- Department of Rehabilitation Medicine, Peking University First Hospital, Beijing 100034, P. R. China;
WANG Ninghua,
Email: wangninghua2003@163.com
The theoretical system of motor re-learning is one of the important technical systems in the field of neurological rehabilitation. It is helpful to improve the curative effect of rehabilitation by deeply understanding this theory system and applying it flexibly to patients with neural system impairment. In this paper, the principles of neurorehabilitation based on motor re-learning (including active training, repetitive reinforcement; task-specific practice, goal-orientated training; rich environment, increasing difficulty; emphasis on feedback and early intervention) are interpreted with available evidence of mechanism and clinical application studies, in order to provide some ideas and directions for the future clinical research of neurological rehabilitation.
Citation： WANG Rongli, WANG Ninghua. Principles of neurorehabilitation based on motor re-learning. West China Medical Journal, 2020, 35(5): 519-526. doi: 10.7507/1002-0179.202003414 Copy
Copyright © the editorial department of West China Medical Journal of West China Medical Publisher. All rights reserved
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| 3. | Maier M, Ballester BR, Verschure PFMJ. Principles of neurorehabilitation after stroke based on motor learning and brain plasticity mechanisms. Front Syst Neurosci, 2019, 13: 74. |
| 4. | Popović DB, Sinkaer T, Popović MB. Electrical stimulation asa means for achieving recovery of function in stroke patients. NeuroRehabilitation, 2009, 25(1): 45-48. |
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- 1. Carr JH, Shepherd RB. 神经康复: 优化运动技能. 王宁华, 黄真, 译. 2版. 北京: 北京大学医学出版社, 2015: 1-17.
- 2. Maier M, Rubio Ballester B, Duff A, et al. Effect of specific over nonspecific VR-based rehabilitation on poststroke motor recovery: a systematic meta-analysis. Neurorehabil Neural Repair, 2019, 33(2): 112-129.
- 3. Maier M, Ballester BR, Verschure PFMJ. Principles of neurorehabilitation after stroke based on motor learning and brain plasticity mechanisms. Front Syst Neurosci, 2019, 13: 74.
- 4. Popović DB, Sinkaer T, Popović MB. Electrical stimulation asa means for achieving recovery of function in stroke patients. NeuroRehabilitation, 2009, 25(1): 45-48.
- 5. Schmidt RA, Lee TD. Motor control and learning: a behavioral emphasis. 5th ed. USA: Human Kinetics, 2011: 362-365.
- 6. Körding KP, Wolpert DM. Bayesian decision theory in sensorimotor control. Trends Cogn Sci, 2016, 10(7): 319-326.
- 7. Birkenmeier RL, Prager EM, Lang CE. Translating animal doses of task-specific training to people with chronic stroke in 1-hour therapy sessions: a proof-of-concept study. Neurorehabil Neural Repair, 2010, 24(7): 620-635.
- 8. Basso DM, Lang CE. Consideration of dose and timing when applying interventions after stroke and spinal cord injury. J Neurol Phys Ther, 2017, 41: S24-S31.
- 9. Veerbeek JM, van Wegen E, van Peppen R, et al. What is the evidence for physical therapy poststroke? A systematic review and meta-analysis. PLoS One, 2014, 9(2): e87987.
- 10. French B, Thomas LH, Coupe J, et al. Repetitive task training for improving functional ability after stroke. Cochrane Database Syst Rev, 2016, 11: CD006073.
- 11. Thomas LH, French B, Coupe J, et al. Repetitive task training for improving functional ability after stroke. Stroke, 2017, 48: 102-104.
- 12. Cepeda NJ, Pashler H, Vul E, et al. Distributed practice in verbal recall tasks: a review and quantitative synthesis. Psychol Bull, 2006, 132(3): 354-380.
- 13. Savion-Lemieux T, Penhune VB. The effects of practice and delay on motor skill learning and retention. Exp Brain Res, 2005, 161(4): 423-431.
- 14. Gerbier E, Toppino TC. The effect of distributed practice: neuroscience, cognition, and education. Trends Neurosci Educ, 2015, 4: 49-59.
- 15. Okamoto T, Endo S, Shirao T, et al. Role of cerebellar cortical protein synthesis in transfer of memory trace of cerebellum-dependent motor learning. J Neurosci, 2011, 31(24): 8958-8966.
- 16. Yamazaki T, Nagao S, Lennon W, et al. Modeling memory consolidation during posttraining periods in cerebellovestibular learning. Proc Natl Acad Sci U S A, 2015, 112(11): 3541-3546.
