卒中后运动神经反馈康复训练研究进展与前景

doi:10.3969/j.issn.0258-8021.2019.06.013

Abstract
Figure/Table
References
Related Citation (15)

Download: PDF (7692 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract Stroke is a comprehensive disease of local dysfunction caused by sudden vascular disease in brain area, which is the first malignant neurological disease in the world. Exercise rehabilitation training plays an important role in the recovery of function after stroke. The key lies in the improvement of the plasticity of the injured nerve tissue in brain area through limb movement induction to achieve the improvement and recovery of motor function. However, traditional passive repeated training approaches cannot arouse patients’ participation and enthusiasm, which seriously affects the rehabilitation effect. In recent years, the model of motor neurofeedback rehabilitation training based on the motor imagery brain-machine interface (MI-BCI) can be driven by the endogenicity of patients′ subjective motor intention to produce plastic changes in the corresponding brain regions, and it can promote limb motor rehabilitation through brain functional recombination. This paper reviewed the application of different motor neurofeedback modes including proprioception and visual feedback in stroke rehabilitation training, discussed the existing challenges and solutions of the current MI-BCI neurofeedback rehabilitation training system, and provided opinions for the future development.

Key words： stroke motor imagery brain-computer interface motor neurofeedback rehabilitation training

Received: 17 January 2019

PACS:

R318

Corresponding Authors: E-mail: richardming@tju.edu.cn

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	Wang Mengya
	Wang Zhongpeng
	Chen Long
	Wan Baikun
	Gu Xiaosong
	Ming Dong

Cite this article:

Wang Mengya,Wang Zhongpeng,Chen Long, et al. Research Progress and Prospects of Motor Neurofeedback Rehabilitation Training after Stroke[J]. Chinese Journal of Biomedical Engineering, 2019, 38(6): 742-752.

URL:

http://cjbme.csbme.org/EN/10.3969/j.issn.0258-8021.2019.06.013 OR http://cjbme.csbme.org/EN/Y2019/V38/I6/742

