A model of motor and sensory axon activation in the median nerve using surface electrical stimulation
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Surface electrical stimulation has the potential to be a powerful and non-invasive treatment for a variety of medical conditions but currently it is difficult to obtain consistent evoked responses. A viable clinical system must be able to adapt to variations in individuals to produce repeatable results. To more fully study the effect of these variations without performing exhaustive testing on human subjects, a system of computer models was created to predict motor and sensory axon activation in the median nerve due to surface electrical stimulation at the elbow. An anatomically-based finite element model of the arm was built to accurately predict voltages resulting from surface electrical stimulation. In addition, two axon models were developed based on previously published models to incorporate physiological differences between sensory and motor axons. This resulted in axon models that could reproduce experimental results for conduction velocity, strength-duration curves and activation threshold. Differences in experimentally obtained action potential shape between the motor and sensory axons were reflected in the models. The models predicted a lower threshold for sensory axons than motor axons of the same diameter, allowing a range of sensory axons to be activated before any motor axons. This system of models will be a useful tool for development of surface electrical stimulation as a method to target specific neural functions.
KeywordsAxon model Motor axon model Sensory axon model Finite element model Surface electrical stimulation
The authors would like to express appreciation to Matthew Schiefer for his assistance in using Ansys and general modeling advice.
Funding was received from the following entities but none of these entities had any role in design of the study or analysis of the data.
• Hope College Dean of Natural and Applied Sciences.
• A grant to ‘Hope College’ from the Howard Hughes Medical Institute through the Precollege and Undergraduate Science Education Program.
• Michigan Space Grant Consortium Undergraduate Fellowship Program.
• Hope College Nyenhuis Faculty Development Fund.
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest
- Birmingham, K., Gradinaru, V., Anikeeva, P., Grill, W. M., Pikov, V., McLaughlin, B., Pasricha, P., Weber, D., Ludwig, K., & Famm, K. (2014). Bioelectronic medicines: A research roadmap. Nature Reviews. Drug Discovery, 13(6), 399–400. https://doi.org/10.1038/nrd4351nrd4351.CrossRefPubMedGoogle Scholar
- Bostock, H., & Rothwell, J. C. (1997). Latent addition in motor and sensory fibres of human peripheral nerve. J Physiol, 498(Pt 1), 277–294. http://www.ncbi.nlm.nih.gov/pubmed/9023784
- Bostock, H., Baker, M., & Reid, G. (1991). Changes in excitability of human motor axons underlying post-ischaemic fasciculations: Evidence for two stable states. The Journal of Physiology, 441, 537–557. http://www.ncbi.nlm.nih.gov/pubmed/1667800
- Boyd, I. A., & Davey, M. R. (1968). Composition of peripheral nerves. Edinburgh: E. & S. Livingstone.Google Scholar
- David, G., Modney, B., Scappaticci, K. A., Barrett, J. N., & Barrett, E. F. (1995). Electrical and morphological factors influencing the depolarizing after-potential in rat and lizard myelinated axons. J Physiol, 489(Pt 1), 141–157. http://www.ncbi.nlm.nih.gov/pubmed/8583398
- Dawson, G. D. (1956). The relative excitability and conduction velocity of sensory and motor nerve fibres in man. The Journal of Physiology, 131(2), 436–451. http://www.ncbi.nlm.nih.gov/pubmed/13320345
- Dimbylow, P. J. (2000). Electromagnetic field calculations in an anatomically realistic voxel model of the human body. In B. J. Klauenberg & D. Miklavcic (Eds.), Radio Frequency Radiation Dosimetry and its Relationship to the Biological Effects of Electromagnetic Fields (p. 127). Dordrecht: Kluwer Academic Publishers.Google Scholar
- Dorgan, S. J., & Reilly, R. B. (1999). A model for human skin impedance during surface functional neuromuscular stimulation. IEEE Transactions on Rehabilitation Engineering, 7(3), 341–348. http://www.ncbi.nlm.nih.gov/pubmed/10498379
- Drillis, R., Contini, R., & Bluestein, M. (1964). Body segment parameters; a survey of measurement techniques. Artificial Limbs, 8, 44–66. http://www.ncbi.nlm.nih.gov/pubmed/14208177
- Elbow Cross Sectional Anatomy. (2014). Electronic Open Reduction Internal Fixation Reference. http://www.eorif.com/elbow-cross-sectional-anatomy
- Erlanger, J., & Blair, E. A. (1938). Comparative observations on motor and sensory fibers with special reference to repetitiousness. American Journal of Physiology, 121, 431–453.Google Scholar
- Feinstein, B., Lindegard, B., Nyman, E., & Wohlfart, G. (1955). Morphologic studies of motor units in normal human muscles. Acta Anatomica (Basel), 23(2), 127–142. http://www.ncbi.nlm.nih.gov/pubmed/14349537
- Geddes, L. A., & Baker, L. E. (1967). The specific resistance of biological material--a compendium of data for the biomedical engineer and physiologist. Medical & Biological Engineering, 5(3), 271–293. http://www.ncbi.nlm.nih.gov/pubmed/6068939
