An EEG Study of Brain Connectivity Dynamics at theResting State

Stavros I. Dimitriadis,Nikolaos A. Laskaris, Vasso Tsirka, Michael Vourkas, Sifis Micheloyannis

Electronics Laboratory, Department of Physics, University of Patras, Patras 26500, GreeceArtificial Intelligence & Information Analysis Laboratory, Department of Informatics, Aristotle University, Thessaloniki, GreeceMedical Division (Laboratory L.Widιn), University of Crete, 71409 Iraklion/Crete, GreeceTechnical High School of Crete, Estavromenos, Iraklion, Crete, Greece

http://users.auth.gr/~stdimitr 1

OutlineIntroduction-Multichannels EEG recordings-resting state (eyes closed)-multifrequency approach (from θ to γ)-time varying connectivity analysis

Methodology-PLV- Network Metrics (Global & Local efficiency)-Detection of hubs across time (hprobability)-Detection of consistent hubs across time- fluctuations of modulatity across frequency bands-Description of time-varying connectivity analysis with a restricted number of functional segregations detected via a distance measure (VI) 2

Outline of the Methodology

Outline

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Outline of the Methodology

Outline

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Results

Discussion

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Intro Method Results Conclusions

Analyzing connectivity in time – varying approach can unfold the “true dynamics” of brain functionality compared to static approach

In this study, we attempted to characterize the resting state from the perspective of complex network analysis and with high temporal resolution.

Intro Method Results Conclusions

MotivationIn this study, we attempted to characterize the resting state from theperspective of complex network analysis and with high temporal resolution.

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The main purpose was to add information related to the dynamics of associated brain connectivity based on EEG signals from different frequency bands (δ to γ).

We adopted a recently developed methodology for time-varying network-analysis of functional connectivity (Dimitriadis et al., 2010), denoted hereafter as “TVFCA”.

Intro Method Results Conclusions

MotivationTVFCA facilitated the detection of systematics behind the emergence of hubs and the formation of functional modules via phase-coupling.

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Summarizing:- We detected a restricted repertoire of segregation motifs and - revealed the deterministic character of changes in functional segregation by adopting entropic measures reflecting the time evolution of brain’s modular structure.

Data acquisition:Resting stateIntro Method Results Conclusion

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18 subjects30 EEG electrodesHorizontal and Vertical EOGTrial duration: 1 x 20 secondsSingle trial analysis

The recording was terminated when at least an EEG-trace without visible artifacts had been recorded for each condition

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Intro Method Results Conclusions

Using a zero-phase band-pass filter (3rd order Butterworth filter), signals were extracted within six different narrow bands ( from 0.5 to 45 Hz)

Filtering

Artifact CorrectionWorking individually for each subband and using EEGLAB (Delorme & Makeig,2004), artifact reduction was performed using ICA

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-Components related to eye movement were identified based on their scalp topography which included frontal sites and their temporal course which followed the EOG signals.

-Components reflecting cardiac activity were recognized from the regular rythmic pattern in their time course widespread in the corresponding ICA component.

Intro Method Results Conclusions

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Introducing time in the Analysis of Functional Connectivity

Brain connectivity may be modulated by rapid changes in time and, additionally, in a frequency-dependent manner.

Selecting the appropriate window for estimating the time-frequency dependent network-properties is crucial for understanding the neural underpinnings of various cognitive functions.

Here , we adopted a frequency dependent criterion of time interval equals to two cycles of the lower frequency limit that corresponds to the - possibly - synchronized oscillations of each brain rhythm (Dimitriadis et al., 2010).

