SINAPSE
Cognitive Engineering Lab
THINK BIG
think different
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
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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
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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 Patterns
Network 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 - 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
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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 !!!
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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
Dimitriadis et al., 2013
Markovian chain
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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
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 informationtheoretic measure called:
Entropy Reduction Rate
ERT stimulus > ERT baseline
Dimitriadis et al., 2013
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Tracking Whole-Brain Connectivity
Dynamics in the Resting State (fMRI)
Allen et al. 2012
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Allen et al. 2012
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Prototyping Functional
Connectivity Graphs
Allen et al. 2012
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Occurrences of Prototypical FCGs
Allen et al. 2012
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Transitions of Prototypical FCGs
Markovian chain
Allen et al. 2012
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Extracting Meaningful Measures
from Markovian Chain
1.
2.
3.
4.
5.
6.
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
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Meta-Analysis of Brain Data
1. Meta-Analysis of Functional Imaging Data e.g. Using
Replicator Dynamics(Neumann et al., 2005)
Neumann et al., 2005
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Detect Motifs in Static/Dynamic FCGs
1. 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)
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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
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Brain Decoding
Co-activated Areas (Foci) vs Co-activated graphs
Co-activated Graphs
Anderson et al., 2007
Co-activated Brain Areas (Foci)
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Brain Decoding
Cognitive States: attention, emotion, language, memory, mental imagery etc.
Brain Diseases/Disorders: Dyslexia, ADHD, Alzheimer etc.
Developmental changes
General goal:
1. Understanding how the brain functions
2. Characterizing individual brain state across different tasks
3. Monitor Individual Cognitive Performance
4. Build significant biomarkers for the prevention of brain
disorders
5. Monitor the improvement of the treatment
(pharmacological/surgery) in brain disease subjects…
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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 : http://neuroinformatics.web.auth.gr/
http://neuroinformatics.gr/
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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 timedependent 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 State
Cereb. 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 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
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Thank you for your attention!
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