No abstract

There is a growing interest in decomposing high-density surface electromyography (HDsEMG) into motor unit spike trains to improve knowledge on the neural control of muscle contraction.However, the reliability of decomposition approaches is sometimes questioned, especially because they require manual editing of the outputs. We aimed to assess the inter-operator reliability of the identification of motor unit spike trains. Eight operators with varying experience in HDsEMG decomposition were provided with the same data extracted using the convolutive kernel compensation method. They were asked to manually edit them following established procedures. Data included signals from three lower leg muscles and different submaximal intensities. After manual analysis, 126 ± 5 motor units were retained (range across operators: 119-134). A total of 3380 rate of agreement values were calculated (28 pairwise comparisons ´ 11 contractions/muscles ´ 4-28 motor units). The median rate of agreement value was 99.6%. Inter-operator reliability was excellent for both mean discharge rate and time at recruitment (intraclass correlation coefficient > 0.99). These results show that when provided with the same decomposed data and the same basic instructions, operators converge toward almost identical results. Our data have been made available so that they can be used for training new operators.

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The force generated by the muscles involved in an action is produced by common synaptic inputs received by the engaged motor neurons. The purpose of our study was to identify the low-dimensional latent components, defined hereafter as neural modules, underlying the discharge rates of the motor units from two knee extensors (vastus medialis and lateralis) and two hand muscles (index and thumb muscles) during isometric contractions. The neural modules were extracted by factor analysis from the pooled motor units and no assumptions were made regarding the orthogonality of the modules or the association between the modules and each muscle. Factor analysis identified two independent neural modules that captured most of the covariance in the discharge rates of the motor units in the synergistic muscles. Although the neural modules were strongly correlated with the discharge rates of motor units in each of the synergistic pair of muscles, not all motor units in a muscle were correlated with the neural module for that muscle. The distribution of motor units across the pair of neural modules differed for each muscle: 80% of the motor units in first dorsal interosseous were more strongly correlated with the neural module for that muscle, whereas the proportion was 70%, 60%, and 45% for the thenar, vastus medialis, and vastus lateralis muscles. All other motor units either belonged to both modules or to the module for the other muscle (15% for vastus lateralis). Based on a simulation of 480 integrate-and-fire neurons receiving independent and common inputs, we demonstrate that factor analysis identifies the three neural modules with high levels of accuracy. Our results indicate that the correlated discharge rates of motor units arise from at least two sources of common synaptic input that are not distributed homogeneously among the motor neurons innervating synergistic muscles.

This study aimed at: (1) Reporting COVID-19 symptoms and duration in professional football players; (2) comparing players' pulmonary function before and after COVID-19; (3) comparing players' metabolic power (P met ) before and after COVID-19. Thirteen male players (Age: 23.9 ± 4.0 years, VȮ 2peak : 49.7 ± 4.0 mL/ kg/min) underwent a medical screening and performed a running incremental step test and a spirometry test after COVID-19. Spirometric data were compared with the ones collected at the beginning of the same season. Players' mean P met of the 10 matches played before COVID-19 was compared with mean P met of the 10 matches played after COVID-19. Players completed a questionnaire on COVID-19 symptoms and duration 6 months following the disease. COVID-19 positivity lasted on average 15 ± 5 days. "General fatigue" and "muscle fatigue" symptoms were reported by all players during COVID-19 and persisted for 77% (general fatigue) and 54% (muscle fatigue) of the players for 37 ± 28 and 38 ± 29 days after the disease, respectively. No significant changes in spirometric measurements were found after COVID-19, even though some impairments at the individual level were observed. Conversely, a linear mixed-effects model analysis showed a significant reduction of P met (−4.1 ± 3.5%) following COVID-19 (t = −2.686, p < 0.05). "General fatigue" and "muscle fatigue" symptoms may persist for several weeks following COVID-19 in professional football players and should be considered for a safer return to sport. Players' capacity to compete at high intensities might be compromised after COVID-19.