Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Review Article
  • Published:

Neural interfaces for the brain and spinal cord—restoring motor function

Abstract

Regaining motor function is of high priority to patients with spinal cord injury (SCI). A variety of electronic devices that interface with the brain or spinal cord, which have applications in neural prosthetics and neurorehabilitation, are in development. Owing to our advancing understanding of activity-dependent synaptic plasticity, new technologies to monitor, decode and manipulate neural activity are being translated to patient populations, and have demonstrated clinical efficacy. Brain–machine interfaces that decode motor intentions from cortical signals are enabling patient-driven control of assistive devices such as computers and robotic prostheses, whereas electrical stimulation of the spinal cord and muscles can aid in retraining of motor circuits and improve residual capabilities in patients with SCI. Next-generation interfaces that combine recording and stimulating capabilities in so-called closed-loop devices will further extend the potential for neuroelectronic augmentation of injured motor circuits. Emerging evidence suggests that integration of closed-loop interfaces into intentional motor behaviours has therapeutic benefits that outlast the use of these devices as prostheses. In this Review, we summarize this evidence and propose that several known plasticity mechanisms, operating in a complementary manner, might underlie the therapeutic effects that are achieved by closing the loop between electronic devices and the nervous system.

Key Points

  • Brain–machine interfaces (BMIs) that record and decode signals from the brain enable volitional control of assistive devices, and modify patterns of cortical activity through the process of neurofeedback

  • The translation of invasive BMIs from animal studies to patients suggests that these technologies could control functional electrical stimulation for the restoration of movement to paralysed limbs

  • Epidural and intraspinal stimulation generates functional limb movements involving the coordinated activity of multiple muscles, and the activation of spinal circuitry in combination with volitional intent could have therapeutic benefits

  • Correlated patterns of spiking activity drive synaptic and structural plasticity, and experimental protocols that involve stimulation of the CNS and PNS have been used to artificially induce specific changes in neural connectivity

  • Neural prostheses that combine recording and stimulation capabilities within wearable or implantable closed-loop devices could replace or augment injured pathways from the cortex to the spinal cord

  • Long-term operation of closed-loop devices may have further therapeutic benefits through several complementary mechanisms of activity-dependent plasticity

This is a preview of subscription content, access via your institution

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Interfacing with the central and peripheral motor system for prosthetic and rehabilitation applications.
Figure 2: Examples of 'closed-loop' connections between the nervous system and electronic devices.
Figure 3: Spinal cord stimulation—electrode designs and experimental outcomes.
Figure 4: Microelectrode arrays stimulate many muscle groups.
Figure 5: Protocols for inducing plasticity according to Hebb's rule.
Figure 6: Possible therapeutic effects of closed-loop neural prostheses.

Similar content being viewed by others

References

  1. Wyndaele, M. & Wyndaele, J.-J. Incidence, prevalence and epidemiology of spinal cord injury: what learns a worldwide literature survey? Spinal Cord 44, 523–529 (2006).

    Article  CAS  PubMed  Google Scholar 

  2. Anderson, K. D. Targeting recovery: priorities of the spinal cord-injured population. J. Neurotrauma 21, 1371–1383 (2004).

    Article  PubMed  Google Scholar 

  3. Devivo, M. J. Epidemiology of traumatic spinal cord injury: trends and future implications. Spinal Cord 50, 365–372 (2012).

    Article  CAS  PubMed  Google Scholar 

  4. Bradbury, E. J. & McMahon, S. B. Spinal cord repair strategies: why do they work? Nat. Rev. Neurosci. 7, 644–653 (2006).

    Article  CAS  PubMed  Google Scholar 

  5. Boulenguez, P. & Vinay, L. Strategies to restore motor functions after spinal cord injury. Curr. Opin. Neurobiol. 19, 587–600 (2009).

    Article  CAS  PubMed  Google Scholar 

  6. Sahni, V. & Kessler, J. A. Stem cell therapies for spinal cord injury. Nat. Rev. Neurol. 6, 363–372 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  7. Sherwood, A. M., Dimitrijevic, M. R. & McKay, W. B. Evidence of subclinical brain influence in clinically complete spinal cord injury: discomplete SCI. J. Neurol. Sci. 110, 90–98 (1992).

