Download PDF
Semin Plast Surg 2024; 38(01): 010-018
DOI: 10.1055/s-0044-1779028
Review Article

Merging Humans and Neuroprosthetics through Regenerative Peripheral Nerve Interfaces

Yucheng Tian
1   Department of Biomedical Engineering, University of Michigan, Ann Arbor, Michigan
,
Alex K. Vaskov
2   Section of Plastic Surgery, Department of Surgery, University of Michigan, Ann Arbor, Michigan
,
Widya Adidharma
2   Section of Plastic Surgery, Department of Surgery, University of Michigan, Ann Arbor, Michigan
,
Paul S. Cederna
1   Department of Biomedical Engineering, University of Michigan, Ann Arbor, Michigan
2   Section of Plastic Surgery, Department of Surgery, University of Michigan, Ann Arbor, Michigan
,
Stephen W.P. Kemp
1   Department of Biomedical Engineering, University of Michigan, Ann Arbor, Michigan
2   Section of Plastic Surgery, Department of Surgery, University of Michigan, Ann Arbor, Michigan
› Author Affiliations
› Further Information
    Permissions and Reprints

Abstract

Limb amputations can be devastating and significantly affect an individual's independence, leading to functional and psychosocial challenges in nearly 2 million people in the United States alone. Over the past decade, robotic devices driven by neural signals such as neuroprostheses have shown great potential to restore the lost function of limbs, allowing amputees to regain movement and sensation. However, current neuroprosthetic interfaces have challenges in both signal quality and long-term stability. To overcome these limitations and work toward creating bionic limbs, the Neuromuscular Laboratory at University of Michigan Plastic Surgery has developed the Regenerative Peripheral Nerve Interface (RPNI). This surgical construct embeds a transected peripheral nerve into a free muscle graft, effectively amplifying small peripheral nerve signals to provide enhanced control signals for a neuroprosthetic limb. Furthermore, the RPNI has the potential to provide sensory feedback to the user and facilitate neuroprosthesis embodiment. This review focuses on the animal studies and clinical trials of the RPNI to recapitulate the promising trajectory toward neurobionics where the boundary between an artificial device and the human body becomes indistinct. This paper also sheds light on the prospects of the improvement and dissemination of the RPNI technology.

Keywords

regenerative peripheral nerve interface - neuroprosthetics - sensory feedback - clinical neural engineering


Publication History

Article published online:
06 February 2024

© 2024. Thieme. All rights reserved.

Thieme Medical Publishers, Inc.
333 Seventh Avenue, 18th Floor, New York, NY 10001, USA

 

