The modular organization of human anatomical brain networks: Accounting for the cost of wiring

Abstract: Brain networks are expected to be modular. However, existing techniques for estimating a network's modules make it difficult to assess the influence of organizational principles such as wiring cost reduction on the detected modules. Here, we present a modification of an existing module detection algorithm that allows us to focus on connections that are unexpected under a cost-reduction wiring rule and to identify modules from among these connections. We apply this technique to anatomical brain networks and show that the modules we detect differ from those detected using the standard technique. We demonstrate that these novel modules are spatially distributed, exhibit unique functional fingerprints, and overlap considerably with rich clubs, giving rise to an alternative and complementary interpretation of the functional roles of specific brain regions. Finally, we demonstrate that, using the modified module detection approach, we can detect modules in a developmental dataset that track normative patterns of maturation. Collectively, these findings support the hypothesis that brain networks are composed of modules and provide additional insight into the function of those modules.