[1] Thrift AG, Cadilhac DA, Thayabaranathan T, et al. Global stroke statistics [J]. International Journal of Stroke, 2013, 9(1): 6-18.
[2] Thrift AG, Thayabaranathan T, Howard G, et al. Global stroke statistics [J]. International Journal of Stroke, 2017, 9(1): 13-32.
[3] Kwakkel G, Kollen BJ, Van d GJ, et al. Probability of regaining dexterity in the flaccid upper limb: impact of severity of paresis and time since onset in acute stroke [J]. Stroke, 2003, 34(9): 2181-2186.
[4] Hatem SM, Saussez G, Faille MD, et al. Rehabilitation of motor function after stroke: A multiple systematic review focused on techniques to stimulate upper extremity recovery [J]. Frontiers in Human Neuroscience, 2016, 10(88): 442.
[5] Sharma N, Baron JC, Rowe JB. Motor imagery after stroke: relating outcome to motor network connectivity [J]. Annals of Neurology, 2009, 66(5): 604-616.
[6] Kleim JA, Jones TA. Principles of experience-dependent neural plasticity: implications for rehabilitation after brain damage [J]. Journal of Speech Language & Hearing Research, 2008, 51(1): 225-239.
[7] Sdhof TC, Malenka RC. Understanding synapses: past, present, and future [J]. Neuron, 2008, 60(3): 469-476.
[8] Cramer SC. Repairing the human brain after stroke: I. Mechanisms of spontaneous recovery [J]. Annals of Neurology, 2010, 63(3): 272-287.
[9] Nudo RJ, Plautz EJ, Frost SB. Role of adaptive plasticity in recovery of function after damage to motor cortex [J]. Muscle & Nerve, 2010, 24(8): 1000-1019.
[10] Carmichael ST. Cellular and molecular mechanisms of neural repair after stroke: Making waves [J]. Annals of Neurology, 2010, 59(5): 735-742.
[11] Kleim JA, Jones TA, Schallert T. Motor enrichment and the induction of plasticity before or after brain injury [J]. Neurochemical Research, 2003, 28(11): 1757-1769.
[12] Flöel A, Werner C, Grittner U, et al. Physical fitness training in Subacute Stroke (PHYS-STROKE) - study protocol for a randomised controlled trial [J]. Trials, 2014, 15(1): 45.
[13] Kondo T, Saeki M, Hayashi Y, et al. Effect of instructive visual stimuli on neurofeedback training for motor imagery-based brain-computer interface [J]. Human Movement Science, 2015, 43: 239-249.
[14] Jeannerod M, Frak V. Mental imaging of motor activity in humans [J]. Current Opinion in Neurobiology, 1999, 9(6): 735-739.
[15] Munzert J, Lorey B, Zentgraf K. Cognitive motor processes: the role of motor imagery in the study of motor representations [J]. Brain Research Reviews, 2009, 60(2): 306-326.
[16] Mulder T. Motor imagery and action observation: cognitive tools for rehabilitation [J]. Journal of Neural Transmission, 2007, 114(10): 1265-1278.
[17] Caldara R, Deiber MP, Andrey C, et al. Actual and mental motor preparation and execution: A spatiotemporal ERP study [J]. Experimental Brain Research, 2004, 159(3): 389-399.
[18] Kasess CH, Windischberger C, Cunnington R, et al. The suppressive influence of SMA on M1 in motor imagery revealed by fMRI and dynamic causal modeling [J]. Neuroimage, 2008, 40(2): 828-837.
[19] Neuper C, Scherer R, Reiner M, et al. Imagery of motor actions: differential effects of kinesthetic and visual-motor mode of imagery in single-trial EEG [J]. Cognitive Brain Research, 2005, 25(3): 668-677.
[20] Hebb DO. The organization of behavior: a neuropsycholocigal theory [J]. Canadian Psychology, 1949, 44(1): 74-76.
[21] Sitaram R, Ros T, Stoeckel L, et al. Closed-loop brain training: the science of neurofeedback [J]. Nature Reviews Neuroscience, 2016, 18(2): 86-100.
[22] Shibata K, Watanabe T, Sasaki Y, et al. Perceptual learning incepted by decoded fMRI neurofeedback without stimulus presentation [J]. Science, 2011, 334(6061): 1413-1415.
[23] Kim DY, Yoo SS, Tegethoff M, et al. The inclusion of functional connectivity information into fMRI-based neurofeedback improves its efficacy in the reduction of cigarette cravings [J]. Journal of Cognitive Neuroscience, 2015, 27(8): 1552-1572.
[24] Clerc M. Brain computer interfaces, principles and practise [J]. Biomedical Engineering Online, 2013, 12(1): 1-4.