  • References

  • 1 Ziegler-Graham K, MacKenzie EJ, Ephraim PL, Travison TG, Brookmeyer R. Estimating the prevalence of limb loss in the United States: 2005 to 2050. Arch Phys Med Rehabil 2008; 89 (03) 422-429
    CrossrefPubMedGoogle Scholar
  • 2 Luchetti M, Cutti AG, Verni G, Sacchetti R, Rossi N. Impact of Michelangelo prosthetic hand: findings from a crossover longitudinal study. J Rehabil Res Dev 2015; 52 (05) 605-618
    CrossrefPubMedGoogle Scholar
  • 3 Cordella F, Ciancio AL, Sacchetti R. et al. Literature review on needs of upper limb prosthesis users. Front Neurosci 2016; 10: 209
    CrossrefPubMedGoogle Scholar
  • 4 Adewole DO, Serruya MD, Harris JP. et al. The evolution of neuroprosthetic interfaces. Crit Rev Biomed Eng 2016; 44 (1–2): 123-152
    CrossrefPubMedGoogle Scholar
  • 5 Rijnbeek EH, Eleveld N, Olthuis W. Update on peripheral nerve electrodes for closed-loop neuroprosthetics. Front Neurosci 2018; 12: 350
    CrossrefPubMedGoogle Scholar
  • 6 Marinelli A, Boccardo N, Tessari F. et al Active upper limb prostheses: a review on current state and upcoming breakthroughs. Prog Biomed Eng (Bristol) 2023; 5 (01) 012001
    CrossrefPubMedGoogle Scholar
  • 7 Kuiken TA, Miller LA, Lipschutz RD. et al. Targeted reinnervation for enhanced prosthetic arm function in a woman with a proximal amputation: a case study. Lancet 2007; 369 (9559) 371-380
    CrossrefPubMedGoogle Scholar
  • 8 Raspopovic S, Capogrosso M, Petrini FM. et al. Restoring natural sensory feedback in real-time bidirectional hand prostheses. Sci Transl Med 2014; 6 (222) 222ra19
    CrossrefPubMedGoogle Scholar
  • 9 Ortiz-Catalan M, Mastinu E, Sassu P, Aszmann O, Brånemark R. Self-contained neuromusculoskeletal arm prostheses. N Engl J Med 2020; 382 (18) 1732-1738
    CrossrefPubMedGoogle Scholar
  • 10 Ortiz-Catalan M, Zbinden J, Millenaar J. et al. A highly integrated bionic hand with neural control and feedback for use in daily life. Sci Robot 2023; 8 (83) eadf7360
    CrossrefPubMedGoogle Scholar
  • 11 Thakor NV, Fifer MS, Hotson G. et al Neuroprosthetic limb control with electrocorticography: approaches and challenges. Paper presented at: 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, held August 26-30, 2014, in Chicago, IL, USA
    PubMedGoogle Scholar
  • 12 Hotson G, McMullen DP, Fifer MS. et al. Individual finger control of a modular prosthetic limb using high-density electrocorticography in a human subject. J Neural Eng 2016; 13 (02) 026017-26017
    CrossrefPubMedGoogle Scholar
  • 13 Agashe HA, Paek AY, Contreras-Vidal JL. Multisession, noninvasive closed-loop neuroprosthetic control of grasping by upper limb amputees. Prog Brain Res 2016; 228: 107-128
    CrossrefPubMedGoogle Scholar
  • 14 Al-Quraishi MS, Elamvazuthi I, Daud SA, Parasuraman S, Borboni A. EEG-based control for upper and lower limb exoskeletons and prostheses: a systematic review. Sensors (Basel) 2018; 18 (10) 3342
    CrossrefPubMedGoogle Scholar
  • 15 Zhai X, Jelfs B, Chan RHM, Tin C. Self-recalibrating surface EMG pattern recognition for neuroprosthesis control based on Convolutional Neural Network. Front Neurosci 2017; 11: 379
    CrossrefPubMedGoogle Scholar
  • 16 Farina D, Vujaklija I, Brånemark R. et al. Toward higher-performance bionic limbs for wider clinical use. Nat Biomed Eng 2023; 7 (04) 473-485
    CrossrefPubMedGoogle Scholar
  • 17 Smith LH, Kuiken TA, Hargrove LJ. Real-time simultaneous and proportional myoelectric control using intramuscular EMG. J Neural Eng 2014; 11 (06) 066013
    CrossrefPubMedGoogle Scholar
  • 18 Yildiz KA, Shin AY, Kaufman KR. Interfaces with the peripheral nervous system for the control of a neuroprosthetic limb: a review. J Neuroeng Rehabil 2020; 17 (01) 43
    CrossrefPubMedGoogle Scholar
