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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

A: firing rates of individual motoneurons for the standard model (black) and a model in which persistent inward currents (PICs) were removed from the MNs (red). B: corticomuscular coherence. C: interspike interval (ISI) histograms from one MN. D: corticomuscular coherence phase for these 2 stimulations. Simulation length was 4,017 s.
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f7: A: firing rates of individual motoneurons for the standard model (black) and a model in which persistent inward currents (PICs) were removed from the MNs (red). B: corticomuscular coherence. C: interspike interval (ISI) histograms from one MN. D: corticomuscular coherence phase for these 2 stimulations. Simulation length was 4,017 s.

Mentions: One feature of the present model is that it uses a realistic representation of the motoneurons, including sodium and calcium PICs (Li and Bennett 2003; Li et al. 2004a,b). Once activated, these currents allow the motoneurons to continue firing with less synaptic input. To investigate the impact of PICs on corticomuscular coherence, the model was simulated with these conductances disabled. To maintain a contraction strength of 5% MVC, a larger rate of independent input to motoneurons was required in this reduced model (8 kHz with PICs vs. 22.9 kHz without). We have seen that the dispersion of motoneuron firing rates can change both the coherence and its phase. Figure 7 A shows that the motoneurons have similar firing rates with and without PICs (Fig. 7A), indicating the success of the increased synaptic input at maintaining firing in the no-PIC simulation. The effect on corticomuscular coherence is shown in Fig. 7B; removing PICs reduced the coherence amplitude, but the location of the subpeaks was similar. The increased independent input required in the absence of PICs meant that the cortical input to the motoneuron was a smaller fraction of the total than when PICs were able to contribute to the current driving the motoneurons to fire. The reduced signal-to-noise ratio in the motoneuron inputs led to the reduction in coherence.


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

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

A: firing rates of individual motoneurons for the standard model (black) and a model in which persistent inward currents (PICs) were removed from the MNs (red). B: corticomuscular coherence. C: interspike interval (ISI) histograms from one MN. D: corticomuscular coherence phase for these 2 stimulations. Simulation length was 4,017 s.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

f7: A: firing rates of individual motoneurons for the standard model (black) and a model in which persistent inward currents (PICs) were removed from the MNs (red). B: corticomuscular coherence. C: interspike interval (ISI) histograms from one MN. D: corticomuscular coherence phase for these 2 stimulations. Simulation length was 4,017 s.
Mentions: One feature of the present model is that it uses a realistic representation of the motoneurons, including sodium and calcium PICs (Li and Bennett 2003; Li et al. 2004a,b). Once activated, these currents allow the motoneurons to continue firing with less synaptic input. To investigate the impact of PICs on corticomuscular coherence, the model was simulated with these conductances disabled. To maintain a contraction strength of 5% MVC, a larger rate of independent input to motoneurons was required in this reduced model (8 kHz with PICs vs. 22.9 kHz without). We have seen that the dispersion of motoneuron firing rates can change both the coherence and its phase. Figure 7 A shows that the motoneurons have similar firing rates with and without PICs (Fig. 7A), indicating the success of the increased synaptic input at maintaining firing in the no-PIC simulation. The effect on corticomuscular coherence is shown in Fig. 7B; removing PICs reduced the coherence amplitude, but the location of the subpeaks was similar. The increased independent input required in the absence of PICs meant that the cortical input to the motoneuron was a smaller fraction of the total than when PICs were able to contribute to the current driving the motoneurons to fire. The reduced signal-to-noise ratio in the motoneuron inputs led to the reduction in 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