1. SINAPSE
THINK BIG
think different
Cognitive Engineering Lab Dynamic Functional Brain Connectivity: Perspectives and Further Directions
Scientific Visitor
Dr.Dimitriadis Stavros (Greece)
Neuroinformatics Group
Aristotele University of Thessaloniki
Dept.of Computer Science
Workshop on Brain Connectivity: Structure and Function in Normal Brain and Disease
Center of Life Sciences Auditorium , May 17rd, 2013

2. Overview
First part: From Time-Varying Functional Connectivity Analysis to Functional Connectivity Microstates (FCμstates): Summarizing dynamic brain activity into a restricted repertoire of meaningful FCΜstates using EEG/fMRI
Second part: Investigating Functional Cooperation from the Human Brain Connectivity via Simple Graph-Theoretic Methods

3. Brain Connectivity
Modes of brain connectivity. Sketches at the top illustrate structural connectivity (fiber pathways), functional connectivity (correlations), and effective connectivity (information flow) among four brain regions in macaque cortex. Matrices at the bottom show binary structural connections (left), symmetric mutual information (middle) and non-symmetric transfer entropy (right). Data was obtained from a large-scale simulation of cortical dynamics (see Honey et al., 2007).

4. Dynamic Functional Connectivity:
One would expect that fast fluctuations of Functional Connectivity (FC) will occur during spontaneous and task-evoked activity while plasticity and development are accompanied by slower and mutually interdependent changes in Structural Connectivity (SC) and FC.
Computational models of large-scale neural dynamics suggest that rapid changes in FC can occur in the course of spontaneous activity, even while SC remains unaltered (Honey et al., 2007; Deco et al., 2009).
Detailed analysis of electromagnetic time series data suggests that functional coupling between remote sites in the brain undergoes continual and rapid fluctuations (Linkenkaer-Hansen et al., 2001; Stam and de Bruin, 2004).

5. DFC Patterns
Interestingly, there is already experimental evidence suggesting that the emergence of a unified neural process is mediated by the continuous formation and dissolution of functional links over multiple time scales (Engel et al., 2001; Varela et al., 2001; Honey et al., 2007; Kitzbichler et al., 2009).

6. DFC Patterns Network Metrics Time Series (NMTS)
Dimitriadis et al., 2010

7. Dynamic Functional Connectivity:
Definition of time-window matters
Dimitriadis et al., 2010

8. Macrostates - Microstates
From a Neuroscience Point of View:
Functional Significance of EEG Microstates:
In spontaneous EEG, four standard classes of microstate were distinguished , whose parameters (Lehmann et al., 1978)
(e.g. duration, occurrences per second, covered percentage of analysis time, transition probabilities (Dimitriadis et al., 2013 under revision in HBM)) change as function of age
While listening to frequent and rare sounds

9. Macrostates - Microstates
From a Neuroscience Point of View:
Can you give an exemplar of Macrostate related to brain functionality ?
We spend a third of our lives doing it !!!

10. From Scalp Potential Microstates to Functional Connectivity Microstates
From a Neuroscience Point of View:
Multi-trial ERP Visual Paradigm

11. From Scalp Potential Microstates to Functional Connectivity Microstates
EEG
Dimitriadis et al., 2013
Markovian chain

12. From Scalp Potential Microstates to Functional Connectivity Microstates
From a Neuroscience Point of View:
Poccurence of Fcμstates

13. From Scalp Potential Microstates to Functional Connectivity Microstates
Clusters related to FCμstates
Topographies of functional clusters related to FCμstates detected for the
‘‘Left’’ ERP-trials
Topographies of functional clusters related to FCμstates detected for the
‘‘Right’’ ERP-trials
Dimitriadis et al., 2013

14. Markov State Models
Symbolic Time Series describe the Evolution of Fcμstates:
e.g ……
(a)Estimate directed Global efficiency in Codebook-networks:
DGE stimulus > DGE baseline
(b)We can quantify the deterministicity of the system based on an information-theoretic measure called:
Entropy Reduction Rate
ERT stimulus > ERT baseline
Dimitriadis et al., 2013

15. Tracking Whole-Brain Connectivity Dynamics in the Resting State (fMRI)
Allen et al. 2012

16. Allen et al. 2012

17. Prototyping Functional Connectivity Graphs
Allen et al. 2012

18. Occurrences of Prototypical FCGs
Allen et al. 2012

19. Transitions of Prototypical FCGs
Markovian chain
Allen et al. 2012

20. Extracting Meaningful Measures from Markovian Chain
Duration of a Functional Connectivity Microstate
occurrences per second
covered percentage of analysis time,
transition probabilities (Dimitriadis et al., 2013 under revision in HBM)
Complexity
Deterministicity (Dimitriadis et al., 2013)
Allen et al ; Dimitriadis et al., 2013

21. Meta-Analysis of Brain Data
Meta-Analysis of Functional Imaging Data e.g. Using
Replicator Dynamics(Neumann et al., 2005)
Neumann et al., 2005

