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

TL;DR

This paper introduces a modified module detection algorithm for brain networks that accounts for wiring cost, revealing spatially distributed modules with unique functions and developmental patterns.

Contribution

It presents a novel algorithm that isolates modules based on unexpected connections, enhancing understanding of brain network organization and development.

Findings

Detected modules differ from standard methods

Modules show distinct spatial and functional properties

Modules track developmental maturation patterns

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.