An EEG Study of Brain Connectivity Dynamics at the Resting State

1. 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, Greece Artificial Intelligence & Information Analysis Laboratory, Department of Informatics, Aristotle University, Thessaloniki, Greece Medical Division (Laboratory L.Widιn), University of Crete, 71409 Iraklion/Crete, Greece Technical High School of Crete, Estavromenos, Iraklion, Crete, Greece http://users.auth.gr/~stdimitr

2. Outline Introduction -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)

3. Outline Outline of the Methodology

4. Outline Outline of the Methodology Results Discussion

5. 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.

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

7. Motivation TVFCA facilitated the detection of systematics behind the emergence of hubs and the formation of functional modules via phase-coupling. • 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.

8. Data acquisition:Resting state 18subjects 30 EEG electrodes Horizontal and Vertical EOG Trial duration: 1 x 20 seconds Single trial analysis The recording was terminated when at least an EEG-trace without visible artifacts had been recorded for each condition

9. Filtering 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) Artifact Correction Working individually for each subband and using EEGLAB (Delorme & Makeig,2004), artifact reduction was performed using ICA -Components related to eye movementwere identified based on their scalp topography which includedfrontal sites and their temporal course which followed the EOGsignals. -Components reflecting cardiac activity were recognizedfrom the regular rythmic pattern in their time course widespreadin the corresponding ICA component.

10. 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).

11. 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

12. 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

13. Elements of Graph Theoretical Analysis -Functional Connectivity Networks and Related Topological Properties Quantifying Fluctuations in Modular Structure -Quantify the contrast regarding community structure at two successive (in time) instances by adopting a metric called VI (Meila, 2007)

14. Hub Distribution as Reflected Over the Scalp After Averaging Across Time Detected hubs mainly frontaland parietal, occipital brain regions

15. Hub Distribution as Reflected Over the Scalp Detecting consistent hubs across subjects and time Detected hubs mainly frontaland parietal, occipital brain regions

16. Dynamic Behavior of Cortical Segregations Fluctuations of functional segregations follow the underlying brain rhythms

17. The Repertoire of Functional Segregations Time Varying Functional connectivity at Resting-state is described by abrupt changes between recurrent bimodal segregations

18. The Repertoire of Functional Segregations Table 1.Relative frequency of the segregation motifs observed in delta band for a single subject Time Varying Functional connectivity at Resting-state is described by abrupt changes between recurrent bimodal segregations Table 2. Entropy of the segregation time series.

19. Conclusions We examined the dynamical behavior of the functional networks corresponding to EEG frequency bands using a nonlinear connectivity estimator and tools derived from the graph theory. 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 and detected 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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