    Article  CAS  PubMed  Google Scholar 

  8. Humphrey, D. R., Schmidt, E. M. & Thompson, W. D. Predicting measures of motor performance from multiple cortical spike trains. Science 170, 758–762 (1970).

    Article  CAS  PubMed  Google Scholar 

  9. Wessberg, J. et al. Real-time prediction of hand trajectory by ensembles of cortical neurons in primates. Nature 408, 361–365 (2000).

    Article  CAS  PubMed  Google Scholar 

  10. Serruya, M. D., Hatsopoulos, N. G., Paninski, L., Fellows, M. R. & Donoghue, J. P. Instant neural control of a movement signal. Nature 416, 141–142 (2002).

    Article  CAS  PubMed  Google Scholar 

  11. Taylor, D. M., Tillery, S. I. & Schwartz, A. B. Direct cortical control of 3D neuroprosthetic devices. Science 296, 1829–1832 (2002).

    Article  CAS  PubMed  Google Scholar 

  12. Carmena, J. M. et al. Learning to control a brain–machine interface for reaching and grasping by primates. PLoS Biol. 1, E42 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Velliste, M., Perel, S., Spalding, M. C., Whitford, A. S. & Schwartz, A. B. Cortical control of a prosthetic arm for self-feeding. Nature 453, 1098–1101 (2008).

    Article  CAS  PubMed  Google Scholar 

  14. Hochberg, L. R. et al. Neuronal ensemble control of prosthetic devices by a human with tetraplegia. Nature 442, 164–171 (2006).

    Article  CAS  PubMed  Google Scholar 

  15. Hochberg, L. R. et al. Reach and grasp by people with tetraplegia using a neurally controlled robotic arm. Nature 485, 372–375 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Polikov, V. S., Tresco, P. A. & Reichert, W. M. Response of brain tissue to chronically implanted neural electrodes. J. Neurosci. Methods 148, 1–18 (2005).

    Article  PubMed  Google Scholar 

  17. Kipke, D. R. et al. Advanced neurotechnologies for chronic neural interfaces: new horizons and clinical opportunities. J. Neurosci. 28, 11830–11838 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Schouenborg, J. Biocompatible multichannel electrodes for long-term neurophysiological studies and clinical therapy—novel concepts and design. Prog. Brain Res. 194, 61–70 (2011).

    Article  PubMed  Google Scholar 

  19. Birbaumer, N. & Cohen, L. G. Brain–computer interfaces: communication and restoration of movement in paralysis. J. Physiol. 579, 621–636 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Wolpaw, J. R., McFarland, D. J., Neat, G. W. & Forneris, C. A. An EEG-based brain–computer interface for cursor control. Electroencephalogr. Clin. Neurophysiol. 78, 252–259 (1991).

    Article  CAS  PubMed  Google Scholar 

  21. Pfurtscheller, G., Neuper, C., Flotzinger, D. & Pregenzer, M. EEG-based discrimination between imagination of right and left hand movement. Electroencephalogr. Clin. Neurophysiol. 103, 642–651 (1997).

    Article  CAS  PubMed  Google Scholar 

  22. Blankertz, B., Dornhege, G., Krauledat, M., Mller, K.-R. & Curio, G. The non-invasive Berlin brain–computer interface: fast acquisition of effective performance in untrained subjects. Neuroimage 37, 539–550 (2007).

    Article  PubMed  Google Scholar 

  23. Wolpaw, J. R. & McFarland, D. J. Control of a two-dimensional movement signal by a noninvasive brain-computer interface in humans. Proc. Natl Acad. Sci. USA 101, 17849–17854 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. McFarland, D. J., Sarnacki, W. A. & Wolpaw, J. R. Electroencephalographic (EEG) control of three-dimensional movement. J. Neural. Eng. 7, 036007 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  25. Onose, G. et al. On the feasibility of using motor imagery EEG-based brain–computer interface in chronic tetraplegics for assistive robotic arm control: a clinical test and long-term post-trial follow-up. Spinal Cord 50, 599–608 (2012).

