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Front Comput Neurosci. 2013 May 13;7:54. doi: 10.3389/fncom.2013.00054. eCollection 2013.

A methodology for assessing the effect of correlations among muscle synergy activations on task-discriminating information.

Author information

  • 1Robotics, Brain and Cognitive Sciences Department, Istituto Italiano di Tecnologia Genoa, Italy ; Communication, Computer and System Sciences Department, Doctoral School on Life and Humanoid Technologies, University of Genoa Genoa, Italy ; Institute of Neuroscience and Psychology, University of Glasgow Glasgow, UK.

Abstract

Muscle synergies have been hypothesized to be the building blocks used by the central nervous system to generate movement. According to this hypothesis, the accomplishment of various motor tasks relies on the ability of the motor system to recruit a small set of synergies on a single-trial basis and combine them in a task-dependent manner. It is conceivable that this requires a fine tuning of the trial-to-trial relationships between the synergy activations. Here we develop an analytical methodology to address the nature and functional role of trial-to-trial correlations between synergy activations, which is designed to help to better understand how these correlations may contribute to generating appropriate motor behavior. The algorithm we propose first divides correlations between muscle synergies into types (noise correlations, quantifying the trial-to-trial covariations of synergy activations at fixed task, and signal correlations, quantifying the similarity of task tuning of the trial-averaged activation coefficients of different synergies), and then uses single-trial methods (task-decoding and information theory) to quantify their overall effect on the task-discriminating information carried by muscle synergy activations. We apply the method to both synchronous and time-varying synergies and exemplify it on electromyographic data recorded during performance of reaching movements in different directions. Our method reveals the robust presence of information-enhancing patterns of signal and noise correlations among pairs of synchronous synergies, and shows that they enhance by 9-15% (depending on the set of tasks) the task-discriminating information provided by the synergy decompositions. We suggest that the proposed methodology could be useful for assessing whether single-trial activations of one synergy depend on activations of other synergies and quantifying the effect of such dependences on the task-to-task differences in muscle activation patterns.

KEYWORDS:

correlations; information theory; muscle synergies; single-trial analysis; task decoding

PMID:
23717277
[PubMed]
PMCID:
PMC3652392
Free PMC Article

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