Cited by 485Neuroscience and Neural EngineeringSpinal Cord Injury ResearchEEG and Brain-Computer Interfaces

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Restoration of grasp following paralysis through brain-controlled stimulation of muscles

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Ethier, C., Oby, E. R., Bauman, M. J., Miller, L. E.·2012·Nature

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Abstract

Patients with spinal cord injury lack the connections between brain and spinal cord circuits that are essential for voluntary movement. Clinical systems that achieve muscle contraction through functional electrical stimulation (FES) have proven to be effective in allowing patients with tetraplegia to regain control of hand movements and to achieve a greater measure of independence in daily activities. In existing clinical systems, the patient uses residual proximal limb movements to trigger pre-programmed stimulation that causes the paralysed muscles to contract, allowing use of one or two basic grasps. Instead, we have developed an FES system in primates that is controlled by recordings made from microelectrodes permanently implanted in the brain. We simulated some of the effects of the paralysis caused by C5 or C6 spinal cord injury by injecting rhesus monkeys with a local anaesthetic to block the median and ulnar nerves at the elbow. Then, using recordings from approximately 100 neurons in the motor cortex, we predicted the intended activity of several of the paralysed muscles, and used these predictions to control the intensity of stimulation of the same muscles. This process essentially bypassed the spinal cord, restoring to the monkeys voluntary control of their paralysed muscles. This achievement is a major advance towards similar restoration of hand function in human patients through brain-controlled FES. We anticipate that in human patients, this neuroprosthesis would allow much more flexible and dexterous use of the hand than is possible with existing FES systems.

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Targeting Recovery: Priorities of the Spinal Cord-Injured Population
Anderson, K. D. (2004) · Journal of Neurotrauma · doi:10.1089/neu.2004.21.1371 · Cited by 2,094
Spinal-cord injury
McDonald, J. W., & Sadowsky, C. L. (2002) · The Lancet · doi:10.1016/s0140-6736(02)07603-1 · Cited by 1,858
Learning to control a brain–machine interface for reaching and grasping by primates
Carmena, J. M., Lebedev, M. A., Crist, R. E., O'Doherty, J. E., Santucci, D. M., Dimitrov, D. F., Patil, P. G., Henriquez, C. S., & Nicolelis, M. A. L. (2003) · PLoS Biology · doi:10.1371/journal.pbio.0000042 · Cited by 1,761
Relation of pyramidal tract activity to force exerted during voluntary movement.
Evarts, E. V. (1968) · Journal of Neurophysiology · doi:10.1152/jn.1968.31.1.14 · Cited by 1,258
Direct control of paralysed muscles by cortical neurons
Moritz, C. T., Perlmutter, S. I., & Fetz, E. E. (2008) · Nature · doi:10.1038/nature07418 · Cited by 610
Spasticity after spinal cord injury
Adams, M. M., & Hicks, A. L. (2005) · Spinal Cord · doi:10.1038/sj.sc.3101757 · Cited by 517
Motor and sensory recovery following incomplete tetraplegia
Waters, R. L., Adkins, R. H., Yakura, J. S., & Sie, I. H. (1994) · Archives of Physical Medicine and Rehabilitation · doi:10.1016/0003-9993(94)90034-5 · Cited by 438
Predicting Measures of Motor Performance from Multiple Cortical Spike Trains
Humphrey, D. R., Schmidt, E. M., & Thompson, W. (1970) · Science · doi:10.1126/science.170.3959.758 · Cited by 372
Efficacy of an implanted neuroprosthesis for restoring hand grasp in tetraplegia: A multicenter study
