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Brain Anatomical Network and Intelligence

Overview of attention for article published in PLoS Computational Biology, May 2009
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Title
Brain Anatomical Network and Intelligence
Published in
PLoS Computational Biology, May 2009
DOI 10.1371/journal.pcbi.1000395
Pubmed ID
Authors

Yonghui Li, Yong Liu, Jun Li, Wen Qin, Kuncheng Li, Chunshui Yu, Tianzi Jiang

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.

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Geographical breakdown

Country Count As %
United States 8 1%
United Kingdom 7 1%
China 4 <1%
Germany 4 <1%
Spain 3 <1%
France 2 <1%
Austria 2 <1%
Brazil 2 <1%
India 2 <1%
Other 12 2%
Unknown 541 92%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 145 25%
Researcher 101 17%
Student > Master 81 14%
Student > Bachelor 42 7%
Professor > Associate Professor 31 5%
Other 115 20%
Unknown 72 12%
Readers by discipline Count As %
Psychology 114 19%
Neuroscience 86 15%
Agricultural and Biological Sciences 73 12%
Medicine and Dentistry 64 11%
Engineering 41 7%
Other 96 16%
Unknown 113 19%