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.
Bibliographical noteFunding Information:
Work partially supported by NIH P41 RR008079 , NIH P30 NS057091 , NIH R01 EB008432 , ONR , NGA , NSF , NSSEFF/AFOSR , and ARO . NJ was additionally supported by NIH NLM Grant T15 LM07356 . This study was supported by grant number RO1 HD050735 from the National Institute of Child Health and Human Development, USA , and Project Grant 496682 from the National Health and Medical Research Council, Australia . Additional support for algorithm development was provided by the NIA , NIBIB , and the National Center for Research Resources ( AG016570, EB01651, RR019771 to PT). The authors would like to thank the feedback provided by Dr. Daniel Yekutieli in the correct interpretation of the hierarchical control of the FDR and also Dr. Ernesto Estrada for his feedback on the correct interpretation of the communicability matrix for directed graphs, and for providing us with further bibliography in the subject. We are also grateful to the twins for their willingness to participate in our studies, and research nurses, Marlene Grace and Ann Eldridge, Queensland Institute of Medical Research, for twin recruitment.
- Anatomical brain connectivity
- Complex networks
- Diffusion weighted MRI
- False discovery rate
- Hierarchical analysis
- Sex and kinship brain network differences
- Topological analysis