Electrical spinal cord stimulation must preserve proprioception to enable locomotion in humans with spinal cord injury

Abstract

Epidural electrical stimulation (EES) of the spinal cord restores locomotion in animal models of spinal cord injury but is less effective in humans. Here we hypothesized that this interspecies discrepancy is due to interference between EES and proprioceptive information in humans. Computational simulations and preclinical and clinical experiments reveal that EES blocks a significant amount of proprioceptive input in humans, but not in rats. This transient deafferentation prevents modulation of reciprocal inhibitory networks involved in locomotion and reduces or abolishes the conscious perception of leg position. Consequently, continuous EES can only facilitate locomotion within a narrow range of stimulation parameters and is unable to provide meaningful locomotor improvements in humans without rehabilitation. Simulations showed that burst stimulation and spatiotemporal stimulation profiles mitigate the cancellation of proprioceptive information, enabling robust control over motor neuron activity. This demonstrates the importance of stimulation protocols that preserve proprioceptive information to facilitate walking with EES.

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Fig. 1: Probability of antidromic collisions during EES in rats and humans.
Fig. 2: EES induces antidromic activity along proprioceptive afferents and disrupts proprioception.
Fig. 3: Effect of EES on the natural modulation of proprioceptive circuits during passive movements.
Fig. 4: Impact of continuous EES on proprioceptive afferent firings during locomotion in rats and humans.
Fig. 5: Interactions between EES and muscle spindle feedback circuits during locomotion in rats and humans.
Fig. 6: Impact of EES frequencies on muscle activity and leg kinematics during locomotion in rats and humans.
Fig. 7: Spatiotemporal EES protocols encoding proprioceptive sensory information.
Fig. 8: High-frequency, low-amplitude bursts of EES recruit motor neurons through temporal summation of EPSPs.

Data availability

Acquired data are available from the corresponding author upon reasonable request.

References

  1. 1.

    Kiehn, O. Decoding the organization of spinal circuits that control locomotion. Nat. Rev. Neurosci. 17, 224–238 (2016).

    CAS  Article  Google Scholar 

  2. 2.

    van den Brand, R. et al. Restoring voluntary control of locomotion after paralyzing spinal cord injury. Science 336, 1182–1185 (2012).

    Article  Google Scholar 

  3. 3.

    Wenger, N. et al. Closed-loop neuromodulation of spinal sensorimotor circuits controls refined locomotion after complete spinal cord injury. Sci. Transl. Med. 6, 255ra133 (2014).

    Article  Google Scholar 

  4. 4.

    Musienko, P. et al. Somatosensory control of balance during locomotion in decerebrated cat. J. Neurophysiol. 107, 2072–2082 (2012).

    Article  Google Scholar 

  5. 5.

    Capogrosso, M. et al. A brain-spine interface alleviating gait deficits after spinal cord injury in primates. Nature 539, 284–288 (2016).

    Article  Google Scholar 

  6. 6.

    Asboth, L. et al. Cortico-reticulo-spinal circuit reorganization enables functional recovery after severe spinal cord contusion. Nat. Neurosci. 21, 576–588 (2018).

    CAS  Article  Google Scholar 

  7. 7.

    Dimitrijevic, M. R., Gerasimenko, Y. & Pinter, M. M. Evidence for a spinal central pattern generator in humans. Ann. NY Acad. Sci. 860, 360–376 (1998).

    CAS  Article  Google Scholar 

  8. 8.

    Minassian, K. et al. Stepping-like movements in humans with complete spinal cord injury induced by epidural stimulation of the lumbar cord: electromyographic study of compound muscle action potentials. Spinal Cord 42, 401–416 (2004).

    CAS  Article  Google Scholar 

  9. 9.

    Herman, R., He, J., D’Luzansky, S., Willis, W. & Dilli, S. Spinal cord stimulation facilitates functional walking in a chronic, incomplete spinal cord injured. Spinal Cord 40, 65–68 (2002).