Peckham, P. H., Keith, M. W., Kilgore, K. L., Grill, J. H., Wuolle, K. S., Thrope, G. B., Gorman, P., Hobby, J., Mulcahey, M. J., Carroll, S., et al. (2001) · Archives of Physical Medicine and Rehabilitation · doi:10.1053/apmr.2001.25910 · Cited by 319
Implantable functional neuromuscular stimulation in the tetraplegic hand
Keith, M. W., Peckham, P. H., Thrope, G. B., Stroh, K. C., Smith, B., Buckett, J. R., Kilgore, K. L., & Jatich, J. W. (1989) · The Journal Of Hand Surgery · doi:10.1016/s0363-5023(89)80017-6 · Cited by 212

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Animal models of axon regeneration after spinal cord injury
Lee, D.-H., & Lee, J. K. (2013) · Neuroscience Bulletin · doi:10.1007/s12264-013-1365-4 · Cited by 63
Intraspinal microstimulation produces over-ground walking in anesthetized cats
Holinski, B. J., Mazurek, K. A., Everaert, D. G., Toossi, A., Lucas‐Osma, A. M., Troyk, P. R., Etienne‐Cummings, R., Stein, R. B., & Mushahwar, V. K. (2016) · Journal of Neural Engineering · doi:10.1088/1741-2560/13/5/056016 · Cited by 63
A muscle-activity-dependent gain between motor cortex and EMG
Naufel, S. N., Glaser, J. I., Kording, K. P., Perreault, E. J., & Miller, L. E. (2018) · Journal of Neurophysiology · doi:10.1152/jn.00329.2018 · Cited by 64
Self-citation
Towards neural co-processors for the brain: combining decoding and encoding in brain–computer interfaces
Rao, R. P. N. (2019) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2019.03.008 · Cited by 63
Intrafascicular peripheral nerve stimulation produces fine functional hand movements in primates
Badi, M., Wurth, S. M., Scarpato, I., Roussinova, E., Losanno, E., Bogaard, A. R., Delacombaz, M., Borgognon, S., Čvančara, P., Fallegger, F., et al. (2021) · Science Translational Medicine · doi:10.1126/scitranslmed.abg6463 · Cited by 62
A High-Performance Keyboard Neural Prosthesis Enabled by Task Optimization
Nuyujukian, P., Fan, J. M., Kao, J. C., Ryu, S. I., & Shenoy, K. V. (2014) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2014.2354697 · Cited by 61
Non-invasive approaches to functional recovery after spinal cord injury: Therapeutic targets and multimodal device interventions
Pizzolato, C., Gunduz, M. A., Palipana, D. B., Wu, J., Grant, G. D., Hall, S., Dennison, R., ZAFONTE, R. D., Lloyd, D. G., & Teng, Y. D. (2021) · Experimental Neurology · doi:10.1016/j.expneurol.2021.113612 · Cited by 64
Brain–Machine Interface Control Algorithms
Shanechi, M. M. (2016) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2016.2639501 · Cited by 61
Targeting Sensory and Motor Integration for Recovery of Movement After CNS Injury
Asan, A. S., McIntosh, J. R., & Carmel, J. B. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2021.791824 · Cited by 71
A cortical–spinal prosthesis for targeted limb movement in paralysed primate avatars
Shanechi, M. M., Hu, R. C., & Williams, Z. M. (2014) · Nature Communications · doi:10.1038/ncomms4237 · Cited by 62
Bidirectional brain-computer interfaces
Hughes, C. L., Herrera, A. J., Gaunt, R. A., & Collinger, J. L. (2020) · Handbook of clinical neurology · doi:10.1016/b978-0-444-63934-9.00013-5 · Cited by 65
Long-term decoding stability of local field potentials from silicon arrays in primate motor cortex during a 2D center out task
Wang, D., Zhang, Q., Li, Y., Wang, Y., Zhu, J., Zhang, S., & Zheng, X. (2014) · Journal of Neural Engineering · doi:10.1088/1741-2560/11/3/036009 · Cited by 60
Preliminary studies on the basic factors of bionics
Ren, L., & Liang, Y. (2014) · Science China Technological Sciences · doi:10.1007/s11431-013-5449-1 · Cited by 58
Bayesian statistics: relevant for the brain?
