Longitudinal development of muscle strength and relationship with motor unit activity and muscle morphological characteristics in youth athletes

Long-chain n-3 polyunsaturated fatty acid supplementation and neuromuscular function in older adults

PURPOSE OF REVIEW

This review aims to explore the latest research investigating the effects of marine-derived long-chain n -3 polyunsaturated fatty acid (LC n -3 PUFA) supplementation on neuromuscular function in older adults.

RECENT FINDINGS

Ageing results in a decline in skeletal muscle strength and mass. There is growing evidence that LC n -3 PUFA supplementation increases muscle strength and mass in healthy older adults, yet the mechanisms underlying these effects remain elusive. Recent studies investigating LC n -3 PUFA supplementation have demonstrated effects on neuromuscular function such as increases in the compound muscle action potential (M-wave) amplitude and surface electromyography alongside increases in muscular strength. Therefore, evidence suggests that LC n -3 PUFA may elicit a beneficial effect at the neuromuscular junction and possess neuroprotective properties in older adults.

SUMMARY

LC n -3 PUFA supplementation may increase or maintain neuromuscular function throughout the ageing process. Further research is warranted to investigate the long-term effects LC n -3 PUFA supplementation on neuromuscular outcomes such as single motor unit properties and cortical/supraspinal networks, utilizing state-of-the-art techniques in neuromuscular physiology.

Prediction of 1-year change in knee extension strength by neuromuscular properties in older adults

Improving muscle strength and preventing muscle weakness are important for older adults. The change in strength can be effectively explained by skeletal muscle mass and neural factors. Neural factors are important for older adults because the variation of neural components is greater in older than in young adults, and any decline in strength cannot solely be explained by a decrease in skeletal muscle mass. The purpose of the present study was to investigate whether skeletal muscle mass or motor unit firing properties could explain the change in muscle strength after 1 year. Thirty-eight older adults (75.0 ± 4.7 years, 156.6 ± 7.7 cm, 55.5 ± 9.4 kg, 26 women) performed maximum voluntary knee extension and their skeletal muscle mass was measured using a bioimpedance device. During a submaximal contraction task, high-density surface electromyography was recorded and the signals were decomposed into individual motor unit firing. As an index of motor unit firing properties, the slope and y-intercept (MU intercept) were calculated from the regression line between recruitment thresholds and firing rates in each participant. After 1 year, their maximum knee extension torque was evaluated again. A stepwise multiple regression linear model with sex and age as covariates indicated that MU intercept was a significant explanation with a negative association for the 1-year change in muscle strength (β =  - 0.493, p = 0.004), but not skeletal muscle mass (p = 0.364). The results suggest that neural components might be predictors of increasing and decreasing muscle strength rather than skeletal muscle mass.

Tutorial: Analysis of central and peripheral motor unit properties from decomposed High-Density surface EMG signals with openhdemg

High-Density surface Electromyography (HD-sEMG) is the most established technique for the non-invasive analysis of single motor unit (MU) activity in humans. It provides the possibility to study the central properties (e.g., discharge rate) of large populations of MUs by analysis of their firing pattern. Additionally, by spike-triggered averaging, peripheral properties such as MUs conduction velocity can be estimated over adjacent regions of the muscles and single MUs can be tracked across different recording sessions. In this tutorial, we guide the reader through the investigation of MUs properties from decomposed HD-sEMG recordings by providing both the theoretical knowledge and practical tools necessary to perform the analyses. The practical application of this tutorial is based on openhdemg, a free and open-source community-based framework for the automated analysis of MUs properties built on Python 3 and composed of different modules for HD-sEMG data handling, visualisation, editing, and analysis. openhdemg is interfaceable with most of the available recording software, equipment or decomposition techniques, and all the built-in functions are easily adaptable to different experimental needs. The framework also includes a graphical user interface which enables users with limited coding skills to perform a robust and reliable analysis of MUs properties without coding.