Motor-Unit Pool Model of Continuous and Discrete Force Variability

Motor Control ◽  
2011 ◽  
Vol 15 (4) ◽  
pp. 439-455 ◽  
Author(s):  
Xiaogang Hu ◽  
Karl M. Newell
Keyword(s):  
Motor Unit ◽  
Force Variability ◽  
Pool Model

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

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.

scholarly journals Spasticity, weakness, force variability, and sustained spontaneous motor unit discharges of resting spastic-paretic biceps brachii muscles in chronic stroke

2013 ◽  
Vol 48 (1) ◽  
pp. 85-92 ◽  
Author(s):  
Shuo-Hsiu Chang ◽  
Gerard e. Francisco ◽  
Ping Zhou ◽  
W. Zev Rymer ◽  
Sheng Li
Keyword(s):  
Motor Unit ◽  
Biceps Brachii ◽  
Chronic Stroke ◽  
Force Variability

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

2021 ◽  
Vol 17 (3) ◽  
pp. e1008707
Author(s):  
Akira Nagamori ◽  
Christopher M. Laine ◽  
Gerald E. Loeb ◽  
Francisco J. Valero-Cuevas
Keyword(s):  
Motor Unit ◽  
Muscle Force ◽  
Critical Path ◽  
Control Strategies ◽  
Discharge Rate ◽  
Muscle Mechanics ◽  
Sensorimotor Control ◽  
Force Variability ◽  
Motor Noise ◽  
Signal Dependence

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.

scholarly journals Sources of Signal-Dependent Noise During Isometric Force Production

2002 ◽  
Vol 88 (3) ◽  
pp. 1533-1544 ◽  
Author(s):  
Kelvin E. Jones ◽  
Antonia F. de C. Hamilton ◽  
Daniel M. Wolpert
Keyword(s):  
Motor Unit ◽  
Isometric Force ◽  
Force Production ◽  
Motor Command ◽  
Force Variability ◽  
Isometric Contractions ◽  
Linear Scaling ◽  
Force Output ◽  
Extensor Pollicis Longus ◽  
Isometric Force Production

It has been proposed that the invariant kinematics observed during goal-directed movements result from reducing the consequences of signal-dependent noise (SDN) on motor output. The purpose of this study was to investigate the presence of SDN during isometric force production and determine how central and peripheral components contribute to this feature of motor control. Peripheral and central components were distinguished experimentally by comparing voluntary contractions to those elicited by electrical stimulation of the extensor pollicis longus muscle. To determine other factors of motor-unit physiology that may contribute to SDN, a model was constructed and its output compared with the empirical data. SDN was evident in voluntary isometric contractions as a linear scaling of force variability (SD) with respect to the mean force level. However, during electrically stimulated contractions to the same force levels, the variability remained constant over the same range of mean forces. When the subjects were asked to combine voluntary with stimulation-induced contractions, the linear scaling relationship between the SD and mean force returned. The modeling results highlight that much of the basic physiological organization of the motor-unit pool, such as range of twitch amplitudes and range of recruitment thresholds, biases force output to exhibit linearly scaled SDN. This is in contrast to the square root scaling of variability with mean force present in any individual motor-unit of the pool. Orderly recruitment by twitch amplitude was a necessary condition for producing linearly scaled SDN. Surprisingly, the scaling of SDN was independent of the variability of motoneuron firing and therefore by inference, independent of presynaptic noise in the motor command. We conclude that the linear scaling of SDN during voluntary isometric contractions is a natural by-product of the organization of the motor-unit pool that does not depend on signal-dependent noise in the motor command. Synaptic noise in the motor command and common drive, which give rise to the variability and synchronization of motoneuron spiking, determine the magnitude of the force variability at a given level of mean force output.

scholarly journals Motor unit recruitment strategies and muscle properties determine the influence of synaptic noise on force steadiness

