Trends in Neurosciences
ReviewCommon drive of motor units in regulation of muscle force
Abstract
The neuromuscular system is responsible for all our interactions with our environment. Although recent decades have witnessed numerous discoveries that have shed light into various properties of this system, the basic principles underlying its overall operation still remain poorly understood. In this article, Carlo J. De Luca and Zeynep Erim discuss the concept of common drive of motor units that provides a possible scheme for the control of motor units, unifying various seemingly isolated findings that have been reported. According to this concept, a pool of motor units that makes up a muscle is controlled collectively during a contraction of that muscle. The unique firing patterns of individual motor units are effected, not by separate command signals sent to these units, but by one common drive to which motor units respond differently. The specific architecture of the system and the orderly gradation in the inherent properties of individual elements enable a single source to control the activities of all the motor units in a given pool. Such an arrangement relieves the CNS from the burden of monitoring and regulating each motor unit separately.
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Tutorial: Analysis of central and peripheral motor unit properties from decomposed High-Density surface EMG signals with openhdemg
2024, Journal of Electromyography and KinesiologyHigh-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.
Tutorial. Surface electromyogram (sEMG) amplitude estimation: Best practices
2023, Journal of Electromyography and KinesiologyThis tutorial intends to provide insight, instructions and “best practices” for those who are novices—including clinicians, engineers and non-engineers—in extracting electromyogram (EMG) amplitude from the bipolar surface EMG (sEMG) signal of voluntary contractions. A brief discussion of sEMG amplitude extraction from high density sEMG (HDsEMG) arrays and feature extraction from electrically elicited contractions is also provided.
This tutorial attempts to present its main concepts in a straightforward manner that is accessible to novices in the field not possessing a wide range of technical background (if any) in this area. Surface EMG amplitude, also referred to as the sEMG envelope [often implemented as root mean square (RMS) sEMG or average rectified value (ARV) sEMG], quantifies the voltage variation of the sEMG signal and is grossly related to the overall neural excitation of the muscle and to peripheral parameters.
The tutorial briefly reviews the physiological origin of the voluntary sEMG signal and sEMG recording, including electrode configurations, sEMG signal transduction, electronic conditioning and conversion by an analog-to-digital converter. These topics have been covered in greater detail in prior tutorials in this series. In depth descriptions of state-of-the-art methods for computing sEMG amplitude are then provided, including guidance on signal pre-conditioning, absolute value vs. square-law detection, selection of appropriate sEMG amplitude smoothing filters and attenuation of measurement noise. The tutorial provides a detailed list of best practices for sEMG amplitude estimation.
Evolution of surface electromyography: From muscle electrophysiology towards neural recording and interfacing
2023, Journal of Electromyography and KinesiologySurface electromyography (EMG) comprises a recording of electrical activity from the body surface generated by muscle fibres during muscle contractions. Its characteristics depend on the fibre membrane potentials and the neural activation signal sent from the motor neurons to the muscles. EMG has been classically used as the primary investigation tool in kinesiology studies in a variety of applications. More recently, surface EMG techniques have evolved from single-channel methods to high-density systems with hundreds of electrodes. High-density EMG recordings can be deconvolved to estimate the discharge times of spinal motor neurons innervating the recorded muscles, with algorithms that have been developed and validated in the last two decades. Within limits and with some variability across muscles, these techniques provide a non-invasive method to study relatively large populations of motor neurons in humans. Surface EMG is thus evolving from a peripheral measure of muscle electrical activity towards a neural recording and neural interfacing signal. These advances in technology have had a major impact on our fundamental understanding of the neural control of movement and have exposed new perspectives in neurotechnologies. Here we provide an overview and perspective of modern EMG technology, as derived from past achievements, and its impact in neurophysiology and neural engineering.
Skeletal muscle models composed of motor units: A review
2023, Journal of Electromyography and KinesiologyThe mathematical muscle models should include several aspects of muscle structure and physiology. First, muscle force is the sum of forces of multiple motor units (MUs), which have different contractile properties and play different roles in generating muscle force. Second, whole muscle activity is an effect of net excitatory inputs to a pool of motoneurons innervating the muscle, which have different excitability, influencing MU recruitment. In this review, we compare various methods for modeling MU twitch and tetanic forces and then discuss muscle models composed of different MU types and number. We first present four different analytical functions used for twitch modeling and show limitations related to the number of twitch describing parameters. We also show that a nonlinear summation of twitches should be considered in modeling tetanic contractions. We then compare different muscle models, most of which are variations of Fuglevand’s model, adopting a common drive hypothesis and the size principle. We pay attention to integrating previously developed models into a consensus model based on physiological data from in vivo experiments on the rat medial gastrocnemius muscle and its respective motoneurons. Finally, we discuss the shortcomings of existing models and potential applications for studying MU synchronization, potentiation, and fatigue.
Consensus for experimental design in electromyography (CEDE) project: Single motor unit matrix
2023, Journal of Electromyography and KinesiologyThe analysis of single motor unit (SMU) activity provides the foundation from which information about the neural strategies underlying the control of muscle force can be identified, due to the one-to-one association between the action potentials generated by an alpha motor neuron and those received by the innervated muscle fibers. Such a powerful assessment has been conventionally performed with invasive electrodes (i.e., intramuscular electromyography (EMG)), however, recent advances in signal processing techniques have enabled the identification of single motor unit (SMU) activity in high-density surface electromyography (HDsEMG) recordings. This matrix, developed by the Consensus for Experimental Design in Electromyography (CEDE) project, provides recommendations for the recording and analysis of SMU activity with both invasive (needle and fine-wire EMG) and non-invasive (HDsEMG) SMU identification methods, summarizing their advantages and disadvantages when used during different testing conditions. Recommendations for the analysis and reporting of discharge rate and peripheral (i.e., muscle fiber conduction velocity) SMU properties are also provided. The results of the Delphi process to reach consensus are contained in an appendix. This matrix is intended to help researchers to collect, report, and interpret SMU data in the context of both research and clinical applications.
Detecting motor unit abnormalities in amyotrophic lateral sclerosis using high-density surface EMG
2022, Clinical NeurophysiologyThe purpose of this study was to detect specific motor unit (MU) abnormalities in people with amyotrophic lateral sclerosis (ALS) compared to controls using high-density surface electromyography (HD-SEMG).
Sixteen people with ALS and 16 control subjects. The participants performed ramp up and sustained contractions at 30% of their maximal voluntary contraction. HD-SEMG signals were recorded in the vastus lateralis muscle and decomposed into individual MU firing behavior using a convolution blind source separation method.
In total, 339 MUs were detected (people with ALS; n = 93, control subjects; n = 246). People with ALS showed significantly higher mean firing rate, recruitment threshold, coefficient of variation of the MU firing rate, MU firing rate at recruitment, and motoneurons excitability than those of control subjects (p < 0.001). The number of MU, MU firing rate, recruitment threshold, and MU firing rate at recruitment were significantly correlated with disease severity (p < 0.001). Multivariable analysis revealed that an increased MU firing rate at recruitment was independently associated with ALS.
These results suggest increased excitability at recruitment, which is consistent with neurodegeneration results in a compensatory increase in MU activity.
Abnormal MU firing behavior provides an important physiological index for understanding the pathophysiology of ALS.