    CAS  Article  Google Scholar 

  10. 10.

    Angeli, C. A. et al. Recovery of over-ground walking after chronic motor complete spinal cord Injury. N. Engl. J. Med. 379, 1244–1250 (2018).

    Article  Google Scholar 

  11. 11.

    Gill, M. L. et al. Neuromodulation of lumbosacral spinal networks enables independent stepping after complete paraplegia. Nat. Med. 377, 1938 (2018).

    Google Scholar 

  12. 12.

    Harkema, S. et al. Effect of epidural stimulation of the lumbosacral spinal cord on voluntary movement, standing, and assisted stepping after motor complete paraplegia: a case study. Lancet 377, 1938–1947 (2011).

    Article  Google Scholar 

  13. 13.

    Angeli, C. A., Edgerton, V. R., Gerasimenko, Y. P. & Harkema, S. J. Altering spinal cord excitability enables voluntary movements after chronic complete paralysis in humans. Brain 137, 1394–1409 (2014).

    Article  Google Scholar 

  14. 14.

    Rattay, F., Minassian, K. & Dimitrijevic, M. R. Epidural electrical stimulation of posterior structures of the human lumbosacral cord: 2. quantitative analysis by computer modeling. Spinal Cord 38, 473–489 (2000).

    CAS  Article  Google Scholar 

  15. 15.

    Capogrosso, M. et al. A computational model for epidural electrical stimulation of spinal sensorimotor circuits. J. Neurosci. 33, 19326–19340 (2013).

    CAS  Article  Google Scholar 

  16. 16.

    Gerasimenko, Y. P. et al. Spinal cord reflexes induced by epidural spinal cord stimulation in normal awake rats. J. Neurosci. Meth. 157, 253–263 (2006).

    Article  Google Scholar 

  17. 17.

    Minassian, K. et al. Human lumbar cord circuitries can be activated by extrinsic tonic input to generate locomotor-like activity. Hum. Mov. Sci. 26, 275–295 (2007).

    CAS  Article  Google Scholar 

  18. 18.

    Moraud, E. M. et al. Mechanisms underlying the neuromodulation of spinal circuits for correcting gait and balance deficits after spinal cord injury. Neuron 89, 814–828 (2016).

    CAS  Article  Google Scholar 

  19. 19.

    Su, C. F., Haghighi, S. S., Oro, J. J. & Gaines, R. W. “Backfiring” in spinal cord monitoring. High thoracic spinal cord stimulation evokes sciatic response by antidromic sensory pathway conduction, not motor tract conduction. Spine 17, 504–508 (1992).

    CAS  Article  Google Scholar 

  20. 20.

    Hunter, J. P. & Ashby, P. Segmental effects of epidural spinal cord stimulation in humans. J. Physiol. (Lond.) 474, 407–419 (1994).

    CAS  Article  Google Scholar 

  21. 21.

    Buonocore, M., Bonezzi, C. & Barolat, G. Neurophysiological evidence of antidromic activation of large myelinated fibres in lower limbs during spinal cord stimulation. Spine 33, E90–E93 (2008).

    Article  Google Scholar 

  22. 22.

    Prochazka, A. Proprioceptive feedback and movement regulation. Compr. Physiol. 76, 125 (1996).

    Google Scholar 

  23. 23.

    Courtine, G. et al. Transformation of nonfunctional spinal circuits into functional states after the loss of brain input. Nat. Neurosci. 12, 1333–1342 (2009).

    CAS  Article  Google Scholar 

  24. 24.

    Capaday, C. & Stein, R. B. Amplitude modulation of the soleus H-reflex in the human during walking and standing. J. Neurosci. 6, 1308–1313 (1986).

    CAS  Article  Google Scholar 

  25. 25.

    Courtine, G., Harkema, S. J., Dy, C. J., Gerasimenko, Y. P. & Dyhre-Poulsen, P. Modulation of multisegmental monosynaptic responses in a variety of leg muscles during walking and running in humans. J. Physiol. (Lond.) 582, 1125–1139 (2007).

