Cited by 599Advanced Neuroimaging Techniques and ApplicationsFunctional Brain Connectivity StudiesComplex Network Analysis Techniques

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Brain Anatomical Network and Intelligence

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Li, Yonghui, Liu, Yong, Li, Jun, Qin, Wen, Li, Kuncheng, Yu, Chunshui, Jiang, Tianzi·2009·PLoS Computational Biology

DOI 10.1371/journal.pcbi.1000395PMC PMC2683575

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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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Self-citation

Authors: Li, Y., Liu, Y., Li, J., Qin, W., Li, K., Yu, C., & Jiang, T.
Affiliations: Chinese Academy of Sciences (CN)
Capital Medical University (CN)
Corresponding author: Yu, Chunshui
Jiang, Tianzi
ORCID: Li, Yonghui
Liu, Yong
Li, Jun
Qin, Wen
Li, Kuncheng
Yu, Chunshui
Jiang, Tianzi
Venue: PLoS Computational Biology
Publisher: Public Library of Science (PLoS)
Published: 2009-05-29
Volume: 5
Issue: 5
Pages: e1000395
ISSN: 1553-734X (ISSN-L)
1553-7358
DOI: 10.1371/journal.pcbi.1000395
PMC: PMC2683575
OpenAlex: W2073017605
Cited by: 597
References: 81
Citations by year: 2026: 9 · 2025: 9 · 2024: 14 · 2023: 18 · 2022: 26 · 2021: 41 · 2020: 29 · 2019: 53 · 2018: 27 · 2017: 40 · 2016: 56 · 2015: 54 · 2014: 43 · 2013: 59 · 2012: 50
FWCI: 29.97
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0/ (unspecified)
Locations: PLoS Computational Biology (publishedVersion, cc-by)
PubMed Central (publishedVersion, cc-by)
PubMed (publishedVersion)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Figshare (submittedVersion, cc-by)
Funders: National Natural Science Foundation of China (30730035, 60675033, 30670601)
Chinese Academy of Sciences
Program for New Century Excellent Talents in University (NCET-07-0568)
Topics: Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
Keywords: intelligence, global efficiency, average path length, individual differences in intelligence, structural connectivity, diffusion tensor tractography, graph theory, information transmission efficiency, IQ
MeSH terms: Adolescent, Adult, Brain, Brain Mapping, Female, Humans, Intelligence, Intelligence Tests, Male, Models, Neurological, Nerve Net, Neural Pathways, Cluster Analysis, Diffusion Magnetic Resonance Imaging

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