Summary: | 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.
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