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 networks Speaker : Jimmy Lu Advisor : Hsing Mei Web Computing Laboratory Computer Science and Information Engineering Department Fu Jen Catholic University

2. Outline Introduction Background Graph theory Complex networks Examine principles (three aspects) Structural, functional, effective connectivity Clinical terms Experiment Future work 2

3. Introduction Examines principles Structural properties Relationship between the structural substrate and dynamic functional and effective connectivity patterns The efficiency of brain structural organization may be an important biological basis for intelligence 3

4. Background – graph theory Adjacency (connection) matrix Characteristic path length Clustering coefficient Connectedness Cycle Degree Distance Distance matrix Graph Path Regular graph Random graph Scale-free graph Small-world networks

5. Background – complex networks Random networks Small-world networks Scale-free networks

6. Background – three aspects Neuroanatomical substrate (structural connectivity) Dynamics of neurons (functional connectivity) Functional segregation Functional integration Causal interaction (effective connectivity)

7. Background - structural Network participation indices In-degree, out-degree transmission coefficient Broadcasters, integrators cooperative Matching index Connectional fingerprint Motif analysis

8. Background - structural Graph theoretical analysis High clustering, short path length Functional proximity Multivariate analysis Multidimensional scaling Hierarchical cluster Evolutionary optimization Inter-cluster connection Connectedness

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13. Background – development Local spatial growth rules Preferential attachment Global network design

14. Background – clinical terms Anatomical Automatic Labeling (AAL) Magnetic resonance imaging (MRI) Diffusion MRI Functional MRI (fMRI) In vivo Cerebral cortex Tractography

15. Background - functional There are some problems in the previous work Only binary networks Nonzero connection probability value to brain region pairs which are unlikely to be connected Etc... But they all have evidence that brain network is close to small-world

16. Background - experiment 79 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) group Diffusion tensor image (DTI)/T1 image/AAL co-register Normalize, inverse Construct binary and weighted network of an individual brain Anatomical network analyses Statistical analysis

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25. Future work

26. Future work From the computer science perspective How about the social network? Other internet overlaid networks? A brain simulator? Etc…?

27. Reference Li Y, Liu Y, Li J, Qin W, Li K, et al. (2009) Brain Anatomical Network and Intelligence. PLoSComput Olaf 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