    Article  CAS  PubMed  Google Scholar 

  26. Rickert, J. et al. Encoding of movement direction in different frequency ranges of motor cortical local field potentials. J. Neurosci. 25, 8815–8824 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Waldert, S. et al. A review on directional information in neural signals for brain–machine interfaces. J. Physiol. Paris 103, 244–254 (2009).

    Article  PubMed  Google Scholar 

  28. Birbaumer, N. et al. A spelling device for the paralysed. Nature 398, 297–298 (1999).

    Article  CAS  PubMed  Google Scholar 

  29. Waldert, S. et al. Hand movement direction decoded from MEG and EEG. J. Neurosci. 28, 1000–1008 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Georgopoulos, A. P., Langheim, F. J., Leuthold, A. C. & Merkle, A. N. Magnetoencephalographic signals predict movement trajectory in space. Exp. Brain Res. 167, 132–135 (2005).

    Article  PubMed  Google Scholar 

  31. Leuthardt, E. C., Schalk, G., Wolpaw, J. R., Ojemann, J. G. & Moran, D. W. A brain–computer interface using electrocorticographic signals in humans. J. Neural Eng. 1, 63–71 (2004).

    Article  PubMed  Google Scholar 

  32. Moran, D. Evolution of brain–computer interface: action potentials, local field potentials and electrocorticograms. Curr. Opin. Neurobiol. 20, 741–745 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Chao, Z. C., Nagasaka, Y. & Fujii, N. Long-term asynchronous decoding of arm motion using electrocorticographic signals in monkeys. Front. Neuroeng. 3, 3 (2010).

    PubMed  PubMed Central  Google Scholar 

  34. Suminski, A. J., Tkach, D. C., Fagg, A. H. & Hatsopoulos, N. G. Incorporating feedback from multiple sensory modalities enhances brain-machine interface control. J. Neurosci. 30, 16777–16787 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Fagg, A. H. et al. Biomimetic brain machine interfaces for the control of movement. J. Neurosci. 27, 11842–11846 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Venkatraman, S. & Carmena, J. M. Active sensing of target location encoded by cortical microstimulation. IEEE Trans. Neural Syst. Rehabil. Eng. 19, 317–324 (2011).

    Article  PubMed  Google Scholar 

  37. O'Doherty, J. E. et al. Active tactile exploration using a brain–machine–brain interface. Nature 479, 228–231 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Green, A. M. & Kalaska, J. F. Learning to move machines with the mind. Trends Neurosci. 34, 61–75 (2011).

    Article  CAS  PubMed  Google Scholar 

  39. Jackson, A. & Fetz, E. E. Interfacing with the computational brain. IEEE Trans. Neural Syst. Rehabil. Eng. 19, 534–541 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  40. Fetz, E. E. Operant conditioning of cortical unit activity. Science 163, 955–958 (1969).

    Article  CAS  PubMed  Google Scholar 

  41. Ganguly, K., Dimitrov, D. F., Wallis, J. D. & Carmena, J. M. Reversible large-scale modification of cortical networks during neuroprosthetic control. Nat. Neurosci. 14, 662–667 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Buch, E. et al. Think to move: a neuromagnetic brain-computer interface (BCI) system for chronic stroke. Stroke 39, 910–917 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  43. Varkuti, B. et al. Resting state changes in functional connectivity correlate with movement recovery for BCI and robot-assisted upper-extremity training after stroke. Neurorehabil. Neural Repair http://dx.doi.org/10.1177/1545968312445910.

  44. Enzinger, C. et al. Brain motor system function in a patient with complete spinal cord injury following extensive brain–computer interface training. Exp. Brain Res. 190, 215–223 (2008).

    Article  PubMed  Google Scholar 

  45. Daly, J. J. & Wolpaw, J. R. Brain–computer interfaces in neurological rehabilitation. Lancet Neurol. 7, 1032–1043 (2008).

