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Neuromotor recovery from stroke: computational models at central, functional, and muscle synergy level

Overview of attention for article published in Frontiers in Computational Neuroscience, January 2013
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  • Above-average Attention Score compared to outputs of the same age and source (59th percentile)

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142 Mendeley
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Title
Neuromotor recovery from stroke: computational models at central, functional, and muscle synergy level
Published in
Frontiers in Computational Neuroscience, January 2013
DOI 10.3389/fncom.2013.00097
Pubmed ID
Authors

Maura Casadio, Irene Tamagnone, Susanna Summa, Vittorio Sanguineti

Abstract

Computational models of neuromotor recovery after a stroke might help to unveil the underlying physiological mechanisms and might suggest how to make recovery faster and more effective. At least in principle, these models could serve: (i) To provide testable hypotheses on the nature of recovery; (ii) To predict the recovery of individual patients; (iii) To design patient-specific "optimal" therapy, by setting the treatment variables for maximizing the amount of recovery or for achieving a better generalization of the learned abilities across different tasks. Here we review the state of the art of computational models for neuromotor recovery through exercise, and their implications for treatment. We show that to properly account for the computational mechanisms of neuromotor recovery, multiple levels of description need to be taken into account. The review specifically covers models of recovery at central, functional and muscle synergy level.

X Demographics

X Demographics

The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 142 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 2 1%
Switzerland 2 1%
United Kingdom 2 1%
United States 2 1%
Czechia 1 <1%
Italy 1 <1%
Canada 1 <1%
India 1 <1%
Unknown 130 92%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 36 25%
Researcher 19 13%
Student > Master 16 11%
Student > Bachelor 9 6%
Student > Doctoral Student 8 6%
Other 30 21%
Unknown 24 17%
Readers by discipline Count As %
Engineering 42 30%
Medicine and Dentistry 17 12%
Neuroscience 15 11%
Agricultural and Biological Sciences 9 6%
Computer Science 6 4%
Other 23 16%
Unknown 30 21%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 05 December 2013.
All research outputs
#13,388,742
of 22,716,996 outputs
Outputs from Frontiers in Computational Neuroscience
#572
of 1,336 outputs
Outputs of similar age
#158,277
of 280,757 outputs
Outputs of similar age from Frontiers in Computational Neuroscience
#51
of 131 outputs
Altmetric has tracked 22,716,996 research outputs across all sources so far. This one is in the 39th percentile – i.e., 39% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,336 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.2. This one has gotten more attention than average, scoring higher than 54% 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 280,757 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 42nd percentile – i.e., 42% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 131 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 59% of its contemporaries.