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Coordinated alpha and gamma control of muscles and spindles in movement and posture

Overview of attention for article published in Frontiers in Computational Neuroscience, October 2015
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Title
Coordinated alpha and gamma control of muscles and spindles in movement and posture
Published in
Frontiers in Computational Neuroscience, October 2015
DOI 10.3389/fncom.2015.00122
Pubmed ID
Authors

Si Li, Cheng Zhuang, Manzhao Hao, Xin He, Juan C. Marquez, Chuanxin M. Niu, Ning Lan

Abstract

Mounting evidence suggests that both α and γ motoneurons are active during movement and posture, but how does the central motor system coordinate the α-γ controls in these tasks remains sketchy due to lack of in vivo data. Here a computational model of α-γ control of muscles and spindles was used to investigate α-γ integration and coordination for movement and posture. The model comprised physiologically realistic spinal circuitry, muscles, proprioceptors, and skeletal biomechanics. In the model, we divided the cortical descending commands into static and dynamic sets, where static commands (α s and γ s ) were for posture maintenance and dynamic commands (α d and γ d ) were responsible for movement. We matched our model to human reaching movement data by straightforward adjustments of descending commands derived from either minimal-jerk trajectories or human EMGs. The matched movement showed smooth reach-to-hold trajectories qualitatively close to human behaviors, and the reproduced EMGs showed the classic tri-phasic patterns. In particular, the function of γ d was to gate the α d command at the propriospinal neurons (PN) such that antagonistic muscles can accelerate or decelerate the limb with proper timing. Independent control of joint position and stiffness could be achieved by adjusting static commands. Deefferentation in the model indicated that accurate static commands of α s and γ s are essential to achieve stable terminal posture precisely, and that the γ d command is as important as the α d command in controlling antagonistic muscles for desired movements. Deafferentation in the model showed that losing proprioceptive afferents mainly affected the terminal position of movement, similar to the abnormal behaviors observed in human and animals. Our results illustrated that tuning the simple forms of α-γ commands can reproduce a range of human reach-to-hold movements, and it is necessary to coordinate the set of α-γ descending commands for accurate and stable control of movement and posture.

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Mendeley readers

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The data shown below were compiled from readership statistics for 98 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 1%
United States 1 1%
Germany 1 1%
Brazil 1 1%
Unknown 94 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 21 21%
Student > Master 18 18%
Student > Doctoral Student 8 8%
Student > Bachelor 8 8%
Researcher 6 6%
Other 17 17%
Unknown 20 20%
Readers by discipline Count As %
Neuroscience 22 22%
Engineering 16 16%
Medicine and Dentistry 8 8%
Sports and Recreations 7 7%
Nursing and Health Professions 6 6%
Other 16 16%
Unknown 23 23%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 23 November 2016.
All research outputs
#18,428,159
of 22,829,683 outputs
Outputs from Frontiers in Computational Neuroscience
#1,053
of 1,343 outputs
Outputs of similar age
#200,503
of 278,739 outputs
Outputs of similar age from Frontiers in Computational Neuroscience
#29
of 36 outputs
Altmetric has tracked 22,829,683 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,343 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.2. This one is in the 13th percentile – i.e., 13% of its peers scored the same or lower than it.
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 278,739 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 16th percentile – i.e., 16% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 36 others from the same source and published within six weeks on either side of this one. This one is in the 16th percentile – i.e., 16% of its contemporaries scored the same or lower than it.