[25] Shindo K, Kawashima K, Ushiba J, et al. Effects of neurofeedback training with an electroencephalogram -based brain-computer interface for hand paralysis in patients with chronic stroke: A preliminary case series study [J]. Journal of Rehabilitation Medicine, 2011, 43(10): 951-957.
[26] Nijholt A, Tan D, Pfurtscheller G, et al. Brain-computer interfacing for intelligent systems [J]. IEEE Intelligent Systems, 2008, 23(3): 72-79.
[27] Vafadar AK, Côté JN, Archambault PS. Effectiveness of functional electrical stimulation in improving clinical outcomes in the upper arm following stroke: A Systematic Review and Meta-Analysis [J]. Biomed Res Int, 2015, 2015: 1-14.
[28] Eraifej J, Clark W, France B, et al. Effectiveness of upper limb functional electrical stimulation after stroke for the improvement of activities of daily living and motor function: a systematic review and meta-analysis [J]. Systematic Reviews, 2017, 6(1): 40.
[29] Yan T, Huichan CWY, Li LSW. Functional electrical stimulation improves motor recovery of the lower extremity and walking ability of subjects with first acute stroke a randomized placebo-controlled trial [J]. Dkgest of the World Latest Medical Information, 2005, 36(1): 80-85.
[30] Ng MFW, Tong RKY, Li LSW. A pilot study of randomized clinical controlled trial of gait training in subacute stroke patients with partial body-weight support electromechanical gait trainer and functional electrical stimulation: six-month follow-up [J]. Stroke, 2008, 39(1): 154-160.
[31] Kutlu M, Freeman CT, Hallewell E, et al. Upper-limb stroke rehabilitation using electrode-array based functional electrical stimulation with sensing and control innovations [J]. Medical Engineering & Physics, 2016, 38(4): 366-379.
[32] Takahashi M, Takeda K, Otaka Y, et al. Event related desynchronization-modulated functional electrical stimulation system for stroke rehabilitation: A feasibility study [J]. Journal of NeuroEngineering and Rehabilitation, 2012, 9(1): 56.
[33] Saito K, Yamaguchi T, Yoshida N, et al. Combined effect of motor imagery and peripheral nerve electrical stimulation on the motor cortex [J]. Experimental Brain Research Experimentelle Hirnforschung Experimentation Cerebrale, 2013, 227(3): 333-342.
[34] Quandt F, Hummel FC. The influence of functional electrical stimulation on hand motor recovery in stroke patients: A review [J]. Experimental & Translational Stroke Medicine, 2014, 6(1): 9.
[35] Rushton DN. Functional electrical stimulation and rehabilitation--an hypothesis [J]. Medical Engineering & Physics, 2003, 25(1): 75-78.
[36] Reynolds C, Osuagwu BA, Vuckovic A. Influence of motor imagination on cortical activation during functional electrical stimulation [J]. Clinical Neurophysiology, 2015, 126(7): 1360-1369.
[37] Grimm F, Walter A, Spüler M, et al. Hybrid neuroprosthesis for the upper limb: Combining brain-controlled neuromuscular stimulation with a multi-joint arm exoskeleton [J]. Frontiers in Neuroscience, 2016, 10: 367.
[38] Chipchase LS, Schabrun SM, Hodges PW. Peripheral electrical stimulation to induce cortical plasticity: a systematic review of stimulus parameters [J]. Clinical Neurophysiology, 2011, 122(3): 456-463.
[39] Ajiboye AB, Willett FR, Young DR, et al. Restoration of reaching and grasping movements through brain-controlled muscle stimulation in a person with tetraplegia: a proof-of-concept demonstration [J]. Lancet, 2017, 389(10081): 1821-1830.
[40] Antonio F, Caterina P, Carmelo C, et al. Positive effects of robotic exoskeleton training of upper limb reaching movements after stroke [J]. Journal of NeuroEngineering and Rehabilitation, 2012, 9(1): 36.
[41] Barsotti M, Leonardis D, Loconsole C, et al. A full upper limb robotic exoskeleton for reaching and grasping rehabilitation triggered by MI-BCI [C] //2015 IEEE International Conference on Rehabilitation Robotics (ICORR). Singapore: IEEE, 2015: 49-54.
[42] Semprini M, Laffranchi M, Sanguineti V, et al. Technological approaches for neurorehabilitation: from robotic devices to brain stimulation and beyond [J]. Frontiers in Neurology, 2018, 9: 212.
