Detecting directional coupling in the human epileptic brain: Limitations and potential pitfalls

Osterhage, Hannes; Mormann, Florian; Wagner, Tobias; Lehnertz, Klaus

doi:10.1103/physreve.77.011914

Cited by 30 publications

(26 citation statements)

References 64 publications

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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.…”

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confidence: 82%

“…This selective signal-gating mechanism could explain the strong positive correlation between amplitude and dPTE in our study, but it also explains why this correlation is not perfect, as the amplitude of the sending population itself is not the only prerequisite for routing information: There is also the sensitivity of receiving populations. However, given the mesoscopic level at which the analysis was performed and the infancy of this field, future modeling approaches need to include the influence of global structural topology (22)(23)(24) and inhomogeneity of neuronal populations to systematically study how patterns of information flow depend on frequency, power, SNR, and the role of regions within the network. Moreover, experimental studies should investigate if, and how, these patterns of information flow optimize integrative cognitive processing.…”

Section: Discussionmentioning

confidence: 99%

“…From these time series, the first 20 artifact-free epochs, containing 4,096 samples (3.2768 s), were selected to obtain stable results (70). These time series were then filtered in classical EEG/MEG frequency bands [delta (0.5-4 Hz), theta (4-8 Hz), alpha1 (8-10 Hz), alpha2 (10-13 Hz), beta (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), and lower gamma (30-48 Hz)], using an offline discrete fast Fourier transform filter that does not distort the phases (BrainWave, version 0.9.150.6; home.kpn.nl/stam7883/brainwave.html). Subsequently, the instantaneous phase for each time series was computed by taking the argument of the analytic signal as computed using the Hilbert transform (see e.g., ref.…”

Section: Methodsmentioning

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.

334

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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confidence: 82%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

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.

334

366

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“…A systematic comparison of methods based on phase dynamics and state-space was carried out in [Smirnov & Andrzejak, 2005], where the authors concluded that neither of the methods is better than the other one. Moreover, some spurious dependencies could be identified for real time series analysis [Osterhage et al, 2008].…”

Section: Introductionmentioning

confidence: 99%

Inferring Indirect Coupling by Means of Recurrences

Zou

Romano

Thiel

et al. 2011

Int. J. Bifurcation Chaos

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The identification of the coupling direction from measured time series taking place in a group of interacting components is an important challenge for many experimental studies. We propose here a method to uncover the coupling configuration using recurrence properties. The approach hinges on a generalization of conditional probability of recurrence, which was originally introduced to detect and quantify even weak coupling directions between two interacting systems, to the case of multivariate time series where indirect interactions might be present. We test our method by an example of three coupled Lorenz systems. Our results confirm that the proposed method has much potential to identify indirect coupling, which is very relevant for experimental time series analysis.

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“…Among them are approaches based on state-space reconstruction [9][10][11][12][13][14], phases [15][16][17][18][19][20][21], information theory [22][23][24][25][26][27], linear correlation [28][29][30], dynamical Bayesian inferrence analysis [31][32][33][34][35] as well as on neural networks [36,37], among others. A comparison between many of these approaches was done in model systems and also in experimental data [21,35,[38][39][40][41][42]. In this study we apply a state-space approach [14] and a phase-based approach [15,18,19].…”

Section: Introductionmentioning

confidence: 99%

Coupling strength versus coupling impact in nonidentical bidirectionally coupled dynamics

Laiou

Andrzejak

2017

Phys. Rev. E

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The understanding of interacting dynamics is important for the characterization of real-world networks. In general real-world networks are heterogeneous in the sense that each node of the network is a dynamics with different properties. For coupled non-identical dynamics symmetric interactions are not straightforwardly defined from the coupling strength values. Thus, a challenging issue is whether we can define a symmetric interaction in this asymmetric setting. To address this problem we introduce the notion of the coupling impact. The coupling impact considers not only the coupling strength but also the energy of the individual dynamics which is conveyed via the coupling. To illustrate this concept, we follow a data-driven approach by analyzing signals from pairs of coupled model dynamics using two different connectivity measures. We find that the coupling impact, but not the coupling strength, correctly detects a symmetric interaction between pairs of coupled dynamics regardless of their degree of asymmetry. Therefore, this approach allows to reveal the real impact that one dynamics has on the other and hence to define symmetric interactions in pairs of non-identical dynamics.

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Detecting directional coupling in the human epileptic brain: Limitations and potential pitfalls

Cited by 30 publications

References 64 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

Inferring Indirect Coupling by Means of Recurrences

Coupling strength versus coupling impact in nonidentical bidirectionally coupled dynamics

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