An EEG Study of Brain Connectivity Dynamics at the Resting State

Report
An EEG Study of Brain Connectivity Dynamics at the
Resting 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
1
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)
2
Outline
Outline of the Methodology
3
Outline
Outline of the Methodology
Results
Discussion
4
Intro
Method
Results
Conclusion
s
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.
5
Intro
Method
Results
Conclusion
s
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”.
6
Intro
Method
Results
Conclusion
s
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.
7
Intro
Method
Results
Data acquisition:Resting state
Conclusion
s
18 subjects
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
8
Intro
Filtering
Method
Results
Conclusion
s
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 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.
9
Intro
Method
Results
Conclusion
s
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 timefrequency 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
10
(Dimitriadis et al., 2010).
Intro
Method
Results
Conclusion
s
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
11
Intro
Method
Results
Conclusion
s
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
Intro
Method
Results
Conclusion
s
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)
13
Intro
Method
Results
Conclusion
s
Hub Distribution as Reflected Over the Scalp
After Averaging Across Time
Detected hubs mainly frontal and parietal, occipital brain regions
14
Intro
Method
Results
Conclusion
s
Hub Distribution as Reflected Over the Scalp
Detecting consistent hubs across subjects and time
Detected hubs mainly frontal and parietal, occipital brain regions
Intro
Method
Results
Conclusion
s
Dynamic Behavior of Cortical Segregations
Fluctuations of
functional segregations
follow the underlying
brain rhythms
16
Intro
Method
Results
Conclusion
s
The Repertoire of Functional Segregations
Time Varying
Functional connectivity
at Resting-state is
described by abrupt
changes between
recurrent bimodal
segregations
17
Intro
Method
Results
Conclusion
s
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
Table 2. Entropy of the segregation time recurrent bimodal
series.
segregations
18
Intro
Method
Results
Conclusions
Conclusion
s
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)
19
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.
20
21

similar documents