[43] Gassert R, Dietz V. Rehabilitation robots for the treatment of sensorimotor deficits: a neurophysiological perspective [J]. Journal of Neuroengineering & Rehabilitation, 2018, 15(1): 46.
[44] Jiang N, Gizzi L, Mrachacz-Kersting N, et al. A brain-computer interface for single-trial detection of gait initiation from movement related cortical potentials [J]. Clinical Neurophysiology, 2015, 126(1): 154-159.
[45] Naros G, Naros I, Grimm F, et al. Reinforcement learning of self-regulated sensorimotor β-oscillations improves motor performance [J]. Neuroimage, 2016, 134: 142-152.
[46] Saleh S, Fluet G, Qiu Qinyin, et al. Neural patterns of reorganization after intensive robot-assisted virtual reality therapy and repetitive task practice in patients with chronic stroke [J]. Front Neurol, 2017, 8: 452.
[47] Ono T, Tomita Y, Inose M, et al. Multimodal sensory feedback associated with motor attempts alters BOLD responses to paralyzed hand movement in chronic stroke patients [J]. Brain Topography, 2015, 28(2): 340-351.
[48] Widmer M, Held JP, Wittmann F, et al. Does motivation matter in upper-limb rehabilitation after stroke? ArmeoSenso-Reward: study protocol for a randomized controlled trial [J]. Trials, 2017, 18(1): 580.
[49] Hortal E, Planelles D, Resquin F, et al. Using a brain-machine interface to control a hybrid upper limb exoskeleton during rehabilitation of patients with neurological conditions [J]. Journal of Neuroengineering & Rehabilitation, 2015, 12(1): 1-16.
[50] Vukelic＇ M, Gharabaghi A. Oscillatory entrainment of the motor cortical network during motor imagery is modulated by the feedback modality [J]. Neuroimage, 2015, 111: 1-11.
[51] Zhang Xin, Xu Guanghua, Xie Jun, et al. An EEG-driven lower limb rehabilitation training system for active and passive co-stimulation [C] //2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Milan: IEEE, 2015: 4582-4585.
[52] Elnady AM, Xin Z, Zhen G X, et al. A single-session preliminary evaluation of an affordable BCI-controlled arm exoskeleton and motor-proprioception platform [J]. Frontiers in Human Neuroscience, 2015, 9: 168.
[53] Zhang Xin, Elnady AM, Randhawa BK, et al. Combining mental training and physical training with goal-oriented protocols in stroke rehabilitation: A feasibility case study [J]. Frontiers in Human Neuroscience, 2018, 12: 125.
[54] Bagarinao E, Yoshida A, Ueno M, et al. Improved volitional recall of motor-imagery-related brain activation patterns using real-time functional MRI-based neurofeedback [J]. Frontiers in Human Neuroscience, 2018, 12: 158.
[55] Mihara M, Miyai I, Hattori N, et al. Neurofeedback using real-time near-infrared spectroscopy enhances motor imagery related cortical activation [J]. PLoS ONE, 2012, 7(3): e32234.
[56] Hwang HJ, Kwon K, Im CH. Neurofeedback-based motor imagery training for brain-computer interface (BCI) [J]. Journal of Neuroscience Methods, 2009, 179(1): 150-156.
[57] Mihara M, Hattori N, Hatakenaka M, et al. Near-infrared spectroscopy-mediated neurofeedback enhances efficacy of motor imagery-based training in poststroke victims: a pilot study [J]. Stroke; 2013, 44(4): 1091-1098.
[58] Omura T, Kanoh S., A basic study on neuro-feedback training to enhance a change of sensory-motor rhythm during motor imagery tasks [C] //2017 10th Biomedical Engineering International Conference (BMEiCON). Hokkaido: IEEE, 2017: 1-5.
[59] Yoo SS, Lee JH, O’leary H, et al. Neurofeedback fMRI-mediated learning and consolidation of regional brain activation during motor imagery [J]. International Journal of Imaging Systems & Technology, 2010, 18(1): 69-78.
[60] Weiskopf N, Mathiak K, Bock SW, et al. Principles of a brain-computer interface (BCI) based on real-time functional magnetic resonance imaging (fMRI) [J]. IEEE Transactions on Biomedical Engineering, 2004, 51(6): 966-970.
[61] Kober SE, Wood G, Kurzmann J, et al. Near-infrared spectroscopy based neurofeedback training increases specific motor imagery related cortical activation compared to sham feedback [J]. Biological Psychology, 2014, 95(1): 21-30.
