Go with the flow: Use of a directed phase lag index (dPLI) to characterize patterns of phase relations in a large-scale model of brain dynamics

Stam, Cornelis J.; Straaten, E.C.W. van

doi:10.1016/j.neuroimage.2012.05.050

Cited by 128 publications

(138 citation statements)

References 47 publications

(68 reference statements)

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“…In contrast, EEG studies in healthy controls have revealed a front-to-back pattern of directed connectivity, particularly in the alpha band (17)(18)(19)(20)(21)(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 assumed strength of the underlying structural connections (22)(23)(24), and the observed EEG patterns strongly depend on the choice of reference (25), which may explain why, controversially, the reverse back-tofront pattern has also been observed in EEG (26)(27)(28). An important advantage of MEG over EEG in this context is that it is referencefree.…”

supporting

confidence: 77%

“…S3), confirming that the observed patterns were not an artifact of the source reconstruction approach. In addition, we compared dPTE to another directional measure, the directed Phase Lag Index (dPLI) (23), which is based on phase differences rather than information flow (Fig. S4).…”

Section: Resultsmentioning

confidence: 99%

“…In particular, highly connected regions in the network, so-called hubs, become phase-lagging with respect to nonhub regions (22,23). Brain networks have their strongest hubs in posterior regions (36,51), which would hence lead to a front-to-back pattern of phase differences.…”

Section: Discussionmentioning

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.

335

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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supporting

confidence: 77%

Section: Resultsmentioning

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.

335

366

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“…To estimate single trial event related functional connectivity be tween hippocampal slow theta sources and cortical activity simulta neously measured with iEEG and MEG, we chose the directed phase lag index or dPLI (Stam and van Straaten, 2012). This measure has been effective at identifying patterns of phase leading and lagging on simulated EEG data obtained from a realistic model of macroscopic structural and functional brain networks (Stam and van Straaten, 2012) and on real EEG data (van Straaten et al, 2015).…”

Section: Phase Coupling Analyses Between Ieeg and Meg Beamforming Soumentioning

confidence: 99%

“…This measure has been effective at identifying patterns of phase leading and lagging on simulated EEG data obtained from a realistic model of macroscopic structural and functional brain networks (Stam and van Straaten, 2012) and on real EEG data (van Straaten et al, 2015). Like the original PLI (Stam et al, 2007), the dPLI quantifies the phase synchronization strength between two signals, disregarding zero lag phase differences that may be due to volume conduction and/or common sources.…”

Section: Phase Coupling Analyses Between Ieeg and Meg Beamforming Soumentioning

confidence: 99%

Slow-theta power decreases during item-place encoding predict spatial accuracy of subsequent context recall

et al. 2016

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a b s t r a c tHuman hippocampal theta oscillations play a key role in accurate spatial coding. Associative encoding involves similar hippocampal networks but, paradoxically, is also characterized by theta power decreases. Here, we inves tigated how theta activity relates to associative encoding of place contexts resulting in accurate navigation. Using MEG, we found that slow theta (2 5 Hz) power negatively correlated with subsequent spatial accuracy for vir tual contextual locations in posterior hippocampus and other cortical structures involved in spatial cognition. A rare opportunity to simultaneously record MEG and intracranial EEG in an epilepsy patient provided crucial in sights: during power decreases, slow theta in right anterior hippocampus and left inferior frontal gyrus phase led the left temporal cortex and predicted spatial accuracy. Our findings indicate that decreased slow theta activ ity reflects local and long range neural mechanisms that encode accurate spatial contexts, and strengthens the view that local suppression of low frequency activity is essential for more efficient processing of detailed information.

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Low‐ and high‐frequency oscillations reveal distinct absence seizure networks

Tenney

Fujiwara

Horn

et al. 2014

Annals of Neurology

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Neuromagnetic activity within frontal and parietal cortical regions provides further confirmation of hemodynamic changes reported using functional magnetic resonance imaging that have been associated with absence seizures. The frequency-dependent nature of these networks has not previously been reported, and the presence of HFOs during absence seizures is a novel finding. Co-occurring frontal and parietal corticothalamic networks may interact to produce a pathological state that contributes to the generation of spike and wave discharges. The clinical and pathophysiological implications of HFOs within the frontal cortical region are unclear and should be further investigated.

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Go with the flow: Use of a directed phase lag index (dPLI) to characterize patterns of phase relations in a large-scale model of brain dynamics

Cited by 128 publications

References 47 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

Slow-theta power decreases during item-place encoding predict spatial accuracy of subsequent context recall

Low‐ and high‐frequency oscillations reveal distinct absence seizure networks

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