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Circuits Generating Corticomuscular Coherence Investigated Using a Biophysically Based Computational Model. I. Descending Systems

Recordings of motor cortical activity typically show oscillations around 10 and 20 Hz; only those at 20 Hz are coherent with electromyograms (EMGs) of contralateral muscles. Experimental measurements of the phase difference between approximately 20-Hz oscillations in cortex and muscle are often diff...

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Detalles Bibliográficos
Autores principales: Williams, Elizabeth R., Baker, Stuart N.
Formato: Texto
Lenguaje:English
Publicado: American Physiological Society 2009
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2637020/
https://www.ncbi.nlm.nih.gov/pubmed/19019981
http://dx.doi.org/10.1152/jn.90362.2008
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author Williams, Elizabeth R.
Baker, Stuart N.
author_facet Williams, Elizabeth R.
Baker, Stuart N.
author_sort Williams, Elizabeth R.
collection PubMed
description Recordings of motor cortical activity typically show oscillations around 10 and 20 Hz; only those at 20 Hz are coherent with electromyograms (EMGs) of contralateral muscles. Experimental measurements of the phase difference between approximately 20-Hz oscillations in cortex and muscle are often difficult to reconcile with the known corticomuscular conduction delays. We investigated the generation of corticomuscular coherence further using a biophysically based computational model, which included a pool of motoneurons connected to motor units that generated EMGs. Delays estimated from the coherence phase–frequency relationship were sensitive to the width of the motor unit action potentials. In addition, the nonlinear properties of the motoneurons could produce complex, oscillatory phase–frequency relationships. This was due to the interaction of cortical inputs to the motoneuron pool with the intrinsic rhythmicity of the motoneurons; the response appeared more linear if the firing rate of motoneurons varied widely across the pool, such as during a strong contraction. The model was able to reproduce the smaller than expected delays between cortex and muscles seen in experiments. However, the model could not reproduce the constant phase over a frequency band sometimes seen in experiments, nor the lack of around 10-Hz coherence. Simple propagation of oscillations from cortex to muscle thus cannot completely explain the observed corticomuscular coherence.
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spelling pubmed-26370202009-02-09 Circuits Generating Corticomuscular Coherence Investigated Using a Biophysically Based Computational Model. I. Descending Systems Williams, Elizabeth R. Baker, Stuart N. J Neurophysiol Articles Recordings of motor cortical activity typically show oscillations around 10 and 20 Hz; only those at 20 Hz are coherent with electromyograms (EMGs) of contralateral muscles. Experimental measurements of the phase difference between approximately 20-Hz oscillations in cortex and muscle are often difficult to reconcile with the known corticomuscular conduction delays. We investigated the generation of corticomuscular coherence further using a biophysically based computational model, which included a pool of motoneurons connected to motor units that generated EMGs. Delays estimated from the coherence phase–frequency relationship were sensitive to the width of the motor unit action potentials. In addition, the nonlinear properties of the motoneurons could produce complex, oscillatory phase–frequency relationships. This was due to the interaction of cortical inputs to the motoneuron pool with the intrinsic rhythmicity of the motoneurons; the response appeared more linear if the firing rate of motoneurons varied widely across the pool, such as during a strong contraction. The model was able to reproduce the smaller than expected delays between cortex and muscles seen in experiments. However, the model could not reproduce the constant phase over a frequency band sometimes seen in experiments, nor the lack of around 10-Hz coherence. Simple propagation of oscillations from cortex to muscle thus cannot completely explain the observed corticomuscular coherence. American Physiological Society 2009-01 2008-11-19 /pmc/articles/PMC2637020/ /pubmed/19019981 http://dx.doi.org/10.1152/jn.90362.2008 Text en Copyright © 2009, American Physiological Society This document may be redistributed and reused, subject to www.the-aps.org/publications/journals/funding_addendum_policy.htm (http://www.the-aps.org/publications/journals/funding_addendum_policy.htm) .
spellingShingle Articles
Williams, Elizabeth R.
Baker, Stuart N.
Circuits Generating Corticomuscular Coherence Investigated Using a Biophysically Based Computational Model. I. Descending Systems
title Circuits Generating Corticomuscular Coherence Investigated Using a Biophysically Based Computational Model. I. Descending Systems
title_full Circuits Generating Corticomuscular Coherence Investigated Using a Biophysically Based Computational Model. I. Descending Systems
title_fullStr Circuits Generating Corticomuscular Coherence Investigated Using a Biophysically Based Computational Model. I. Descending Systems
title_full_unstemmed Circuits Generating Corticomuscular Coherence Investigated Using a Biophysically Based Computational Model. I. Descending Systems
title_short Circuits Generating Corticomuscular Coherence Investigated Using a Biophysically Based Computational Model. I. Descending Systems
title_sort circuits generating corticomuscular coherence investigated using a biophysically based computational model. i. descending systems
topic Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2637020/
https://www.ncbi.nlm.nih.gov/pubmed/19019981
http://dx.doi.org/10.1152/jn.90362.2008
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