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      Common muscle synergies for balance and walking

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          Abstract

          Little is known about the integration of neural mechanisms for balance and locomotion. Muscle synergies have been studied independently in standing balance and walking, but not compared. Here, we hypothesized that reactive balance and walking are mediated by a common set of lower-limb muscle synergies. In humans, we examined muscle activity during multidirectional support-surface perturbations during standing and walking, as well as unperturbed walking at two speeds. We show that most muscle synergies used in perturbations responses during standing were also used in perturbation responses during walking, suggesting common neural mechanisms for reactive balance across different contexts. We also show that most muscle synergies using in reactive balance were also used during unperturbed walking, suggesting that neural circuits mediating locomotion and reactive balance recruit a common set of muscle synergies to achieve task-level goals. Differences in muscle synergies across conditions reflected differences in the biomechanical demands of the tasks. For example, muscle synergies specific to walking perturbations may reflect biomechanical challenges associated with single limb stance, and muscle synergies used during sagittal balance recovery in standing but not walking were consistent with maintaining the different desired center of mass motions in standing vs. walking. Thus, muscle synergies specifying spatial organization of muscle activation patterns may define a repertoire of biomechanical subtasks available to different neural circuits governing walking and reactive balance and may be recruited based on task-level goals. Muscle synergy analysis may aid in dissociating deficits in spatial vs. temporal organization of muscle activity in motor deficits. Muscle synergy analysis may also provide a more generalizable assessment of motor function by identifying whether common modular mechanisms are impaired across the performance of multiple motor tasks.

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          Most cited references100

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

          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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            Central programming of postural movements: adaptation to altered support-surface configurations.

            We studied the extent to which automatic postural actions in standing human subjects are organized by a limited repertoire of central motor programs. Subjects stood on support surfaces of various lengths, which forced them to adopt different postural movement strategies to compensate for the same external perturbations. We assessed whether a continuum or a limited set of muscle activation patterns was used to produce different movement patterns and the extent to which movement patterns were influenced by prior experience. Exposing subjects standing on a normal support surface to brief forward and backward horizontal surface perturbations elicited relatively stereotyped patterns of leg and trunk muscle activation with 73- to 110-ms latencies. Activity began in the ankle joint muscles and then radiated in sequence to thigh and then trunk muscles on the same dorsal or ventral aspect of the body. This activation pattern exerted compensatory torques about the ankle joints, which restored equilibrium by moving the body center of mass forward or backward. This pattern has been termed the ankle strategy because it restores equilibrium by moving the body primarily around the ankle joints. To successfully maintain balance while standing on a support surface short in relation to foot length, subjects activated leg and trunk muscles at similar latencies but organized the activity differently. The trunk and thigh muscles antagonistic to those used in the ankle strategy were activated in the opposite proximal-to-distal sequence, whereas the ankle muscles were generally unresponsive. This activation pattern produced a compensatory horizontal shear force against the support surface but little, if any, ankle torque. This pattern has been termed the hip strategy, because the resulting motion is focused primarily about the hip joints. Exposing subjects to horizontal surface perturbations while standing on support surfaces intermediate in length between the shortest and longest elicited more complex postural movements and associated muscle activation patterns that resembled ankle and hip strategies combined in different temporal relations. These complex postural movements were executed with combinations of torque and horizontal shear forces and motions of ankle and hip joints. During the first 5-20 practice trials immediately following changes from one support surface length to another, response latencies were unchanged. The activation patterns, however, were complex and resembled the patterns observed during well-practiced stance on surfaces of intermediate lengths.(ABSTRACT TRUNCATED AT 400 WORDS)
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              Motor patterns in human walking and running.

              Despite distinct differences between walking and running, the two types of human locomotion are likely to be controlled by shared pattern-generating networks. However, the differences between their kinematics and kinetics imply that corresponding muscle activations may also be quite different. We examined the differences between walking and running by recording kinematics and electromyographic (EMG) activity in 32 ipsilateral limb and trunk muscles during human locomotion, and compared the effects of speed (3-12 km/h) and gait. We found that the timing of muscle activation was accounted for by five basic temporal activation components during running as we previously found for walking. Each component was loaded on similar sets of leg muscles in both gaits but generally on different sets of upper trunk and shoulder muscles. The major difference between walking and running was that one temporal component, occurring during stance, was shifted to an earlier phase in the step cycle during running. These muscle activation differences between gaits did not simply depend on locomotion speed as shown by recordings during each gait over the same range of speeds (5-9 km/h). The results are consistent with an organization of locomotion motor programs having two parts, one that organizes muscle activation during swing and another during stance and the transition to swing. The timing shift between walking and running reflects therefore the difference in the relative duration of the stance phase in the two gaits.
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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
                02 May 2013
                2013
                : 7
                : 48
                Affiliations
                The Wallace H. Coulter Department of Biomedical Engineering, Georgia Tech and Emory University Atlanta, GA, USA
                Author notes

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

                Reviewed by: Yuri P. Ivanenko, IRCCS Fondazione Santa Lucia, Italy; Thierry Pozzo, INSERM, France

                *Correspondence: Lena H. Ting, The Wallace H. Coulter Department of Biomedical Engineering, Georgia Tech and Emory University, 313 Ferst Drive, Atlanta, GA 30332-0535, USA. e-mail: lting@ 123456emory.edu
                Article
                10.3389/fncom.2013.00048
                3641709
                23653605
                3350b921-a1cd-4f6a-90fa-c5fb126cf532
                Copyright © 2013 Chvatal and Ting.

                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
                : 21 December 2012
                : 08 April 2013
                Page count
                Figures: 8, Tables: 1, Equations: 1, References: 114, Pages: 14, Words: 11850
                Categories
                Neuroscience
                Original Research Article

                Neurosciences
                locomotion,posture,muscle synergy,motor control,electromyography
                Neurosciences
                locomotion, posture, muscle synergy, motor control, electromyography

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