    CAS  Article  Google Scholar 

  26. 26.

    Dy, C. J. et al. Phase-dependent modulation of percutaneously elicited multisegmental muscle responses after spinal cord injury. J. Neurophysiol. 103, 2808–2820 (2010).

    Article  Google Scholar 

  27. 27.

    Wenger, N. et al. Spatiotemporal neuromodulation therapies engaging muscle synergies improve motor control after spinal cord injury. Nat. Med. 22, 138–145 (2016).

    CAS  Article  Google Scholar 

  28. 28.

    Mignardot, J.-B. et al. A multidirectional gravity-assist algorithm that enhances locomotor control in patients with stroke or spinal cord injury. Sci. Transl. Med. 9, eaah3621 (2017).

    Article  Google Scholar 

  29. 29.

    Conway, B. A., Hultborn, H. & Kiehn, O. Proprioceptive input resets central locomotor rhythm in the spinal cat. Exp. Brain Res. 68, 643–656 (1987).

    CAS  Article  Google Scholar 

  30. 30.

    Prochazka, A. Quantifying proprioception. Prog. Brain. Res. 123, 133–142 (1999).

    CAS  Article  Google Scholar 

  31. 31.

    Mendell, L. M. & Henneman, E. Terminals of single Ia fibers: location, density, and distribution within a pool of 300 homonymous motoneurons. J. Neurophysiol. 34, 171–187 (1971).

    CAS  Article  Google Scholar 

  32. 32.

    Segev, I., Fleshman, J. W. Jr. & Burke, R. E. Computer simulation of group Ia EPSPs using morphologically realistic models of cat alpha-motoneurons. J. Neurophysiol. 64, 648–660 (1990).

    CAS  Article  Google Scholar 

  33. 33.

    Collins, W. F. III, Honig, M. G. & Mendell, L. M. Heterogeneity of group Ia synapses on homonymous alpha-motoneurons as revealed by high-frequency stimulation of Ia afferent fibers. J. Neurophysiol. 52, 980–993 (1984).

    Article  Google Scholar 

  34. 34.

    Koerber, H. R. & Mendell, L. M. Modulation of synaptic transmission at Ia-afferent connections on motoneurons during high-frequency afferent stimulation: dependence on motor task. J. Neurophysiol. 65, 1313–1320 (1991).

    CAS  Article  Google Scholar 

  35. 35.

    Bawa, P. & Chalmers, G. Responses of human motoneurons to high-frequency stimulation of Ia afferents. Muscle Nerve 38, 1604–1615 (2008).

    Article  Google Scholar 

  36. 36.

    Carhart, M. R., He, J., Herman, R., D'Luzansky, S. & Willis, W. T. Epidural spinal-cord stimulation facilitates recovery of functional walking following incomplete spinal-cord injury. IEEE Trans. Neural Syst. Rehabil. Eng. 12, 32–42 (2004).

    Article  Google Scholar 

  37. 37.

    Proske, U. & Gandevia, S. C. The proprioceptive senses: their roles in signaling body shape, body position and movement, and muscle force. Physiol. Rev. 92, 1651–1697 (2012).

    CAS  Article  Google Scholar 

  38. 38.

    Dietz, V. Proprioception and locomotor disorders. Nat. Rev. Neurosci. 3, 781–790 (2002).

    CAS  Article  Google Scholar 

  39. 39.

    Tuthill, J. C. & Azim, E. Proprioception. Curr. Biol. 28, R194–R203 (2018).

    CAS  Article  Google Scholar 

  40. 40.

    Sanes, J. N., Mauritz, K. H., Dalakas, M. C. & Evarts, E. V. Motor control in humans with large-fiber sensory neuropathy. Hum. Neurobiol. 4, 101–114 (1985).

    CAS  PubMed  Google Scholar 

  41. 41.

