Force variability is mostly not motor noise: Theoretical implications for motor control

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.

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Model-Based Analysis of Muscle Strength and EMG-Force Relation with respect to Different Patterns of Motor Unit Loss

Neural Plasticity ◽

10.1155/2021/5513224 ◽

2021 ◽

Vol 2021 ◽

pp. 1-9

Author(s):

Chengjun Huang ◽

Maoqi Chen ◽

Yingchun Zhang ◽

Sheng Li ◽

Ping Zhou

Keyword(s):

Muscle Strength ◽

Motor Neuron ◽

Motor Unit ◽

Muscle Force ◽

Motor Units ◽

Force Variability ◽

Valuable Insight ◽

Model Based ◽

Force Relation ◽

Pool Model

This study presents a model-based sensitivity analysis of the strength of voluntary muscle contraction with respect to different patterns of motor unit loss. A motor unit pool model was implemented including simulation of a motor neuron pool, muscle force, and surface electromyogram (EMG) signals. Three different patterns of motor unit loss were simulated, including (1) motor unit loss restricted to the largest ones, (2) motor unit loss restricted to the smallest ones, and (3) motor unit loss without size restriction. The model outputs including muscle force amplitude, variability, and the resultant EMG-force relation were quantified under two different motor neuron firing strategies. It was found that motor unit loss restricted to the largest ones had the most dominant impact on muscle strength and significantly changed the EMG-force relation, while loss restricted to the smallest motor units had a pronounced effect on force variability. These findings provide valuable insight toward our understanding of the neurophysiological mechanisms underlying experimental observations of muscle strength, force control, and EMG-force relation in both normal and pathological conditions.

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Discharge Rate Variability Influences the Variation in Force Fluctuations Across the Working Range of a Hand Muscle

Journal of Neurophysiology ◽

10.1152/jn.01122.2004 ◽

2005 ◽

Vol 93 (5) ◽

pp. 2449-2459 ◽

Cited By ~ 272

Author(s):

Chet T. Moritz ◽

Benjamin K. Barry ◽

Michael A. Pascoe ◽

Roger M. Enoka

Keyword(s):

Motor Unit ◽

Discharge Rate ◽

Motor Units ◽

Peak Discharge ◽

High Threshold ◽

Force Variability ◽

Recruitment Threshold ◽

Unit Model ◽

Wide Range ◽

Significant Difference

The goal of this study was to improve the ability of a motor unit model to predict experimentally measured force variability across a wide range of forces. Motor unit discharge characteristics were obtained from 38 motor units of the first dorsal interosseus muscle. Motor unit discharges were recorded in separate isometric contractions that ranged from 4 to 85% of the maximal voluntary contraction (MVC) force above recruitment threshold. High-threshold motor units exhibited both greater minimal and peak discharge rates compared with low-threshold units ( P < 0.01). Minimal discharge rate increased from 7 to 23 pps, and peak discharge rate increased from 14 to 38 pps with an increase in recruitment threshold. Relative discharge rate variability (CV) decreased exponentially for each motor unit from an average of 30 to 13% as index finger force increased above recruitment threshold. In separate experiments, force variability was assessed at eight force levels from 2 to 95% MVC. The CV for force decreased from 4.9 to 1.4% as force increased from 2 to 15% MVC ( P < 0.01) and remained constant at higher forces (1.2–1.9%; P = 0.14). When the motor unit model was revised using these experimental findings, discharge rate variability was the critical factor that resulted in no significant difference between simulated and experimental force variability ( P = 0.22) at all force levels. These results support the hypothesis that discharge rate variability is a major determinant of the trends in isometric force variability across the working range of a muscle.

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Motor-Unit Recruitment Plays an Important Role in Determining the Relationship Between Muscle Force and Force Variability

Biomedical Journal of Scientific & Technical Research ◽

10.26717/bjstr.2018.08.001722 ◽

2018 ◽

Vol 8 (5) ◽

Author(s):

Wan X Yao

Keyword(s):

Motor Unit ◽

Muscle Force ◽

Motor Unit Recruitment ◽

Force Variability ◽

The Relationship

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Experimentally Valid Predictions of Muscle Force and EMG in Models of Motor-Unit Function Are Most Sensitive to Neural Properties

Journal of Neurophysiology ◽

10.1152/jn.00577.2007 ◽

2007 ◽

Vol 98 (3) ◽

pp. 1581-1590 ◽

Cited By ~ 32

Author(s):

Kevin G. Keenan ◽

Francisco J. Valero-Cuevas

Keyword(s):

Motor Neuron ◽

Motor Unit ◽

Large Scale ◽

Computational Models ◽

Muscle Force ◽

State Of The Art ◽

Force Variability ◽

Muscle Properties ◽

Unit Function ◽

Parameter Values

Computational models of motor-unit populations are the objective implementations of the hypothesized mechanisms by which neural and muscle properties give rise to electromyograms (EMGs) and force. However, the variability/uncertainty of the parameters used in these models—and how they affect predictions—confounds assessing these hypothesized mechanisms. We perform a large-scale computational sensitivity analysis on the state-of-the-art computational model of surface EMG, force, and force variability by combining a comprehensive review of published experimental data with Monte Carlo simulations. To exhaustively explore model performance and robustness, we ran numerous iterative simulations each using a random set of values for nine commonly measured motor neuron and muscle parameters. Parameter values were sampled across their reported experimental ranges. Convergence after 439 simulations found that only 3 simulations met our two fitness criteria: approximating the well-established experimental relations for the scaling of EMG amplitude and force variability with mean force. An additional 424 simulations preferentially sampling the neighborhood of those 3 valid simulations converged to reveal 65 additional sets of parameter values for which the model predictions approximate the experimentally known relations. We find the model is not sensitive to muscle properties but very sensitive to several motor neuron properties—especially peak discharge rates and recruitment ranges. Therefore to advance our understanding of EMG and muscle force, it is critical to evaluate the hypothesized neural mechanisms as implemented in today's state-of-the-art models of motor unit function. We discuss experimental and analytical avenues to do so as well as new features that may be added in future implementations of motor-unit models to improve their experimental validity.

