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      Investigating reduction of dimensionality during single-joint elbow movements: a case study on muscle synergies

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          Abstract

          A long standing hypothesis in the neuroscience community is that the central nervous system (CNS) generates the muscle activities to accomplish movements by combining a relatively small number of stereotyped patterns of muscle activations, often referred to as “muscle synergies.” Different definitions of synergies have been given in the literature. The most well-known are those of synchronous, time-varying and temporal muscle synergies. Each one of them is based on a different mathematical model used to factor some EMG array recordings collected during the execution of variety of motor tasks into a well-determined spatial, temporal or spatio-temporal organization. This plurality of definitions and their separate application to complex tasks have so far complicated the comparison and interpretation of the results obtained across studies, and it has always remained unclear why and when one synergistic decomposition should be preferred to another one. By using well-understood motor tasks such as elbow flexions and extensions, we aimed in this study to clarify better what are the motor features characterized by each kind of decomposition and to assess whether, when and why one of them should be preferred to the others. We found that three temporal synergies, each one of them accounting for specific temporal phases of the movements could account for the majority of the data variation. Similar performances could be achieved by two synchronous synergies, encoding the agonist-antagonist nature of the two muscles considered, and by two time-varying muscle synergies, encoding each one a task-related feature of the elbow movements, specifically their direction. Our findings support the notion that each EMG decomposition provides a set of well-interpretable muscle synergies, identifying reduction of dimensionality in different aspects of the movements. Taken together, our findings suggest that all decompositions are not equivalent and may imply different neurophysiological substrates to be implemented.

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          Most cited references21

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          Neural population dynamics during reaching

          Most theories of motor cortex have assumed that neural activity represents movement parameters. This view derives from an analogous approach to primary visual cortex, where neural activity represents patterns of light. Yet it is unclear how well that analogy holds. Single-neuron responses in motor cortex appear strikingly complex, and there is marked disagreement regarding which movement parameters are represented. A better analogy might be with other motor systems, where a common principle is rhythmic neural activity. We found that motor cortex responses during reaching contain a brief but strong oscillatory component, something quite unexpected for a non-periodic behavior. Oscillation amplitude and phase followed naturally from the preparatory state, suggesting a mechanistic role for preparatory neural activity. These results demonstrate unexpected yet surprisingly simple structure in the population response. That underlying structure explains many of the confusing features of individual-neuron responses.
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            Locomotor primitives in newborn babies and their development.

            How rudimentary movements evolve into sophisticated ones during development remains unclear. It is often assumed that the primitive patterns of neural control are suppressed during development, replaced by entirely new patterns. Here we identified the basic patterns of lumbosacral motoneuron activity from multimuscle recordings in stepping neonates, toddlers, preschoolers, and adults. Surprisingly, we found that the two basic patterns of stepping neonates are retained through development, augmented by two new patterns first revealed in toddlers. Markedly similar patterns were observed also in the rat, cat, macaque, and guineafowl, consistent with the hypothesis that, despite substantial phylogenetic distances and morphological differences, locomotion in several animal species is built starting from common primitives, perhaps related to a common ancestral neural network.
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              Muscle synergy patterns as physiological markers of motor cortical damage.

              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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                Author and article information

                Journal
                Front Comput Neurosci
                Front Comput Neurosci
                Front. Comput. Neurosci.
                Frontiers in Computational Neuroscience
                Frontiers Media S.A.
                1662-5188
                28 February 2013
                2013
                : 7
                : 11
                Affiliations
                [1] 1Section for Computational Sensomotorics, Department of Cognitive Neurology, Hertie Institute of Clinical Brain Research and Center for Integrative Neuroscience, University Clinic Tübingen Tübingen, Germany
                [2] 2Deparment of Robotics, Brain and Cognitive Sciences, Istituto Italiano di Tecnologia Genoa, Italy
                [3] 3UR CIAMS, EA 4532 - Motor Control and Perception Team, Université Paris-Sud 11 Orsay, France
                [4] 4Department of Communication, Computer and System Sciences, University of Genoa Genoa, Italy
                [5] 5Institute of Neuroscience and Psychology, University of Glasgow Glasgow, UK
                [6] 6Center for Neuroscience and Cognitive Systems @ UniTn, Istituto Italiano di Tecnologia Rovereto, Italy
                [7] 7Institut Universitaire de France, Université de Bourgogne, UFR STAPS Dijon, France
                [8] 8INSERM U887, Motricité-Plasticité Dijon, France
                Author notes

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

                Reviewed by: Guy Cheron, Université Libre de Bruxelles, Belgium; Jinsook Roh, Rehabilitation Institute of Chicago, USA

                *Correspondence: Enrico Chiovetto, Section for Computational Sensomotorics, Department of Cognitive Neurology, Hertie Institute for Clinical Brain Research, Otfried-Müller-Str. 25, 72076 Tübingen, Germany. e-mail: enrico.chiovetto@ 123456klinikum.uni-tuebingen.de
                Article
                10.3389/fncom.2013.00011
                3584318
                23450667
                91a00684-bfe0-4b4d-925d-56ceb7f016da
                Copyright © 2013 Chiovetto, Berret, Delis, Panzeri and Pozzo.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in other forums, provided the original authors and source are credited and subject to any copyright notices concerning any third-party graphics etc.

                History
                : 16 November 2012
                : 10 February 2013
                Page count
                Figures: 8, Tables: 1, Equations: 5, References: 31, Pages: 12, Words: 9463
                Categories
                Neuroscience
                Original Research Article

                Neurosciences
                dimensionality reduction,elbow rotations,emg,muscle synergies,non-negative matrix factorization,triphasic pattern

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