Kording, K. P. (2014) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2014.01.003 · Cited by 58
Implantable Neural Interfaces and Wearable Tactile Systems for Bidirectional Neuroprosthetics Systems
Cutrone, A., & Micera, S. (2019) · Advanced Healthcare Materials · doi:10.1002/adhm.201801345 · Cited by 58
Corticospinal neuroprostheses to restore locomotion after spinal cord injury
Borton, D. A., Bonizzato, M., Beauparlant, J., DiGiovanna, J., Moraud, E. M., Wenger, N., Евгеньевич, М. П., Minev, I. R., Lacour, S. P., Millán, J. D. R., et al. (2013) · Neuroscience Research · doi:10.1016/j.neures.2013.10.001 · Cited by 60
Implantable neurotechnologies: electrical stimulation and applications
Nag, S., & Thakor, N. V. (2016) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-015-1442-0 · Cited by 56
A Hebbian learning rule gives rise to mirror neurons and links them to control theoretic inverse models
Hanuschkin, A., Ganguli, S., & Hahnloser, R. H. R. (2013) · Frontiers in Neural Circuits · doi:10.3389/fncir.2013.00106 · Cited by 54
Movement representation in the primary motor cortex and its contribution to generalizable EMG predictions
Oby, E. R., Éthier, C., & Miller, L. E. (2012) · Journal of Neurophysiology · doi:10.1152/jn.00331.2012 · Cited by 57
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Brain computer interface learning for systems based on electrocorticography and intracortical microelectrode arrays
Hiremath, S. V., Chen, W., Wang, W., Foldes, S., Yang, Y., Tyler-Kabara, E. C., Collinger, J. L., & Boninger, M. L. (2015) · Frontiers in Integrative Neuroscience · doi:10.3389/fnint.2015.00040 · Cited by 53
Implantable neurotechnologies: bidirectional neural interfaces—applications and VLSI circuit implementations
Greenwald, E., Masters, M. R., & Thakor, N. V. (2016) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-015-1429-x · Cited by 53
Real-time linear prediction of simultaneous and independent movements of two finger groups using an intracortical brain-machine interface
Nason, S. R., Mender, M. J., Vaskov, A. K., Willsey, M. S., Kumar, N. G., Kung, T. A., Patil, P. G., & Chestek, C. A. (2021) · Neuron · doi:10.1016/j.neuron.2021.08.009 · Cited by 55
Creating new functional circuits for action via brain-machine interfaces
Orsborn, A. L., & Carmena, J. M. (2013) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2013.00157 · Cited by 55
A Spatio-Temporal Fractal Model for a CPS Approach to Brain-Machine-Body Interfaces
Xue, Y., Rodríguez, S. H., & Bogdan, P. (2016) · Design, Automation, and Test in Europe · doi:10.3850/9783981537079_0502 · Cited by 52
Independent Mobility Achieved through a Wireless Brain-Machine Interface
Libedinsky, C., So, R. Q., Xu, Z., Kyar, T. K., Ho, D., Lim, C., Chan, L., Chua, Y., Yao, L., Cheong, J. H., et al. (2016) · PLoS ONE · doi:10.1371/journal.pone.0165773 · Cited by 52
Progress towards restoring upper limb movement and sensation through intracortical brain-computer interfaces
Collinger, J. L., Gaunt, R. A., & Schwartz, A. B. (2018) · Current Opinion in Biomedical Engineering · doi:10.1016/j.cobme.2018.11.005 · Cited by 53
Single-unit activity, threshold crossings, and local field potentials in motor cortex differentially encode reach kinematics
Perel, S., Sadtler, P. T., Oby, E. R., Ryu, S. I., Tyler‐Kabara, E. C., Batista, A. P., & Chase, S. M. (2015) · Journal of Neurophysiology · doi:10.1152/jn.00293.2014 · Cited by 55
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A Novel FES Strategy for Poststroke Rehabilitation Based on the Natural Organization of Neuromuscular Control
Cheung, V. C. K., Niu, C. M., Li, S., Xie, Q., & Lan, N. (2018) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2018.2874132 · Cited by 53
Personalized Brain–Computer Interface and Its Applications
Ma, Y., Gong, A., Nan, W., Ding, P., Wang, F., & Fu, Y. (2022) · Journal of Personalized Medicine · doi:10.3390/jpm13010046 · Cited by 52
Personalized strategies of neurostimulation: from static biomarkers to dynamic closed-loop assessment of neural function