22. Detect Motifs in Static/Dynamic FCGs
Discovery of group-consistent graph substructure patterns
Without a-priori definition of the n-motifs
Monitoring Motif in A Dynamic Way
gSpan - algorithm
(Iakovidou et al., 2012)

23. Mining Large Numbers of FCGs
Multivariate
Univariate
Power Spectrum /foci
N N*(N-1)/2 = O(N2) N*(N-1)= O(N2)
Increment of Degrees of Freedom
Increment of the discriminative power to decode
Different Brain States Simultaneously
Anderson et al., 2007

24. Co-activated Areas (Foci) vs Co-activated graphs
Brain Decoding
Co-activated Areas (Foci) vs Co-activated graphs
Co-activated Graphs
Co-activated Brain Areas (Foci)
Anderson et al., 2007

25. Brain Decoding
Cognitive States: attention, emotion, language, memory, mental imagery etc.
Brain Diseases/Disorders: Dyslexia, ADHD, Alzheimer etc.
Developmental changes
General goal:
Understanding how the brain functions
Characterizing individual brain state across different tasks
Monitor Individual Cognitive Performance
Build significant biomarkers for the prevention of brain disorders
Monitor the improvement of the treatment (pharmacological/surgery) in brain disease subjects…

26. Neuroinformatics Group
Aristotele University of Thessaloniki (Greece)
Dr. Nikolaos Laskaris, Assistant Professor, Dept. of Informatics
Dr. Dimitrios Adamos, Researcher (Music Department/AUTH - Music Cognition)
Dr. Efstratios Kosmidis,Lecturer of Physiology, Medical School, AUTH
Dr.Areti Tzelepi, Researcher ,Institute of Communication and Computer Systems
Group Websites :

27. References
[1] Lehmann D & Skrandies W (1980) Reference-free identification of components of checkerboard-evoked multichannel potential fields. Electroenceph Clin Neurophysiol 48:
[2] Deco, G., Jirsa, V., McIntosh, A.R., Sporns, O., Kötter, R., Key role of coupling, delay, and noise in resting brain fluctuations. Proc. Natl. Acad. Sci. U. S. A. 106,10302–10307
[3] Honey, C.J., Kötter, R., Breakspear, M., Sporns, O., Network structure of cerebral cortex shapes functional connectivity on multiple time scales. Proc. Natl. Acad. Sci. U. S. A. 104, 10240–10245
[4] Linkenkaer-Hansen, K., Nikouline, V.V., Palva, J.M., Ilmoniemi, R.J., Long-range temporal correlations and scaling behavior in human brain oscillations. J. Neurosci. 21,
1370–1377.
[5] Stam, C.J., de Bruin, E.A., Scale-free dynamics of global functional connectivity in the human brain. Hum. Brain Mapp. 22, 97–104.
[6]Dimitriadis SI, Laskaris NA, Tzelepi A. On the quantization of time-varying phase synchrony patterns into distinct  Functional Connectivity Microstates (FCμstates) in a multi-trial visual ERP paradigm. IN PRESS 2013
[7] Dimitriadis SI, Laskaris NA, Tsirka V, Vourkas M, Micheloyannis S, Fotopoulos S.Tracking brain dynamics via time-dependent network analysis. Journal of Neuroscience Methods Volume 193, Issue 1, 30 October 2010, pp
8) Dimitriadis SI, Laskaris NA, Tzelepi A, Economou G.Analyzing Functional Brain Connectivity by means of Commute Times: a new approach and its application to track event-related dynamics. IEEE (TBE)Transactions on Biomedical Engineering, Volume 59, Issue 5, May 2012, pp  
[9]Allen et al., Tracking Whole-Brain Connectivity Dynamics in the Resting State
Cereb. Cortex (2012)doi:  /cercor/bhs352.
[10] Federico Cirett Gal´an and Carole R. Beal.EEG Estimates of Engagement and Cognitive Workload Predict Math Problem Solving Outcomes. UMAP 2012, LNCS 7379, pp. 51–62, 2012.
[11] Mohammed Mostafa Yehia. EEG - Mental Task Discrimination by Digital Signal Processing
[12] Jack Culpepper. Discriminating Mental States Using EEG Represented by Power Spectral Density

28. References
[11] Cheng-Jian Lina & Ming-Hua Hsieh.Classification of mental task from EEG data using neural networks based on
particle swarm optimization. Neurocomputing 72 (2009) 1121– 1130
[12] Charles W. Anderson , Zlatko Sijercic. Classification of EEG Signals from Four Subjects During Five Mental Tasks Proceedings of the Conference on Engineering Applications in Neural Networks (EANN’96)
[13] Iakovidou N, Dimitriadis SI, Tsichlas K, Laskaris NA, Manolopoulos Y. On the Discovery of Group-Consistent Graph Substructure Patterns from brain networks. Neuroscience Methods ,Volume 213, Issue 2, 15 March 2013, pp. 204–213

29. Thank you for your attention!