Hierarchical topological network analysis of anatomical human brain connectivity and differences related to sex and kinship

Duarte-Carvajalino, Julio M., Jahanshad, Neda, Lenglet, Christophe, McMahon, Katie L., de Zubicaray, Greig I., Martin, Nicholas G., Wright, Margaret J., Thompson, Paul M. and Sapiro, Guillermo (2012) Hierarchical topological network analysis of anatomical human brain connectivity and differences related to sex and kinship. NeuroImage, 59 4: 3784-3804. doi:10.1016/j.neuroimage.2011.10.096

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Author Duarte-Carvajalino, Julio M.
Jahanshad, Neda
Lenglet, Christophe
McMahon, Katie L.
de Zubicaray, Greig I.
Martin, Nicholas G.
Wright, Margaret J.
Thompson, Paul M.
Sapiro, Guillermo
Title Hierarchical topological network analysis of anatomical human brain connectivity and differences related to sex and kinship
Journal name NeuroImage   Check publisher's open access policy
ISSN 1053-8119
Publication date 2012-02-15
Year available 2011
Sub-type Article (original research)
DOI 10.1016/j.neuroimage.2011.10.096
Volume 59
Issue 4
Start page 3784
End page 3804
Total pages 21
Place of publication Maryland Heights, MO, U.S.A.
Publisher Academic Press
Collection year 2012
Language eng
Formatted abstract
Modern non-invasive brain imaging technologies, such as diffusion weighted magnetic resonance imaging (DWI), enable the mapping of neural fiber tracts in the white matter, providing a basis to reconstruct a detailed map of brain structural connectivity networks. Brain connectivity networks differ from random networks in their topology, which can be measured using small worldness, modularity, and high-degree nodes (hubs). Still, little is known about how individual differences in structural brain network properties relate to age, sex, or genetic differences. Recently, some groups have reported brain network biomarkers that enable differentiation among individuals, pairs of individuals, and groups of individuals. In addition to studying new topological features, here we provide a unifying general method to investigate topological brain networks and connectivity differences between individuals, pairs of individuals, and groups of individuals at several levels of the data hierarchy, while appropriately controlling false discovery rate (FDR) errors. We apply our new method to a large dataset of high quality brain connectivity networks obtained from High Angular Resolution Diffusion Imaging (HARDI) tractography in 303 young adult twins, siblings, and unrelated people. Our proposed approach can accurately classify brain connectivity networks based on sex (93% accuracy) and kinship (88.5% accuracy). We find statistically significant differences associated with sex and kinship both in the brain connectivity networks and in derived topological metrics, such as the clustering coefficient and the communicability matrix.

Highlights: ► We analyze structural brain connectivity network differences due to sex or kinship. ► We use a large dataset of brain connectivity networks from HARDI tractography. ► We analyze the connectivity networks as well as the topological metrics derived. ► We use robust pattern recognition and statistical analysis. ► We perform a hierarchical analysis of topological brain connectivity differences.
Keyword Anatomical brain connectivity
Complex networks
Diffusion weighted MRI
Topological analysis
Hierarchical analysis
False discovery rate
Sex and kinship brain network differences
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ
Additional Notes Available online 12 November 2011.

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2012 Collection
School of Medicine Publications
School of Psychology Publications
Centre for Advanced Imaging Publications
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Citation counts: TR Web of Science Citation Count  Cited 19 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 21 times in Scopus Article | Citations
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Created: Wed, 04 Jan 2012, 10:44:59 EST by Sandrine Ducrot on behalf of Centre for Advanced Imaging