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The extraction of neural strategies from the surface EMG

Journal of Applied Physiology ◽

10.1152/japplphysiol.01070.2003 ◽

2004 ◽

Vol 96 (4) ◽

pp. 1486-1495 ◽

Cited By ~ 861

Author(s):

Dario Farina ◽

Roberto Merletti ◽

Roger M. Enoka

Keyword(s):

Motor Unit ◽

Neural Control ◽

Muscle Activation ◽

Control Strategies ◽

Discharge Rate ◽

Motor Units ◽

Surface Emg ◽

Global Features ◽

Emg Signal ◽

The Many

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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Recruitment and derecruitment characteristics of motor units in a hand muscle of young and old adults

Journal of Applied Physiology ◽

10.1152/japplphysiol.00807.2009 ◽

2010 ◽

Vol 108 (6) ◽

pp. 1659-1667 ◽

Cited By ~ 17

Author(s):

Mark Jesunathadas ◽

Adam R. Marmon ◽

James M. Gibb ◽

Roger M. Enoka

Keyword(s):

Motor Unit ◽

Discharge Rate ◽

Age Groups ◽

Motor Units ◽

Motor Unit Recruitment ◽

Force Variability ◽

Old Adults ◽

Hand Muscle ◽

The Difference ◽

Ramp Contractions

The significant decline in motor neuron number after ∼60 yr of age is accompanied by a remodeling of the neuromuscular system so that average motor unit force increases and the ability of old adults to produce an intended force declines. One possible explanation for the loss of movement precision is that the remodeling increases the difference in recruitment forces between successively recruited motor units in old adults and this augments force variability at motor unit recruitment. The purpose of the study was to compare the forces and discharge characteristics of motor units in a hand muscle of young and old adults at motor unit recruitment and derecruitment. The difference in recruitment force between pairs of motor units did not differ between young ( n = 54) and old adults ( n = 56; P = 0.702). However, old adults had a greater proportion of contractions in which motor units discharged action potentials transiently before discharging continuously during the ramp increase in force (young: 0.32; old: 0.41; P = 0.045). Force variability at motor unit recruitment was greater for old adults compared with young adults ( P ≤ 0.010), but discharge rate and discharge variability did not differ between age groups ( P ≥ 0.729). These results suggest that the difference in force between the recruitment of successive motor units does not differ between age groups, but that motor unit recruitment may be more transient and could contribute to the greater variability in force observed in old adults during graded ramp contractions.

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Frequency Modulation of Motor Unit Discharge Has Task-Dependent Effects on Fluctuations in Motor Output

Journal of Neurophysiology ◽

10.1152/jn.00390.2005 ◽

2005 ◽

Vol 94 (4) ◽

pp. 2878-2887 ◽

Cited By ~ 28

Author(s):

Carol J. Mottram ◽

Evangelos A. Christou ◽

François G. Meyer ◽

Roger M. Enoka

Keyword(s):

Motor Unit ◽

Frequency Modulation ◽

Discharge Rate ◽

Motor Output ◽

Unit Discharge ◽

Rate Of Increase ◽

Output Force ◽

Position Task ◽

Motor Unit Discharge ◽

Target Force

The rate of change in the fluctuations in motor output differs during the performance of fatiguing contractions that involve different types of loads. The purpose of this study was to examine the contribution of frequency modulation of motor unit discharge to the fluctuations in the motor output during sustained contractions with the force and position tasks. In separate tests with the upper arm vertical and the elbow flexed to 1.57 rad, the seated subjects maintained either a constant upward force at the wrist (force task) or a constant elbow angle (position task). The force and position tasks were performed in random order at a target force equal to 3.6 ± 2.1% (mean ± SD) of the maximal voluntary contraction (MVC) force above the recruitment threshold of an isolated motor unit from the biceps brachii. Each subject maintained the two tasks for an identical duration (161 ± 93 s) at a mean target force of 22.4 ± 13.6% MVC. As expected, the rate of increase in the fluctuations in motor output (force task: SD for detrended force; position task: SD for vertical acceleration) was greater for the position task than the force task ( P < 0.001). The amplitude of the coefficient of variation (CV) and the power spectra for motor unit discharge were similar between tasks ( P > 0.1) and did not change with time ( P > 0.1), and could not explain the different rates of increase in motor output fluctuations for the two tasks. Nonetheless, frequency modulation of motor unit discharge differed during the two tasks and predicted ( P < 0.001) both the CV for discharge rate (force task: 1–3, 12–13, and 14–15 Hz; position task: 0–1, and 1–2 Hz) and the fluctuations in motor output (force task: 5–6, 9–10, 12–13, and 14–15 Hz; position task: 6–7, 14–15, 17–19, 20–21, and 23–24 Hz). Frequency modulation of motor unit discharge rate differed for the force and position tasks and influenced the ability to sustain steady contractions.

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