Identifying true brain interaction from EEG data using the imaginary part of coherency

Nolte, Guido; Bai, Ou; Wheaton, Lewis A.; Mari, Zoltán; Vorbach, Sherry; Hallett, Mark

doi:10.1016/j.clinph.2004.04.029

Cited by 1,578 publications

(1,744 citation statements)

References 34 publications

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“…It has been shown in PascualMarqui et al (2011) to give an improved connectivity measure as compared to the imaginary coherence proposed by Nolte et al (2004).…”

Section: Data Conditioningmentioning

confidence: 94%

“…Nolte et al, 2004) between time series x and y in the frequency band ω is: ( 1) which is based on the cross-spectrum given by the covariance and variances of the signals, and where i is the imaginary unit ( 1  ). The squared modulus of the coherence is: …”

Section: Data Conditioningmentioning

confidence: 99%

“…Also, the confounding effect of volume conduction in the conventional computation of EEG coherence has been criticized, and omission of zero phase angle coherence values was proposed as remedy (Nolte et al, 2004). Moreover, since the waveform of an EEG time series from a head surface electrode depends on the chosen reference, conventional head surface coherence is reference-dependent (examples in Lehmann et al, 2006).…”

Section: Introductionmentioning

confidence: 99%

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Reduced functional connectivity between cortical sources in five meditation traditions detected with lagged coherence using EEG tomography

Lehmann¹,

Faber²,

Tei

et al. 2012

NeuroImage

141

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Brain functional states are established by functional connectivities between brain regions. In experienced meditators (13 Tibetan Buddhists, 15 QiGong, 14 Sahaja Yoga, 14 Ananda Marga Yoga, 15 Zen), 19-channel EEG was recorded before, during and after that meditation exercise which their respective tradition regards as route to the most desirable meditative state. The head surface EEG data were recomputed (sLORETA) into 19 cortical regional source model time series. All 171 functional connectivities between regions were computed as 'lagged coherence' for the eight EEG frequency bands (delta through gamma). This analysis removes ambiguities of localization, volume conduction-induced inflation of coherence, and reference-dependence. All significant differences (corrected for multiple testing) between meditation compared to no-task rest before and after meditation showed lower coherence during meditation, in all five traditions and eight (inhibitory as well as excitatory) frequency bands. Conventional coherence between the original head surface EEG time series very predominantly also showed reduced coherence during meditation. The topography of the functional connectivities was examined via PCAbased computation of principal connectivities. When going into and out of meditation, significantly different connectivities revealed clearly different topographies in the delta frequency band and minor differences in the beta-2 band. The globally reduced functional interdependence between brain regions in meditation suggests that interaction between the self process functions is minimized, and that constraints on the self process by other processes are minimized, thereby leading to the subjective experience of noninvolvement, detachment and letting go, as well as of all-oneness and dissolution of ego borders during meditation. AbstractBrain functional states are established by functional connectivities between brain regions. In experienced meditators (13 Tibetan Buddhists, 15 QiGong, 14 Sahaja Yoga, 14 Ananda Marga Yoga, 15 Zen), 19-channel EEG was recorded before, during and after that meditation exercise which their respective tradition regards as route to the most desirable meditative state. The head surface EEG data were recomputed (sLORETA) into 19 cortical regional source model time series. All 171 functional connectivities between regionswere computed as 'lagged coherence' for the eight EEG frequency bands (delta through gamma). This analysis removes ambiguities of localization, volume conduction-induced inflation of coherence, and reference-dependence. All significant differences (corrected for multiple testing) between meditation compared to no-task rest before and after meditation showed lower coherence during meditation, in all five traditions and eight (inhibitory as well as excitatory) frequency bands. Conventional coherence between the original head surface EEG time series very predominantly also showed reduced coherence during meditation. The topography of the functional connectivities was examined ...

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“…It has been shown in PascualMarqui et al (2011) to give an improved connectivity measure as compared to the imaginary coherence proposed by Nolte et al (2004).…”

Section: Data Conditioningmentioning

confidence: 94%

Section: Data Conditioningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Reduced functional connectivity between cortical sources in five meditation traditions detected with lagged coherence using EEG tomography

Lehmann¹,

Faber²,

Tei

et al. 2012

NeuroImage

141

View full text Add to dashboard Cite

show abstract

“…It has been intensely used in EEG literature to describe the dependency between two scalp locations, however it is inflated by the artificial in-phase (zero-lag) correlation engendered by volume conduction (Nunez and Srinivasan 2006). Recently Nolte et al (2004) proposed to consider instead the "imaginary part" of the coherency (the non-squared coherence)…”