  • 19 Waldert S. Invasive vs. non-invasive neuronal signals for brain-machine interfaces: will one prevail?. Front Neurosci 2016; 10: 1-4
    CrossrefPubMedGoogle Scholar
  • 20 Fifer MS, Acharya S, Benz HL, Mollazadeh M, Crone NE, Thakor NV. Toward electrocorticographic control of a dexterous upper limb prosthesis: building brain-machine interfaces. IEEE Pulse 2012; 3 (01) 38-42
    CrossrefPubMedGoogle Scholar
  • 21 Navarro X, Krueger TB, Lago N, Micera S, Stieglitz T, Dario P. A critical review of interfaces with the peripheral nervous system for the control of neuroprostheses and hybrid bionic systems. J Peripher Nerv Syst 2005; 10 (03) 229-258
    CrossrefPubMedGoogle Scholar
  • 22 Nagappan PG, Chen H, Wang D-Y. Neuroregeneration and plasticity: a review of the physiological mechanisms for achieving functional recovery postinjury. Mil Med Res 2020; 7 (01) 30
    PubMedGoogle Scholar
  • 23 Russell C, Roche AD, Chakrabarty S. Peripheral nerve bionic interface: a review of Electrodes. Int J Intell Robot Appl 2019; 3 (01) 11-18
    CrossrefPubMedGoogle Scholar
  • 24 Ordonez JS, Pikov V, Wiggins H. et al Cuff electrodes for very small diameter nerves—prototyping and first recordings in vivo. Paper presented at: 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society held August 26-30, 2014, in Chicago, IL, USA
    PubMedGoogle Scholar
  • 25 González-González MA, Kanneganti A, Joshi-Imre A. et al. Thin film multi-electrode softening cuffs for selective neuromodulation. Sci Rep 2018; 8 (01) 16390
    CrossrefPubMedGoogle Scholar
  • 26 Rossini PM, Micera S, Benvenuto A. et al. Double nerve intraneural interface implant on a human amputee for robotic hand control. Clin Neurophysiol 2010; 121 (05) 777-783
    CrossrefPubMedGoogle Scholar
  • 27 Boretius T, Badia J, Pascual-Font A. et al. A transverse intrafascicular multichannel electrode (TIME) to interface with the peripheral nerve. Biosens Bioelectron 2010; 26 (01) 62-69
    CrossrefPubMedGoogle Scholar
  • 28 George JA, Page DM, Davis TS. et al. Long-term performance of Utah slanted electrode arrays and intramuscular electromyographic leads implanted chronically in human arm nerves and muscles. J Neural Eng 2020; 17 (05) 056042
    CrossrefPubMedGoogle Scholar
  • 29 Yan D, Jiman AA, Bottorff EC. et al. Ultraflexible and stretchable intrafascicular peripheral nerve recording device with axon-dimension, cuff-less microneedle electrode array. Small 2022; 18 (21) e2200311
    CrossrefPubMedGoogle Scholar
  • 30 Kung TA, Bueno RA, Alkhalefah GK, Langhals NB, Urbanchek MG, Cederna PS. Innovations in prosthetic interfaces for the upper extremity. Plast Reconstr Surg 2013; 132 (06) 1515-1523
    CrossrefPubMedGoogle Scholar
  • 31 Raspopovic S, Valle G, Petrini FM. Sensory feedback for limb prostheses in amputees. Nat Mater 2021; 20 (07) 925-939
    CrossrefPubMedGoogle Scholar
  • 32 Tian Y, Slepyan A, Iskarous MM, Sankar S, Hunt CL, Thakor NV. Real-time, dynamic sensory feedback using neuromorphic tactile signals and transcutaneous electrical nerve stimulation. Paper presented at: 2022 IEEE Biomedical Circuits and Systems Conference (BioCAS) held October 13-15, 2022 , in Taipei, Taiwan
    PubMedGoogle Scholar
  • 33 Rackerby R, Lukosch S, Munro D. Understanding and measuring the cognitive load of amputees for rehabilitation and prosthesis development. Arch Rehabil Res Clin Transl 2022; 4 (03) 100216
    PubMedGoogle Scholar
  • 34 Tan DW, Schiefer MA, Keith MW, Anderson JR, Tyler J, Tyler DJ. A neural interface provides long-term stable natural touch perception. Sci Transl Med 2014; 6 (257) 257ra138
    PubMedGoogle Scholar
  • 35 Ghafoor U, Kim S, Hong K-S. Selectivity and longevity of peripheral-nerve and machine interfaces: a review. Front Neurorobot 2017; 11: 1-21
    CrossrefPubMedGoogle Scholar
