A fast and intuitive method for calculating dynamic network reconfiguration and node flexibility

• Published in: Frontiers in neuroscience Vol. 17; p. 1025428
• Main Authors: Chinichian, Narges, Kruschwitz, Johann D., Reinhardt, Pablo, Palm, Maximilian, Wellan, Sarah A., Erk, Susanne, Heinz, Andreas, Walter, Henrik, Veer, Ilya M.
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Research Foundation 09.02.2023; Frontiers Media S.A
• Subjects: Algorithms; Alzheimer's disease; Brain; Cognitive ability; community detection; Computational neuroscience; dynamic functional connectivity; dynamical network analysis; Flexibility; Mental task performance; Methods; network neuroscience; Neuroscience; Neurosciences; Short term memory; task-based fMRI; template-based flexibility; Time series; network neuroscience; task-based fMRI; dynamic functional connectivity; modular structure; dynamical network analysis; template-based flexibility; community detection
• ISSN: 1662-453X; 1662-4548; 1662-453X
• DOI: 10.3389/fnins.2023.1025428

Dynamic interactions between brain regions, either during rest or performance of cognitive tasks, have been studied extensively using a wide variance of methods. Although some of these methods allow elegant mathematical interpretations of the data, they can easily become computationally expensive or difficult to interpret and compare between subjects or groups. Here, we propose an intuitive and computationally efficient method to measure dynamic reconfiguration of brain regions, also termed flexibility. Our flexibility measure is defined in relation to an a-priori set of biologically plausible brain modules (or networks) and does not rely on a stochastic data-driven module estimation, which, in turn, minimizes computational burden. The change of affiliation of brain regions over time with respect to these a-priori template modules is used as an indicator of brain network flexibility. We demonstrate that our proposed method yields highly similar patterns of whole-brain network reconfiguration (i.e., flexibility) during a working memory task as compared to a previous study that uses a data-driven, but computationally more expensive method. This result illustrates that the use of a fixed modular framework allows for valid, yet more efficient estimation of whole-brain flexibility, while the method additionally supports more fine-grained (e.g. node and group of nodes scale) flexibility analyses restricted to biologically plausible brain networks.