z-logo
open-access-imgOpen Access
Model-Based Analysis of Muscle Strength and EMG-Force Relation with respect to Different Patterns of Motor Unit Loss
Author(s) -
Chengjun Huang,
Maoqi Chen,
Yingchun Zhang,
Sheng Li,
Ping Zhou
Publication year - 2021
Publication title -
neural plasticity
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.288
H-Index - 68
eISSN - 2090-5904
pISSN - 1687-5443
DOI - 10.1155/2021/5513224
Subject(s) - motor unit , motor unit recruitment , electromyography , neurophysiology , muscle contraction , physical medicine and rehabilitation , neuroscience , computer science , anatomy , psychology , medicine
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.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.
Having issues? You can contact us here
Accelerating Research

Address

John Eccles House
Robert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom