Modularity maximization as a flexible and generic framework for brain network exploratory analysis
Farnaz Zamani Esfahlani,
Youngheun Jo,
Maria Grazia Puxeddu,
Haily Merritt,
Jacob C. Tanner,
Sarah Greenwell,
Riya Patel,
Joshua Faskowitz,
Richard F. Betzel
Affiliations
Farnaz Zamani Esfahlani
Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, United States
Youngheun Jo
Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, United States
Maria Grazia Puxeddu
Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, United States; Department of Computer, Control and Management Engineering “Antonio Ruberti”, Sapienza University of Rome, Rome 00185, Italy; IRCCS Fondazione Santa Lucia, Rome 00179, Italy
Haily Merritt
Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN 47405, United States; Cognitive Science Program, Indiana University, Bloomington, IN 47405, United States
Jacob C. Tanner
Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN 47405, United States; Cognitive Science Program, Indiana University, Bloomington, IN 47405, United States
Sarah Greenwell
Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, United States
Riya Patel
Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, United States
Joshua Faskowitz
Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, United States; Program in Neuroscience, Indiana University, Bloomington, IN 47405, United States
Richard F. Betzel
Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, United States; Cognitive Science Program, Indiana University, Bloomington, IN 47405, United States; Program in Neuroscience, Indiana University, Bloomington, IN 47405, United States; Network Science Institute, Indiana University, Bloomington, IN 47405, United States; Corresponding author.
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.