1932

Abstract

The fields of human motor control, motor learning, and neurorehabilitation have long been linked by the intuition that understanding how we move (and learn to move) leads to better rehabilitation. In reality, these fields have remained largely separate. Our knowledge of the neural control of movement has expanded, but principles that can directly impact rehabilitation efficacy remain somewhat sparse. This raises two important questions: What can basic studies of motor learning really tell us about rehabilitation, and are we asking the right questions to improve the lives of patients? This review aims to contextualize recent advances in computational and behavioral studies of human motor learning within the framework of neurorehabilitation. We also discuss our views of the current challenges facing rehabilitation and outline potential clinical applications from recent theoretical and basic studies of motor learning and control.

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/content/journals/10.1146/annurev-neuro-080317-062245
2018-07-08
2024-03-29
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Literature Cited

  1. Ballester BR, Maier M, San Segundo Mozo RM, Castañeda V, Duff A, Verschure PFMJ 2016. Counteracting learned non-use in chronic stroke patients with reinforcement-induced movement therapy. J. Neuroeng. Rehabil. 13:74
    [Google Scholar]
  2. Bell JA, Wolke ML, Ortez RC, Jones TA, Kerr AL 2015. Training intensity affects motor rehabilitation efficacy following unilateral ischemic insult of the sensorimotor cortex in C57BL/6 mice. Neurorehabil. Neural Repair 29:590–98
    [Google Scholar]
  3. Bernstein NA 1967. The Co-ordination and Regulation of Movements Oxford, UK: Pergamon Press
  4. Bittmann MF, Patton JL, Huang FC 2017. Customized therapy using distributions of reaching errors. IEEE Int. Conf. Rehabil. Robot 2017:658–63
    [Google Scholar]
  5. Bloom R, Przekop A, Sanger TD 2010. Prolonged electromyogram biofeedback improves upper extremity function in children with cerebral palsy. J. Child Neurol. 25:1480–84
    [Google Scholar]
  6. Brunner I, Skouen JS, Hofstad H, Aßmuss J, Becker F et al. 2016. Is upper limb virtual reality training more intensive than conventional training for patients in the subacute phase after stroke? An analysis of treatment intensity and content. BMC Neurol 16:219
    [Google Scholar]
  7. Cameirão MS, Faria AL, Paulino T, Alves J, Bermúdez I, Badia S 2016. The impact of positive, negative and neutral stimuli in a virtual reality cognitive-motor rehabilitation task: a pilot study with stroke patients. J. Neuroeng. Rehabil. 13:70
    [Google Scholar]
  8. Choi JT, Vining EP, Reisman DS, Bastian AJ 2009. Walking flexibility after hemispherectomy: split-belt treadmill adaptation and feedback control. Brain 132:722–33
    [Google Scholar]
  9. Dietz V, Zijlstra W, Duysens J 1994. Human neuronal interlimb coordination during split-belt locomotion. Exp. Brain Res. 101:513–20
    [Google Scholar]
  10. Ebersbach G, Ebersbach A, Edler D, Kaufhold O, Kusch M et al. 2010. Comparing exercise in Parkinson's disease—the Berlin LSVT BIG study. Mov. Disord. 25:1902–8
    [Google Scholar]
  11. Fernandez-Ruiz J, Diaz R, Hall-Haro C, Vergara P, Mischner J et al. 2003. Normal prism adaptation but reduced after-effect in basal ganglia disorders using a throwing task. Eur. J. Neurosci. 18:689–94
    [Google Scholar]
  12. Hardwick RM, Rajan VA, Bastian AJ, Krakauer JW, Celnik PA 2017. Motor learning in stroke: Trained patients are not equal to untrained patients with less impairment. Neurorehabil. Neural Repair 31:178–89
    [Google Scholar]
  13. Hasson CJ, Manczurowsky J, Yen SC 2015. A reinforcement learning approach to gait training improves retention. Front. Hum. Neurosci. 9:459
    [Google Scholar]
  14. Horak FB, Diener HC 1994. Cerebellar control of postural scaling and central set in stance. J. Neurophysiol. 72:479–93
    [Google Scholar]
  15. Huang HJ, Kram R, Ahmed AA 2012. Reduction of metabolic cost during motor learning of arm reaching dynamics. J. Neurosci. 32:2182–90
    [Google Scholar]
  16. Izawa J, Shadmehr R 2011. Learning from sensory and reward prediction errors during motor adaptation. PLOS Comput. Biol. 7:e1002012
    [Google Scholar]
  17. Kitago T, Ryan SL, Mazzoni P, Krakauer JW, Haith AM 2013. Unlearning versus savings in visuomotor adaptation: comparing effects of washout, passage of time, and removal of errors on motor memory. Front. Hum. Neurosci. 7:307
