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      Control of motor output during steady submaximal contractions is modulated by contraction history

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          Abstract

          The purpose of the study was to investigate the influence of contraction history on force steadiness and the associated EMG activity during submaximal isometric contractions performed with the dorsiflexor muscles. The key feature of the protocol was a triangular ramp contraction performed in the middle of a steady contraction at a lower target force. The target force during the ramp contraction was 20% MVC greater than that during the steady contraction. Thirty-seven healthy individuals (21 men and 16 women) performed the submaximal tasks with the ankle dorsiflexors. Electromyography (EMG) signals were recorded from tibialis anterior with a pair of surface electrodes. The coefficient of variation for force was significantly greater during the second steady contraction compared with the first one at each of the seven target forces ( p < 0.015; d = 0.38–0.92). Although the average applied force during the steady contractions before and after the triangular contraction was the same ( p = 0.563), the mean EMG amplitude for the steady contractions performed after the triangular contraction was significantly greater at each of the seven target forces ( p < 0.0001; d = 0.44–0.68). Also, there were significant differences in mean EMG frequency between the steady contractions performed before and after the triangular contraction ( p < 0.01; d = 0.13–0.82), except at 10 and 20% MVC force. The greater force fluctuations during a steady submaximal contraction after an intervening triangular contraction indicate a change in the discharge characteristics of the involved motor units.

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          A power primer.

          One possible reason for the continued neglect of statistical power analysis in research in the behavioral sciences is the inaccessibility of or difficulty with the standard material. A convenient, although not comprehensive, presentation of required sample sizes is provided here. Effect-size indexes and conventional values for these are given for operationally defined small, medium, and large effects. The sample sizes necessary for .80 power to detect effects at these levels are tabled for eight standard statistical tests: (a) the difference between independent means, (b) the significance of a product-moment correlation, (c) the difference between independent rs, (d) the sign test, (e) the difference between independent proportions, (f) chi-square tests for goodness of fit and contingency tables, (g) one-way analysis of variance, and (h) the significance of a multiple or multiple partial correlation.
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            The extraction of neural strategies from the surface EMG.

            This brief review examines some of the methods used to infer central control strategies from surface electromyogram (EMG) recordings. Among the many uses of the surface EMG in studying the neural control of movement, the review critically evaluates only some of the applications. The focus is on the relations between global features of the surface EMG and the underlying physiological processes. Because direct measurements of motor unit activation are not available and many factors can influence the signal, these relations are frequently misinterpreted. These errors are compounded by the counterintuitive effects that some system parameters can have on the EMG signal. The phenomenon of crosstalk is used as an example of these problems. The review describes the limitations of techniques used to infer the level of muscle activation, the type of motor unit recruited, the upper limit of motor unit recruitment, the average discharge rate, and the degree of synchronization between motor units. Although the global surface EMG is a useful measure of muscle activation and assessment, there are limits to the information that can be extracted from this signal.
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              The extraction of neural strategies from the surface EMG: an update.

              A surface EMG signal represents the linear transformation of motor neuron discharge times by the compound action potentials of the innervated muscle fibers and is often used as a source of information about neural activation of muscle. However, retrieving the embedded neural code from a surface EMG signal is extremely challenging. Most studies use indirect approaches in which selected features of the signal are interpreted as indicating certain characteristics of the neural code. These indirect associations are constrained by limitations that have been detailed previously (Farina D, Merletti R, Enoka RM. J Appl Physiol 96: 1486-1495, 2004) and are generally difficult to overcome. In an update on these issues, the current review extends the discussion to EMG-based coherence methods for assessing neural connectivity. We focus first on EMG amplitude cancellation, which intrinsically limits the association between EMG amplitude and the intensity of the neural activation and then discuss the limitations of coherence methods (EEG-EMG, EMG-EMG) as a way to assess the strength of the transmission of synaptic inputs into trains of motor unit action potentials. The debated influence of rectification on EMG spectral analysis and coherence measures is also discussed. Alternatively, there have been a number of attempts to identify the neural information directly by decomposing surface EMG signals into the discharge times of motor unit action potentials. The application of this approach is extremely powerful, but validation remains a central issue.
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                Author and article information

                Contributors
                abdxsup@gmail.com
                Journal
                Exp Brain Res
                Exp Brain Res
                Experimental Brain Research
                Springer Berlin Heidelberg (Berlin/Heidelberg )
                0014-4819
                1432-1106
                23 January 2024
                23 January 2024
                2024
                : 242
                : 3
                : 675-683
                Affiliations
                [1 ]Movement Neuroscience Laboratory, Department of Physical Therapy, Movement, and Rehabilitation Sciences, Northeastern University, ( https://ror.org/04t5xt781) Boston, MA 02115 USA
                [2 ]Faculty of Sport Sciences, Sivas Cumhuriyet University, ( https://ror.org/04f81fm77) Sivas, Turkey
                [3 ]Department of Integrative Physiology, University of Colorado Boulder, ( https://ror.org/02ttsq026) Boulder, CO USA
                Author notes

                Communicated by Winston D Byblow.

                Author information
                http://orcid.org/0000-0002-4581-5567
                http://orcid.org/0000-0002-7881-2397
                Article
                6774
                10.1007/s00221-023-06774-8
                10894765
                38260992
                be832a5e-4d63-4f52-8ea3-8a5b5aa770a9
                © The Author(s) 2024

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 13 November 2023
                : 22 December 2023
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100000890, National Multiple Sclerosis Society;
                Award ID: RG-2206-39688
                Award Recipient :
                Funded by: Northeastern University USA
                Categories
                Research Article
                Custom metadata
                © Springer-Verlag GmbH Germany, part of Springer Nature 2024

                Neurosciences
                force steadiness,muscle contraction,emg power density spectrum,neural drive to muscle,synaptic input

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