1. The document examines the relationship between brain anatomical networks and intelligence by analyzing structural, functional, and effective connectivity patterns. 2. It reviews concepts from graph theory and complex networks that are relevant for studying brain networks, including small-world networks and scale-free networks. 3. An experiment analyzed diffusion tensor images and other data from 79 subjects to construct and analyze anatomical brain networks and investigate their relationships with general and high intelligence.

1. Brian networks2010/02/06Brain Anatomical Network and Intelligence&Organization, development and function of complex brain networksSpeaker : Jimmy LuAdvisor : Hsing MeiWeb Computing LaboratoryComputer Science and Information Engineering DepartmentFu Jen Catholic University

2. OutlineIntroductionBackgroundGraph theoryComplex networksExamine principles (three aspects)Structural, functional, effective connectivityClinical termsExperimentFuture work2

3. IntroductionExamines principlesStructural propertiesRelationship between the structural substrate and dynamic functional and effective connectivity patternsThe efficiency of brain structural organization may be an important biological basis for intelligence3

4. Background – graph theoryAdjacency (connection) matrixCharacteristic path lengthClustering coefficientConnectednessCycleDegreeDistanceDistance matrixGraphPathRegular graphRandom graphScale-free graphSmall-world networks

5. Background – complex networksRandom networksSmall-world networksScale-free networks

6. Background – three aspectsNeuroanatomical substrate (structural connectivity)Dynamics of neurons (functional connectivity)Functional segregationFunctional integrationCausal interaction (effective connectivity)

7. Background - structuralNetwork participation indicesIn-degree, out-degreetransmission coefficientBroadcasters, integratorscooperativeMatching indexConnectional fingerprintMotif analysis

8. Background - structuralGraph theoretical analysisHigh clustering, short path lengthFunctional proximityMultivariate analysisMultidimensional scalingHierarchical clusterEvolutionary optimizationInter-cluster connectionConnectedness

9. Background - structural

10. Background - structural

11. Background - structural

12. Background - structural

13. Background – developmentLocal spatial growth rulesPreferential attachmentGlobal network design

14. Background – clinical termsAnatomical Automatic Labeling (AAL)Magnetic resonance imaging (MRI)Diffusion MRIFunctional MRI (fMRI)In vivoCerebral cortexTractography

15. Background - functionalThere are some problems in the previous workOnly binary networksNonzero connection probability value to brain region pairs which are unlikely to be connectedEtc...But they all have evidence that brain network is close to small-world

16. Background - experiment79 subjects (44 males and 35 females, mean age = 23.8 years, range = 17-33 years)intelligence quotient (IQ) test (FSIQ, PIQ, VIQ)GI (general intelligence)/HI (high intelligence) groupDiffusion tensor image (DTI)/T1 image/AAL co-registerNormalize, inverseConstruct binary and weighted network of an individual brainAnatomical network analysesStatistical analysis

17. Background - experiment

18. Background - experiment

19. Background - experiment

20. Background - experiment

21. Background - experiment

22. Background - experiment

23. Background - experiment

24. Background - experiment

25. Future work

26. Future workFrom the computer science perspectiveHow about the social network?Other internet overlaid networks?A brain simulator?Etc…?

27. ReferenceLi Y, Liu Y, Li J, Qin W, Li K, et al. (2009) Brain Anatomical Network and Intelligence. PLoSComputOlaf Sporns, Dante R. Chialvo, Marcus Kaiser and Claus C. Hilgetag (2004) Organization, development and function of complex brain networks. TRENDS in Cognitive Sciences Vol.8 No.9