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The Propagation of Movement Variability in Time: A Methodological Approach for Discrete Movements with Multiple Degrees of Freedom

Overview of attention for article published in Frontiers in Computational Neuroscience, October 2017
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Title
The Propagation of Movement Variability in Time: A Methodological Approach for Discrete Movements with Multiple Degrees of Freedom
Published in
Frontiers in Computational Neuroscience, October 2017
DOI 10.3389/fncom.2017.00093
Pubmed ID
Authors

Melanie Krüger, Andreas Straube, Thomas Eggert

Abstract

In recent years, theory-building in motor neuroscience and our understanding of the synergistic control of the redundant human motor system has significantly profited from the emergence of a range of different mathematical approaches to analyze the structure of movement variability. Approaches such as the Uncontrolled Manifold method or the Noise-Tolerance-Covariance decomposition method allow to detect and interpret changes in movement coordination due to e.g., learning, external task constraints or disease, by analyzing the structure of within-subject, inter-trial movement variability. Whereas, for cyclical movements (e.g., locomotion), mathematical approaches exist to investigate the propagation of movement variability in time (e.g., time series analysis), similar approaches are missing for discrete, goal-directed movements, such as reaching. Here, we propose canonical correlation analysis as a suitable method to analyze the propagation of within-subject variability across different time points during the execution of discrete movements. While similar analyses have already been applied for discrete movements with only one degree of freedom (DoF; e.g., Pearson's product-moment correlation), canonical correlation analysis allows to evaluate the coupling of inter-trial variability across different time points along the movement trajectory for multiple DoF-effector systems, such as the arm. The theoretical analysis is illustrated by empirical data from a study on reaching movements under normal and disturbed proprioception. The results show increased movement duration, decreased movement amplitude, as well as altered movement coordination under ischemia, which results in a reduced complexity of movement control. Movement endpoint variability is not increased under ischemia. This suggests that healthy adults are able to immediately and efficiently adjust the control of complex reaching movements to compensate for the loss of proprioceptive information. Further, it is shown that, by using canonical correlation analysis, alterations in movement coordination that indicate changes in the control strategy concerning the use of motor redundancy can be detected, which represents an important methodical advance in the context of neuromechanics.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 57 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 57 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 15 26%
Researcher 8 14%
Student > Master 7 12%
Student > Doctoral Student 4 7%
Student > Bachelor 4 7%
Other 8 14%
Unknown 11 19%
Readers by discipline Count As %
Sports and Recreations 10 18%
Psychology 6 11%
Nursing and Health Professions 4 7%
Medicine and Dentistry 4 7%
Engineering 4 7%
Other 13 23%
Unknown 16 28%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 17 December 2017.
All research outputs
#7,540,801
of 23,005,189 outputs
Outputs from Frontiers in Computational Neuroscience
#414
of 1,353 outputs
Outputs of similar age
#123,691
of 325,897 outputs
Outputs of similar age from Frontiers in Computational Neuroscience
#16
of 31 outputs
Altmetric has tracked 23,005,189 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,353 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.1. This one has gotten more attention than average, scoring higher than 68% 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 325,897 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 54% of its contemporaries.
We're also able to compare this research output to 31 others from the same source and published within six weeks on either side of this one. This one is in the 48th percentile – i.e., 48% of its contemporaries scored the same or lower than it.