    [Google Scholar]
  18. Krakauer JW, Mazzoni P 2011. Human sensorimotor learning: adaptation, skill, and beyond. Curr. Opin. Neurobiol. 21:636–44
    [Google Scholar]
  19. Kuo AD, Donelan JM 2010. Dynamic principles of gait and their clinical implications. Phys. Ther. 90:157–74
    [Google Scholar]
  20. Lamontagne A, Fung J 2004. Faster is better: implications for speed-intensive gait training after stroke. Stroke 35:2543–48
    [Google Scholar]
  21. Lang CE, Lohse KR, Birkenmeier RL 2015. Dose and timing in neurorehabilitation: prescribing motor therapy after stroke. Curr. Opin. Neurol. 28:549–55
    [Google Scholar]
  22. Lang CE, Strube MJ, Bland MD, Waddell KJ, Cherry-Allen KM et al. 2016. Dose response of task-specific upper limb training in people at least 6 months poststroke: a phase II, single-blind, randomized, controlled trial. Ann. Neurol. 80:342–54
    [Google Scholar]
  23. Latash ML, Nicholas JJ 1996. Motor control research in rehabilitation medicine. Disabil. Rehabil. 18:293–99
    [Google Scholar]
  24. Laver KE, George S, Thomas S, Deutsch JE, Crotty M 2015. Virtual reality for stroke rehabilitation. Cochrane Database Syst. Rev. 2015:CD008349
    [Google Scholar]
  25. Lewis RF, Zee DS 1993. Ocular motor disorders associated with cerebellar lesions: pathophysiology and topical localization. Rev. Neurol. 149:665–77
    [Google Scholar]
  26. Long AW, Roemmich RT, Bastian AJ 2016. Blocking trial-by-trial error correction does not interfere with motor learning in human walking. J. Neurophysiol. 115:2341–48
    [Google Scholar]
  27. Malhotra PA, Soto D, Li K, Russell C 2013. Reward modulates spatial neglect. J. Neurol. Neurosurg. Psychiatry 84:366–69
    [Google Scholar]
  28. Martin TA, Keating JG, Goodkin HP, Bastian AJ, Thach WT 1996. Throwing while looking through prisms. I. Focal olivocerebellar lesions impair adaptation. Brain 119:1183–98
    [Google Scholar]
  29. Mazzoni P, Krakauer JW 2006. An implicit plan overrides an explicit strategy during visuomotor adaptation. J. Neurosci. 26:3642–45
    [Google Scholar]
  30. McDougle SD, Boggess MJ, Crossley MJ, Parvin D, Ivry RB, Taylor JA 2016. Credit assignment in movement-dependent reinforcement learning. PNAS 113:6797–802
    [Google Scholar]
  31. Morton SM, Bastian AJ 2006. Cerebellar contributions to locomotor adaptations during splitbelt treadmill walking. J. Neurosci. 26:9107–16
    [Google Scholar]
  32. Pavlov IP 1927. Conditioned Reflexes: An Investigation of the Physiological Activity of the Cerebral Cortex Oxford, UK: Oxford Univ. Press
  33. Pekny SE, Izawa J, Shadmehr R 2015. Reward-dependent modulation of movement variability. J. Neurosci. 35:4015–24
    [Google Scholar]
  34. Reisman DS, Block HJ, Bastian AJ 2005. Interlimb coordination during locomotion: What can be adapted and stored?. J. Neurophysiol. 94:2403–15
    [Google Scholar]
  35. Reisman DS, McLean H, Keller J, Danks KA, Bastian AJ 2013. Repeated split-belt treadmill training improves poststroke step length asymmetry. Neurorehabil. Neural Repair 27:460–68
    [Google Scholar]
  36. Reisman DS, Wityk R, Silver K, Bastian AJ 2007. Locomotor adaptation on a split-belt treadmill can improve walking symmetry post-stroke. Brain 130:1861–72
    [Google Scholar]
  37. Reisman DS, Wityk R, Silver K, Bastian AJ 2009. Split-belt treadmill adaptation transfers to overground walking in persons poststroke. Neurorehabil. Neural Repair 23:735–44
    [Google Scholar]
  38. Rode G, Lacour S, Jacquin-Courtois S, Pisella L, Michel C et al. 2015. Long-term sensorimotor and therapeutical effects of a mild regime of prism adaptation in spatial neglect. A double-blind RCT essay. Ann. Phys. Rehabil. Med. 58:40–53
    [Google Scholar]
  39. Roemmich RT, Hack N, Akbar U, Hass CJ 2014a. Effects of dopaminergic therapy on locomotor adaptation and adaptive learning in persons with Parkinson's disease. Behav. Brain Res. 268:31–39
    [Google Scholar]
  40. Roemmich RT, Long AW, Bastian AJ 2016. Seeing the errors you feel enhances locomotor performance but not learning. Curr. Biol. 26:2707–16
    [Google Scholar]
  41. Roemmich RT, Nocera JR, Stegemöller EL, Hassan A, Okun MS, Hass CJ 2014b. Locomotor adaptation and locomotor adaptive learning in Parkinson's disease and normal aging. Clin. Neurophysiol. 125:313–19
    [Google Scholar]