  • 36 Urbanchek MG, Kung TA, Frost CM. et al. Development of a regenerative peripheral nerve interface for control of a neuroprosthetic limb. BioMed Res Int 2016; 5726730
    PubMedGoogle Scholar
  • 37 Lowe AL, Thakor NV. Cut wires: the electrophysiology of regenerated tissue. Bioelectron Med 2021; 7 (01) 1
    CrossrefPubMedGoogle Scholar
  • 38 Woo SL, Urbanchek MG, Leach MK, Moon JD, Cederna PS, Langhals NB. Quantification of regenerative peripheral nerve interface signal transmission. Plast Reconstr Surg 2012; 130: 55-56
    CrossrefPubMedGoogle Scholar
  • 39 Vu PP, Chestek CA, Nason SR, Kung TA, Kemp SWP, Cederna PS. The future of upper extremity rehabilitation robotics: research and practice. Muscle Nerve 2020; 61 (06) 708-718
    CrossrefPubMedGoogle Scholar
  • 40 Frost CM, Ursu DC, Flattery SM. et al. Regenerative peripheral nerve interfaces for real-time, proportional control of a neuroprosthetic hand. J Neuroeng Rehabil 2018; 15 (01) 108
    CrossrefPubMedGoogle Scholar
  • 41 Ursu DC, Urbanchek MG, Nedic A, Cederna PS, Gillespie RB. In vivo characterization of regenerative peripheral nerve interface function. J Neural Eng 2016; 13 (02) 026012
    CrossrefPubMedGoogle Scholar
  • 42 Vu PP, Vaskov AK, Irwin ZT. et al. A regenerative peripheral nerve interface allows real-time control of an artificial hand in upper limb amputees. Sci Transl Med 2020; 12 (533) eaay2857
    CrossrefPubMedGoogle Scholar
  • 43 Kung TA, Langhals NB, Martin DC, Johnson PJ, Cederna PS, Urbanchek MG. Regenerative peripheral nerve interface viability and signal transduction with an implanted electrode. Plast Reconstr Surg 2014; 133 (06) 1380-1394
    CrossrefPubMedGoogle Scholar
  • 44 Ursu D, Nedic A, Urbanchek M, Cederna P, Gillespie RB. Adjacent regenerative peripheral nerve interfaces produce phase-antagonist signals during voluntary walking in rats. J Neuroeng Rehabil 2017; 14 (01) 33
    CrossrefPubMedGoogle Scholar
  • 45 Irwin ZT, Schroeder KE, Vu PP. et al. Chronic recording of hand prosthesis control signals via a regenerative peripheral nerve interface in a rhesus macaque. J Neural Eng 2016; 13 (04) 046007
    CrossrefPubMedGoogle Scholar
  • 46 Vu PP, Irwin ZT, Bullard AJ. et al. Closed-loop continuous hand control via chronic recording of regenerative peripheral nerve interfaces. IEEE Trans Neural Syst Rehabil Eng 2018; 26 (02) 515-526
    CrossrefPubMedGoogle Scholar
  • 47 Vu PP, Vaskov AK, Lee C. et al. Long-term upper-extremity prosthetic control using regenerative peripheral nerve interfaces and implanted EMG electrodes. J Neural Eng 2023; 20 (02) 026039
    CrossrefPubMedGoogle Scholar
  • 48 Moran SL, Berger RA. Biomechanics and hand trauma: what you need. Hand Clin 2003; 19 (01) 17-31
    CrossrefPubMedGoogle Scholar
  • 49 Vaskov AK, Vu PP, North N. et al. Surgically implanted electrodes enable real-time finger and grasp pattern recognition for prosthetic hands. IEEE Trans Robot 2022; 38 (05) 2841-2857
    CrossrefPubMedGoogle Scholar
  • 50 Hargrove LJ, Miller LA, Turner K, Kuiken TA. Myoelectric pattern recognition outperforms direct control for transhumeral amputees with targeted muscle reinnervation: a randomized clinical trial. Sci Rep 2017; 7 (01) 13840
    CrossrefPubMedGoogle Scholar
  • 51 Geng Y, Zhang F, Yang L, Zhang Y, Li G. Reduction of the effect of arm position variation on real-time performance of Motion Classification. Annu Int Conf IEEE Eng Med Biol Soc 2012; DOI: 10.1109/EMBC.2012.6346539.
    CrossrefPubMedGoogle Scholar
  • 52 Hwang H-J, Hahne JM, Müller K-R. Real-time robustness evaluation of regression based myoelectric control against arm position change and donning/doffing. PLoS ONE 2017; 12 (11) e0186318
    CrossrefPubMedGoogle Scholar
  • 53 Resnik L, Huang HH, Winslow A, Crouch DL, Zhang F, Wolk N. Evaluation of EMG pattern recognition for upper limb prosthesis control: a case study in comparison with direct myoelectric control. J Neuroeng Rehabil 2018; 15 (01) 23
