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• W3202330493 startingPage "118607" @default.
• W3202330493 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 multiple frontiers for future research and applications." @default.
• W3202330493 created "2021-10-11" @default.
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• W3202330493 creator A5090820512 @default.
• W3202330493 date "2021-12-01" @default.
• W3202330493 modified "2023-10-17" @default.
• W3202330493 title "Modularity maximization as a flexible and generic framework for brain network exploratory analysis" @default.
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