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Restoring cortical control of functional movement in a human with quadriplegia

Overview of attention for article published in Nature, April 2016
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  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (99th percentile)
  • High Attention Score compared to outputs of the same age and source (99th percentile)

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mendeley
503 Mendeley
citeulike
2 CiteULike
Title
Restoring cortical control of functional movement in a human with quadriplegia
Published in
Nature, April 2016
DOI 10.1038/nature17435
Pubmed ID
Authors

Chad E. Bouton, Ammar Shaikhouni, Nicholas V. Annetta, Marcia A. Bockbrader, David A. Friedenberg, Dylan M. Nielson, Gaurav Sharma, Per B. Sederberg, Bradley C. Glenn, W. Jerry Mysiw, Austin G. Morgan, Milind Deogaonkar, Ali R. Rezai, Bouton, Chad E, Shaikhouni, Ammar, Annetta, Nicholas V, Bockbrader, Marcia A, Friedenberg, David A, Nielson, Dylan M, Sharma, Gaurav, Sederberg, Per B, Glenn, Bradley C, Mysiw, W Jerry, Morgan, Austin G, Deogaonkar, Milind, Rezai, Ali R, Bouton CE, Shaikhouni A, Annetta NV, Bockbrader MA, Friedenberg DA, Nielson DM, Sharma G, Sederberg PB, Glenn BC, Mysiw WJ, Morgan AG, Deogaonkar M, Rezai AR

Abstract

Millions of people worldwide suffer from diseases that lead to paralysis through disruption of signal pathways between the brain and the muscles. Neuroprosthetic devices are designed to restore lost function and could be used to form an electronic 'neural bypass' to circumvent disconnected pathways in the nervous system. It has previously been shown that intracortically recorded signals can be decoded to extract information related to motion, allowing non-human primates and paralysed humans to control computers and robotic arms through imagined movements. In non-human primates, these types of signal have also been used to drive activation of chemically paralysed arm muscles. Here we show that intracortically recorded signals can be linked in real-time to muscle activation to restore movement in a paralysed human. We used a chronically implanted intracortical microelectrode array to record multiunit activity from the motor cortex in a study participant with quadriplegia from cervical spinal cord injury. We applied machine-learning algorithms to decode the neuronal activity and control activation of the participant's forearm muscles through a custom-built high-resolution neuromuscular electrical stimulation system. The system provided isolated finger movements and the participant achieved continuous cortical control of six different wrist and hand motions. Furthermore, he was able to use the system to complete functional tasks relevant to daily living. Clinical assessment showed that, when using the system, his motor impairment improved from the fifth to the sixth cervical (C5-C6) to the seventh cervical to first thoracic (C7-T1) level unilaterally, conferring on him the critical abilities to grasp, manipulate, and release objects. This is the first demonstration to our knowledge of successful control of muscle activation using intracortically recorded signals in a paralysed human. These results have significant implications in advancing neuroprosthetic technology for people worldwide living with the effects of paralysis.

Twitter Demographics

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Mendeley readers

The data shown below were compiled from readership statistics for 503 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 25 5%
United Kingdom 8 2%
Spain 3 <1%
Germany 2 <1%
Italy 2 <1%
Switzerland 2 <1%
Singapore 2 <1%
France 1 <1%
Chile 1 <1%
Other 10 2%
Unknown 447 89%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 152 30%
Researcher 114 23%
Student > Master 59 12%
Student > Bachelor 48 10%
Professor 27 5%
Other 103 20%
Readers by discipline Count As %
Engineering 134 27%
Neuroscience 86 17%
Agricultural and Biological Sciences 81 16%
Medicine and Dentistry 61 12%
Computer Science 33 7%
Other 108 21%

Attention Score in Context

This research output has an Altmetric Attention Score of 2438. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 07 November 2017.
All research outputs
#221
of 8,779,839 outputs
Outputs from Nature
#54
of 49,031 outputs
Outputs of similar age
#15
of 277,412 outputs
Outputs of similar age from Nature
#4
of 1,009 outputs
Altmetric has tracked 8,779,839 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 99th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 49,031 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 76.1. This one has done particularly well, scoring higher than 99% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 277,412 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 99% of its contemporaries.
We're also able to compare this research output to 1,009 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 99% of its contemporaries.