2012 ◽  
Vol 107 (12) ◽  
pp. 3357-3369 ◽  
Author(s):  
Jakob L. Dideriksen ◽  
Francesco Negro ◽  
Roger M. Enoka ◽  
Dario Farina
Keyword(s):  
Motor Unit ◽  
Low Frequency ◽  
Spike Trains ◽  
Motor Unit Recruitment ◽  
Force Variability ◽  
Muscle Properties ◽  
Single Motor Unit ◽  
Force Steadiness ◽  
Low Frequency Oscillations ◽  
Synaptic Noise

Motoneurons receive synaptic inputs from tens of thousands of connections that cause membrane potential to fluctuate continuously (synaptic noise), which introduces variability in discharge times of action potentials. We hypothesized that the influence of synaptic noise on force steadiness during voluntary contractions is limited to low muscle forces. The hypothesis was examined with an analytical description of transduction of motor unit spike trains into muscle force, a computational model of motor unit recruitment and rate coding, and experimental analysis of interspike interval variability during steady contractions with the abductor digiti minimi muscle. Simulations varied contraction force, level of synaptic noise, size of motor unit population, recruitment range, twitch contraction times, and level of motor unit short-term synchronization. Consistent with the analytical derivations, simulations and experimental data showed that force variability at target forces above a threshold was primarily due to low-frequency oscillations in neural drive, whereas the influence of synaptic noise was almost completely attenuated by two low-pass filters, one related to convolution of motoneuron spike trains with motor unit twitches (temporal summation) and the other attributable to summation of single motor unit forces (spatial summation). The threshold force above which synaptic noise ceased to influence force steadiness depended on recruitment range, size of motor unit population, and muscle contractile properties. This threshold was low (<10% of maximal force) for typical values of these parameters. Results indicate that motor unit recruitment and muscle properties of a typical muscle are tuned to limit the influence of synaptic noise on force steadiness to low forces and that the inability to produce a constant force during stronger contractions is mainly attributable to the common low-frequency oscillations in motoneuron discharge rates.

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

2005 ◽  
Vol 93 (5) ◽  
pp. 2449-2459 ◽  
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.

scholarly journals Visual information processing in older adults: reaction time and motor unit pool modulation

2018 ◽  
Vol 120 (5) ◽  
pp. 2630-2639 ◽  
Author(s):  
MinHyuk Kwon ◽  
Evangelos A. Christou
Keyword(s):  
Older Adults ◽  
Information Processing ◽  
Reaction Time ◽  
Motor Unit ◽  
Visual Feedback ◽  
Force Control ◽  
Visual Information ◽  
Visual Information Processing ◽  
Force Variability ◽  
Premotor Time

Presently, there is no evidence that magnification of visual feedback has motor implications beyond impairments in force control during a visuomotor task. We hypothesized that magnification of visual feedback would increase visual information processing, alter the muscle activation, and exacerbate the response time in older adults. To test this hypothesis, we examined whether magnification of visual feedback during a reaction time task alters the premotor time and the motor unit pool activation of older adults. Participants responded as fast as possible to a visual stimulus while they maintained a steady ankle dorsiflexion force (15% maximum) either with low-gain or high-gain visual feedback of force. We quantified the following: 1) response time and its components (premotor and motor time), 2) force variability, and 3) motor unit pool activity of the tibialis anterior muscle. Older adults exhibited longer premotor time and greater force variability than young adults. Only in older adults, magnification of visual feedback lengthened the premotor time and exacerbated force variability. The slower premotor time in older adults with high-gain visual feedback was associated with increased force variability and an altered modulation of the motor unit pool. In conclusion, our findings provide novel evidence that magnification of visual feedback also exacerbates premotor time during a reaction time task in older adults, which is correlated with force variability and an altered modulation of motor unit pool. Thus these findings suggest that visual information processing deficiencies in older adults could result in force control and reaction time impairments. NEW & NOTEWORTHY It is unknown whether magnification of visual feedback has motor implications beyond impairments in force control for older adults. We examined whether it impairs reaction time and motor unit pool activation. The findings provide novel evidence that magnification of visual feedback exacerbates reaction time by lengthening premotor time, which implicates time for information processing in older adults, which is correlated with force variability and an altered modulation of motor unit pool.

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