  The modular structure of brain networks supports specialized information
processing, complex dynamics, and cost-efficient spatial embedding.
Inter-individual variation in modular structure has been linked to differences
in performance, disease, and development. There exist many data-driven methods
for detecting and comparing modular structure, the most popular of which is
modularity maximization. Although modularity maximization is a general
framework that can be modified and reparamaterized to address domain-specific
research questions, its application to neuroscientific datasets has, thus far,
been narrow. Here, we highlight several strategies in which the
``out-of-the-box'' version of modularity maximization can be extended to
address questions specific to neuroscience. First, we present approaches for
detecting ``space-independent'' modules and for applying modularity
maximization to signed matrices. Next, we show that the modularity maximization
frame is well-suited for detecting task- and condition-specific modules.
Finally, we highlight the role of multi-layer models in detecting and tracking
modules across time, tasks, subjects, and modalities. In summary, modularity
maximization is a flexible and general framework that can be adapted to detect
modular structure resulting from a wide range of hypotheses. This article
highlights opens multiple frontiers for future research and applications.
