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Dimensionality of joint torques and muscle patterns for reaching

Overview of attention for article published in Frontiers in Computational Neuroscience, January 2014
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Title
Dimensionality of joint torques and muscle patterns for reaching
Published in
Frontiers in Computational Neuroscience, January 2014
DOI 10.3389/fncom.2014.00024
Pubmed ID
Authors

Marta Russo, Mattia D'Andola, Alessandro Portone, Francesco Lacquaniti, Andrea d'Avella

Abstract

Muscle activities underlying many motor behaviors can be generated by a small number of basic activation patterns with specific features shared across movement conditions. Such low-dimensionality suggests that the central nervous system (CNS) relies on a modular organization to simplify control. However, the relationship between the dimensionality of muscle patterns and that of joint torques is not fixed, because of redundancy and non-linearity in mapping the former into the latter, and needs to be investigated. We compared the torques acting at four arm joints during fast reaching movements in different directions in the frontal and sagittal planes and the underlying muscle patterns. The dimensionality of the non-gravitational components of torques and muscle patterns in the spatial, temporal, and spatiotemporal domains was estimated by multidimensional decomposition techniques. The spatial organization of torques was captured by two or three generators, indicating that not all the available coordination patterns are employed by the CNS. A single temporal generator with a biphasic profile was identified, generalizing previous observations on a single plane. The number of spatiotemporal generators was equal to the product of the spatial and temporal dimensionalities and their organization was essentially synchronous. Muscle pattern dimensionalities were higher than torques dimensionalities but also higher than the minimum imposed by the inherent non-negativity of muscle activations. The spatiotemporal dimensionality of the muscle patterns was lower than the product of their spatial and temporal dimensionality, indicating the existence of specific asynchronous coordination patterns. Thus, the larger dimensionalities of the muscle patterns may be required for CNS to overcome the non-linearities of the musculoskeletal system and to flexibly generate endpoint trajectories with simple kinematic features using a limited number of building blocks.

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Geographical breakdown

Country Count As %
United States 3 3%
Netherlands 2 2%
Switzerland 1 <1%
United Kingdom 1 <1%
Austria 1 <1%
Unknown 103 93%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 29 26%
Researcher 18 16%
Student > Master 18 16%
Other 9 8%
Student > Bachelor 7 6%
Other 14 13%
Unknown 16 14%
Readers by discipline Count As %
Engineering 28 25%
Agricultural and Biological Sciences 21 19%
Neuroscience 20 18%
Sports and Recreations 7 6%
Medicine and Dentistry 5 5%
Other 10 9%
Unknown 20 18%
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 24 March 2014.
All research outputs
#20,656,161
of 25,373,627 outputs
Outputs from Frontiers in Computational Neuroscience
#1,116
of 1,463 outputs
Outputs of similar age
#243,187
of 319,280 outputs
Outputs of similar age from Frontiers in Computational Neuroscience
#14
of 18 outputs
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