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

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          Abstract

          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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          Most cited references 47

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          Models of recruitment and rate coding organization in motor-unit pools.

          1. Isometric muscle force and the surface electromyogram (EMG) were simulated from a model that predicted recruitment and firing times in a pool of 120 motor units under different levels of excitatory drive. The EMG-force relationships that emerged from simulations using various schedules of recruitment and rate coding were compared with those observed experimentally to determine which of the modeled schemes were plausible representations of the actual organization in motor-unit pools. 2. The model was comprised of three elements: a motoneuron model, a motor-unit force model, and a model of the surface EMG. Input to the neuron model was an excitatory drive function representing the net synaptic input to motoneurons during voluntary muscle contractions. Recruitment thresholds were assigned such that many motoneurons had low thresholds and relatively few neurons had high thresholds. Motoneuron firing rate increased as a linear function of excitatory drive between recruitment threshold and peak firing rate levels. The sequence of discharge times for each motoneuron was simulated as a random renewal process. 3. Motor-unit twitch force was estimated as an impulse response of a critically damped, second-order system. Twitch amplitudes were assigned according to rank in the recruitment order, and twitch contraction times were inversely related to twitch amplitude. Nonlinear force-firing rate behavior was simulated by varying motor-unit force gain as a function of the instantaneous firing rate and the contraction time of the unit. The total force exerted by the muscle was computed as the sum of the motor-unit forces. 4. Motor-unit action potentials were simulated on the basis of estimates of the number and location of motor-unit muscle fibers and the propagation velocity of the fiber action potentials. The number of fibers innervated by each unit was assumed to be directly proportional to the twitch force. The area of muscle encompassing unit fibers was proportional to the number of fibers innervated, and the location of motor-unit territories were randomly assigned within the muscle cross section. Action-potential propagation velocities were estimated from an inverse function of contraction time. The train of discharge times predicted from the motoneuron model determined the occurrence of each motor-unit action potential. The surface EMG was synthesized as the sum of all motor-unit action-potential trains. 5. Two recruitment conditions were tested: narrow (limit of recruitment 70% maximum excitation).(ABSTRACT TRUNCATED AT 400 WORDS)
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            The relation between the surface electromyogram and muscular force.

            1. Motor units in the first dorsal interosseus muscle of normal human subjects were recorded by needle electrodes, together with the surface electromyogram (e.m.g.). The wave form contributed by each motor unit to the surface e.m.g. was determined by signal averaging. 2. The peak-to-peak amplitude of the wave form contributed to the surface e.m.g. by a motor unit increased approximately as the square root of the threshold force at which the unit was recruited. The peak-to-peak duration of the wave form was independent of the threshold force. 3. Large and small motor units are uniformly distributed throughout this muscle, and the muscle fibres making up a motor unit may be widely dispersed. 4. The rectified surface e.m.g. was computed as a function of force, based on the sample of motor units recorded. The largest contribution of motor unit recruitment occurs at low force levels, while the contribution of increased firing rate becomes more important at higher force levels. 5. Possible bases for the common experimental observation that the mean rectified surface e.m.g. varies linearly with the force generated by a muscle are discussed. E.m.g. potentials and contractile responses may both sum non-linearly at moderate to high force levels, but in such a way that the rectified surface e.m.g. is still approximately linearly related to the force produced by the muscle.
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              Multichannel Blind Source Separation Using Convolution Kernel Compensation

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                Author and article information

                Contributors
                Journal
                Neural Plast
                Neural Plast
                np
                Neural Plasticity
                Hindawi
                2090-5904
                1687-5443
                2021
                22 June 2021
                : 2021
                Affiliations
                1Guangdong Work Injury Rehabilitation Center, Guangzhou, China
                2Department of Physical Medicine and Rehabilitation, University of Texas Health Science Center at Houston, TX, USA
                3TIRR Memorial Hermann Research Center, Houston, TX, USA
                4Institute of Rehabilitation Engineering, University of Health and Rehabilitation Sciences, Qingdao, China
                5Department of Biomedical Engineering, University of Houston, Houston, TX, USA
                Author notes

                Academic Editor: Mou-Xiong Zheng

                Article
                10.1155/2021/5513224
                8245245
                34257638
                Copyright © 2021 Chengjun Huang et al.

                This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                Funding
                Funded by: Guangzhou Science and Technology Program
                Award ID: 201704030039
                Funded by: Natural Science Foundation of Shandong Province
                Award ID: ZR2020KF012
                Categories
                Research Article

                Neurosciences

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