    CrossrefPubMedGoogle Scholar
  • 54 Lee C, Vaskov AK, Gonzalez MA. et al. Use of regenerative peripheral nerve interfaces and intramuscular electrodes to improve prosthetic grasp selection: a case study. J Neural Eng 2022; 19 (06) 066010
    CrossrefPubMedGoogle Scholar
  • 55 Dweiri YM, Eggers TE, Gonzalez-Reyes LE, Drain JP, McCallum GA, Duran D. Stable detection of movement intent from peripheral nerves: chronic study in dogs. Proc IEEE 2017; 105 (01) 50-65
    CrossrefPubMedGoogle Scholar
  • 56 Vu PP, Lu CW, Vaskov AK. et al. Restoration of proprioceptive and cutaneous sensation using regenerative peripheral nerve interfaces in humans with upper limb amputations. Plast Reconstr Surg 2022; 149 (06) 1149e-1154e
    CrossrefPubMedGoogle Scholar
  • 57 Valle G, Mazzoni A, Iberite F. et al. Biomimetic intraneural sensory feedback enhances sensation naturalness, tactile sensitivity, and manual dexterity in a bidirectional prosthesis. Neuron 2018; 100 (01) 37-45.e7
    CrossrefPubMedGoogle Scholar
  • 58 Valle G, D'Anna E, Strauss I. et al. Hand control with invasive feedback is not impaired by increased cognitive load. Front Bioeng Biotechnol 2020; 8: 287
    CrossrefPubMedGoogle Scholar
  • 59 Chee L, Valle G, Preatoni G, Basla C, Marazzi M, Raspopovic S. Cognitive benefits of using non-invasive compared to implantable neural feedback. Sci Rep 2022; 12 (01) 16696
    CrossrefPubMedGoogle Scholar
  • 60 Gonzalez MA. et al. Characterizing sensory thresholds and intensity sensitivity of regenerative peripheral nerve interfaces: a case study. Paper presented at: 2022 International Conference on Rehabilitation Robotics (ICORR) held July 25-29, 2022 , in Rotterdam, the Netherlands
    PubMedGoogle Scholar
  • 61 Abyzova E, Dogadina E, Rodriguez RD. et al. Beyond Tissue replacement: The Emerging role of smart implants in healthcare. Mater Today Bio 2023; 22: 100784
    CrossrefPubMedGoogle Scholar
  • 62 Cracchiolo M, Valle G, Petrini F. et al. Decoding of grasping tasks from intraneural recordings in trans-radial amputee. J Neural Eng 2020; 17 (02) 026034
    CrossrefPubMedGoogle Scholar
  • 63 George JA, Davis TS, Brinton MR, Clark GA. Intuitive neuromyoelectric control of a dexterous bionic arm using a modified Kalman filter. J Neurosci Methods 2020; 330: 108462
    CrossrefPubMedGoogle Scholar
  • 64 Wang W, Jiang Y, Zhong D. et al. Neuromorphic sensorimotor loop embodied by monolithically integrated, low-voltage, soft e-skin. Science 2023; 380 (6646) 735-742
    CrossrefPubMedGoogle Scholar
  • 65 Shokur S, Mazzoni A, Schiavone G, Weber DJ, Micera S. A modular strategy for next-generation upper-limb sensory-motor neuroprostheses. Med (N Y) 2021; 2 (08) 912-937
    PubMedGoogle Scholar
  • 66 Svientek SR, Ursu DC, Cederna PS, Kemp SWP. Fabrication of the composite regenerative peripheral nerve interface (C-RPNI) in the adult rat. J Vis Exp 2020; x (156) 156
    PubMedGoogle Scholar
  • 67 Kubiak CA, Kemp SWP, Cederna PS. Regenerative peripheral nerve interface for management of postamputation neuroma. JAMA Surg 2018; 153 (07) 681-682
    CrossrefPubMedGoogle Scholar
  • 68 McGimpsey G, Bradford TC. Limb Prosthetics Services and Devices. Worcester, MA: Bioengineering Institute Center for Neuroprosthetics Worcester Polytechnic Institution; 2008: 1-35
    Google Scholar
  • 69 Resnik L, Meucci MR, Lieberman-Klinger S. et al. Advanced upper limb prosthetic devices: implications for upper limb prosthetic rehabilitation. Arch Phys Med Rehabil 2012; 93 (04) 710-717
    CrossrefPubMedGoogle Scholar
  • 70 Won SM, Cai L, Gutruf P, Rogers JA. Wireless and battery-free technologies for neuroengineering. Nat Biomed Eng 2023; 7 (04) 405-423
    CrossrefPubMedGoogle Scholar