- 17. Dettmers C, Teske U, Hamzei F, et al. Distributed form of constraint-induced movement therapy improves functional outcome and quality of life after stroke. Arch Phys Med Rehabil, 2005, 86(2): 204-209.
- 18. Kwakkel G, Veerbeek JM, van Wegen EE, et al. Constraint-induced movement therapy after stroke. Lancet Neurol, 2015, 14(2): 224-234.
- 19. Billinger SA, Boyne P, Coughenour E, et al. Does aerobic exercise and the FITT principle fit into stroke recovery?. Curr Neurol Neurosci Rep, 2015, 15(2): 519.
- 20. Lohse KR, Lang CE, Boyd LA. Is more better? Using metadata to explore dose-response relationships in stroke rehabilitation. Stroke, 2014, 45(7): 2053-2058.
- 21. Dromerick AW, Lang CE, Birkenmeier RL, et al. Very early constraint-induced movement during stroke rehabilitation (VECTORS): a single-center RCT. Neurology, 2009, 73(3): 195-201.
- 22. Kwakkel G. Intensity of practice after stroke: more is better. Schweiz Arch Neurol Psychiatr, 2009, 160(7): 295-298.
- 23. Hayward KS, Barker RN, Carson RG, et al. The effect of alteringa single component of a rehabilitation programme on the functional recovery of stroke patients: a systematic review and meta-analysis. Clin Rehabil, 2014, 28(2): 107-117.
- 24. Taub E, Uswatte G, Mark VW, et al. The learned nonuse phenomenon: implications for rehabilitation. Eura Medicophys, 2006, 42(3): 241-256.
- 25. Han CE, Arbib MA, Schweighofer N. Stroke rehabilitation reaches a threshold. PLoS Comput Biol, 2008, 4(8): e1000133.
- 26. Johansen-Berg H, Dawes H, Guy C, et al. Correlation between motor improvements and altered fMRI activity after rehabilitative therapy. Brain, 2002, 125(Pt 12): 2731-2742.
- 27. Ballester BR, Maier M, San Segundo Mozo RM, et al. Counteracting learned non-use in chronic stroke patients with reinforcement-induced movement therapy. J Neuroeng Rehabil, 2016, 13(1): 74.
- 28. Ballester BR, Lathe A, Duarte E, et al. A wearable bracelet device for promoting arm use in stroke patients//Faisal A, Krebs HI, Pedotti A. Proceedings of the 3rd international congress on neurotechnology, electronics and informatics. Lisbon: INSTICC, 2015: 24-31.
- 29. Winstein CJ, Wolf SL, Dromerick AW, et al. Effect of a task-oriented rehabilitation program on upper extremity recovery following motor stroke: the ICARE randomized clinical trial. JAMA, 2016, 315(6): 571-581.
- 30. Boyd LA, Vidoni ED, Wessel BD. Motor learning after stroke: is skill acquisition a prerequisite for contralesional neuroplastic change?. Neurosci Lett, 2010, 482(1): 21-25.
- 31. Wilkins KB, Owen M, Ingo C, et al. Neural plasticity in moderate to severe chronic stroke following a device-assisted task-specific arm/hand intervention. Front Neurol, 2017, 8: 284.
- 32. Singer BJ, Vallence AM, Cleary S, et al. The effect of EMG triggered electrical stimulation plus task practice on arm function in chronic stroke patients with moderate-severe arm deficits. Restor Neurol Neurosci, 2013, 31(6): 681-691.
- 33. Wu C, Trombly CA, Lin K, et al. A kinematic study of contextual effects on reaching performance in persons with and without stroke: influences of object availability. Arch Phys Med Rehabil, 2000, 81(1): 95-101.
- 34. Gauggel S, Fischer S. The effect of goal setting on motor performance and motor learning in brain-damaged patients. Neuropsychol Rehabil, 2001, 11: 33-44.
- 35. Nathan DE, Prost RW, Guastello SJ, et al. Investigating the neural correlates of goal-oriented upper extremity movements. NeuroRehabilitation, 2012, 31(4): 421-428.
- 36. Fu MJ, Knutson JS, Chae J. Stroke rehabilitation using virtual environments. Phys Med Rehabil Clin N Am, 2015, 26(4): 747-757.
- 37. Yue Z, Zhang X, Wang J. Hand rehabilitation robotics on poststroke motor recovery. Behav Neurol, 2017, 2017: 3908135.
