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SINAPSETHINK BIG

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Cognitive Engineering Lab

Dynamic Functional Brain Connectivity: Perspectives and Further Directions

Scientific VisitorDr.Dimitriadis Stavros (Greece)

Neuroinformatics GroupAristotele 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

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OverviewFirst 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

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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).

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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).

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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).

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DFC PatternsNetwork Metrics Time Series (NMTS)

Dimitriadis et al., 2010

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Dynamic Functional Connectivity:Definition of time-window matters

Dimitriadis et al., 2010

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Macrostates - MicrostatesFrom 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

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Macrostates - MicrostatesFrom 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 !!!

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From Scalp Potential Microstates to Functional Connectivity Microstates

From a Neuroscience Point of View:

Multi-trial ERP Visual Paradigm

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From Scalp Potential Microstates to Functional Connectivity Microstates

EEG

Markovian chainDimitriadis et al., 2013

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From Scalp Potential Microstates to Functional Connectivity Microstates

From a Neuroscience Point of View:

Poccurence of Fcμstates

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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

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Markov State Models

Dimitriadis et al., 2013

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 deterministicity of the system based on an information-theoretic measure called:Entropy Reduction RateERT stimulus > ERT baseline

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Tracking Whole-Brain Connectivity Dynamics in the Resting State (fMRI)

Allen et al. 2012

16Allen et al. 2012

17Allen et al. 2012

Prototyping Functional Connectivity Graphs

18Allen et al. 2012

Occurrences of Prototypical FCGs

19Allen et al. 2012

Transitions of Prototypical FCGs

Markovian chain

20Allen et al. 2012 ; Dimitriadis et al., 2013

Extracting Meaningful Measures from Markovian Chain

1. Duration of a Functional Connectivity Microstate2. occurrences per second3. covered percentage of analysis time, 4. transition probabilities (Dimitriadis et al., 2013 under revision in HBM)5. Complexity6. Deterministicity (Dimitriadis et al., 2013)

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Meta-Analysis of Brain Data

Neumann et al., 2005

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

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Detect Motifs in Static/Dynamic FCGs

(Iakovidou et al., 2012)

1. Discovery of group-consistent graph substructure patterns

Monitoring Motif in A Dynamic Way

Without a-priori definition of the n-motifs

gSpan - algorithm

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Mining Large Numbers of FCGs

Univariate Power Spectrum /foci

Multivariate

Anderson et al., 2007

Increment of Degrees of Freedom

Increment of the discriminative power to decodeDifferent Brain States Simultaneously

N N*(N-1)/2 = O(N2) N*(N-1)= O(N2)

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Brain DecodingCo-activated Areas (Foci) vs Co-activated graphs

Anderson et al., 2007

Co-activated Graphs Co-activated Brain Areas (Foci)

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Brain DecodingCognitive States: attention, emotion, language, memory, mental imagery etc.

Brain Diseases/Disorders: Dyslexia, ADHD, Alzheimer etc.

Developmental changes

General goal:1. Understanding how the brain functions2. Characterizing individual brain state across different tasks3. Monitor Individual Cognitive Performance4. Build significant biomarkers for the prevention of brain

disorders5. Monitor the improvement of the treatment

(pharmacological/surgery) in brain disease subjects…

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Neuroinformatics GroupAristotele University of Thessaloniki (Greece)

Dr. Nikolaos Laskaris, Assistant Professor, Dept. of Informatics

Group Websites : http://neuroinformatics.web.auth.gr/ http://neuroinformatics.gr/

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

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References[1] Lehmann D & Skrandies W (1980) Reference-free identification of components of checkerboard-evoked multichannel potential fields. Electroenceph Clin Neurophysiol 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]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. 145-155.8) 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.1302-1309. [9]Allen et al., Tracking Whole-Brain Connectivity Dynamics in the Resting StateCereb. Cortex (2012)doi: 10.1093/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

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References[11] Cheng-Jian Lina & Ming-Hua Hsieh.Classification of mental task from EEG data using neural networks based onparticle 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

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Thank you for your attention!