Carè, M., Chiappalone, M., & Cota, V. R. (2024) · Frontiers in Neuroscience · doi:10.3389/fnins.2024.1363128 · Cited by 51
Adversarial Domain Adaptation for Stable Brain-Machine Interfaces
Farshchian, A., Gallego, J. A., Cohen, J. P., Bengio, Y., Miller, L. E., & Solla, S. A. (2018) · arXiv · doi:10.48550/arxiv.1810.00045 · Cited by 48
Self-citation
Systems Neuroengineering: Understanding and Interacting with the Brain
Edelman, B. J., Johnson, N., Sohrabpour, A., Tong, S., Thakor, N., & He, B. (2015) · Engineering · doi:10.15302/j-eng-2015078 · Cited by 45
Physiological properties of brain-machine interface input signals
Slutzky, M. W., & Flint, R. D. (2017) · Journal of Neurophysiology · doi:10.1152/jn.00070.2017 · Cited by 47
Continuous Closed-Loop Decoder Adaptation with a Recursive Maximum Likelihood Algorithm Allows for Rapid Performance Acquisition in Brain-Machine Interfaces
Dangi, S., Gowda, S., Moorman, H. G., Orsborn, A. L., So, K., Shanechi, M. M., & Carmena, J. M. (2014) · Neural Computation · doi:10.1162/neco_a_00632 · Cited by 45
Current Challenges Facing the Translation of Brain Computer Interfaces from Preclinical Trials to Use in Human Patients
Murphy, M. D., Guggenmos, D. J., Bundy, D. T., & Nudo, R. J. (2016) · Frontiers in Cellular Neuroscience · doi:10.3389/fncel.2015.00497 · Cited by 46
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Ueyama, Y., & Miyashita, E. (2013) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2013.2273473 · Cited by 43
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Tomlinson, T., & Miller, L. E. (2016) · Advances in experimental medicine and biology · doi:10.1007/978-3-319-47313-0_20 · Cited by 44
Self-citation
Donut‐Shaped Stretchable Kirigami: Enabling Electronics to Integrate with the Deformable Muscle
Morikawa, Y., Yamagiwa, S., Sawahata, H., Numano, R., Koida, K., & Kawano, T. (2019) · Advanced Healthcare Materials · doi:10.1002/adhm.201900939 · Cited by 43
Neurotechnologies to restore hand functions
Losanno, E., Mender, M. J., Chestek, C. A., Shokur, S., & Micera, S. (2023) · Nature Reviews Bioengineering · doi:10.1038/s44222-023-00054-4 · Cited by 45
Comparing temporal aspects of visual, tactile, and microstimulation feedback for motor control
Godlove, J. M., Whaite, E. O., & Batista, A. P. (2014) · Journal of Neural Engineering · doi:10.1088/1741-2560/11/4/046025 · Cited by 46
Towards autonomous neuroprosthetic control using Hebbian reinforcement learning
Mahmoudi, B., Pohlmeyer, E. A., Prins, N. W., Geng, S., & Sanchez, J. C. (2013) · Journal of Neural Engineering · doi:10.1088/1741-2560/10/6/066005 · Cited by 42
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Mazurek, K. A., & Schieber, M. H. (2017) · Neuron · doi:10.1016/j.neuron.2017.11.006 · Cited by 42
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Ajiboye, A. B., Simeral, J. D., Donoghue, J. P., Hochberg, L. R., & Kirsch, R. F. (2012) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2012.2209882 · Cited by 44
Activity-dependent plasticity and spinal cord stimulation for motor recovery following spinal cord injury
Samejima, S., Henderson, R. P., Pradarelli, J. J., Mondello, S. E., & Moritz, C. T. (2022) · Experimental Neurology · doi:10.1016/j.expneurol.2022.114178 · Cited by 42
Enabling Low-Power, Multi-Modal Neural Interfaces Through a Common, Low-Bandwidth Feature Space
Irwin, Z. T., Thompson, D. E., Schroeder, K. E., Tat, D. M., Hassani, A., Bullard, A. J., Woo, S. L., Urbanchek, M. G., Sachs, A. J., Cederna, P. S., et al. (2015) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2015.2501752 · Cited by 43
Leveraging neural dynamics to extend functional lifetime of brain-machine interfaces
Kao, J. C., Ryu, S. I., & Shenoy, K. V. (2017) · Scientific Reports · doi:10.1038/s41598-017-06029-x · Cited by 42
Corticospinal Motor Circuit Plasticity After Spinal Cord Injury: Harnessing Neuroplasticity to Improve Functional Outcomes