- 38. Lin CH, Knowlton BJ, Chiang MC, et al. Brain-behavior correlates of optimizing learning through interleaved practice. Neuroimage, 2011, 56(3): 1758-1772.
- 39. Lage GM, Ugrinowitsch H, Apolinário-Souza T, et al. Repetition and variation in motor practice: a review of neural correlates. Neurosci Biobehav Rev, 2015, 57: 132-141.
- 40. Darekar A, McFadyen BJ, Lamontagne A, et al. Efficacy of virtual reality-based intervention on balance and mobility disorders post-stroke: a scoping review. J Neuroeng Rehabil, 2015, 12: 46.
- 41. Nielsen JB, Willerslev-Olsen M, Christiansen L, et al. Science-based neurorehabilitation: recommendations for neurorehabilitation from basic science. J Mot Behav, 2015, 47(1): 7-17.
- 42. Wickens CD, Hutchins S, Carolan T, et al. Effectiveness of part-task training and increasing-difficulty training strategies: a meta-analysis approach. Hum Factors, 2013, 55(2): 461-470.
- 43. Andrieux M, Danna J, Thon B. Self-control of task difficulty during training enhances motor learning of a complex coincidence-anticipation task. Res Q Exerc Sport, 2012, 83(1): 27-35.
- 44. Gendolla GHE. Self-relevance of performance, task difficulty, and task engagement assessed as cardiovascular response. Motiv Emot, 1999, 23: 45-66.
- 45. 王荣丽, 周志浩, 席宇诚, 等. 机器人辅助脑瘫儿童踝关节康复临床初步研究. 北京大学学报(医学版), 2018, 50(2): 207-212.
- 46. Franceschini M, Mazzoleni S, Goffredo M, et al. Upper limb robot-assisted rehabilitation versus physical therapy on subacute stroke patients: a follow-up study. J Bodyw Mov Ther, 2020, 24(1): 194-198.
- 47. Lucca LF. Virtual reality and motor rehabilitation of the upper limb after stroke: a generation of progress?. J Rehabil Med, 2009, 41(12): 1003-1100.
- 48. Keshner EA, Fung J. The quest to apply VR technology to rehabilitation: tribulations and treasures. J Vestib Res, 2017, 27(1): 1-5.
- 49. Shariat A, Najafabadi MG, Ansari NN, et al. The effects of cycling with and without functional electrical stimulation on lower limb dysfunction in patients post-stroke: a systematic review with meta-analysis. NeuroRehabilitation, 2019, 44(3): 389-412.
- 50. 王荣丽, 王宁华. 功能性电刺激踏车疗法在脑卒中早期康复中的疗效研究. 中国康复医学杂志, 2020, 35(2): 147-150.
- 51. Driver J, Noesselt T. Multisensory interplay reveals crossmodal influences on “sensory-specific” brain regions, neural responses, and judgments. Neuron, 2008, 57(1): 11-23.
- 52. Gentile G, Petkova VI, Ehrsson HH. Integration of visual and tactile signals from the hand in the human brain: an FMRI study. J Neurophysiol, 2011, 105(2): 910-922.
- 53. McGann M. Perceptual modalities: modes of presentation or modes of interaction?. J Conscious Stud, 2010, 17: 72-94.
- 54. Gomez-Rodriguez M, Peters J, Hill J, et al. Closing the sensorimotor loop: haptic feedback facilitates decoding of motor imagery. J Neural Eng, 2011, 8(3): 036005.
- 55. Ross JM, Balasubramaniam R. Physical and neural entrainment to rhythm: human sensorimotor coordination across tasks and effector systems. Front Hum Neurosci, 2014, 8: 576.
- 56. Schaefer RS. Auditory rhythmic cueing in movement rehabilitation: findings and possible mechanisms. Philos TransR Soc Lond B Biol Sci, 2014, 369(1658): 20130402.
- 57. Nombela C, Hughes LE, Owen AM, et al. Into the groove: can rhythm influence Parkinson’s disease?. Neurosci Biobehav Rev, 2013, 37(10 Pt 2): 2564-2570.
- 58. Moore E, Schaefer RS, Bastin ME, et al. Diffusion tensor MRI tractography reveals increased fractional anisotropy (FA) in arcuate fasciculus following music-cued motor training. Brain Cogn, 2017, 116: 40-46.
- 59. van Wegen E, de Goede C, Lim I, et al. The effect of rhythmic somatosensory cueing on gait in patients with Parkinson’s disease. J Neurol Sci, 2006, 248(1/2): 210-214.
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