Speaker: Jimmy Lu Topics: Graph Analysis of Brain Networks - Measures and Tools Date: 2010.06.02 WECO Lab, CSIE, FJU

1. Graph Analysis of Brain
Networks—Measures and
Tools
Speaker : Jimmy Lu
Advisor : Hsing Mei
Web Computing Laboratory(WECO Lab)
Computer Science and Information Engineering Department
Fu Jen Catholic University

2. Steps of Graph Analysis
 Define the network nodes. (EEG, MEG, MRI, DTI,
etc.)
 Estimate a continuous measure of association
between nodes
 Generate an association matrix, a binary adjacency
matrix or undirected graph.
 Calculate the network parameters of interest in
this graphical model of a brain network
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3. Steps of generate structural and functional brain networks
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4. Computational modeling of structural and functional brain netowrks
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5. Illustrative anatomical, functional, effective connectivity networks
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6. Network measures
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7. Measures of network topology
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8. Construction of brain networks
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9. Graph analysis tools
 Brain connectivity toolbox (BCT)
 A toolbox of Matlab
 Contributor: Olaf Sporns, Mikail Rubinov, Rolf Kotter,
Patric Hagmann
 Define functions for analyzing complex graph (ex:
degree, shortest path, etc.)
 The functions are the collection of many research
results
 The web site also provide some useful information. (ex:
connectivity datasets, tools for visualization, etc.)
 Open source. We can define our own measures.
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10. http://www.brain-connectivity-toolbox.net
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11. Reference
 [1] Ed Bullmore, Olaf Sporns, “Complex brain networks: graph
theoretical analysis of structural and functional systems”, Nature
Reviews Neuroscience 10, 186-198 (March 2009)
 [2] Mikail Rubinov, Olaf Sporns, “Complex network measures of brain
connectivity: uses and interpretations”, NeuroImage (2009)
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