Modularity maximization as a flexible and generic framework for brain network exploratory analysis

TL;DR

This paper demonstrates that modularity maximization is a versatile framework for analyzing brain networks, adaptable to various data types, conditions, and temporal dynamics, thereby broadening its neuroscientific applications.

Contribution

It introduces several extensions of modularity maximization tailored for neuroscience, including methods for signed matrices, space-independent modules, and multi-layer temporal tracking.

Findings

Extended modularity maximization for signed matrices.

Detection of condition-specific modules.

Tracking modules across time and modalities.

Abstract

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.