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      The number and choice of muscles impact the results of muscle synergy analyses

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          Abstract

          One theory for how humans control movement is that muscles are activated in weighted groups or synergies. Studies have shown that electromyography (EMG) from a variety of tasks can be described by a low-dimensional space thought to reflect synergies. These studies use algorithms, such as nonnegative matrix factorization, to identify synergies from EMG. Due to experimental constraints, EMG can rarely be taken from all muscles involved in a task. However, it is unclear if the choice of muscles included in the analysis impacts estimated synergies. The aim of our study was to evaluate the impact of the number and choice of muscles on synergy analyses. We used a musculoskeletal model to calculate muscle activations required to perform an isometric upper-extremity task. Synergies calculated from the activations from the musculoskeletal model were similar to a prior experimental study. To evaluate the impact of the number of muscles included in the analysis, we randomly selected subsets of between 5 and 29 muscles and compared the similarity of the synergies calculated from each subset to a master set of synergies calculated from all muscles. We determined that the structure of synergies is dependent upon the number and choice of muscles included in the analysis. When five muscles were included in the analysis, the similarity of the synergies to the master set was only 0.57 ± 0.54; however, the similarity improved to over 0.8 with more than ten muscles. We identified two methods, selecting dominant muscles from the master set or selecting muscles with the largest maximum isometric force, which significantly improved similarity to the master set and can help guide future experimental design. Analyses that included a small subset of muscles also over-estimated the variance accounted for (VAF) by the synergies compared to an analysis with all muscles. Thus, researchers should use caution using VAF to evaluate synergies when EMG is measured from a small subset of muscles.

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          Merging of healthy motor modules predicts reduced locomotor performance and muscle coordination complexity post-stroke.

          David J Clark, Lena Ting, Felix Zajac … (2010)
          Evidence suggests that the nervous system controls motor tasks using a low-dimensional modular organization of muscle activation. However, it is not clear if such an organization applies to coordination of human walking, nor how nervous system injury may alter the organization of motor modules and their biomechanical outputs. We first tested the hypothesis that muscle activation patterns during walking are produced through the variable activation of a small set of motor modules. In 20 healthy control subjects, EMG signals from eight leg muscles were measured across a range of walking speeds. Four motor modules identified through nonnegative matrix factorization were sufficient to account for variability of muscle activation from step to step and across speeds. Next, consistent with the clinical notion of abnormal limb flexion-extension synergies post-stroke, we tested the hypothesis that subjects with post-stroke hemiparesis would have altered motor modules, leading to impaired walking performance. In post-stroke subjects (n = 55), a less complex coordination pattern was shown. Fewer modules were needed to account for muscle activation during walking at preferred speed compared with controls. Fewer modules resulted from merging of the modules observed in healthy controls, suggesting reduced independence of neural control signals. The number of modules was correlated to preferred walking speed, speed modulation, step length asymmetry, and propulsive asymmetry. Our results suggest a common modular organization of muscle coordination underlying walking in both healthy and post-stroke subjects. Identification of motor modules may lead to new insight into impaired locomotor coordination and the underlying neural systems.
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            Muscle synergy patterns as physiological markers of motor cortical damage.

            Vincent Cheung, Andrea Turolla, Michela Agostini … (2012)
            The experimental findings herein reported are aimed at gaining a perspective on the complex neural events that follow lesions of the motor cortical areas. Cortical damage, whether by trauma or stroke, interferes with the flow of descending signals to the modular interneuronal structures of the spinal cord. These spinal modules subserve normal motor behaviors by activating groups of muscles as individual units (muscle synergies). Damage to the motor cortical areas disrupts the orchestration of the modules, resulting in abnormal movements. To gain insights into this complex process, we recorded myoelectric signals from multiple upper-limb muscles in subjects with cortical lesions. We used a factorization algorithm to identify the muscle synergies. Our factorization analysis revealed, in a quantitative way, three distinct patterns of muscle coordination-including preservation, merging, and fractionation of muscle synergies-that reflect the multiple neural responses that occur after cortical damage. These patterns varied as a function of both the severity of functional impairment and the temporal distance from stroke onset. We think these muscle-synergy patterns can be used as physiological markers of the status of any patient with stroke or trauma, thereby guiding the development of different rehabilitation approaches, as well as future physiological experiments for a further understanding of postinjury mechanisms of motor control and recovery.
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              Shared and specific muscle synergies in natural motor behaviors.

