Motor unit control strategies of endurance- versus resistance-trained individuals

2015 ◽  
Vol 52 (5) ◽  
pp. 832-843 ◽  
Author(s):  
Trent J. Herda ◽  
Jacob A. Siedlik ◽  
Michael A. Trevino ◽  
Michael A. Cooper ◽  
Joseph P. Weir
2021 ◽  
Vol 53 (8S) ◽  
pp. 165-166
Author(s):  
Sunggun Jeon ◽  
William Miller ◽  
Jun Seob Song ◽  
Xin Ye

Author(s):  
S. Jayne Garland ◽  
Courtney L. Pollock ◽  
Tanya D. Ivanova

2021 ◽  
Vol 17 (3) ◽  
pp. e1008707
Author(s):  
Akira Nagamori ◽  
Christopher M. Laine ◽  
Gerald E. Loeb ◽  
Francisco J. Valero-Cuevas

Variability in muscle force is a hallmark of healthy and pathological human behavior. Predominant theories of sensorimotor control assume ‘motor noise’ leads to force variability and its ‘signal dependence’ (variability in muscle force whose amplitude increases with intensity of neural drive). Here, we demonstrate that the two proposed mechanisms for motor noise (i.e. the stochastic nature of motor unit discharge and unfused tetanic contraction) cannot account for the majority of force variability nor for its signal dependence. We do so by considering three previously underappreciated but physiologically important features of a population of motor units: 1) fusion of motor unit twitches, 2) coupling among motoneuron discharge rate, cross-bridge dynamics, and muscle mechanics, and 3) a series-elastic element to account for the aponeurosis and tendon. These results argue strongly against the idea that force variability and the resulting kinematic variability are generated primarily by ‘motor noise.’ Rather, they underscore the importance of variability arising from properties of control strategies embodied through distributed sensorimotor systems. As such, our study provides a critical path toward developing theories and models of sensorimotor control that provide a physiologically valid and clinically useful understanding of healthy and pathologic force variability.


Author(s):  
Sunggun Jeon ◽  
William M. Miller ◽  
Xin Ye

Background: This study examined the motor unit (MU) control strategies for non-fatiguing isometric elbow flexion tasks at 40% and 70% maximal voluntary isometric contraction. Methods: Nineteen healthy individuals performed two submaximal tasks with similar torque levels: contracting against an immovable object (force task), and maintaining the elbow joint angle against an external load (position task). Surface electromyographic (EMG) signals were collected from the agonist and antagonist muscles. The signals from the agonist were decomposed into individual action potential trains. The linear regression analysis was used to examine the MU recruitment threshold (RT) versus mean firing rates (MFR), and RT versus derecruitment threshold (DT) relationships. Results: Both agonist and antagonist muscles’ EMG amplitudes did not differ between two tasks. The linear slopes of the MU RT versus MFR and RT versus DT relationships during the position task were more negative (p = 0.010) and more positive (p = 0.023), respectively, when compared to the force task. Conclusions: To produce a similar force output, the position task may rely less on the recruitment of relatively high-threshold MUs. Additionally, as the force output decreases, MUs tend to derecruit at a higher force level during the position task.


2004 ◽  
Vol 96 (4) ◽  
pp. 1486-1495 ◽  
Author(s):  
Dario Farina ◽  
Roberto Merletti ◽  
Roger M. Enoka

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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