%0 Journal Article %A Teddy J. Akiki %A Chadi G. Abdallah %T Determining the Hierarchical Architecture of the Human Brain Using Subject-Level Clustering of Functional Networks %D 2018 %R 10.1101/350462 %J bioRxiv %P 350462 %X Optimal integration and segregation of neuronal connections are necessary for efficient large-scale network communication between distributed cortical regions while allowing for modular specialization. This network dynamic is enabled at the cortical mesoscale by the organization of nodes into communities. Previous in vivo efforts to map the mesoscale architecture in humans had several limitations. Here we characterize a consensus multiresolution mesoscale organization of the functional cortical network. We derive this consensus from the clustering of subject-level networks. We show that this subject-derived consensus framework yields clusters that better map to the individual, compared to the widely-used group-derived consensus approach. We also show that using clustering methods tailored for correlation networks results in functional communities that better fit anatomical metadata. We applied this analysis to magnetic resonance imaging data from 1003 healthy individuals part of the Human Connectome Project. The hierarchical atlas will be made publicly available for future investigators. %U https://www.biorxiv.org/content/biorxiv/early/2018/06/23/350462.full.pdf