    Article  PubMed  Google Scholar 

  46. Wang, W. et al. Neural interface technology for rehabilitation: exploiting and promoting neuroplasticity. Phys. Med. Rehabil. Clin. N. Am. 21, 157–178 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Donaldson, N., Perkins, T. A., Fitzwater, R., Wood, D. E. & Middleton, F. FES cycling may promote recovery of leg function after incomplete spinal cord injury. Spinal Cord 38, 680–682 (2000).

    Article  CAS  PubMed  Google Scholar 

  48. Graupe, D. An overview of the state of the art of noninvasive FES for independent ambulation by thoracic level paraplegics. Neurol. Res. 24, 431–442 (2002).

    Article  PubMed  Google Scholar 

  49. Thrasher, T. A. & Popovic, M. R. Functional electrical stimulation of walking: function, exercise and rehabilitation. Ann. Readapt. Med. Phys. 51, 452–460 (2008).

    Article  CAS  PubMed  Google Scholar 

  50. Keith, M. W. Neuroprostheses for the upper extremity. Microsurgery 21, 256–263 (2001).

    Article  CAS  PubMed  Google Scholar 

  51. Popovic, M. B. Control of neural prostheses for grasping and reaching. Med. Eng. Phys. 25, 41–50 (2003).

    Article  PubMed  Google Scholar 

  52. Rupp, R. & Gerner, H. J. Neuroprosthetics of the upper extremity—clinical application in spinal cord injury and challenges for the future. Acta Neurochir. Suppl. 97, 419–426 (2007).

    CAS  PubMed  Google Scholar 

  53. Everaert, D. G., Thompson, A. K., Chong, S. L. & Stein, R. B. Does functional electrical stimulation for foot drop strengthen corticospinal connections? Neurorehabil. Neural Repair 24, 168–177 (2010).

    Article  PubMed  Google Scholar 

  54. Popovic, M. B., Popovic, D. B., Sinkjaer, T., Stefanovic, A. & Schwirtlich, L. Clinical evaluation of functional electrical therapy in acute hemiplegic subjects. J. Rehabil. Res. Dev. 40, 443–453 (2003).

    Article  PubMed  Google Scholar 

  55. Khaslavskaia, S. & Sinkjaer, T. Motor cortex excitability following repetitive electrical stimulation of the common peroneal nerve depends on the voluntary drive. Exp. Brain Res. 162, 497–502 (2005).

    Article  PubMed  Google Scholar 

  56. Craggs, M. D. Cortical control of motor prostheses: using the cord-transected baboon as the primate model for human paraplegia. Adv. Neurol. 10, 91–101 (1975).

    CAS  PubMed  Google Scholar 

  57. Moritz, C. T., Perlmutter, S. I. & Fetz, E. E. Direct control of paralysed muscles by cortical neurons. Nature 456, 639–642 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Pohlmeyer, E. A. et al. Toward the restoration of hand use to a paralyzed monkey: brain-controlled functional electrical stimulation of forearm muscles. PLoS ONE 4, e5924 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Ethier, C., Oby, E. R., Bauman, M. J. & Miller, L. E. Restoration of grasp following paralysis through brain-controlled stimulation of muscles. Nature 485, 368–371 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Chadwick, E. K. et al. Continuous neuronal ensemble control of simulated arm reaching by a human with tetraplegia. J. Neural Eng. 8, 034003 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Jackson, A., Moritz, C. T., Mavoori, J., Lucas, T. H. & Fetz, E. E. The neurochip BCI: towards a neural prosthesis for upper limb function. IEEE Trans. Neural Syst. Rehabil. Eng. 14, 187–190 (2006).

    Article  PubMed  Google Scholar 

  62. Pfurtscheller, G., Müller, G. R., Pfurtscheller, J., Gerner, H. J. & Rupp, R. 'Thought'—control of functional electrical stimulation to restore hand grasp in a patient with tetraplegia. Neurosci. Lett. 351, 33–36 (2003).

