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      Physical and electrophysiological motor unit characteristics are revealed with simultaneous high-density electromyography and ultrafast ultrasound imaging

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          Abstract

          Electromyography and ultrasonography provide complementary information about electrophysiological and physical (i.e. anatomical and mechanical) muscle properties. In this study, we propose a method to assess the electrical and physical properties of single motor units (MUs) by combining High-Density surface Electromyography (HDsEMG) and ultrafast ultrasonography (US). Individual MU firings extracted from HDsEMG were used to identify the corresponding region of muscle tissue displacement in US videos. The time evolution of the tissue velocity in the identified region was regarded as the MU tissue displacement velocity. The method was tested in simulated conditions and applied to experimental signals to study the local association between the amplitude distribution of single MU action potentials and the identified displacement area. We were able to identify the location of simulated MUs in the muscle cross-section within a 2 mm error and to reconstruct the simulated MU displacement velocity (cc > 0.85). Multiple regression analysis of 180 experimental MUs detected during isometric contractions of the biceps brachii revealed a significant association between the identified location of MU displacement areas and the centroid of the EMG amplitude distribution. The proposed approach has the potential to enable non-invasive assessment of the electrical, anatomical, and mechanical properties of single MUs in voluntary contractions.

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          Models of recruitment and rate coding organization in motor-unit pools.

          1. Isometric muscle force and the surface electromyogram (EMG) were simulated from a model that predicted recruitment and firing times in a pool of 120 motor units under different levels of excitatory drive. The EMG-force relationships that emerged from simulations using various schedules of recruitment and rate coding were compared with those observed experimentally to determine which of the modeled schemes were plausible representations of the actual organization in motor-unit pools. 2. The model was comprised of three elements: a motoneuron model, a motor-unit force model, and a model of the surface EMG. Input to the neuron model was an excitatory drive function representing the net synaptic input to motoneurons during voluntary muscle contractions. Recruitment thresholds were assigned such that many motoneurons had low thresholds and relatively few neurons had high thresholds. Motoneuron firing rate increased as a linear function of excitatory drive between recruitment threshold and peak firing rate levels. The sequence of discharge times for each motoneuron was simulated as a random renewal process. 3. Motor-unit twitch force was estimated as an impulse response of a critically damped, second-order system. Twitch amplitudes were assigned according to rank in the recruitment order, and twitch contraction times were inversely related to twitch amplitude. Nonlinear force-firing rate behavior was simulated by varying motor-unit force gain as a function of the instantaneous firing rate and the contraction time of the unit. The total force exerted by the muscle was computed as the sum of the motor-unit forces. 4. Motor-unit action potentials were simulated on the basis of estimates of the number and location of motor-unit muscle fibers and the propagation velocity of the fiber action potentials. The number of fibers innervated by each unit was assumed to be directly proportional to the twitch force. The area of muscle encompassing unit fibers was proportional to the number of fibers innervated, and the location of motor-unit territories were randomly assigned within the muscle cross section. Action-potential propagation velocities were estimated from an inverse function of contraction time. The train of discharge times predicted from the motoneuron model determined the occurrence of each motor-unit action potential. The surface EMG was synthesized as the sum of all motor-unit action-potential trains. 5. Two recruitment conditions were tested: narrow (limit of recruitment 70% maximum excitation).(ABSTRACT TRUNCATED AT 400 WORDS)
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            Analysis of motor units with high-density surface electromyography.

            Although the behaviour of individual motor units is classically studied with intramuscular EMG, recently developed techniques allow its analysis also from EMG recorded in multiple locations over the skin surface (high-density surface EMG). The analysis of motor units from the surface EMG is useful when the insertion of needles is not desirable or not possible. Moreover, surface EMG allows the measure of motor unit properties which are difficult to assess with invasive technology (e.g., muscle fiber conduction velocity or location of innervation zones) and may increase the number of detectable motor units with respect to selective intramuscular recordings. Although some limitations remain, both the discharge pattern and muscle fiber properties of individual motor units can currently be analyzed non-invasively. This review presents the conditions and methodologies which allow the investigation of motor units with surface EMG.
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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
                marco.carbonaro@polito.it
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                25 May 2022
                25 May 2022
                2022
                : 12
                : 8855
                Affiliations
                [1 ]GRID grid.4800.c, ISNI 0000 0004 1937 0343, Laboratory for Engineering of the Neuromuscular System (LISiN), Department of Electronics and Telecommunication, , Politecnico di Torino, ; 10129 Turin, Italy
                [2 ]GRID grid.4800.c, ISNI 0000 0004 1937 0343, PoliToBIOMed Laboratory, , Politecnico di Torino, ; 10129 Turin, Italy
                [3 ]GRID grid.4800.c, ISNI 0000 0004 1937 0343, Biolab, Department of Electronics and Telecommunications, , Politecnico di Torino, ; 10129 Turin, Italy
                [4 ]GRID grid.25627.34, ISNI 0000 0001 0790 5329, Musculoskeletal Sciences and Sports Medicine Research Centre, Department of Life Sciences, , Manchester Metropolitan University, ; Manchester, M15 6BH UK
                Article
                12999
                10.1038/s41598-022-12999-4
                9133081
                35614312
                034b406a-a7fc-457b-8cdb-b0361bdd0ea0
                © The Author(s) 2022

                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
                : 21 December 2021
                : 6 May 2022
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100000268, Biotechnology and Biological Sciences Research Council;
                Award ID: BB/L018632/1
                Award Recipient :
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                © The Author(s) 2022

                Uncategorized
                biomedical engineering,neurophysiology
                Uncategorized
                biomedical engineering, neurophysiology

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