- Goffredo, M., Schmid, M., Conforto, S., Bilotti, F., Palma, C., Vegni, L., & D’Alessio, T. (2014). A two-step model to optimise transcutaneous electrical stimulation of the human upper arm. Compel-the International Journal for Computation and Mathematics in Electrical and Electronic Engineering, 33(4), 1329–1345. https://doi.org/10.1108/Compel-04-2013-0118.CrossRefGoogle Scholar
- Grill, W., & Mortimer, J. T. (1997). Inversion of the current-distance relationship by transient depolarization. IEEE Transactions on Biomedical Engineering, 44(1), 1–9. http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=9214779
- Grinberg, Y., Schiefer, M. A., Tyler, D. J., & Gustafson, K. J. (2008). Fascicular perineurium thickness, size, and position affect model predictions of neural excitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 16(6), 572–581. http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=19144589
- Hines, M. L., & Carnevale, N. T. (1997). The NEURON simulation environment. Neural Computation, 9(6), 1179–1209. http://www.ncbi.nlm.nih.gov/pubmed/9248061
- Kiernan, M. C., Mogyoros, I., & Burke, D. (1996). Differences in the recovery of excitability in sensory and motor axons of human median nerve. Brain, 119\(Pt 4), 1099–1105. http://www.ncbi.nlm.nih.gov/pubmed/8813274Google Scholar
- Kilgore, K. L., & Bhadra, N. (2004). Nerve conduction block utilising high-frequency alternating current. Medical & Biological Engineering & Computing, 42(3), 394–406. http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=15191086
- Kuhn, A., Keller, T., Lawrence, M., & Morari, M. (2010). The influence of electrode size on selectivity and comfort in transcutaneous electrical stimulation of the forearm. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 18(3), 255–262. https://doi.org/10.1109/TNSRE.2009.2039807.CrossRefPubMedGoogle Scholar
- McIntyre, C., Richardson, A. G., & Grill, W. M. (2002). Modeling the excitability of mammalian nerve fibers: Influence of afterpotentials on the recovery cycle. Journal of Neurophysiology, 87(2), 995–1006. http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=11826063
- Miles, J. D., Kilgore, K. L., Bhadra, N., & Lahowetz, E. A. (2007). Effects of ramped amplitude waveforms on the onset response of high-frequency mammalian nerve block. Journal of Neural Engineering, 4(4), 390–398. http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=18057506
- Mogyoros, I., Kiernan, M. C., & Burke, D. (1996). Strength-duration properties of human peripheral nerve. Brain, 119(Pt 2), 439–447. http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=8800939
- Panizza, M., Nilsson, J., Roth, B. J., Rothwell, J., & Hallett, M. (1994). The time constants of motor and sensory peripheral nerve fibers measured with the method of latent addition. Electroencephalography and Clinical Neurophysiology, 93(2), 147–154. http://www.ncbi.nlm.nih.gov/pubmed/7512921
- Roper, J., & Schwarz, J. R. (1989). Heterogeneous distribution of fast and slow potassium channels in myelinated rat nerve fibres. The Journal of Physiology, 416, 93–110. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1189205/
- Schiefer, M. A., Triolo, R. J., & Tyler, D. J. (2008). A model of selective activation of the femoral nerve with a flat interface nerve electrode for a lower extremity neuroprosthesis. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 16(2), 195–204. http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=18403289
- Sha, N., Kenney, L. P. J., Heller, B. W., Barker, A. T., Howard, D., & Moatamedi, M. (2008). A Finite Element Model to Identify Electrode Influence on Current Distribution in the Skin, 32(8), 639–643. https://doi.org/10.1111/j.1525-1594.2008.00615.x.
- Sunderland, S. (1978). Nerves and nerve injury (2nd ed.). New York: Churchill Livingstone.Google Scholar
- Suresh, S., Smith, L., & Tyler, D. J. (2006). Fascicular anatomy of upper extremity nerves for Neuroprosthesis development. Chicago: Biomedical Engineering Society.Google Scholar
- Veale, J. L., Mark, R. F., & Rees, S. (1973). Differential sensitivity of motor and sensory fibres in human ulnar nerve. Journal of Neurology, Neurosurgery, and Psychiatry, 36(1), 75–86. http://www.ncbi.nlm.nih.gov/pubmed/4348037
- Warman, E. N., Grill, W. M., & Durand, D. (1992). Modeling the effects of electric fields on nerve fibers: Determination of excitation thresholds. IEEE Transactions on Biomedical Engineering, 39(12), 1244–1254. http://www.ncbi.nlm.nih.gov/pubmed/1487287
- Wesselink, W. A., Holsheimer, J., & Boom, H. B. (1999). A model of the electrical behaviour of myelinated sensory nerve fibres based on human data. Medical & Biological Engineering & Computing, 37(2), 228–235. http://www.ncbi.nlm.nih.gov/pubmed/10396827
- Wongsarnpigoon, A., Woock, J. P., & Grill, W. M. (2010). Efficiency analysis of waveform shape for electrical excitation of nerve fibers. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 18(3), 319–328. https://doi.org/10.1109/TNSRE.2010.2047610.CrossRefPubMedPubMedCentralGoogle Scholar