    Article  CAS  PubMed  Google Scholar 

  63. Muller-Putz, G. R., Scherer, R., Pfurtscheller, G. & Rupp, R. EEG-based neuroprosthesis control: a step towards clinical practice. Neurosci. Lett. 382, 169–174 (2005).

    Article  CAS  PubMed  Google Scholar 

  64. Daly, J. J. et al. Feasibility of a new application of noninvasive brain computer interface (BCI): a case study of training for recovery of volitional motor control after stroke. J. Neurol. Phys. Ther. 33, 203–211 (2009).

    Article  PubMed  Google Scholar 

  65. Meadows, P. M. Implant technology and usability. Artif. Organs 32, 581–585 (2008).

    Article  PubMed  Google Scholar 

  66. Tyler, D. J. & Durand, D. M. Functionally selective peripheral nerve stimulation with a flat interface nerve electrode. IEEE Trans. Neural Syst. Rehabil. Eng. 10, 294–303 (2002).

    Article  PubMed  Google Scholar 

  67. Normann, R. A. et al. Coordinated, multi-joint, fatigue-resistant feline stance produced with intrafascicular hind limb nerve stimulation. J. Neural Eng. 9, 026019 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Brindley, G. S. The first 500 patients with sacral anterior root stimulator implants: general description. Paraplegia 32, 795–805 (1994).

    CAS  PubMed  Google Scholar 

  69. Schuettler, M. et al. Realization of an active book for multichannel intrathecal root stimulation in spinal cord injury—preliminary results. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2011, 2965–2968 (2011).

    Google Scholar 

  70. Epstein, L. J. & Palmieri, M. Managing chronic pain with spinal cord stimulation. Mt Sinai J. Med. 79, 123–132 (2012).

    Article  PubMed  Google Scholar 

  71. Compton, A. K., Shah, B. & Hayek, S. M. Spinal cord stimulation: a review. Curr. Pain Headache Rep. 16, 35–42 (2012).

    Article  PubMed  Google Scholar 

  72. Minassian, K., Hofstoetter, U., Tansey, K. & Mayr, W. Neuromodulation of lower limb motor control in restorative neurology. Clin. Neurol. Neurosurg. 114, 489–497 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  73. Bizzi, E., Mussa-Ivaldi, F. A. & Giszter, S. F. Computations underlying the execution of movement: a biological perspective. Science 253, 287–291 (1991).

    Article  CAS  PubMed  Google Scholar 

  74. Giszter, S. F., Mussa-Ivaldi, F. A. & Bizzi, E. Convergent force fields organized in the frog's spinal cord. J. Neurosci. 13, 467–491 (1993).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Tresch, M. C. & Bizzi, E. Responses to spinal microstimulation in the chronically spinalized rat and their relationship to spinal systems activated by low threshold cutaneous stimulation. Exp. Brain Res. 129, 401–416 (1999).

    Article  CAS  PubMed  Google Scholar 

  76. Mushahwar, V. K. & Horch, K. W. Selective activation of muscle groups in the feline hindlimb through electrical microstimulation of the ventral lumbo-sacral spinal cord. IEEE Trans. Rehabil. Eng. 8, 11–21 (2000).

    Article  CAS  PubMed  Google Scholar 

  77. Mushahwar, V. K., Collins, D. F. & Prochazka, A. Spinal cord microstimulation generates functional limb movements in chronically implanted cats. Exp. Neurol. 163, 422–429 (2000).

    Article  CAS  PubMed  Google Scholar 

  78. Lemay, M. A., Grasse, D. & Grill, W. M. Hindlimb endpoint forces predict movement direction evoked by intraspinal microstimulation in cats. IEEE Trans. Neural Syst. Rehabil. Eng. 17, 379–389 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  79. Barthélemy, D., Leblond, H., Provencher, J. & Rossignol, S. Nonlocomotor and locomotor hindlimb responses evoked by electrical microstimulation of the lumbar cord in spinalized cats. J. Neurophysiol. 96, 3273–3292 (2006).

    Article  PubMed  Google Scholar 

  80. Guevremont, L. et al. Locomotor-related networks in the lumbosacral enlargement of the adult spinal cat: activation through intraspinal microstimulation. IEEE Trans. Neural Syst. Rehabil. Eng. 14, 266–272 (2006).

