SINAPSE THINK BIG think different

1. SINAPSE THINK BIG think different Cognitive Engineering LabDynamic 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 Macrostaterelated 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. 1 2 3 4 2 3 11 10 …… (a)Estimate directed Global efficiency in Codebook-networks: DGE stimulus > DGE baseline (b)We can quantify the deterministicityof 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. 2012 ; 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. 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. DimitriosAdamos, Researcher (Music Department/AUTH - Music Cognition) Dr.EfstratiosKosmidis,Lecturer of Physiology, Medical School, AUTH Dr.AretiTzelepi,Researcher ,Institute of Communication and Computer Systems Group Websites : http://neuroinformatics.web.auth.gr/ http://neuroinformatics.gr/

27. References [1] Lehmann D & Skrandies W (1980) Reference-free identification of components of checkerboard-evoked multichannel potential fields. ElectroencephClinNeurophysiol 48:609-621. [2] Deco, G., Jirsa, V., McIntosh, A.R., Sporns, O., Kötter, R., 2009. 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., 2007. 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., 2001. Long-range temporal correlations and scaling behavior in human brain oscillations. J. Neurosci. 21, 1370–1377. [5]Stam, C.J., de Bruin, E.A., 2004. Scale-free dynamics of global functional connectivity in the human brain. Hum. Brain Mapp. 22, 97–104. [6]DimitriadisSI, 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. 145-155. 8)Dimitriadis SI, Laskaris NA, Tzelepi A, EconomouG.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.1302-1309. [9]Allen et al., Tracking Whole-Brain Connectivity Dynamics in the Resting State Cereb. Cortex (2012)doi: 10.1093/cercor/bhs352. [10] Federico CirettGal´an and Carole R. Beal.EEGEstimates of Engagement and Cognitive Workload Predict Math Problem Solving Outcomes. UMAP 2012, LNCS 7379, pp. 51–62, 2012. [11] Mohammed MostafaYehia. 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-JianLina & Ming-HuaHsieh.Classificationof mental task from EEG data using neural networks based on particle swarm optimization. Neurocomputing 72 (2009) 1121– 1130 [12] Charles W. Anderson , ZlatkoSijercic.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!