  42. Rossetti Y, Rode G, Pisella L, Farné A, Li L, Boisson D, Perenin MT 1998. Prism adaptation to a rightward optical deviation rehabilitates left hemispatial neglect. Nature 395:166–69
    [Google Scholar]
  43. Saposnik G, Cohen LG, Mamdani M, Pooyania S, Ploughman M et al. 2016. Efficacy and safety of non-immersive virtual reality exercising in stroke rehabilitation (EVREST): a randomised, multicentre, single-blind, controlled trial. Lancet Neurol 15:1019–27
    [Google Scholar]
  44. Saposnik G, Teasell R, Mamdani M, Hall J, McIlroy W et al. 2010. Effectiveness of virtual reality using Wii gaming technology in stroke rehabilitation: a pilot randomized clinical trial and proof of principle. Stroke 41:1477–84
    [Google Scholar]
  45. Scheidt RA, Stoeckmann T 2007. Reach adaptation and final position control amid environmental uncertainty after stroke. J. Neurophysiol. 97:2824–36
    [Google Scholar]
  46. Schmidt RT, Lee TD 2005. Motor Control and Learning: A Behavioral Emphasis Champaign, IL: Human Kinetics
  47. Schultz W 1998. Predictive reward signal of dopamine neurons. J. Neurophysiol. 80:1–27
    [Google Scholar]
  48. Shadmehr R, Huang HJ, Ahmed AA 2016. A representation of effort in decision-making and motor control. Curr. Biol. 26:1929–34
    [Google Scholar]
  49. Shmuelof L, Huang VS, Haith AM, Delnicki RJ, Mazzoni P, Krakauer JW 2012. Overcoming motor forgetting through reinforcement of learned actions. J. Neurosci. 32:14617–21
    [Google Scholar]
  50. Skinner BF 1953. Science and Human Behavior New York: The Macmillan Company
  51. Smith MA, Ghazizadeh A, Shadmehr R 2006. Interacting adaptive processes with different timescales underlie short-term motor learning. PLOS Biol 4:e179
    [Google Scholar]
  52. Smith MA, Shadmehr R 2005. Intact ability to learn internal models of arm dynamics in Huntington's disease but not cerebellar degeneration. J. Neurophysiol. 93:2809–21
    [Google Scholar]
  53. Statton MA, Toliver A, Bastian AJ 2016. A dual-learning paradigm can simultaneously train multiple characteristics of walking. J. Neurophysiol. 115:2692–700
    [Google Scholar]
  54. Stergiou N, Harbourne R, Cavanaugh J 2006. Optimal movement variability: a new theoretical perspective for neurologic physical therapy. J. Neurol. Phys. Ther. 30:120–29
    [Google Scholar]
  55. Sutton RS, Barto AG 1998. Reinforcement Learning: An Introduction Cambridge, MA: MIT Press
  56. Taylor JA, Ivry RB 2014. Cerebellar and prefrontal cortex contributions to adaptation, strategies, and reinforcement learning. Prog. Brain Res. 210:217–53
    [Google Scholar]
  57. Ten Brink AF, Visser-Meily JM, Nijboer TC 2015. Study protocol of ‘Prism Adaptation in Rehabilitation’: a randomized controlled trial in stroke patients with neglect. BMC Neurol 15:5
    [Google Scholar]
  58. Therrien AS, Wolpert DM, Bastian AJ 2016. Effective reinforcement learning following cerebellar damage requires a balance between exploration and motor noise. Brain 139:101–14
    [Google Scholar]
  59. Thorndike EL 1911. Animal Intelligence New York: The Macmillan Company
  60. Torres-Oviedo G, Bastian AJ 2010. Seeing is believing: effects of visual contextual cues on learning and transfer of locomotor adaptation. J. Neurosci. 30:17015–22
    [Google Scholar]
  61. Torres-Oviedo G, Bastian AJ 2012. Natural error patterns enable transfer of motor learning to novel contexts. J. Neurophysiol. 107:346–56
    [Google Scholar]
  62. Tseng YW, Diedrichsen J, Krakauer JW, Shadmehr R, Bastian AJ 2007. Sensory prediction errors drive cerebellum-dependent adaptation of reaching. J. Neurophysiol. 98:54–62
    [Google Scholar]
  63. Waddell KJ, Strube MJ, Bailey RR, Klaesner JW, Birkenmeier RL et al. 2017. Does task-specific training improve upper limb performance in daily life poststroke?. Neurorehabil. Neural Repair 31:290–300
    [Google Scholar]
  64. Wu HG, Miyamoto YR, Gonzalez Castro LN, Ölveczky BP, Smith MA 2014. Temporal structure of motor variability is dynamically regulated and predicts motor learning ability. Nat. Neurosci. 17:312–21
    [Google Scholar]
  65. Young SJ, van Doornik J, Sanger TD 2011. Finger muscle control in children with dystonia. Mov. Disord. 26:1290–96
    [Google Scholar]
  66. Zeiler SR, Krakauer JW 2013. The interaction between training and plasticity in the poststroke brain. Curr. Opin. Neurol. 26:609–16
    [Google Scholar]
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