Intro Method Results Conclusions

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Elements of Graph Theoretical Analysis-Functional Connectivity Networks and Related Topological Properties

-Construct Functional Connectivity Graphs (FCGs) with PLI

-Adopted two network metrics (global & spatial local efficiency)

-Identifying Significant Edges Based on Dijkstra’s Algorithm

-Identifying Hubs - Compute HubPro across time - Detect consistent hubs across subjects

Intro Method Results Conclusions

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Elements of Graph Theoretical Analysis-Functional Connectivity Networks and Related Topological Properties

-Construct Functional Connectivity Graphs (FCGs) with PLI

-Adopted two network metrics (global & spatial local efficiency)

-Identifying Significant Edges Based on Dijkstra’s Algorithm

-Identifying Hubs - Compute HubPro across time - Detect consistent hubs across subjects

Intro Method Results Conclusions

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Elements of Graph Theoretical Analysis-Functional Connectivity Networks and Related Topological Properties

Quantifying Fluctuations in Modular Structure

-Quantify the contrast regarding community structure at twosuccessive (in time) instances by adopting a metric called VI (Meila, 2007)

Intro Method Results Conclusions

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Hub Distribution as Reflected Over the ScalpAfter Averaging Across Time

Detected hubs mainly frontal and parietal, occipital brain regions

Intro Method Results Conclusions

Hub Distribution as Reflected Over the ScalpDetecting consistent hubs across subjects and time

Detected hubs mainly frontal and parietal, occipital brain regions

Intro Method Results Conclusions

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Dynamic Behavior of Cortical Segregations

Fluctuations of functional segregations follow the underlying brain rhythms

Intro Method Results Conclusions

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The Repertoire of Functional Segregations

Time Varying Functional connectivity at Resting-state is described by abrupt changes between recurrent bimodal segregations

Intro Method Results Conclusions

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The Repertoire of Functional Segregations

Time Varying Functional connectivity at Resting-state is described by abrupt changes between recurrent bimodal segregations

Table 1.Relative frequency of the segregation motifs observed in delta band for a single subject

Table 2. Entropy of the segregation time series.

ConclusionsWe examined the dynamical behavior of the functional networks correspondingto EEG frequency bands using a nonlinear connectivity estimatorand tools derived from the graph theory.

Intro Method Results Conclusions

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The appearance and behavior of hubs in the cortex (as reflected at the EEG channels), and their evolution in time were studied during the resting, “eyes closed,” condition anddetected over frontal, parietal and occipital brain regions.(Laufs et al., 2003; Mantini et al., 2007).

The evolution of functional connectivity can be described via short-lasting bimodal functional segregations.

This was a novel way to describe the complexity in brain-activity measurements.

Deviating from previous approaches in which various entropic estimators (Rezek & Roberts, 1998)

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References

[1]Dimitriadis SI, Laskaris NA, Tsirka V, Vourkas M, Micheloyannis S (2010a) Tracking brain dynamics via time-dependent network analysis. J Neurosci Methods 193:145–155[2] Meila, M. (2007). Comparing clusterings - an information based distance. Journal of Multivariate Analysis, 98, 873-895.[3] Jung, T. P., Humphries, C., Lee, T. W., Makeig, S., McKeown, M. J., et al. (1998). Extended ICA removes artifacts from electroencephalographic recordings.Advances in Neural Information Processing Systems, 10, 894-900.[4] Onton, J., Westerfield M., Townsend, J., & Makeig S. (2006). Imaging human EEG dynamics using independent component analysis. Neuroscience &Biobehavioral Reviews, 30, 808-822.[5] Delorme, A. & Makeig S. (2004). EEGLAB: an open source toolbox for analysis of single trial EEG dynamics. Journal of Neuroscience Methods, 134, 9-21.[6] Rezek, I. & Roberts, S. J. (1998). Stochastic complexity measures for physiological signal analysis. IEEE Transactions on Biomedical Engineering, 44(9), 1186-1191.[7] Laufs, H., Krakow, K., Sterzer, P., Eger, E., Beyerle, A., et al. (2003). Electroencephalographic signatures of attentional and cognitive default modes in spontaneous brain activity fluctuations at rest. Proceedings of the National Academy of Sciences of the United States of America, 100, 11053-11058.[8] Mantini, D., Perrucci, M. G., Del Gratta, C., Romani, G. L., & Corbetta, M. (2007). Electrophysiological signatures of resting state networks in the human brain. Proceedings of the National Academy of Sciences of the United States of America, 104, 13170-13175.

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