense Advanced Research Projects Agency (N66001-11-C-4190)
- Topics
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neural Engineering
- Physical Sciences › Engineering › Biomedical Engineering › Muscle activation and electromyography studies
- Physical Sciences › Engineering › Biomedical Engineering › Prosthetics and Rehabilitation Robotics
- Keywords
- regenerative peripheral nerve interface, myoelectric signals, prosthetic control, peripheral nerve regeneration, free muscle graft, electromyography, denervation, treadmill walking, rat model
- MeSH terms
- Animals, Electrodes, Implanted, Electromyography, Gait, Hindlimb, Male, Nerve Regeneration, Peripheral Nerves, Rats, Inbred F344, Walking, Muscle, Skeletal, Transplants, Rats
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