    Article  PubMed  Google Scholar 

  81. Bamford, J. A. & Mushahwar, V. K. Intraspinal microstimulation for the recovery of function following spinal cord injury. Prog. Brain Res. 194, 227–239 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  82. Moritz, C. T., Lucas, T. H., Perlmutter, S. I. & Fetz, E. E. Forelimb movements and muscle responses evoked by microstimulation of cervical spinal cord in sedated monkeys. J. Neurophysiol. 97, 110–120 (2007).

    Article  PubMed  Google Scholar 

  83. Zimmermann, J. B., Seki, K. & Jackson, A. Reanimating the arm and hand with intraspinal microstimulation. J. Neural Eng. 8, 054001 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  84. 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).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  86. 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  PubMed  PubMed Central  Google Scholar 

  87. Dominici, N. et al. Versatile robotic interface to evaluate, enable and train locomotion and balance after neuromotor disorders. Nat. Med. 18, 1142–1147 (2012).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  89. Wernig, A. Long-term body-weight supported treadmill training and subsequent follow-up in persons with chronic SCI: effects on functional walking ability and measures of subjective well-being. Spinal Cord 44, 265–266 (2006).

    Article  CAS  PubMed  Google Scholar 

  90. Dunlop, S. A. Activity-dependent plasticity: implications for recovery after spinal cord injury. Trends Neurosci. 31, 410–418 (2008).

    Article  CAS  PubMed  Google Scholar 

  91. Caporale, N. & Dan, Y. Spike timing-dependent plasticity: a Hebbian learning rule. Annu. Rev. Neurosci. 31, 25–46 (2008).

    Article  CAS  PubMed  Google Scholar 

  92. Butz, M., Wörgötter, F. & van Ooyen, A. Activity-dependent structural plasticity. Brain Res. Rev. 60, 287–305 (2009).

    Article  PubMed  Google Scholar 

  93. Caroni, P., Donato, F. & Muller, D. Structural plasticity upon learning: regulation and functions. Nat. Rev. Neurosci. 13, 478–490 (2012).

    Article  CAS  PubMed  Google Scholar 

  94. Wolpaw, J. R. What can the spinal cord teach us about learning and memory? Neuroscientist 16, 532–549 (2010).

    Article  PubMed  Google Scholar 

  95. Hebb, D. The Organization of Behavior; A Neuropsychological Theory (John Wiley and Sons, New York, 1949).

    Google Scholar 

  96. Garraway, S. M. & Hochman, S. Modulatory actions of serotonin, norepinephrine, dopamine, and acetylcholine in spinal cord deep dorsal horn neurons. J. Neurophysiol. 86, 2183–2194 (2001).

    Article  CAS  PubMed  Google Scholar 

  97. Rossignol, S. Plasticity of connections underlying locomotor recovery after central and/or peripheral lesions in the adult mammals. Philos. Trans. R. Soc. Lond. B Biol. Sci. 361, 1647–1671 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  98. Rossignol, S. & Frigon, A. Recovery of locomotion after spinal cord injury: some facts and mechanisms. Annu. Rev. Neurosci. 34, 413–440 (2011).

    Article  CAS  PubMed  Google Scholar 

  99. Iriki, A., Keller, A., Pavlides, C. & Asanuma, H. Long-lasting facilitation of pyramidal tract input to spinal interneurons. Neuroreport 1, 157–160 (1990).

    CAS  PubMed  Google Scholar 

  100. Randic, M., Jiang, M. C. & Cerne, R. Long-term potentiation and long-term depression of primary afferent neurotransmission in the rat spinal cord. J. Neurosci. 13, 5228–5241 (1993).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  101. Carmel, J. B., Berrol, L. J., Brus-Ramer, M. & Martin, J. H. Chronic electrical stimulation of the intact corticospinal system after unilateral injury restores skilled locomotor control and promotes spinal axon outgrowth. J. Neurosci. 30, 10918–10926 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  102. Ridding, M. C. & Rothwell, J. C. Is there a future for therapeutic use of transcranial magnetic stimulation? Nat. Rev. Neurosci. 8, 559–567 (2007).