Kazim, S. F., Bowers, C. A., Cole, C. D., Varela, S., Karimov, Z., Martinez, E., Ogulnick, J. V., & Schmidt, M. H. (2021) · Molecular Neurobiology · doi:10.1007/s12035-021-02484-w · Cited by 45
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Byun, D., Cho, S. J., & Kim, S. (2013) · Journal of Micromechanics and Microengineering · doi:10.1088/0960-1317/23/12/125010 · Cited by 39
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Bouton, C. E. (2017) · Journal of Internal Medicine · doi:10.1111/joim.12610 · Cited by 40
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Krucoff, M. O., Miller, J. P., Saxena, T. K., Bellamkonda, R., Rahimpour, S., Harward, S. C., Lad, S. P., & Turner, D. A. (2018) · Neurosurgery · doi:10.1093/neuros/nyy128 · Cited by 38
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Schearer, E. M., Liao, Y.-W., Perreault, E. J., Tresch, M. C., Memberg, W. D., Kirsch, R. F., & Lynch, K. (2013) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2013.2282903 · Cited by 37
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Hsieh, H.-L., & Shanechi, M. M. (2018) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006168 · Cited by 38
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Cloutier, A., & Yang, J. (2013) · Critical Reviews in Biomedical Engineering · doi:10.1615/critrevbiomedeng.2013007887 · Cited by 36
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Now is the Critical Time for Engineered Neuroplasticity
Moritz, C. T. (2018) · Neurotherapeutics · doi:10.1007/s13311-018-0637-0 · Cited by 37
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Guan, C., Aflalo, T., KADLEC, K. H., Gámez, J., Rosario, E. R., Bari, A., Pouratian, N., & Andersen, R. A. (2023) · Journal of Neural Engineering · doi:10.1088/1741-2552/acd3b1 · Cited by 38
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Self-citation
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Perruchoud, D., Pisotta, I., Carda, S., Murray, M. M., & Ionta, S. (2016) · Journal of Neural Engineering · doi:10.1088/1741-2560/13/4/041001 · Cited by 32
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Authors: Ethier, C., Oby, E. R., Bauman, M. J., & Miller, L. E.
Affiliations: Northwestern University (US)
University of Pittsburgh (US)
Corresponding author: Miller, L. E.
ORCID: Oby, E. R.
Bauman, M. J.
Miller, L. E.
Venue: Nature
Publisher: Springer Science and Business Media LLC
Published: 2012-04-18
Volume: 485
Issue: 7398
Pages: 368-371
ISSN: 0028-0836 (ISSN-L)
1476-4687
DOI: 10.1038/nature10987
PMC: PMC3358575
OpenAlex: W1988067967
Cited by: 485
References: 26
Citations by year: 2026: 9 · 2025: 22 · 2024: 24 · 2023: 27 · 2022: 24 · 2021: 33 · 2020: 29 · 2019: 28 · 2018: 42 · 2017: 44 · 2016: 46 · 2015: 38 · 2014: 55 · 2013: 46 · 2012: 18
FWCI: 20.69
Type: journal article
Language: en
Open access: green
www.ncbi.nlm.nih.gov
Licence: www.springer.com/tdm (tdm)
Locations: Nature (publishedVersion)
PubMed (publishedVersion)
PubMed Central (acceptedVersion)
RePEc: Research Papers in Economics (submittedVersion)
Funders: National Institute of Neurological Disorders and Stroke (R01NS053603, F31NS071737, #NS053603)
Topics: Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neural Engineering
Health Sciences › Medicine › Clinical Neurology › Spinal Cord Injury Research
Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
Keywords: brain-computer interface, functional electrical stimulation, paralysis, spinal cord injury, motor cortex, neural prostheses, hand grasping, rhesus monkeys, tetraplegia, hand function recovery, prosthetic hand control
MeSH terms: Animals, Electric Stimulation, Electrodes, Implanted, Hand, Macaca mulatta, Microelectrodes, Motor Cortex, Movement, Muscles, Nerve Block, Paralysis, Quadriplegia, Spinal Cord Injuries, Hand Strength, Prostheses and Implants

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Restoration of grasp following paralysis through brain-controlled stimulation of muscles

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