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• W2137206293 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. ► 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." @default.
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• W2137206293 date "2012-02-01" @default.
• W2137206293 modified "2023-09-24" @default.
• W2137206293 title "Hierarchical topological network analysis of anatomical human brain connectivity and differences related to sex and kinship" @default.
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• W2137206293 doi "https://doi.org/10.1016/j.neuroimage.2011.10.096" @default.
• W2137206293 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/3551467" @default.
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