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      Inter-Person Differences in Isometric Coactivations of Triceps Surae and Tibialis Anterior Decrease in Young, but Not in Older Adults After 14 Days of Bed Rest

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          Abstract

          We examined activation patterns of the gastrocnemius medialis (GM), gastrocnemius lateralis (GL), soleus (SO), and tibialis anterior (TA) muscles in eight older (58.4 ± 3.3 years) and seven young (23.1 ± 2.9 years) participants, before and after 14 days of horizontal bed rest. Visual feedback on the exerted muscle torque was provided to the participants. The discharge patterns of individual motor units (MUs) were studied in three repetitions of isometric plantar flexion at 30 and 60% of Maximum Voluntary Contraction (MVC), before, and 1 day after the 14-day bed rest, respectively. In the GL and GM muscles, the older participants demonstrated higher MU discharge rates than the young, regardless of the contraction level, both before and after the bed rest. In the TA and SO muscles, the differences between the older and young participants were less consistent. Detailed analysis revealed person-specific changes in the MU discharge rates after the bed rest. To quantify the coactivation patterns we calculated the correlation coefficients between the cumulative spike trains of identified MUs from each muscle, and measured the root mean square difference of the correlation coefficients between the trials of the same session (intra-session variability) and between different sessions (inter-session variability) in each participant (intra-person comparison) and across participants (inter-person comparison). In the intra-person comparison, the inter-session variability was higher than the intra-session variability, either before or after the bed rest. At 60% MVC torque, the young demonstrated higher inter-person variability of coactivation than the older participants, but this variability decreased significantly after the bed rest. In older participants, inter-person variability was consistently lower at 60% than at 30% MVC torque. In young participants, inter-person variability became lower at 60% than at 30% MVC torque only after the bed rest. Precaution is required when analyzing the MU discharge and coactivation patterns, as individual persons demonstrate individual adaptations to aging or bed rest.

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          Most cited references48

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          Combinations of muscle synergies in the construction of a natural motor behavior.

          A central issue in motor control is how the central nervous system generates the muscle activity patterns necessary to achieve a variety of behavioral goals. The many degrees of freedom of the musculoskeletal apparatus provide great flexibility but make the control problem extremely complex. Muscle synergies--coherent activations, in space or time, of a group of muscles--have been proposed as building blocks that could simplify the construction of motor behaviors. To evaluate this hypothesis, we developed a new method to extract invariant spatiotemporal components from the simultaneous recordings of the activity of many muscles. We used this technique to analyze the muscle patterns of intact and unrestrained frogs during kicking, a natural defensive behavior. Here we show that combinations of three time-varying muscle synergies underlie the variety of muscle patterns required to kick in different directions, that the recruitment of these synergies is related to movement kinematics, and that there are similarities among the synergies extracted from different behaviors.
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            Multichannel Blind Source Separation Using Convolution Kernel Compensation

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              Accurate identification of motor unit discharge patterns from high-density surface EMG and validation with a novel signal-based performance metric.

              A signal-based metric for assessment of accuracy of motor unit (MU) identification from high-density surface electromyograms (EMG) is introduced. This metric, so-called pulse-to-noise-ratio (PNR), is computationally efficient, does not require any additional experimental costs and can be applied to every MU that is identified by the previously developed convolution kernel compensation technique.
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                Author and article information

                Contributors
                Journal
                Front Physiol
                Front Physiol
                Front. Physiol.
                Frontiers in Physiology
                Frontiers Media S.A.
                1664-042X
                28 January 2022
                2021
                : 12
                : 809243
                Affiliations
                [1] 1System Software Laboratory, Institute of Computer Science, Faculty of Electrical Engineering and Computer Science, University of Maribor , Maribor, Slovenia
                [2] 2Science and Research Centre Koper, Institute for Kinesiology Research , Koper, Slovenia
                [3] 3Department of Health Sciences, Alma Mater Europaea – ECM , Maribor, Slovenia
                Author notes

                Edited by: Nicolas Place, University of Lausanne, Switzerland

                Reviewed by: Taian Martins Vieira, Politecnico di Torino, Italy; Ryota Akagi, Shibaura Institute of Technology, Japan

                *Correspondence: Aleš Holobar, ales.holobar@ 123456um.si

                This article was submitted to Exercise Physiology, a section of the journal Frontiers in Physiology

                Article
                10.3389/fphys.2021.809243
                8832055
                39b0db4c-d4b7-491e-99e6-2b522bddd848
                Copyright © 2022 Divjak, Sedej, Murks, Gerževič, Marusic, Pišot, Šimunič and Holobar.

                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) and the copyright owner(s) 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
                : 04 November 2021
                : 24 December 2021
                Page count
                Figures: 8, Tables: 1, Equations: 0, References: 48, Pages: 14, Words: 8646
                Funding
                Funded by: Javna Agencija za Raziskovalno Dejavnost RS, doi 10.13039/501100004329;
                Award ID: J2-7357
                Award ID: J2-1731
                Award ID: L7 9421
                Award ID: P2-0041
                Funded by: European Regional Development Fund, doi 10.13039/501100008530;
                Award ID: 042–2/2009
                Categories
                Physiology
                Original Research

                Anatomy & Physiology
                high density electromyography,muscle disuse,motor units,discharge rate,aging
                Anatomy & Physiology
                high density electromyography, muscle disuse, motor units, discharge rate, aging

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