Temporal dynamics of primary motor cortex gamma oscillation amplitude and piper corticomuscular coherence changes during motor control

Muthukumaraswamy, Suresh

doi:10.1007/s00221-011-2775-z

Cited by 45 publications

(26 citation statements)

References 36 publications

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“…We used a simple finger movement task adapted from Muthukumaraswamy (2011), which involved simple abductions of the right hand index finger performed to an auditory cue. The cue consisted of a simple auditory tone (1000 Hz), played via a piezo electric device connected via plastic tubing to ear-inserts, followed by an inter-stimulus interval of 3.4–4.5 s. This gave approximately 130 epochs of data per ten minute recording session.…”

Section: Methodsmentioning

confidence: 99%

High precision anatomy for MEG

et al. 2014

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Precise MEG estimates of neuronal current flow are undermined by uncertain knowledge of the head location with respect to the MEG sensors. This is either due to head movements within the scanning session or systematic errors in co-registration to anatomy. Here we show how such errors can be minimized using subject-specific head-casts produced using 3D printing technology. The casts fit the scalp of the subject internally and the inside of the MEG dewar externally, reducing within session and between session head movements. Systematic errors in matching to MRI coordinate system are also reduced through the use of MRI-visible fiducial markers placed on the same cast. Bootstrap estimates of absolute co-registration error were of the order of 1 mm. Estimates of relative co-registration error were < 1.5 mm between sessions. We corroborated these scalp based estimates by looking at the MEG data recorded over a 6 month period. We found that the between session sensor variability of the subject's evoked response was of the order of the within session noise, showing no appreciable noise due to between-session movement. Simulations suggest that the between-session sensor level amplitude SNR improved by a factor of 5 over conventional strategies. We show that at this level of coregistration accuracy there is strong evidence for anatomical models based on the individual rather than canonical anatomy; but that this advantage disappears for errors of greater than 5 mm. This work paves the way for source reconstruction methods which can exploit very high SNR signals and accurate anatomical models; and also significantly increases the sensitivity of longitudinal studies with MEG.

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Section: Methodsmentioning

confidence: 99%

High precision anatomy for MEG

et al. 2014

View full text Add to dashboard Cite

show abstract

“…We used a simple finger movement task adapted from Muthukumaraswamy (2011), which involved abductions of the right hand index finger performed to an auditory cue. The cue consisted of a simple auditory tone (1000 Hz), played via a piezoelectric device connected via plastic tubing to ear-inserts, followed by an inter-stimulus interval of 3.4–4.5 s. This gave approximately 145 epochs of data per ten minute recording session.…”

Section: Methodsmentioning

confidence: 99%

Discrimination of cortical laminae using MEG

et al. 2014

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Typically MEG source reconstruction is used to estimate the distribution of current flow on a single anatomically derived cortical surface model. In this study we use two such models representing superficial and deep cortical laminae. We establish how well we can discriminate between these two different cortical layer models based on the same MEG data in the presence of different levels of co-registration noise, Signal-to-Noise Ratio (SNR) and cortical patch size. We demonstrate that it is possible to make a distinction between superficial and deep cortical laminae for levels of co-registration noise of less than 2 mm translation and 2° rotation at SNR > 11 dB. We also show that an incorrect estimate of cortical patch size will tend to bias layer estimates. We then use a 3D printed head-cast (Troebinger et al., 2014) to achieve comparable levels of co-registration noise, in an auditory evoked response paradigm, and show that it is possible to discriminate between these cortical layer models in real data.

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“…It has been recognised through application of time-varying coherence measures that CMC coherence fluctuates even when a subject attempts to maintain the same motor output (Muthukumaraswamy, 2011). As discussed earlier, the techniques introduced here allow us to focus on the fluctuations within the phase coupling rather than on the averaged measure of coupling.…”

Section: Neurophysiological Datamentioning

confidence: 99%

Markers of criticality in phase synchronization

Botcharova

Farmer

Berthouze

2014

Front. Syst. Neurosci.

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The concept of the brain as a critical dynamical system is very attractive because systems close to criticality are thought to maximize their dynamic range of information processing and communication. To date, there have been two key experimental observations in support of this hypothesis: (i) neuronal avalanches with power law distribution of size and (ii) long-range temporal correlations (LRTCs) in the amplitude of neural oscillations. The case for how these maximize dynamic range of information processing and communication is still being made and because a significant substrate for information coding and transmission is neural synchrony it is of interest to link synchronization measures with those of criticality. We propose a framework for characterizing criticality in synchronization based on an analysis of the moment-to-moment fluctuations of phase synchrony in terms of the presence of LRTCs. This framework relies on an estimation of the rate of change of phase difference and a set of methods we have developed to detect LRTCs. We test this framework against two classical models of criticality (Ising and Kuramoto) and recently described variants of these models aimed to more closely represent human brain dynamics. From these simulations we determine the parameters at which these systems show evidence of LRTCs in phase synchronization. We demonstrate proof of principle by analysing pairs of human simultaneous EEG and EMG time series, suggesting that LRTCs of corticomuscular phase synchronization can be detected in the resting state and experimentally manipulated. The existence of LRTCs in fluctuations of phase synchronization suggests that these fluctuations are governed by non-local behavior, with all scales contributing to system behavior. This has important implications regarding the conditions under which one should expect to see LRTCs in phase synchronization. Specifically, brain resting states may exhibit LRTCs reflecting a state of readiness facilitating rapid task-dependent shifts toward and away from synchronous states that abolish LRTCs.

show abstract

Temporal dynamics of primary motor cortex gamma oscillation amplitude and piper corticomuscular coherence changes during motor control

Cited by 45 publications

References 36 publications

High precision anatomy for MEG

High precision anatomy for MEG

Discrimination of cortical laminae using MEG

Markers of criticality in phase synchronization

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