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

Lookup NU author(s): Dr Elizabeth Williams, Professor Stuart BakerORCiD



Williams ER, Baker SN. Circuits generating corticomuscular coherence investigated using a biophysically based computational model. I. Descending systems. J Neurophysiol 101: 31-41, 2009. First published November 19, 2008; doi:10.1152/jn.90362.2008. 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.

Publication metadata

Author(s): Williams ER, Baker SN

Publication type: Article

Publication status: Published

Journal: Journal of Neurophysiology

Year: 2009

Volume: 101

Issue: 1

Pages: 31-41

Date deposited: 09/06/2010

ISSN (print): 0022-3077

ISSN (electronic): 1522-1598

Publisher: American Physiological Society


DOI: 10.1152/jn.90362.2008


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