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

The DOI matches its Crossref record; 81 of the 81 references it cites were found in a registry or an index.

Brain Anatomical Network and Intelligence

Open access
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
Open this paper in CoChat — free accountOpen PDF

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.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1371/journal.pcbi.1000395

Cites

81 of 81 references verified0 citation errors · 0 not checkable

Statistics

Figures

Figure 1 Schematic representation of the transformation of the AAL template into individual DTI space. Both the T1 template and the AAL template showed in the right column are in the MNI space, with image dimensions of 181 mm×217 mm×181 mm and voxel dimensions of 1 mm×1 mm×1 mm. Both the rT1 image and the transformed AAL template overlaid on it showed in the left column are in the DTI native space of one randomly selected individual, with image dimensions of 256 mm×256 mm×45 mm and voxel dimensions of 1 mm×1 mm×3 mm. The homologous brain regions in AAL template were coded in different colors because the areas in the left and right hemispheres were considered separately.
Figure 1 Schematic representation of the transformation of the AAL template into individual DTI space. Both the T1 template and the AAL template showed in the right column are in the MNI space, with image dimensions of 181 mm×217 mm×181 mm and voxel dimensions of 1 mm×1 mm×1 mm. Both the rT1 image and the transformed AAL template overlaid on it showed in the left column are in the DTI native space of one randomly selected individual, with image dimensions of 256 mm×256 mm×45 mm and voxel dimensions of 1 mm×1 mm×3 mm. The homologous brain regions in AAL template were coded in different colors because the areas in the left and right hemispheres were considered separately.CC BY · PubMed Central
Figure 2 Mean map of the binary connectivity matrixes averaged across the 79 subjects. A 90×90 symmetric matrix in which the x and y axes correspond to the regions listed in Table 1 (labeled by the abbreviations defined in Table 1) and each entry represents the percentage of subjects that have a connection between the corresponding pair of brain regions. The value of each entry ranged from 0 (black color on the map), indicating that no subject showed a connection between the corresponding pair of brain regions, to 1 (white color in the map), indicating that the two regions were connected in all subjects. The regions in the left and right hemispheres are ordered separately. Abbreviations: LH, Left Hemisphere; RH, Right Hemisphere.
Figure 2 Mean map of the binary connectivity matrixes averaged across the 79 subjects. A 90×90 symmetric matrix in which the x and y axes correspond to the regions listed in Table 1 (labeled by the abbreviations defined in Table 1) and each entry represents the percentage of subjects that have a connection between the corresponding pair of brain regions. The value of each entry ranged from 0 (black color on the map), indicating that no subject showed a connection between the corresponding pair of brain regions, to 1 (white color in the map), indicating that the two regions were connected in all subjects. The regions in the left and right hemispheres are ordered separately. Abbreviations: LH, Left Hemisphere; RH, Right Hemisphere.CC BY · PubMed Central
Figure 3 3D presentation of the binary connectivity matrixes averaged across the 79 subjects. (A), (B) and (C): A 3D presentation of the network in anatomical space, in which the green points correspond to the 90 AAL regions defined in Table 1 and the lines correspond to the connection between corresponding pairs of brain regions. The colors of the lines represents the percentage of subjects that have a connection between the corresponding pair of brain regions, ranging from 0.5 (yellow color), indicating that at least half of the subjects showed a connection between the corresponding pair of brain regions, to 1 (red color), indicating that the two regions were connected in all subjects. Abbreviations: LH, Left Hemisphere; RH, Right Hemisphere.
Figure 3 3D presentation of the binary connectivity matrixes averaged across the 79 subjects. (A), (B) and (C): A 3D presentation of the network in anatomical space, in which the green points correspond to the 90 AAL regions defined in Table 1 and the lines correspond to the connection between corresponding pairs of brain regions. The colors of the lines represents the percentage of subjects that have a connection between the corresponding pair of brain regions, ranging from 0.5 (yellow color), indicating that at least half of the subjects showed a connection between the corresponding pair of brain regions, to 1 (red color), indicating that the two regions were connected in all subjects. Abbreviations: LH, Left Hemisphere; RH, Right Hemisphere.CC BY · PubMed Central
Figure 4 Six well-known major white matter tracts reconstructed in three randomly selected subjects. Please note that the fiber bundles showed here may be only parts of a specific major white matter tract, rather than the entire tract.
Figure 4 Six well-known major white matter tracts reconstructed in three randomly selected subjects. Please note that the fiber bundles showed here may be only parts of a specific major white matter tract, rather than the entire tract.CC BY · PubMed Central
Figure 5 Significant partial correlation between the total number of edges and intelligence tests scores. was found to be positively correlated to FSIQ and PIQ.
Figure 5 Significant partial correlation between the total number of edges and intelligence tests scores. was found to be positively correlated to FSIQ and PIQ.CC BY · PubMed Central
Figure 6 Significant partial correlation between Lp and intelligence tests scores. In the case of binary networks, was found to be negatively correlated to FSIQ and PIQ; and in the case of weighted networks, was found to be negatively correlated to FSIQ, PIQ and VIQ.
Figure 6 Significant partial correlation between Lp and intelligence tests scores. In the case of binary networks, was found to be negatively correlated to FSIQ and PIQ; and in the case of weighted networks, was found to be negatively correlated to FSIQ, PIQ and VIQ.CC BY · PubMed Central
Figure 7 Significant partial correlation between E_glob and intelligence tests scores. was found to be positively correlated to FSIQ, PIQ and VIQ in both the binary and weighted networks.
Figure 7 Significant partial correlation between E_glob and intelligence tests scores. was found to be positively correlated to FSIQ, PIQ and VIQ in both the binary and weighted networks.CC BY · PubMed Central

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 81 references

Cited by

Sorted by citations

Items 301–400 of 599

Related

See all

Metadata

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
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
Licence
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

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review