AUTHOR SUMMARY The human brain is characterized by a complex pattern of anatomical wiring, in the form of white-matter tracts that link large volumes of neural tissue. The organization of this pattern is likely driven by many factors, including evolutionary adaptability, robustness to perturbations, and a separation of the timescales necessary to produce a diverse repertoire of neural dynamics. In this study, we sought to disentangle two such factors—the drive to decrease the cost of wiring, and the putative drive to increase the efficiency of the network topology—and we explored the impacts of these factors on the brain’s modular organization. The contributions of this work include a new algorithmic approach to community detection and novel insights into the role of modules in human brain function. 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 allowed us to focus on connections that are unexpected under a cost-reduction wiring rule and to identify modules from among these connections. We applied this technique to anatomical brain networks and showed that the modules we detected differ from those detected using the standard technique. We demonstrated 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 demonstrated 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.