    Cole, J. Pride and a Daily Marathon (MIT Press, Boston, MA, USA, 1995).

    Google Scholar 

  42. 42.

    Dietz, V. & Duysens, J. Significance of load receptor input during locomotion: a review. Gait Posture 11, 102–110 (2000).

    CAS  Article  Google Scholar 

  43. 43.

    Rossignol, S., Dubuc, R. & Gossard, J.-P. Dynamic sensorimotor interactions in locomotion. Physiol. Rev. 86, 89–154 (2006).

    Article  Google Scholar 

  44. 44.

    Hultborn, H. & Nielsen, J. B. Spinal control of locomotion-from cat to man. Acta Physiol. (Oxf.) 189, 111–121 (2007).

    CAS  Article  Google Scholar 

  45. 45.

    Prochazka, A. & Yakovenko, S. Predictive and reactive tuning of the locomotor CPG. Integr. Comp. Biol. 47, 474–481 (2007).

    Article  Google Scholar 

  46. 46.

    Edgerton, V. R., Tillakaratne, N. J. K., Bigbee, A. J., de Leon, R. D. & Roy, R. R. Plasticity of the spinal neural circuitry after injury. Annu. Rev. Neurosci. 27, 145–167 (2004).

    CAS  Article  Google Scholar 

  47. 47.

    Takeoka, A., Vollenweider, I., Courtine, G. & Arber, S. Muscle spindle feedback directs locomotor recovery and circuit reorganization after spinal cord injury. Cell 159, 1626–1639 (2014).

    CAS  Article  Google Scholar 

  48. 48.

    Park, S.-W., Wolf, S. L., Blanton, S., Winstein, C. & Nichols-Larsen, D. S. The EXCITE Trial: predicting a clinically meaningful motor activity log outcome. Neurorehabil. Neural. Repair. 22, 486–493 (2008).

    Article  Google Scholar 

  49. 49.

    Saal, H. P. & Bensmaia, S. J. Biomimetic approaches to bionic touch through a peripheral nerve interface. Neuropsychologia 79 Pt B, 344–353 (2015).

    Article  Google Scholar 

  50. 50.

    Wagner, F. et al. Targeted neurotechnologies restore walking in humans with spinal cord injury. Nature https://doi.org/10.1038/s41593-018-0262-6 (2018).

  51. 51.

    Hines, M. L. & Carnevale, N. T. The NEURON simulation environment. Neural Comput. 9, 1179–1209 (1997).

    CAS  Article  Google Scholar 

  52. 52.

    Delp, S. L. et al. OpenSim: open-source software to create and analyze dynamic simulations of movement. IEEE Trans. Biomed. Eng. 54, 1940–1950 (2007).

    Article  Google Scholar 

  53. 53.

    Burke, R. E. Group Ia synaptic input to fast and slow twitch motor units of cat triceps surae. J. Physiol. (Lond.) 196, 605–630 (1968).

    CAS  Article  Google Scholar 

  54. 54.

    Munson, J. B., Fleshman, J. W. & Sypert, G. W. Properties of single-fiber spindle group II EPSPs in triceps surae motoneurons. J. Neurophysiol. 44, 713–725 (1980).

    CAS  Article  Google Scholar 

  55. 55.

    Harrison, P. J. & Taylor, A. Individual excitatory post-synaptic potentials due to muscle spindle Ia afferents in cat triceps surae motoneurones. J. Physiol. (Lond.) 312, 455–470 (1981).

    CAS  Article  Google Scholar 

  56. 56.

    McIntyre, C. C. & Grill, W. M. Extracellular stimulation of central neurons: influence of stimulus waveform and frequency on neuronal output. J. Neurophysiol. 88, 1592–1604 (2002).

    Article  Google Scholar 

  57. 57.

    Johnson, W. L., Jindrich, D. L., Roy, R. R. & Reggie Edgerton, V. A three-dimensional model of the rat hindlimb: musculoskeletal geometry and muscle moment arms. J. Biomech. 41, 610–619 (2008).