Section: Group Blind Source Separationmentioning

confidence: 99%

On the “Dependence” of “Independent” Group EEG Sources; an EEG Study on Two Large Databases

Congedo

John

Ridder³

et al. 2009

Brain Topogr

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Abstract. The aim of this work is to study the coherence profile (dependence) of robust eyes-closed resting EEG sources isolated by group blind source separation (gBSS). We employ a test-retest strategy using two large sample normative databases (N=57 and N=84). Using a BSS method in the complex Fourier domain, we show that we can rigourously study the out-of-phase dependence of the exctracted components, albeit they are extracted so as to be in-phase independent (by BSS definition). Our focus on lagged communication between components effectively yields dependence measures unbiased by volume conduction effects, which is a major concern about the validity of any dependence measures issued by EEG measurements. We are able to show the organization of the extracted components in two networks. Within each network components oscillate coherently with multiple-frequency dynamics, whereas between networks they exchange information at non-random multiple time-lag rates.

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“…Increasing the size of the brain volumes studied makes the chances of observing signal leakage statistically more likely, and for this reason an effective means to reduce leakage between the data matrices and is of key importance. It is well known that leakage gives rise to a zerophase-lag linear interaction between projected signals, this fact has been exploited in previous methods (Nolte et al, 2004, Stam et al, 2007, Brookes et al, 2012b, Hipp et al, 2012 where zerophase-lag interaction is removed prior to connectivity assessment. In this paper we implement a multivariate extension to previous work (see appendix (Brookes et al, 2012b, Hipp et al, 2012) in which linear regression is employed to supress zero-phase-lag interaction between the seed and test…”

mentioning

confidence: 99%

Measuring temporal, spectral and spatial changes in electrophysiological brain network connectivity

et al. 2014

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ABSTRACT:The topic of functional connectivity in neuroimaging is expanding rapidly and many studies now focus on coupling between spatially separate brain regions. These studies show that a relatively small number of large scale networks exist within the brain, and that healthy function of these networks is disrupted in many clinical populations. To date, the vast majority of studies probing connectivity employ techniques that compute time averaged correlation over several minutes, and between specific pre-defined brain locations. However, increasing evidence suggests that functional connectivity is non-stationary in time. Further, electrophysiological measurements show that connectivity is dependent on the frequency band of neural oscillations. It is also conceivable that networks exhibit a degree of spatial inhomogeneity, i.e. the large scale networks that we observe may result from the time average of multiple transiently synchronised sub-networks, each with their own spatial signature. This means that the next generation of neuroimaging tools to compute functional connectivity must account for spatial inhomogeneity, spectral non-uniformity and temporal non-stationarity. Here, we present a means to achieve this via application of windowed canonical correlation analysis (CCA) to source space projected MEG data. We describe generation of time-frequency connectivity plots, showing the temporal and spectral distribution of coupling between brain regions. Moreover, CCA over voxels provides a means to assess spatial nonuniformity within short time-frequency windows. The feasibility of this technique is demonstrated in simulation and in a resting state MEG experiment where we elucidate multiple distinct spatiotemporal-spectral modes of covariation between the left and right sensorimotor areas. 3 1) INTRODUCTION:Traditional analysis of neuroimaging data has focussed on the identification of significant changes in some metric of interest that are time locked to a particular task. Such methodologies usually rely on knowledge of task timing, and in some cases accurate models of the temporal evolution of neuroimaging signals which are then compared to measured data. These techniques have proved effective in highlighting brain regions that are involved in sensory and cognitive tasks. However, the last decade has seen a 'paradigm shift' in functional brain imaging (Raichle, 2009), with traditional analyses increasingly complemented by analysis of functional connectivity (Biswal et al., 1995, Beckmann et al., 2005, Fox et al., 2005, Fox and Raichle, 2007, Deco and Corbetta, 2011. Here, researchers seek to elucidate spatial patterns of temporal covariation between brain regions.Significant statistical interdependency (e.g. assessed via temporal correlation (Biswal et al., 1995) or independent component analysis (Beckmann et al., 2005)) between signals originating in two or more spatially separate anatomical regions is usually taken to mean that those regions are 'connected'. Functional magnetic resonance imaging (fMRI) has...

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Identifying true brain interaction from EEG data using the imaginary part of coherency

Cited by 1,578 publications

References 34 publications

Reduced functional connectivity between cortical sources in five meditation traditions detected with lagged coherence using EEG tomography

Reduced functional connectivity between cortical sources in five meditation traditions detected with lagged coherence using EEG tomography

On the “Dependence” of “Independent” Group EEG Sources; an EEG Study on Two Large Databases

Measuring temporal, spectral and spatial changes in electrophysiological brain network connectivity

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