              Andrea d'Avella, Emilio Bizzi (2005)
              Selecting the appropriate muscle pattern to achieve a given goal is an extremely complex task because of the dimensionality of the search space and because of the nonlinear and dynamical nature of the transformation between muscle activity and movement. To investigate whether the central nervous system uses a modular architecture to achieve motor coordination we characterized the motor output over a large set of movements. We recorded electromyographic activity from 13 muscles of the hind limb of intact and freely moving frogs during jumping, swimming, and walking in naturalistic conditions. We used multidimensional factorization techniques to extract invariant amplitude and timing relationships among the muscle activations. A decomposition of the instantaneous muscle activations as combinations of nonnegative vectors, synchronous muscle synergies, revealed a spatial organization in the muscle patterns. A decomposition of the same activations as a combination of temporal sequences of nonnegative vectors, time-varying muscle synergies, further uncovered specific characteristics in the muscle activation waveforms. A mixture of synergies shared across behaviors and synergies for specific behaviors captured the invariances across the entire dataset. These results support the hypothesis that the motor controller has a modular organization.
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                Author and article information

                Journal
                Journal ID (nlm-ta): Front Comput Neurosci
                Journal ID (iso-abbrev): Front Comput Neurosci
                Journal ID (publisher-id): Front. Comput. Neurosci.
                Title: Frontiers in Computational Neuroscience
                Publisher: Frontiers Media S.A.
                ISSN (Electronic): 1662-5188
                Publication date (Electronic): 08 August 2013
                Publication date Collection: 2013
                Volume: 7
                Electronic Location Identifier: 105
                Affiliations
                [1] 1Mechanical Engineering, University of Washington Seattle, WA, USA
                [2] 2Sensorimotor Performance Program, Rehabilitation Institute of Chicago Chicago, IL, USA
                [3] 3Biomedical Engineering, Northwestern University Evanston, IL, USA
                [4] 4Department of Physical Medicine and Rehabilitation, Northwestern University Feinberg School of Medicine Chicago, IL, USA
                Author notes

                Edited by: Andrea D'Avella, IRCCS Fondazione Santa Lucia, Italy

                Reviewed by: Ioannis Delis, Istituto Italiano di Tecnologia, Italy; Vincent C. K. Cheung, Massachusetts Institute of Technology, USA

                *Correspondence: Katherine M. Steele, Mechanical Engineering, University of Washington, Stevens Way, Box 352600, Seattle, WA 98195, USA e-mail: kmsteele@ 123456uw.edu
                Article
                DOI: 10.3389/fncom.2013.00105
                PMC ID: 3737463
                PubMed ID: 23964232
                SO-VID: eb552f56-4062-42c3-a7ef-79ebc24bfec5
                Copyright © Copyright © 2013 Steele, Tresch and Perreault.
                License:

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                Date received : 03 May 2013
                Date accepted : 13 July 2013
                Page count
                Figures: 7, Tables: 1, Equations: 0, References: 25, Pages: 9, Words: 6750
                Categories
                Subject: Neuroscience
                Subject: Original Research Article

                ScienceOpen disciplines: Neurosciences
                Keywords: muscle synergy,electromyography,simulation,nonnegative matrix factorization,musculoskeletal model
                Data availability:
                ScienceOpen disciplines: Neurosciences
                Keywords: muscle synergy, electromyography, simulation, nonnegative matrix factorization, musculoskeletal model

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