    Article  Google Scholar 

  58. 58.

    Johnson, W. L., Jindrich, D. L., Zhong, H., Roy, R. R. & Edgerton, V. R. Application of a rat hindlimb model: a prediction of force spaces reachable through stimulation of nerve fascicles. IEEE Trans. Biomed. Eng. 58, 3328–3338 (2011).

    Article  Google Scholar 

  59. 59.

    Delp, S. L. et al. An interactive graphics-based model of the lower extremity to study orthopaedic surgical procedures. IEEE Trans. Biomed. Eng. 37, 757–767 (1990).

    CAS  Article  Google Scholar 

  60. 60.

    Wojtusch, J. & von Stryk, O. HuMoD - A versatile and open database for the investigation, modeling and simulation of human motion dynamics on actuation level. IEEE-RAS International Conference on Humanoid Robots (Humanoids) 15, 74–79 (2015).

    Article  Google Scholar 

  61. 61.

    Hník, P. & Lessler, M. J. Changes in muscle spindle activity of the chronically de-efferented gastrocnemius of the rat. Pflugers Arch. 341, 155–170 (1973).

    Article  Google Scholar 

  62. 62.

    Albert, F., Bergenheim, M., Ribot-Ciscar, E. & Roll, J.-P. The Ia afferent feedback of a given movement evokes the illusion of the same movement when returned to the subject via muscle tendon vibration. Exp. Brain Res. 172, 163–174 (2006).

    Article  Google Scholar 

  63. 63.

    Restuccia, D. et al. Somatosensory evoked potentials after multisegmental lower limb stimulation in focal lesions of the lumbosacral spinal cord. J. Neurol. Neurosurg. Psychiatry 69, 91–95 (2000).

    CAS  Article  Google Scholar 

  64. 64.

    Vallbo, A. B. & al-Falahe, N. A. Human muscle spindle response in a motor learning task. J. Physiol. (Lond.) 421, 553–568 (1990).

    CAS  Article  Google Scholar 

  65. 65.

    Roll, J.-P., Albert, F., Ribot-Ciscar, E. & Bergenheim, M. “Proprioceptive signature” of cursive writing in humans: a multi-population coding. Exp. Brain Res. 157, 359–368 (2004).

    Article  Google Scholar 

  66. 66.

    Han, J., Waddington, G., Adams, R., Anson, J. & Liu, Y. Assessing proprioception: A critical review of methods. J. Sport Health Sci. 5, 80–90 (2016).

    Article  Google Scholar 

  67. 67.

    Ishikawa, K., Ott, K., Porter, R. W. & Stuart, D. Low frequency depression of the H wave in normal and spinal man. Exp. Neurol. 15, 140–156 (1966).

    CAS  Article  Google Scholar 

  68. 68.

    Calancie, B. et al. Evidence that alterations in presynaptic inhibition contribute to segmental hypo- and hyperexcitability after spinal cord injury in man. Electroencephalogr. Clin. Neurophysiol. 89, 177–186 (1993).

    CAS  Article  Google Scholar 

  69. 69.

    Schindler-Ivens, S. & Shields, R. K. Low frequency depression of H-reflexes in humans with acute and chronic spinal-cord injury. Exp. Brain Res. 133, 233–241 (2000).

    CAS  Article  Google Scholar 

  70. 70.

    Vallery, H. et al. Multidirectional transparent support for overground gait training. IEEE Int. Conf. Rehabil. Robot. 2013, 6650512–6650517 (2013).

