EEG coherency II: experimental comparisons of multiple measures

Nunez, Paul L.; Silberstein, Richard B.; Shi, Zhiping; Carpenter, Matthew R; Srinivasan, Ramesh; Tucker, Don M.; Doran, Scott M.; Cadusch, Peter J.; Wijesinghe, Ranjith S.

doi:10.1016/s1388-2457(98)00043-1

Cited by 284 publications

(215 citation statements)

References 22 publications

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“…Moreover, local measures are reference-dependent and hence functional connectivity measures should be performed on reference-free measurements of neuronal activity (Nunez et al, 1999). Source reconstruction has been proven to be a reliable approach to accurately detect functional connectivity (Gross et al, 2001;Hillebrand et al, 2012;Schoffelen and Gross, 2009).…”

Section: Source Reconstructionmentioning

confidence: 99%

Intrinsic Coupling Modes in Source-Reconstructed Electroencephalography

et al. 2014

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Intrinsic coupling of neuronal assemblies constitutes a key feature of ongoing brain activity, yielding the rich spatiotemporal patterns observed in neuroimaging data and putatively supporting cognitive processes. Intrinsic coupling has been investigated in electrophysiological recordings using two types of functional connectivity measures: amplitude and phase coupling. These two coupling modes differ in their likely causes and functions, and have been proposed to provide complementary insights into intrinsic neuronal interactions. Here, we investigate the relationship between amplitude and phase coupling in source-reconstructed electroencephalography (EEG). Volume conduction is a key obstacle for connectivity analysis in EEG-we therefore also test the envelope correlation of orthogonalized signals and the phase lag index. Functional connectivity between six seed source regions (bilateral visual, sensorimotor, and auditory cortices) and all other cortical voxels was computed. For all four measures, coupling between homologous sensory areas in both hemispheres was significantly higher than with other voxels at the same physical distance. The frequency of significant coupling differed between sensory areas: 10 Hz for visual, 30 Hz for auditory, and 40 Hz for sensorimotor cortices. By contrasting envelope correlations and phase locking values, we observed two distinct clusters of voxels showing a different relationship between amplitude and phase coupling. Large clusters contiguous to the seed regions showed an identity (1:1) relationship between amplitude and phase coupling, whereas a cluster located around the contralateral homologous regions showed higher phase than amplitude coupling. These results show a relationship between intrinsic coupling modes that is distinct from the effect of volume conduction.

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Section: Source Reconstructionmentioning

confidence: 99%

Intrinsic Coupling Modes in Source-Reconstructed Electroencephalography

et al. 2014

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“…Instead, the use of surface Laplacians has been encouraged because they are essentially reference-free and additionally reduce the volume conduction effect [Nunez, 1995;Pernier et al, 1988;Perrin et al, 1987Perrin et al, , 1989Schiff, 2005]. Nevertheless, Nunez et al [1997Nunez et al [ , 1999 demonstrated that although surface Laplacian methods remove most of the distortions caused by the common reference electrode and volume conduction, common reference methods, like average reference and digitally linked mastoids, produce reasonably reliable results for longrange coherence. Moreover, the surface Laplacian acts as a spatial band-pass filter of scalp potential, thus emphasizing the locally synchronized sources and deemphasizing the long-range synchronized sources.…”

Section: Reference Analysismentioning

confidence: 99%

Decrease in early right alpha band phase synchronization and late gamma band oscillations in processing syntax in music

Ruiz

Koelsch

Bhattacharya

2008

Human Brain Mapping

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The present study investigated the neural correlates associated with the processing of musicsyntactical irregularities as compared with regular syntactic structures in music. Previous studies reported an early ($200 ms) right anterior negative component (ERAN) by traditional event-related-potential analysis during music-syntactical irregularities, yet little is known about the underlying oscillatory and synchronization properties of brain responses which are supposed to play a crucial role in general cognition including music perception. First we showed that the ERAN was primarily represented by low frequency (<8 Hz) brain oscillations. Further, we found that music-syntactical irregularities as compared with music-syntactical regularities, were associated with (i) an early decrease in the alpha band (9-10 Hz) phase synchronization between right fronto-central and left temporal brain regions, and (ii) a late ($500 ms) decrease in gamma band (38-50 Hz) oscillations over fronto-central brain regions. These results indicate a weaker degree of long-range integration when the musical expectancy is violated. In summary, our results reveal neural mechanisms of music-syntactic processing that operate at different levels of cortical integration, ranging from early decrease in long-range alpha phase synchronization to late local gamma oscillations. Hum Brain Mapp 30:1207Mapp 30: -1225Mapp 30: , 2009. V V C 2008 Wiley-Liss, Inc.

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“…The essential feature of such models is low skull conductivity compared to brain, CSF, skull, or scalp tissue. This model has been used extensively in simulation studies of surface Laplacians (Nunez et al, 1994;Srinivasan et al, 1996), EEG coherence estimates (Nunez et al, 1997(Nunez et al, ,1999Srinivasan et al, 1998) and forms the basis for a number of approaches to source localization (Lopes da Silva 2004). The thickness and conductivity ratios of the spherical shells are the essential features of the model.…”

Section: Spatial Filtering Of Scalp Potentialsmentioning

confidence: 99%

Source analysis of EEG oscillations using high-resolution EEG and MEG

Srinivasan

Winter

Nunez

2006

Progress in Brain Research

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112

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We investigated spatial properties of the source distributions that generate scalp electroencephalographic (EEG) oscillations. The inherent complexity of the spatiotemporal dynamics of EEG oscillations indicates that conceptual models that view source activity as consisting of only of a few "equivalent dipoles" are inadequate. We present an approach that uses volume conduction models to characterize the distinct spatial filtering of cortical source activity by averagereference EEG, high-resolution EEG, and magnetoencephalography (MEG). By comparing these three measures, we can make inferences about the sources of EEG oscillations without having to make prior assumptions about the sources. We apply this approach to spontaneous EEG oscillations observed with eyes closed at rest. Both EEG and MEG recordings show robust alpha rhythms over posterior regions of the cortex; however, the dominant frequency of these rhythms varies between EEG and MEG recordings. Frontal alpha and theta rhythms are generated almost exclusively by superficial radial dipole layers that generate robust EEG signals but very little MEG signals; these sources are presumably mainly in the gyral crowns of frontal cortex. MEG and high-resolution EEG estimates of alpha rhythms provide evidence of local tangential and radial sources in the posterior cortex, lying mainly on sulcal and gyral surfaces. Despite the detailed information about local radial and tangential sources potentially afforded by high-resolution EEG and MEG, it is also evident that the alpha and theta rhythms receive contributions from non-local source activity, for instance large dipole layers distributed over lobeal or (potentially) even larger spatial scales.

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EEG coherency II: experimental comparisons of multiple measures

Cited by 284 publications

References 22 publications

Intrinsic Coupling Modes in Source-Reconstructed Electroencephalography

Intrinsic Coupling Modes in Source-Reconstructed Electroencephalography

Decrease in early right alpha band phase synchronization and late gamma band oscillations in processing syntax in music

Source analysis of EEG oscillations using high-resolution EEG and MEG

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