Connectivity measures applied to human brain electrophysiological data

Greenblatt, R.E.; Pflieger, Mark E.; Ossadtchi, Alexei

doi:10.1016/j.jneumeth.2012.02.025

Cited by 143 publications

(97 citation statements)

References 114 publications

(156 reference statements)

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“…The opposite pattern was observed in the theta (4-8 Hz) band (PAx = −0.50, P < 0.001). The patterns in the gamma (30)(31)(32)(33)(34)(35)(36)(37)(38)(39)(40)(41)(42)(43)(44)(45)(46)(47)(48) and delta (0.5-4 Hz) bands were more dispersed [although still significant (P < 0.001); PAx = 0.26 and −0.29, respectively]. We describe the results for the alpha2 and theta band in more detail below, as the patterns for these bands (i) were most pronounced and (ii) resulted in high PAx values.…”

Section: Resultsmentioning

confidence: 99%

“…Moreover, the large number of sensors (several hundreds) in modern whole-head MEG systems allow for sophisticated spatial filtering approaches to accurately reconstruct time series of neuronal activation across the cortex (29,30). The directed functional connectome can subsequently be reconstructed by estimating information flow between these time series, using either model-based or data-driven approaches (31)(32)(33)(34). Here, we used a recently introduced, sensitive, yet computationally efficient, data-driven measure of information flow, the phase transfer entropy (PTE) (35), to test the hypothesis that resting-state MEG data are characterized by a dominant front-to-back pattern in the alpha band.…”

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confidence: 99%

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Direction of information flow in large-scale resting-state networks is frequency-dependent

Hillebrand

Tewarie

Dellen

et al. 2016

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

311

366

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Normal brain function requires interactions between spatially separated, and functionally specialized, macroscopic regions, yet the directionality of these interactions in large-scale functional networks is unknown. Magnetoencephalography was used to determine the directionality of these interactions, where directionality was inferred from time series of beamformer-reconstructed estimates of neuronal activation, using a recently proposed measure of phase transfer entropy. We observed well-organized posterior-to-anterior patterns of information flow in the higher-frequency bands (alpha1, alpha2, and beta band), dominated by regions in the visual cortex and posterior default mode network. Opposite patterns of anterior-toposterior flow were found in the theta band, involving mainly regions in the frontal lobe that were sending information to a more distributed network. Many strong information senders in the theta band were also frequent receivers in the alpha2 band, and vice versa. Our results provide evidence that large-scale resting-state patterns of information flow in the human brain form frequencydependent reentry loops that are dominated by flow from parietooccipital cortex to integrative frontal areas in the higher-frequency bands, which is mirrored by a theta band anterior-to-posterior flow.information flow | phase transfer entropy | resting-state networks | magnetoencephalography | atlas-based beamforming T he brain is an extremely complex system (1-3) containing, at the macroscopic scale, interconnected functional units (4) with more-or-less specific information processing capabilities (5). However, cognitive functions require the coordinated activity of these spatially separated units, where the oscillatory nature of neuronal activity may provide a possible mechanism (6-9). A complete description of these interactions, in terms of both strength and directionality, is therefore necessary for the understanding of both normal and abnormal brain functioning.Functional interactions may be inferred from statistical dependencies between the time series of neuronal activity at different sites, so-called functional connectivity (10). Indeed, interactions in large-scale functional networks have been observed using Electroencephalography, Magnetoencephalography (EEG/MEG) and functional Magnetic Resonance Imaging (fMRI) (e.g., refs. 11-14). However, as yet, little is known about the directionality of these interactions in large-scale functional networks during the resting state. Estimating directionality from fMRI is challenging due to its limited temporal resolution and indirect relation to neuronal activity (15, 16). In contrast, EEG studies in healthy controls have revealed a front-to-back pattern of directed connectivity, particularly in the alpha band (17-22), consistent with modeling studies that have shown that such patterns may arise due to differences in the number of anatomical connections (the degree) of anterior and posterior regions (22, 23). However, modeled patterns of information flow depend on the a...

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Section: Resultsmentioning

confidence: 99%

mentioning

confidence: 99%

Direction of information flow in large-scale resting-state networks is frequency-dependent

Hillebrand

Tewarie

Dellen

et al. 2016

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

311

366

View full text Add to dashboard Cite

show abstract

“…Effective connectivity is aligned with "causal" connectivity and relies on directional information, e.g., the influence of one neural system on another (Friston, 1994;Nolte, Ziehe, & Nikulin, 2008). Estimates of causal connectivity includes measures of information flow such as with Granger causality (1969) and multivariate autoregressive modeling and conditional information measures (Greenblatt, Pflieger, & Ossadtchi, 2012;Kaminski, Ding, Truccolo, & Bressler, 2001) and the PSI (Ewald, Avarvand, & Nolte, 2013;Nolte et al, 2008). A problem with Granger causality is high noise sensitivity and reliance on multivariate model fitting.…”

Section: Structural Functional and Effective Connectivitymentioning

confidence: 99%

Network Connectivity and LORETA Z Score Biofeedback

Thatcher¹,

Biver²,

North³

2015

Z Score Neurofeedback

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“…There are various methods, e.g., partial directed coherence, coherence, and phase synchronization (PS), to quantify the functional connectivity from different statistical aspects [15], [16]. Extensive studies have demonstrated that PS is a suitable method to examine the rhythmic interaction between two oscillation signals, especially when the amplitudes of the two oscillations are weakly correlated [5], [17], [18].…”

Section: E Phase Synchronization Analysismentioning

confidence: 99%

Functional Brain Networks for Sensory Maintenance in Top-Down Selective Attention to Audiovisual Inputs

Hong

Sun

Tong

2013

IEEE Trans. Neural Syst. Rehabil. Eng.

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Sensory maintenance in top-down selective attention to audiovisual inputs involves distributed cortical activations, while the connectivity between the widespread cortical regions has not been well understood. Graph theory has been demonstrated to be a useful tool in the analysis of brain networks. In this study, we used graph theoretical analysis to investigate the functional brain networks for sensory maintenance in top-down selective attention to audiovisual inputs. Electroencephalograms (EEGs) of 30 channels were recorded from 13 young healthy subjects during a passive view task and a top-down intersensory selective attention task. Phase synchronization indices of EEG signals in pair were computed to construct weighted brain networks. We found small-world properties of the brain networks during both passive view state and top-down selective attentional state in α, β, and γ bands. In addition, the significantly increased clustering coefficient and decreased characteristic path length were observed for brain networks during attentional state compared with passive view state in both β band and γ band. Our results suggest that functional brain networks in higher frequency bands, i.e., β band and γ band, are integrated in different ways during attentional state compared with passive view state.

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Connectivity measures applied to human brain electrophysiological data

Cited by 143 publications

References 114 publications

Direction of information flow in large-scale resting-state networks is frequency-dependent

Direction of information flow in large-scale resting-state networks is frequency-dependent

Network Connectivity and LORETA Z Score Biofeedback

Functional Brain Networks for Sensory Maintenance in Top-Down Selective Attention to Audiovisual Inputs

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