Differentially Categorized Structural Connectome Hubs are Involved in   Differential Microstructural Basis and Functional Implications and Contribute   to Individual Identification
  Human brain structural networks contain sets of centrally embedded hub regions that enable efficient information communication. However, it remains largely unknown about categories of structural brain hubs and their microstructural, functional and cognitive characteristics as well as contributions to individual identification. Here, we employed three multi-modal imaging data sets with structural MRI, diffusion MRI and resting-state functional MRI to construct individual structural brain networks, identify brain hubs based on eight commonly used graph-nodal metrics, and perform comprehensive validation analysis. We found three categories of structural hubs in the brain networks, namely, aggregated, distributed and connector hubs. Spatially, these distinct categories of hubs were primarily located in the default-mode system and additionally in the visual and limbic systems for aggregated hubs, in the frontoparietal system for distributed hubs, and in the sensorimotor and ventral attention systems for connector hubs. Importantly, these three categories of hubs exhibited various distinct characteristics, with the highest level of microstructural organization in the aggregated hubs, the largest wiring cost and topological vulnerability in the distributed hubs, and the highest functional associations and cognitive flexibility in the connector hubs, although they behaved better regarding these characteristics compared to non-hubs. Finally, all three categories of hub indices displayed high across-session spatial similarities and acted as a structural fingerprint with high predictive rates (100%, 100% and 84.2%) for individual identification. Collectively, our findings highlighted three categories of brain hubs with differential microstructural, functional and cognitive associations, which may shed light on the topological mechanisms of the human connectome. 
Xindi Wang, Qixiang Lin, Mingrui Xia, Yong He
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