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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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Related in: MedlinePlus

Results from simulations (4,017 s) using white-noise cortical inputs (A–G) and a cortical input modulated to produce spectral peaks at 10 and 20 Hz (H–N). A and H: power spectrum of cortical input. B and I: power spectrum of rectified electromyogram (EMG). C and J: coherence between cortical input and rectified EMG. D and K: motoneuron spike autocorrelation, averaged over all cells in the motoneuron pool. E and L: cross-correlation between motoneuron spikes, averaged over all possible pairs of motoneurons in the pool (E was smoothed using a Gaussian kernel, width 0.5 ms). F and M: power spectrum of the population spike activity of the motoneurons. Bin width of 1 ms in time domain plots. G and N: average coherence between motoneuron spike trains (averaged over 100 pairs of motoneurons, chosen at random).
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f2: Results from simulations (4,017 s) using white-noise cortical inputs (A–G) and a cortical input modulated to produce spectral peaks at 10 and 20 Hz (H–N). A and H: power spectrum of cortical input. B and I: power spectrum of rectified electromyogram (EMG). C and J: coherence between cortical input and rectified EMG. D and K: motoneuron spike autocorrelation, averaged over all cells in the motoneuron pool. E and L: cross-correlation between motoneuron spikes, averaged over all possible pairs of motoneurons in the pool (E was smoothed using a Gaussian kernel, width 0.5 ms). F and M: power spectrum of the population spike activity of the motoneurons. Bin width of 1 ms in time domain plots. G and N: average coherence between motoneuron spike trains (averaged over 100 pairs of motoneurons, chosen at random).

Mentions: The model was simulated to determine the properties of corticomuscular coherence that would result from simple feedforward propagation of oscillations from the cortex to the motoneurons. A contraction strength of 5% MVC was simulated (involving activity in 177 motoneurons; based on Wani and Guha 1975) because this corresponds to the weak forces commonly used to investigate corticomuscular coherence experimentally in man. Although it is difficult to assess the strength of contraction as a percentage of MVC in animal studies, these also usually use weak contractions for which 5% MVC probably represents a reasonable estimated level. Figure 2 A shows the power spectrum of the cortical input; this was flat, since the cortical input was generated as white noise. The EMG power, however, revealed peaks at 10, 20, and 30 Hz (Fig. 2B). Although coherence between cortical input and EMG was significant over the entire frequency range (2–70 Hz), there were clear peaks at approximately 10, 20, and 30 Hz, with coherence values of 0.039, 0.054, and 0.049, respectively (Fig. 2C).


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

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

Results from simulations (4,017 s) using white-noise cortical inputs (A–G) and a cortical input modulated to produce spectral peaks at 10 and 20 Hz (H–N). A and H: power spectrum of cortical input. B and I: power spectrum of rectified electromyogram (EMG). C and J: coherence between cortical input and rectified EMG. D and K: motoneuron spike autocorrelation, averaged over all cells in the motoneuron pool. E and L: cross-correlation between motoneuron spikes, averaged over all possible pairs of motoneurons in the pool (E was smoothed using a Gaussian kernel, width 0.5 ms). F and M: power spectrum of the population spike activity of the motoneurons. Bin width of 1 ms in time domain plots. G and N: average coherence between motoneuron spike trains (averaged over 100 pairs of motoneurons, chosen at random).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

f2: Results from simulations (4,017 s) using white-noise cortical inputs (A–G) and a cortical input modulated to produce spectral peaks at 10 and 20 Hz (H–N). A and H: power spectrum of cortical input. B and I: power spectrum of rectified electromyogram (EMG). C and J: coherence between cortical input and rectified EMG. D and K: motoneuron spike autocorrelation, averaged over all cells in the motoneuron pool. E and L: cross-correlation between motoneuron spikes, averaged over all possible pairs of motoneurons in the pool (E was smoothed using a Gaussian kernel, width 0.5 ms). F and M: power spectrum of the population spike activity of the motoneurons. Bin width of 1 ms in time domain plots. G and N: average coherence between motoneuron spike trains (averaged over 100 pairs of motoneurons, chosen at random).
Mentions: The model was simulated to determine the properties of corticomuscular coherence that would result from simple feedforward propagation of oscillations from the cortex to the motoneurons. A contraction strength of 5% MVC was simulated (involving activity in 177 motoneurons; based on Wani and Guha 1975) because this corresponds to the weak forces commonly used to investigate corticomuscular coherence experimentally in man. Although it is difficult to assess the strength of contraction as a percentage of MVC in animal studies, these also usually use weak contractions for which 5% MVC probably represents a reasonable estimated level. Figure 2 A shows the power spectrum of the cortical input; this was flat, since the cortical input was generated as white noise. The EMG power, however, revealed peaks at 10, 20, and 30 Hz (Fig. 2B). Although coherence between cortical input and EMG was significant over the entire frequency range (2–70 Hz), there were clear peaks at approximately 10, 20, and 30 Hz, with coherence values of 0.039, 0.054, and 0.049, respectively (Fig. 2C).

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