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Long-Term Training with a Brain-Machine Interface-Based Gait Protocol Induces Partial Neurological Recovery in Paraplegic Patients

Overview of attention for article published in Scientific Reports, August 2016
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • One of the highest-scoring outputs from this source (#5 of 38,339)
  • 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)

Mentioned by

news
180 news outlets
blogs
16 blogs
twitter
396 tweeters
facebook
29 Facebook pages
googleplus
9 Google+ users
video
1 video uploader

Readers on

mendeley
2 Mendeley
citeulike
1 CiteULike
Title
Long-Term Training with a Brain-Machine Interface-Based Gait Protocol Induces Partial Neurological Recovery in Paraplegic Patients
Published in
Scientific Reports, August 2016
DOI 10.1038/srep30383
Pubmed ID
Authors

Ana R. C. Donati, Solaiman Shokur, Edgard Morya, Debora S. F. Campos, Renan C. Moioli, Claudia M. Gitti, Patricia B. Augusto, Sandra Tripodi, Cristhiane G. Pires, Gislaine A. Pereira, Fabricio L. Brasil, Simone Gallo, Anthony A. Lin, Angelo K. Takigami, Maria A. Aratanha, Sanjay Joshi, Hannes Bleuler, Gordon Cheng, Alan Rudolph, Miguel A. L. Nicolelis, Donati, Ana R C, Shokur, Solaiman, Morya, Edgard, Campos, Debora S F, Moioli, Renan C, Gitti, Claudia M, Augusto, Patricia B, Tripodi, Sandra, Pires, Cristhiane G, Pereira, Gislaine A, Brasil, Fabricio L, Gallo, Simone, Lin, Anthony A, Takigami, Angelo K, Aratanha, Maria A, Joshi, Sanjay, Bleuler, Hannes, Cheng, Gordon, Rudolph, Alan, Nicolelis, Miguel A L

Abstract

Brain-machine interfaces (BMIs) provide a new assistive strategy aimed at restoring mobility in severely paralyzed patients. Yet, no study in animals or in human subjects has indicated that long-term BMI training could induce any type of clinical recovery. Eight chronic (3-13 years) spinal cord injury (SCI) paraplegics were subjected to long-term training (12 months) with a multi-stage BMI-based gait neurorehabilitation paradigm aimed at restoring locomotion. This paradigm combined intense immersive virtual reality training, enriched visual-tactile feedback, and walking with two EEG-controlled robotic actuators, including a custom-designed lower limb exoskeleton capable of delivering tactile feedback to subjects. Following 12 months of training with this paradigm, all eight patients experienced neurological improvements in somatic sensation (pain localization, fine/crude touch, and proprioceptive sensing) in multiple dermatomes. Patients also regained voluntary motor control in key muscles below the SCI level, as measured by EMGs, resulting in marked improvement in their walking index. As a result, 50% of these patients were upgraded to an incomplete paraplegia classification. Neurological recovery was paralleled by the reemergence of lower limb motor imagery at cortical level. We hypothesize that this unprecedented neurological recovery results from both cortical and spinal cord plasticity triggered by long-term BMI usage.

Twitter Demographics

The data shown below were collected from the profiles of 396 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 2 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 5 250%
France 2 100%
Singapore 2 100%
Israel 1 50%
Brazil 1 50%
Ireland 1 50%
China 1 50%
Iran, Islamic Republic of 1 50%
Canada 1 50%
Other 7 350%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 81 4050%
Student > Master 59 2950%
Researcher 49 2450%
Student > Bachelor 43 2150%
Student > Doctoral Student 22 1100%
Other 55 2750%
Readers by discipline Count As %
Engineering 102 5100%
Neuroscience 49 2450%
Medicine and Dentistry 47 2350%
Agricultural and Biological Sciences 31 1550%
Computer Science 18 900%
Other 62 3100%

Attention Score in Context

This research output has an Altmetric Attention Score of 1809. 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 15 October 2017.
All research outputs
#443
of 8,515,850 outputs
Outputs from Scientific Reports
#5
of 38,339 outputs
Outputs of similar age
#35
of 260,555 outputs
Outputs of similar age from Scientific Reports
#2
of 3,358 outputs
Altmetric has tracked 8,515,850 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 38,339 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 14.8. 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 260,555 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 3,358 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.