    Article  CAS  PubMed  Google Scholar 

  103. Huang, Y.-Z., Chen, R.-S., Rothwell, J. C. & Wen, H.-Y. The after-effect of human theta burst stimulation is NMDA receptor dependent. Clin. Neurophysiol. 118, 1028–1032 (2007).

    Article  CAS  PubMed  Google Scholar 

  104. Belci, M., Catley, M., Husain, M., Frankel, H. L. & Davey, N. J. Magnetic brain stimulation can improve clinical outcome in incomplete spinal cord injured patients. Spinal Cord 42, 417–419 (2004).

    Article  CAS  PubMed  Google Scholar 

  105. Kuppuswamy, A. et al. Action of 5 Hz repetitive transcranial magnetic stimulation on sensory, motor and autonomic function in human spinal cord injury. Clin. Neurophysiol. 122, 2452–2461 (2011).

    Article  CAS  PubMed  Google Scholar 

  106. Taylor, J. L. & Martin, P. G. Voluntary motor output is altered by spike-timing-dependent changes in the human corticospinal pathway. J. Neurosci. 29, 11708–11716 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  107. Cortes, M., Thickbroom, G. W., Valls-Sole, J., Pascual-Leone, A. & Edwards, D. J. Spinal associative stimulation: a non-invasive stimulation paradigm to modulate spinal excitability. Clin. Neurophysiol. 122, 2254–2259 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  108. Leukel, C., Taube, W., Beck, S. & Schubert, M. Pathway-specific plasticity in the human spinal cord. Eur. J. Neurosci. 35, 1622–1629 (2012).

    Article  PubMed  Google Scholar 

  109. Jackson, A., Mavoori, J. & Fetz, E. E. Long-term motor cortex plasticity induced by an electronic neural implant. Nature 444, 56–60 (2006).

    Article  CAS  PubMed  Google Scholar 

  110. Rebesco, J. M., Stevenson, I. H., Körding, K. P., Solla, S. A. & Miller, L. E. Rewiring neural interactions by micro-stimulation. Front. Syst. Neurosci. 4, 39 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  111. Fetz, E. E., Nishimura, Y., Eaton, R. W. & Perlmutter, S. I. Primate corticospinal connections can be strengthened by prolonged spike-triggered stimulation of spinal cord during free behaviour. Presented at the 40th Society for Neuroscience Annual Meeting (San Diego, CA, 2010).

    Google Scholar 

  112. Fujiwara, T. et al. Motor improvement and corticospinal modulation induced by hybrid assistive neuromuscular dynamic stimulation (HANDS) therapy in patients with chronic stroke. Neurorehabil. Neural Repair. 23, 125–132 (2009).

    Article  PubMed  Google Scholar 

  113. Gad, P. et al. Forelimb EMG-based trigger to control an electronic spinal bridge to enable hindlimb stepping after a complete spinal cord lesion in rats. J. Neuroeng. Rehabil. 9, 38 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  114. Ortner, R., Allison, B. Z., Korisek, G., Gaggl, H. & Pfurtscheller, G. An SSVEP BCI to control a hand orthosis for persons with tetraplegia. IEEE Trans. Neural Syst. Rehabil. Eng. 19, 1–5 (2011).

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

A. Jackson is supported by a Wellcome Trust Research Career Development Fellowship (086561). J. B. Zimmermann is supported by a Wellcome Trust Ph.D. Studentship (087223).

Author information

Authors and Affiliations

Authors

Contributions

Both authors contributed equally to researching data for the article, discussions of content, writing, and to the review and editing of the manuscript before submission.

Corresponding author

Correspondence to Andrew Jackson.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Jackson, A., Zimmermann, J. Neural interfaces for the brain and spinal cord—restoring motor function. Nat Rev Neurol 8, 690–699 (2012). https://doi.org/10.1038/nrneurol.2012.219

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nrneurol.2012.219

This article is cited by

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing