Literature DB >> 28390003

Modifying motor unit territory placement in the Fuglevand model.

Jason W Robertson1,2,3, Jamie A Johnston4,5.   

Abstract

The Fuglevand model is often used to address challenging questions in neurophysiology; however, there are elements of the neuromuscular system unaccounted for in the model. For instance, in some muscles, slow and fast motor units (MUs) tend to reside deep and superficially in the muscle, respectively, necessarily altering the development of surface electromyogram (EMG) power during activation. Thus, the objective of this study was to replace the randomized MU territory (MUT) placement algorithm in the Fuglevand model with an optimized method capable of reflecting these observations. To accomplish this, a weighting term was added to a previously developed optimization algorithm to encourage regionalized MUT placement. The weighting term consequently produced significantly different muscle fibre type content in the deep and superficial portions of the muscle. The relation between simulated EMG and muscle force was found to be significantly affected by regionalization. These changes were specifically a function of EMG power, as force was unaffected by regionalization. These findings suggest that parameterizing MUT regionalization will allow the model to produce a larger variety of EMG-force relations, as is observed physiologically, and could potentially simulate the loss of specific MU types as observed in ageing and clinical populations.

Keywords:  Electromyography (EMG); Motor units; Neuromuscular modelling; Simulation

Mesh:

Year:  2017        PMID: 28390003     DOI: 10.1007/s11517-017-1645-7

Source DB:  PubMed          Journal:  Med Biol Eng Comput        ISSN: 0140-0118            Impact factor:   2.602


  62 in total

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Journal:  IEEE Trans Biomed Eng       Date:  2001-06       Impact factor: 4.538

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Authors:  Jamie A Johnston; Gabriele Formicone; Thomas M Hamm; Marco Santello
Journal:  Exp Brain Res       Date:  2010-11-03       Impact factor: 1.972

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Journal:  J Electromyogr Kinesiol       Date:  2009-09-15       Impact factor: 2.368

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Journal:  J Physiol       Date:  1996-09-01       Impact factor: 5.182

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Authors:  R L Lieber; J Fridén
Journal:  Muscle Nerve       Date:  2000-11       Impact factor: 3.217

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  1 in total

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

Authors:  Chengjun Huang; Maoqi Chen; Yingchun Zhang; Sheng Li; Ping Zhou
Journal:  Neural Plast       Date:  2021-06-22       Impact factor: 3.599

  1 in total

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