Reinnervation of Muscle Targets Enhances the Separability of Motor Unit Signals Following Peripheral Nerve Transfers
Source abstract
Abstract
ABSTRACT After amputation, advanced prosthetic limbs offer a promising means of restoring motor function. However, state‐of‐the‐art prostheses often rely on aggregate electromyogram (EMG) signals to decode motor intention, which limits their ability to replicate natural limb movements. Decomposing EMG signals into individual motor unit components has shown potential for more natural control, but distinguishing between individual units can be challenging when nearby signals overlap. This study demonstrates that muscle target reinnervation surgeries can naturally increase physical separation between motor unit signals, thereby mitigating this overlap. Reinnervation of individual motor units is evaluated in a rodent hindlimb model after direct nerve‐to‐muscle implantation. Histological and electrophysiological analyses reveal that structural changes following reinnervation surgery result in beneficial motor unit signal changes, particularly improving spatial separation between motor unit signals compared to those in intact muscle. This spatial separation contributed to fewer instances of complex, overlapping signals in reinnervated muscle recordings. Motor unit signals were leveraged to provide a proof‐of‐concept of precise control of a virtual prosthesis for the first time after direct nerve‐to‐muscle implantation surgery. These findings highlight the potential of reinnervated muscle targets as key biological interfaces that facilitate motor unit separation, reducing the burden on decomposition algorithms and improving prosthetic control.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Muscle activation and electromyography studies · Neuroscience and Neural Engineering · Transcranial Magnetic Stimulation Studies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Biomedical Engineering Johns Hopkins School of Medicine Baltimore Maryland USA
Department of Electrical Engineering Johns Hopkins University Baltimore Maryland USA
Department of Plastic and Reconstructive Surgery Johns Hopkins School of Medicine Baltimore Maryland USA
Deparatment of Electronics and Telecommunication Engineering Pune Institute of Computer Technology Pune India
Funding
National Science Foundation · Kavli Foundation · Johns Hopkins University
References (39)
Show source-provided references
1. 10.1016/j.jhsa.2023.05.01810.1016/j.jhsa.2023.05.018 ↗
2. 10.3389/fnsys.2015.0016210.3389/fnsys.2015.00162 ↗
3. 10.1097/PHM.0b013e3181587f6c10.1097/PHM.0b013e3181587f6c ↗
4. 10.1007/978-1-4684-3884-0_210.1007/978-1-4684-3884-0_2 ↗
5. 10.1016/j.pmrj.2010.06.01610.1016/j.pmrj.2010.06.016 ↗
6. 10.1109/MSP.2012.220348010.1109/MSP.2012.2203480 ↗
7. 10.1038/s41551‐016‐002510.1038/s41551‐016‐0025 ↗
8. 10.1088/1741‐2552/aaeb0f10.1088/1741‐2552/aaeb0f ↗
9. 10.3389/fnins.2017.0042110.3389/fnins.2017.00421 ↗
10. 10.1109/TBME.1982.32488110.1109/TBME.1982.324881 ↗
11. 10.1109/TSP.2007.89610810.1109/TSP.2007.896108 ↗
12. 10.1016/j.jelekin.2024.10288610.1016/j.jelekin.2024.102886 ↗
13. Reflections on EMG signal decomposition
14. 10.1109/TBME.2022.319211910.1109/TBME.2022.3192119 ↗
15. 10.1016/j.jelekin.2020.10251010.1016/j.jelekin.2020.102510 ↗
16. 10.7554/eLife.8855110.7554/eLife.88551 ↗
17. 10.1126/sciadv.abo504010.1126/sciadv.abo5040 ↗
18. 10.1126/scitranslmed.aay285710.1126/scitranslmed.aay2857 ↗
19. 10.3109/0309364040916775610.3109/03093640409167756 ↗
20. K. N.Quinn P. L.Perkins andS.Wang “Rodent Forelimb Model of Direct Neurotization for Improved Studies of Muscle Reinnervation and Prosthesis Control ” in2023 11th International IEEE/EMBS Conference on Neural Engineering (NER) (IEEE 2023) 1–4.10.1109/NER52421.2023.10123865 ↗
21. 10.1016/j.apmr.2008.05.01610.1016/j.apmr.2008.05.016 ↗
22. 10.1016/j.expneurol.2021.11371710.1016/j.expneurol.2021.113717 ↗
23. 10.1016/S0363‐5023(97)80121‐910.1016/S0363‐5023(97)80121‐9 ↗
24. 10.3390/ijms2122865210.3390/ijms21228652 ↗
25. 10.1371/journal.pone.014977210.1371/journal.pone.0149772 ↗
26. 10.1055/s‐2007‐100665610.1055/s‐2007‐1006656 ↗
27. 10.1097/GOX.000000000000277910.1097/GOX.0000000000002779 ↗
28. 10.1088/1741‐2552/aaed8510.1088/1741‐2552/aaed85 ↗
29. Electrophysiological Motor Unit Number Estimation (MUNE) Measuring Compound Muscle Action Potential (CMAP) in Mouse Hindlimb Muscles
30. 10.1016/j.toxicon.2022.04.00310.1016/j.toxicon.2022.04.003 ↗
31. 10.1109/18.6111510.1109/18.61115 ↗
32. 10.1126/sciadv.aau295610.1126/sciadv.aau2956 ↗
33. 10.1016/j.expneurol.2016.08.01910.1016/j.expneurol.2016.08.019 ↗
34. 10.1001/archneur.1971.0049004008601010.1001/archneur.1971.00490040086010 ↗
35. 10.1002/mus.2713810.1002/mus.27138 ↗
36. Volume Loss During Muscle Reinnervation Surgery Is Correlated With Reduced EMG Signals, but Not Reduced Force Output
37. 10.1097/PRS.000000000001091110.1097/PRS.0000000000010911 ↗
38. Solving the Spike Sorting Problem With Kilosort
39. A. A.Orsini M.Bhatt P. L.Perkins et al. “Spatial Analysis of Neuromuscular Junctions Activation in Three‐Dimensional Histology‐Based Muscle Reconstructions ”arXiv(2025).