Measuring electrophysiological connectivity by power envelope correlation: a technical review on MEG methods

O'Neill, George; Barratt, Eleanor L.; Hunt, Benjamin A. E.; Tewarie, Prejaas; Brookes, Matthew J.

doi:10.1088/0031-9155/60/21/r271

Cited by 123 publications

(112 citation statements)

References 118 publications

(140 reference statements)

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“…Another limitation is that we used envelope-based correlations which are known to be strongly influenced by volume conduction. We opted for this measure because it is widely used (Cabral et al, 2014;Hipp et al, 2012;O'Neill, Barratt, Hunt, Tewarie, & Brookes, 2015) and captures predominantly slow power modulations, which is closer to what the BOLD signal would capture. However, we did conduct our analyses using coherence and imaginary part of coherence (Figures S6 and S7) and found no major differences.…”

Section: Limitationsmentioning

confidence: 99%

Using structural connectivity to augment community structure in EEG functional connectivity

Glomb

Mullier

Carboni

et al. 2019

Preprint

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Recently, EEG recording techniques (high-density EEG) as well as source analysis have improved markedly. Yet, analyzing whole-cortex EEG signals in source space is not standard, partly because EEG signals suffer from volume conduction, introducing spurious functional connections (statistical dependencies) between nearby sources. Genuine functional connectivity (FC) reflecting functional relationships is impossible to disentangle from spurious FC, impairing the applicability of network neuroscience to EEG. Here, we use information from white matter structural connectivity (SC) to attenuate the impact of volume conduction on EEG-FC. We confirm that FC (power envelope correlations) is predicted by the SC beyond the impact of Euclidean distance. We then smooth the EEG signal in the space spanned by graphs derived from SC. Thereby, FC between nearby, structurally connected brain regions is increased while FC between non-connected regions remains unchanged. We hypothesize that this results in an increase in genuine relative to spurious FC. We analyze changes in FC due to smoothing by assessing the resemblance between EEG-and fMRI-FC, and find that smoothing increases resemblance, both in terms of overall correlation and community structure. This suggests that information from the SC can indeed be used to attenuate the effect of volume conduction on EEG source signals. Author summaryIn this study, we combine high-density EEG recorded during resting state with white matter connectivity obtained from diffusion MRI and fiber tracking. We leverage the additional information contained in the structural connectome towards augmenting the source level EEG functional connectivity. In particular, it is known -and confirmed in this study -that the activity of brain regions that possess a direct anatomical connection is, on average, more strongly correlated than that of regions that have no such direct link. We use the structural connectome to define a graph and smooth the source reconstructed EEG signal in the space spanned by this graph. We compare the resulting "filtered" signal correlation matrices to those obtained from fMRI and find that such "graph filtering" improves the agreement between EEG and fMRI functional connectivity structure. Thus suggests that structural connectivity can be used to attenuate some of the limitations imposed by volume conduction.

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Section: Limitationsmentioning

confidence: 99%

Using structural connectivity to augment community structure in EEG functional connectivity

Glomb

Mullier

Carboni

et al. 2019

Preprint

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“…Like phase-coupling, amplitude-coupling may not only result from, and thus reflect, neuronal interactions, but may also regulate these interactions by temporally aligning distant processes associated with fluctuating oscillations von Nicolai et al, 2014). Also amplitude-coupling is expressed in wellstructured cortical networks that match known anatomical and functional connectivity (Hipp et al, 2012;Siems et al, 2016), resemble fMRI correlation patterns (Brookes et al, 2011;Deco and Corbetta, 2011;Destexhe et al, 1999;Hipp and Siegel, 2015;Mantini et al, 2007;Nir et al, 2008;O'Neill et al, 2015), and are more stable than phase-coupling networks (Colclough et al, 2016;Wang et al, 2014). Amplitude-coupling is largely driven by amplitude dynamics below 0.1 Hz (Hipp et al, 2012), which may reflect the slow establishment and decay of communicating networks (Destexhe et al, 1999;Leopold et al, 2003;Mantini et al, 2007;Larson-Prior et al, 2011;Hipp et al, 2012;Engel et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Dissociated cortical phase- and amplitude-coupling patterns in the human brain

