Literature DB >> 33402020

Task-dependent recruitment across ankle extensor muscles and between mechanical demands is driven by the metabolic cost of muscle contraction.

Adrian K M Lai1, Taylor J M Dick2, Andrew A Biewener3, James M Wakeling1.   

Abstract

The nervous system is faced with numerous strategies for recruiting a large number of motor units within and among muscle synergists to produce and control body movement. This is challenging, considering multiple combinations of motor unit recruitment may result in the same movement. Yet vertebrates are capable of performing a wide range of movement tasks with different mechanical demands. In this study, we used an experimental human cycling paradigm and musculoskeletal simulations to test the theory that a strategy of prioritizing the minimization of the metabolic cost of muscle contraction, which improves mechanical efficiency, governs the recruitment of motor units within a muscle and the coordination among synergist muscles within the limb. Our results support our hypothesis, for which measured muscle activity and model-predicted muscle forces in soleus-the slower but stronger ankle plantarflexor-is favoured over the weaker but faster medial gastrocnemius (MG) to produce plantarflexor force to meet increased load demands. However, for faster-contracting speeds induced by faster-pedalling cadence, the faster MG is favoured. Similar recruitment patterns were observed for the slow and fast fibres within each muscle. By contrast, a commonly used modelling strategy that minimizes muscle excitations failed to predict force sharing and known physiological recruitment strategies, such as orderly motor unit recruitment. Our findings illustrate that this common strategy for recruiting motor units within muscles and coordination between muscles can explain the control of the plantarflexor muscles across a range of mechanical demands.

Entities:  

Keywords:  human; motor unit recruitment; muscle; musculoskeletal modelling

Mesh:

Year:  2021        PMID: 33402020      PMCID: PMC7879759          DOI: 10.1098/rsif.2020.0765

Source DB:  PubMed          Journal:  J R Soc Interface        ISSN: 1742-5662            Impact factor:   4.118


  45 in total

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Journal:  J Electromyogr Kinesiol       Date:  2000-12       Impact factor: 2.368

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Journal:  Exp Brain Res       Date:  1983       Impact factor: 1.972

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Journal:  J Neurophysiol       Date:  1980-03       Impact factor: 2.714

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Journal:  J Biomech       Date:  1981       Impact factor: 2.712

8.  A muscle's force depends on the recruitment patterns of its fibers.

Authors:  James M Wakeling; Sabrina S M Lee; Allison S Arnold; Maria de Boef Miara; Andrew A Biewener
Journal:  Ann Biomed Eng       Date:  2012-02-17       Impact factor: 3.934

9.  Why are Antagonist Muscles Co-activated in My Simulation? A Musculoskeletal Model for Analysing Human Locomotor Tasks.

Authors:  Adrian K M Lai; Allison S Arnold; James M Wakeling
Journal:  Ann Biomed Eng       Date:  2017-09-12       Impact factor: 3.934

10.  Quantifying Achilles tendon force in vivo from ultrasound images.

Authors:  Taylor J M Dick; Allison S Arnold; James M Wakeling
Journal:  J Biomech       Date:  2016-08-08       Impact factor: 2.712

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