    CAS  PubMed  Google Scholar 

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Acknowledgements

We thank K. Bartholdi, A. Bichat, and L. Baud for their help with the rat experiments, and we thank all the individuals involved in the STIMO clinical study. This research was supported by the HBP Neurorobotics Platform, funded from the European Union’s Horizon 2020 Framework Programme for Research and Innovation under the Specific Grant Agreement No. 720270 (Human Brain Project SGA1). Financial support was provided by the European Union’s Horizon 2020 Framework Programme for Research and Innovation under Specific Grant Agreements No. 720270 (Human Brain Project SGA1) and No. 785907 (Human Brain Project SGA2); RESTORE: Eurostars E10889, Wings for Life, GTXmedical, Consolidator Grant from the European Research Council (ERC-2015-CoG HOW2WALKAGAIN 682999), Wyss Center for Neuroengineering, National Center of Competence in Research (NCCR) Robotics of the Swiss National Science Foundation, the Commission of Technology and Innovation (CTI) Innosuisse (CTI) OptiStim 25761.1, International Foundation for Research in Paraplegia (IRP), the Michel-Adrien Voirol Foundation, the Firmenich Foundation, the Pictet Group Charitable Foundation, the Panacée Foundation, and the Marie-Curie EPFL fellowship program.

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E.F., M.C., K.M., S.M., and G.C. conceived the study. E.F. and M.C. designed the computational model and E.F. performed the simulations. J.B. performed the surgery in humans. E.F., K.M., F.W., J.B.M., and C.G.LG. performed the experiments. A.R. and E.F. built the robotic platform to control rat ankle kinematics. E.F. performed the data analyses and prepared the figures. G.C. wrote the manuscript with E.F., M.C., and K.M., and all the authors contributed to its editing. G.C., S.M., M.C., and J.B. supervised the work.

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Correspondence to Gregoire Courtine.

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G.C, J.B., and S.M. are founders and shareholders of GTXmedical SA, a company developing neuroprosthetic systems in direct relationship with the present work. E.F., M.C., G.C., and S.M. hold several patents related to electrical spinal cord stimulation.

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Supplementary Figure 1 Impact of continuous EES on the threshold to detection of passive movement test performance.

Scatter plots reporting the detection angle and and plots reporting the error rate (percentage correct trials ± 95% CI) on the TTDPM test performance without EES and when delivering continuous EES at 0.8 and 1.5 times motor response threshold amplitudes and a range of EES frequencies. Different EES frequencies were tested on subject #1 (10 Hz, 50 Hz, 100 Hz) and subject #3 (30 Hz, 50 Hz). At 1.5 motor response threshold amplitude, EES frequencies below 50 Hz induced spasms in the muscles and were thus not tested. Grey dots report the detection angle for successful trials, while pink dots and red crosses indicate false positive and failure to detect movement within the allowed range of motion, respectively (n = 65 for subject #1 and n = 66 for subject #3). *, P < 0.05, Clopper-Pearson non-overlapping intervals, two-sided.

Supplementary Figure 2 Effect of EES on the natural modulation of proprioceptive circuits during passive movements: extended data.

a, Configuration of the experimental setup for subject #1 and #3, as described in Fig. 3a. b, Plots showing EES pulses, EMG activity of the vastus medialis, and changes in knee joint angle during passive oscillations of the knee when EES is delivered at 60 Hz in subject #2 — similar results were achieved in subject #1 and #3. Conventions as in Fig. 3b.

Supplementary Figure 3 Impact of EES amplitude on muscle activity and leg kinematics during locomotion on a treadmill: Subject #1.

a, AIS leg motor score. b, Configuration of electrodes targeting the left and right posterior roots projecting to the L1 and L4 segments. Continuous EES was delivered through these electrodes to facilitate locomotion. c, EMG activity of flexor (semitendinosus/tibialis anterior) and extensor (rectus femoris/soleus) muscles spanning the right knee and ankle joints, together with the changes in the knee ankle and foot height trajectories over four gait cycles without EES and with EES delivered at 0.9, 1.2 and 1.5 motor response threshold amplitude — similar results were obtained for 30 gait cycles (analyzed in d). EES frequency was set to 40Hz. d, Violin plots reporting the root mean square activity of the recorded muscles, the range of motion of the knee and ankle angles, and the step height for different gait cycles (n = 53 gait cycles). Small grey dots represent the different data points, while the large white dots represent the median of the different distributions. Box and whiskers report the interquartile range and the adjacent values, respectively. *, P < 0.05, ***, P < 0.001, Wilcoxon rank-sum two-sided test with Bonferroni correction for multiple comparisons.