Siems

Siegel

2018

Preprint

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Highlights• Systematic comparison of cortical phase-and amplitude-coupling patterns • Demonstration of genuine amplitude coupling independent of phase coupling bias• Amplitude-and phase coupling patterns differ across many cortical regions and frequencies AbstractCoupling of neuronal oscillations may reflect and facilitate the communication between neuronal populations. Two primary neuronal coupling modes have been described:phase-coupling and amplitude-coupling. Theoretically, both coupling modes are independent, but so far, their neuronal relationship remains unclear. Here, we combined MEG, source-reconstruction and simulations to systematically compare cortical phasecoupling and amplitude-coupling patterns in the human brain. Importantly, we took into account a critical bias of amplitude-coupling measures due to phase-coupling. We found differences between both coupling modes across a broad frequency range and most of the cortex. Furthermore, by combining empirical measurements and simulations we ruled out that these results were caused by methodological biases, but instead reflected genuine neuronal amplitude coupling. Overall, our results suggest that cortical phaseand amplitude-coupling patterns are non-redundant, which may reflect at least partly distinct neuronal mechanisms. Furthermore, our findings highlight and clarify the compound nature of amplitude coupling measures.

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“…With this in mind, a growing body of work has begun to show that, via appropriate modeling of MEG data, networks of electrophysiological functional connectivity can be mapped (27)(28)(29). The rich temporal complexity of MEG signals means that multiple ways to characterize functional connectivity exist (30).…”

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confidence: 99%

Relationships between cortical myeloarchitecture and electrophysiological networks

Hunt

Tewarie

Mougin

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

101

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The human brain relies upon the dynamic formation and dissolution of a hierarchy of functional networks to support ongoing cognition. However, how functional connectivities underlying such networks are supported by cortical microstructure remains poorly understood. Recent animal work has demonstrated that electrical activity promotes myelination. Inspired by this, we test a hypothesis that gray-matter myelin is related to electrophysiological connectivity. Using ultra-high field MRI and the principle of structural covariance, we derive a structural network showing how myelin density differs across cortical regions and how separate regions can exhibit similar myeloarchitecture. Building upon recent evidence that neural oscillations mediate connectivity, we use magnetoencephalography to elucidate networks that represent the major electrophysiological pathways of communication in the brain. Finally, we show that a significant relationship exists between our functional and structural networks; this relationship differs as a function of neural oscillatory frequency and becomes stronger when integrating oscillations over frequency bands. Our study sheds light on the way in which cortical microstructure supports functional networks. Further, it paves the way for future investigations of the gray-matter structure/function relationship and its breakdown in pathology.network | functional connectivity | myelination | magnetoencephalography | MRI T he way in which integration of functionally specific brain regions supports ongoing cognition is one of the most important questions in neuroscience, and noninvasive in vivo imaging provides a tool to investigate this interregional connectivity in terms of both brain function and structure. Functional connectivity refers to statistical interdependencies between patterns of brain "activity" measured at separate cortical locations (1) and, even in the "resting state," measured spontaneous brain activity defines nonrandom networks that are related to cognitive processes (2). The way in which these functional networks are supported by structural white-matter pathways is reasonably well understood (3). However, it is likely that the structure-function association extends to graymatter morphology, for which fundamental understanding is lacking. Structural morphology of the cortex is known to vary significantly between individuals, and does so in an organized fashion. For example, individuals with a high cortical volume in Broca's area typically exhibit high cortical volume in Wernicke's area, reflecting a language network (4). Similar observations can be made between other associated cortical regions (5, 6). This is known as structural covariance (7-9) and it allows the formation of matrices showing how structural properties of individual brain regions covary over subjects. In this paper, we assess structural covariance based upon cortical myeloarchitecture and probe its relationship to functional networks that are assessed based upon measured spontaneous brain activity.Noninvasive map...

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Measuring electrophysiological connectivity by power envelope correlation: a technical review on MEG methods

Cited by 123 publications

References 118 publications

Using structural connectivity to augment community structure in EEG functional connectivity

Using structural connectivity to augment community structure in EEG functional connectivity

Dissociated cortical phase- and amplitude-coupling patterns in the human brain

Relationships between cortical myeloarchitecture and electrophysiological networks

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