Measuring the cortical correlation structure of spontaneous oscillatory activity with EEG and MEG

Siems, Marcus; Pape, Anna-Antonia; Hipp, Joerg F.; Siegel, Markus

doi:10.1016/j.neuroimage.2016.01.055

Cited by 102 publications

(130 citation statements)

References 47 publications

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“…Indeed, the so-called FCD (FC dynamics) allows for a better characterization of the data and a more accurate constraint on the model. We will show that the maximal richness of the MEG FCD is present around the carrier frequency of 10–16 Hz, in the same range where static band-limited amplitude correlations are known to be maximal in electrophysiological data (Brookes et al, 2011b, Hipp and Siegel, 2015, Siems et al, 2016). …”

Section: Introductionmentioning

confidence: 73%

Single or multiple frequency generators in on-going brain activity: A mechanistic whole-brain model of empirical MEG data

et al. 2017

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During rest, envelopes of band-limited on-going MEG signals co-vary across the brain in consistent patterns, which have been related to resting-state networks measured with fMRI. To investigate the genesis of such envelope correlations, we consider a whole-brain network model assuming two distinct fundamental scenarios: one where each brain area generates oscillations in a single frequency, and a novel one where each brain area can generate oscillations in multiple frequency bands. The models share, as a common generator of damped oscillations, the normal form of a supercritical Hopf bifurcation operating at the critical border between the steady state and the oscillatory regime. The envelopes of the simulated signals are compared with empirical MEG data using new methods to analyse the envelope dynamics in terms of their phase coherence and stability across the spectrum of carrier frequencies.Considering the whole-brain model with a single frequency generator in each brain area, we obtain the best fit with the empirical MEG data when the fundamental frequency is tuned at 12 Hz. However, when multiple frequency generators are placed at each local brain area, we obtain an improved fit of the spatio-temporal structure of on-going MEG data across all frequency bands. Our results indicate that the brain is likely to operate on multiple frequency channels during rest, introducing a novel dimension for future models of large-scale brain activity.

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Section: Introductionmentioning

confidence: 73%

Single or multiple frequency generators in on-going brain activity: A mechanistic whole-brain model of empirical MEG data

et al. 2017

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“…As mentioned earlier that volume conduction effect has influence on the readings of both modalities. This is demonstrated in the experiments conducted by many researchers for different head models (Vorwerk, et al) (Siems, Pape, Hipp, & Siegel, 2016). They also found that tissue anisotropy and the white matter has major conduction effect for EEG while MEG is only affected by white matter anisotropy (Haueisen, et al, 2002) (Siems, Pape, Hipp, & Siegel, 2016).…”

Section: Introductionmentioning

confidence: 83%

“…This is demonstrated in the experiments conducted by many researchers for different head models (Vorwerk, et al) (Siems, Pape, Hipp, & Siegel, 2016). They also found that tissue anisotropy and the white matter has major conduction effect for EEG while MEG is only affected by white matter anisotropy (Haueisen, et al, 2002) (Siems, Pape, Hipp, & Siegel, 2016). These two phenomena also affect source reconstruction accuracy where EEG is more susceptible to muscle artifacts and different head models while MEG is less prone to both factors (Wolters, et al, 2006).…”

Section: Introductionmentioning

confidence: 87%

Are Sources of EEG and MEG rhythmic activity the same? An analysis based on BC-VARETA

Yuan

Riaz

Razzaq

et al. 2019

Preprint

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In the resting state (closed or open eyes) the electroencephalogram (EEG) and the magnetoencephalogram (MEG) exibit rhythmic brain activity is typically the 10 Hz alpha rhythm. It has a topographic frequency spectral distribution that is, quite similar for both modalities--something not surprising since both EEG and MEG are generated by the same basic oscillations in thalamocortical circuitry. However, different physical aspects underpin the two types of signals. Does this difference lead to a different distribution of reconstructed sources for EEG and MEG rhythms? This question is important for the transferal of results from one modality to the other but has surprisingly received scant attention till now. We address this issue by comparing eyes open EEG source spectra recorded from 77 subjects from the Cuban Human Brain Mapping project with the MEG of 63 subjects from the Human Connectome Project. Source spectra for each voxel and frequency were obtained via a novel sparsecovariance inverse method (BC-VARETA) based on individualized BEM head models with subject-specific regularization parameters (noise to signal ratio). We circumvent the zero inflated statistical issue arising from sparse estimation by employing a novel dimensionality reduction technique known as Zero-inflated Factor Analysis (ZIFA). Both minimum energy and Hotelling's T-2 tests showed that ZIFA scores for MEG and EEG sources were significantly different at all frequency bands. These results exclude a simple identification of MEG and EEG sources of resting-state EEG rhythms. Further study is required to determine the relative contribution of instrumental, physical or physiological mechanisms to these differences.

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“…Prior work has suggested that such infraslow correlations, often computed in the form of envelope correlations of the scalp EEG, intracranial EEG (icEEG), and magnetoencephalography, may form a basis for distant spatial coupling in the brain, both related to and independent of fMRI-defined functional networks. [13][14][15][16][17] In a recent study, we examined magnitude-squared coherence (MSC) of low-frequency amplitude modulations (<0.15 Hz) of band power time series in the icEEG (ie, the second spectrum) for evidence of correlation between different parts of the brain and their correspondence with the fMRI-defined default mode network. 18 Although we reported a lack of support for the default mode network with our measures, we found that MSC was strongest for intercontact distances between 0 and 5 cm, and grew weaker with greater intercontact distance, and that in general, envelope coupling was strongest in the delta band and decreased with increasing frequency.…”

Section: Introductionmentioning

confidence: 99%

Seizure susceptibility and infraslow modulatory activity in the intracranial electroencephalogram

et al. 2018

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Measuring the cortical correlation structure of spontaneous oscillatory activity with EEG and MEG

Cited by 102 publications

References 47 publications

Single or multiple frequency generators in on-going brain activity: A mechanistic whole-brain model of empirical MEG data

Single or multiple frequency generators in on-going brain activity: A mechanistic whole-brain model of empirical MEG data

Are Sources of EEG and MEG rhythmic activity the same? An analysis based on BC-VARETA

Seizure susceptibility and infraslow modulatory activity in the intracranial electroencephalogram

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