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

• Main Authors: Esfahlani, Farnaz Zamani, Jo, Youngheun, Puxeddu, Maria Grazia, Merritt, Haily, Tanner, Jacob C, Greenwell, Sarah, Patel, Riya, Faskowitz, Joshua, Betzel, Richard F
• Format: Journal Article
• Language: English
• Published: 29.06.2021
• Subjects: Quantitative Biology - Neurons and Cognition
• DOI: 10.48550/arxiv.2106.15428

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.