Supplementary Figure 4 Impact of EES frequency and amplitude on muscle activity and leg kinematics during locomotion on a treadmill: Subject #2.

The results displayed in Fig. 6 and Supplementary Figure 3 for subject #1 are reported for subject #2 using the same conventions. Recordings in panels a and c were repeated for 29 and 20 gait cycles and analyzed in panels b and c, respectively. The statistics in panel d were computed over n = 37 gait cycles. *, P < 0.05, ***, P < 0.001, Wilcoxon rank-sum two-sided test with Bonferroni correction for multiple comparisons.

Supplementary Figure 5 Impact of EES frequency and amplitude on muscle activity and leg kinematics during locomotion on a treadmill: Subject #3.

The results displayed in Fig. 6 and Supplementary Figure 3 for subject #1 are reported for subject #3 using the same conventions. Recordings in panels a and c were repeated for 51 and 25 gait cycles and were analyzed in panels b and c, respectively. The statistics in panel b and d were computed over n = 77 and n = 51 gait cycles, respectively. *, P < 0.05, ***, P < 0.001, Wilcoxon rank-sum two-sided test with Bonferroni correction for multiple comparisons.

Supplementary Figure 6 High-frequency, low-amplitude EES protocols preserve proprioceptive information and promote motor patterns formation.

Impact of continuous high-frequency low-amplitude EES protocols (600 Hz, 20% recruited afferents) on the modulation of the muscle spindle feedback circuits, following the same conventions as in Fig. 5. For comparison, the impact of continuous EES on the group-Ia afferent firings is also reported.

Supplementary Figure 7 Integrate-and-fire motor neuron model.

Schematic of the integrate and fire model and of the different synapses contacting this cell. b, Simulated inhibitory and excitatory post synaptic potentials (IPSPs/EPSPs) induced by the activation of a single Ia-inhibitory interneuron or a single group-Ia afferent fiber, respectively. c, Excitation threshold of our multicompartmental alpha motoneuron model. d, Number and amplitude of experimental and modeled EPSP/IPSPs induced from the synaptic contacts originating from group-Ia afferents (s1), group-II excitatory interneurons (s2), and Ia-inhibitory interneurons (s3).

Supplementary Figure 8 Adaptation of the rat neural network to humans.

a, Model layout of the hybrid rat-human computational model used to tune the human neural network weights. W1, w2, w3 and w4 represent the weights of the neural network connections that have been modified to adapt the rat neural network to the human one. b, Systematic search results. W1 and w3 were ranged together between 1 and 2 times the weight used in the rat network, while w2 and w4 were ranged between 1 and 4 times. Bar plots report the percentage of simulations that fulfilled the defined fitness criteria. Selected weights that have been used for further simulations are highlighted with an arrow. c, Effect of EES on the natural activity of Ia-inhibitory interneurons and on the production of motor patterns during locomotion, in the hybrid rat-human model for the selected set of synaptic weights. Panels on the left report the average firing rate profiles of the Ia-inhibitory interneuron populations associated to either the flexor or the extensor network, as well as their modulation depth (mean ± SEM, n = 11 gait cycles). Similarly, right-most panels represent the average firing rate profiles of motoneurons and their mean firing rate activity during the phase in which they are active (mean ± SEM, n = 11 gait cycles). Effects of different EES frequencies and amplitudes are reported on the top and bottom panels, respectively.

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Formento, E., Minassian, K., Wagner, F. et al. Electrical spinal cord stimulation must preserve proprioception to enable locomotion in humans with spinal cord injury. Nat Neurosci 21, 1728–1741 (2018). https://doi.org/10.1038/s41593-018-0262-6

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