Brain anatomical network and intelligence

Li, Yonghui, Liu, Yong, Li, Jun, Qin, Wen, Li, Kuncheng, Yu, Chunshui and Jiang, Tianzi (2009) Brain anatomical network and intelligence. PLoS Computational Biology, 5 5: . doi:10.1371/journal.pcbi.1000395


Author Li, Yonghui
Liu, Yong
Li, Jun
Qin, Wen
Li, Kuncheng
Yu, Chunshui
Jiang, Tianzi
Title Brain anatomical network and intelligence
Journal name PLoS Computational Biology   Check publisher's open access policy
ISSN 1553-734X
1553-7358
Publication date 2009-05-29
Sub-type Article (original research)
DOI 10.1371/journal.pcbi.1000395
Open Access Status DOI
Volume 5
Issue 5
Total pages 17
Place of publication San Francisco, CA, United States
Publisher Public Library of Science
Language eng
Abstract Intuitively, higher intelligence might be assumed to correspond to more efficient information transfer in the brain, but no direct evidence has been reported from the perspective of brain networks. In this study, we performed extensive analyses to test the hypothesis that individual differences in intelligence are associated with brain structural organization, and in particular that higher scores on intelligence tests are related to greater global efficiency of the brain anatomical network. We constructed binary and weighted brain anatomical networks in each of 79 healthy young adults utilizing diffusion tensor tractography and calculated topological properties of the networks using a graph theoretical method. Based on their IQ test scores, all subjects were divided into general and high intelligence groups and significantly higher global efficiencies were found in the networks of the latter group. Moreover, we showed significant correlations between IQ scores and network properties across all subjects while controlling for age and gender. Specifically, higher intelligence scores corresponded to a shorter characteristic path length and a higher global efficiency of the networks, indicating a more efficient parallel information transfer in the brain. The results were consistently observed not only in the binary but also in the weighted networks, which together provide convergent evidence for our hypothesis. Our findings suggest that the efficiency of brain structural organization may be an important biological basis for intelligence.
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status Non-UQ
Additional Notes Article # e1000395

Document type: Journal Article
Sub-type: Article (original research)
Collections: Queensland Brain Institute Publications
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Created: Fri, 21 Oct 2011, 13:04:34 EST by Debra McMurtrie on behalf of Queensland Brain Institute