[62] Berman BD, Horovitz SG, Venkataraman G, et al. Self-modulation of primary motor cortex activity with motor and motor imagery tasks using real-time fMRI-based neurofeedback [J]. Neuroimage, 2012, 59(2): 917-925.
[63] Johnston SJ, Boehm SG, Healy D, et al. Neurofeedback: A promising tool for the self-regulation of emotion networks [J]. Neuroimage, 2010, 49(1): 1066-1072.
[64] Wu Hong, Liang Shuang, Hang Wenlong, et al. Evaluation of motor training performance in 3D virtual environment via combining brain-computer interface and haptic feedback [J]. Procedia Computer Science, 2017, 107: 256-261.
[65] Jarosiewicz B, Masse NY, Bacher D, et al. Advantages of closed-loop calibration in intracortical brain-computer interfaces for people with tetraplegia [J]. Journal of Neural Engineering, 2013, 10(4): 046012.
[66] Berger AM, Davelaar EJ. Frontal alpha oscillations and attentional control: A virtual reality neurofeedback study [J]. Neuroscience, 2017, 378: 189-197.
[67] Vukelic＇ M, Gharabaghi A. Oscillatory entrainment of the motor cortical network during motor imagery is modulated by the feedback modality [J]. Neuroimage, 2015, 111: 1-11.
[68] Liu D, Chen W, Lee K, et al. Brain-actuated gait trainer with visual and proprioceptive feedback [J]. Journal of Neural Engineering, 2017, 14(5): 056017.
[69] Suminski AJ, Tkach DC, Hatsopoulos N G. Exploiting multiple sensory modalities in brain-machine interfaces [J]. Neural Networks, 2009, 22(9): 1224-1234.
[70] Ander RM, Markus S, Vittorio C, et al. Proprioceptive feedback and brain computer interface (BCI) based neuroprostheses [J]. PLoS ONE, 2016, 7(10): e47048.
[71] Kus R, Valbuena D, Zygierewicz J, et al. Asynchronous BCI based on motor imagery with automated calibration and neurofeedback training [J]. IEEE Transactions on Neural Systems & Rehabilitation Engineering, 2012, 20(6): 823-835.
[72] Ang K, Chua KS, Phua KS, et al. A randomized controlled trial of EEG-based motor imagery brain-computer interface robotic rehabilitation for stroke [J]. Clinical Eeg & Neuroscience, 2014, 46(4): 310-320.
[73] Shindo K, Kawashima K, Ushiba J, et al. Effects of neurofeedback training with an electroencephalogram-based brain-computer interface for hand paralysis in patients with chronic stroke: A preliminary case series study [J]. Journal of Rehabilitation Medicine, 2011, 43(10): 951-957.
[74] Royter V, Gharabaghi A. Brain state-dependent closed-loop modulation of paired associative stimulation controlled by sensorimotor desynchronization [J]. Frontiers in Cellular Neuroscience, 2016, 10: 115.
[75] Kobayashi M, Pascualleone A. Transcranial magnetic stimulation in neurology [J]. Lancet Neurology, 2003, 2(3): 145-56.
[76] Rizzolatti G, Fadiga L, Gallese V, et al. Premotor cortex and the recognition of motor actions [J]. Brain Res Cogn Brain Res, 1996, 3(2): 131-141.
[77] Rizzolatti G, Craighero L. The mirror-neuron system - Annual review of neuroscience, [J]. Annual Reviews, 2004, 27: 169-192.
[78] Hari R, Kujala MV. Brain basis of human social interaction: from concepts to brain imaging [J]. Physiological Reviews, 2009, 89(2): 453-479.
[79] Taube W, Mouthon M, Leukel C, et al. Brain activity during observation and motor imagery of different balance tasks: An fMRI study [J]. Cortex, 2015, 64: 102-114.
[80] Takahashi H, Shibuya T, Kato M, et al. Enhanced activation in the extrastriate body area by goal-directed actions [J]. Psychiatry & Clinical Neurosciences, 2010, 62(2): 214-219.
[81] Abe M, Schambra H, Wassermann EM, et al. Reward improves long-term retention of a motor memory through induction of offline memory gains [J]. Current Biology, 2011, 21(7): 557-562.
[82] Widmer M, Ziegler N, Held J, et al. Rewarding feedback promotes motor skill consolidation via striatal activity [J]. Progress in Brain Research, 2016, 229: 303-323.
[83] Hamel R, Savoie FA, Lacroix A, et al. Added value of money on motor performance feedback: Increased left central beta-band power for rewards and fronto-central theta-band power for punishments [J]. NeuroImage, 2018, 179: 63-78.