Presentation on theme: "SINAPSE THINK BIG think different"— Presentation transcript:
1 SINAPSETHINK BIGthink differentCognitive Engineering Lab Dynamic Functional Brain Connectivity: Perspectives and Further DirectionsScientific VisitorDr.Dimitriadis Stavros (Greece)Neuroinformatics GroupAristotele University of ThessalonikiDept.of Computer ScienceWorkshop on Brain Connectivity: Structure and Function in Normal Brain and DiseaseCenter of Life Sciences Auditorium , May 17rd, 2013
2 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/fMRISecond part: Investigating Functional Cooperation from the Human Brain Connectivity via Simple Graph-Theoretic Methods
3 Brain ConnectivityModes 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 PatternsInterestingly, 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 mattersDimitriadis 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 ageWhile 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 EEGDimitriadis et al., 2013Markovian 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 FCμstatesTopographies of functional clusters related to FCμstates detected for the‘‘Left’’ ERP-trialsTopographies of functional clusters related to FCμstates detected for the‘‘Right’’ ERP-trialsDimitriadis et al., 2013
14 Markov State ModelsSymbolic 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 RateERT stimulus > ERT baselineDimitriadis et al., 2013
15 Tracking Whole-Brain Connectivity Dynamics in the Resting State (fMRI) 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 chainAllen et al. 2012
20 Extracting Meaningful Measures from Markovian Chain Duration of a Functional Connectivity Microstateoccurrences per secondcovered percentage of analysis time,transition probabilities (Dimitriadis et al., 2013 under revision in HBM)ComplexityDeterministicity (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. UsingReplicator Dynamics(Neumann et al., 2005)Neumann et al., 2005
22 Detect Motifs in Static/Dynamic FCGs Discovery of group-consistent graph substructure patternsWithout a-priori definition of the n-motifsMonitoring Motif in A Dynamic WaygSpan - algorithm(Iakovidou et al., 2012)
23 Mining Large Numbers of FCGs MultivariateUnivariatePower Spectrum /fociN N*(N-1)/2 = O(N2) N*(N-1)= O(N2)Increment of Degrees of FreedomIncrement of the discriminative power to decodeDifferent Brain States SimultaneouslyAnderson et al., 2007
24 Co-activated Areas (Foci) vs Co-activated graphs Brain DecodingCo-activated Areas (Foci) vs Co-activated graphsCo-activated GraphsCo-activated Brain Areas (Foci)Anderson et al., 2007
25 Brain DecodingCognitive States: attention, emotion, language, memory, mental imagery etc.Brain Diseases/Disorders: Dyslexia, ADHD, Alzheimer etc.Developmental changesGeneral goal:Understanding how the brain functionsCharacterizing individual brain state across different tasksMonitor Individual Cognitive PerformanceBuild significant biomarkers for the prevention of brain disordersMonitor 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 InformaticsDr. Dimitrios Adamos, Researcher (Music Department/AUTH - Music Cognition)Dr. Efstratios Kosmidis,Lecturer of Physiology, Medical School, AUTHDr.Areti Tzelepi, Researcher ,Institute of Communication and Computer SystemsGroup Websites :
27 References Lehmann D & Skrandies W (1980) Reference-free identification of components of checkerboard-evoked multichannel potential fields. Electroenceph Clin Neurophysiol 48: 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 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 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. Stam, C.J., de Bruin, E.A., Scale-free dynamics of global functional connectivity in the human brain. Hum. Brain Mapp. 22, 97–104.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 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, pp8) 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 Allen et al., Tracking Whole-Brain Connectivity Dynamics in the Resting StateCereb. Cortex (2012)doi: /cercor/bhs352. 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. Mohammed Mostafa Yehia. EEG - Mental Task Discrimination by Digital Signal Processing Jack Culpepper. Discriminating Mental States Using EEG Represented by Power Spectral Density
28 References 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 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) 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