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Circuits generating corticomuscular coherence investigated using a biophysically based computational model. I. Descending systems.

Williams ER, Baker SN - J. Neurophysiol. (2008)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Institute of Neuroscience, Newcastle University, Henry Wellcome Building, Newcastle upon Tyne, NE2 4HH, UK.

ABSTRACT
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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A: schematic of the model. B: waveform of motoneuron excitatory postsynaptic potential (EPSP). C: dependence of motoneuron firing rate on rate of excitatory synaptic input for motoneurons (MNs) 1 (top curve), 60, 100, and 177 (bottom curve). D: example raw data output from the model.
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f1: A: schematic of the model. B: waveform of motoneuron excitatory postsynaptic potential (EPSP). C: dependence of motoneuron firing rate on rate of excitatory synaptic input for motoneurons (MNs) 1 (top curve), 60, 100, and 177 (bottom curve). D: example raw data output from the model.

Mentions: The model builds on a model that we previously published (Baker and Lemon 1998), consisting of a pool of realistic motoneurons that receive common input from the motor cortex (Fig. 1 A). EMG and force are simulated from the firing of the motoneurons. By simulating these population recordings, which are typically measured experimentally, we were able directly to compare results from our simulations with experimental estimates of corticomuscular coherence.


Circuits generating corticomuscular coherence investigated using a biophysically based computational model. I. Descending systems.

Williams ER, Baker SN - J. Neurophysiol. (2008)

A: schematic of the model. B: waveform of motoneuron excitatory postsynaptic potential (EPSP). C: dependence of motoneuron firing rate on rate of excitatory synaptic input for motoneurons (MNs) 1 (top curve), 60, 100, and 177 (bottom curve). D: example raw data output from the model.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2637020&req=5

f1: A: schematic of the model. B: waveform of motoneuron excitatory postsynaptic potential (EPSP). C: dependence of motoneuron firing rate on rate of excitatory synaptic input for motoneurons (MNs) 1 (top curve), 60, 100, and 177 (bottom curve). D: example raw data output from the model.
Mentions: The model builds on a model that we previously published (Baker and Lemon 1998), consisting of a pool of realistic motoneurons that receive common input from the motor cortex (Fig. 1 A). EMG and force are simulated from the firing of the motoneurons. By simulating these population recordings, which are typically measured experimentally, we were able directly to compare results from our simulations with experimental estimates of corticomuscular coherence.

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Institute of Neuroscience, Newcastle University, Henry Wellcome Building, Newcastle upon Tyne, NE2 4HH, UK.

ABSTRACT
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.

Show MeSH
Related in: MedlinePlus