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

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.

Full Text

Duke Authors

Cited Authors

  • Duarte-Carvajalino, JM; Jahanshad, N; Lenglet, C; McMahon, KL; de Zubicaray, GI; Martin, NG; Wright, MJ; Thompson, PM; Sapiro, G

Published Date

  • February 15, 2012

Published In

Volume / Issue

  • 59 / 4

Start / End Page

  • 3784 - 3804

PubMed ID

  • 22108644

Electronic International Standard Serial Number (EISSN)

  • 1095-9572

Digital Object Identifier (DOI)

  • 10.1016/j.neuroimage.2011.10.096

Language

  • eng

Citation Source

  • PubMed