Multi-scale detection of hierarchical community architecture in structural and functional brain networks

Abstract

Community detection algorithms have been widely used to study the organization of complex networks like the brain. These techniques provide a partition of brain regions (or nodes) into clusters (or communities), where nodes within a community are densely interconnected with one another. In their simplest application, community detection algorithms are agnostic to the presence of community hierarchies: clusters embedded within clusters of other clusters. To address this limitation, we exercise a multi-scale extension of a common community detection technique, and we apply the tool to synthetic graphs and to graphs derived from human neuroimaging data, including structural and functional imaging data. Our multi-scale community detection algorithm links a graph to copies of itself across neighboring topological scales, thereby becoming sensitive to conserved community organization across levels of the hierarchy. We demonstrate that this method is sensitive to topological inhomogeneities of the graph’s hierarchy by providing a local measure of community stability and inter-scale reliability across topological scales. We compare the brain’s structural and functional network architectures, and we demonstrate that structural graphs display a more prominent hierarchical community organization than functional graphs. Finally, we build an explicitly multimodal multiplex graph that combines both structural and functional connectivity in a single model, and we identify the topological scales where resting state functional connectivity and underlying structural connectivity show similar versus unique hierarchical community architecture. Together, our results demonstrate the advantages of the multi-scale community detection algorithm in studying hierarchical community structure in brain graphs, and they illustrate its utility in modeling multimodal neuroimaging data.

Low-Dimensional Spatio-Temporal Dynamics Underlie Cortex-Wide Neural Activity

“…More broadly, our results are consistent with a hierarchical control of information processing in the brain (Mearns et al, 2019; Deco and Kringelbach, 2017; Ashourvan et al, 2019; Park and Friston, 2013; Botvinick, 2008). Behavioral state changes slowly and reflects the animal’s broad behavioral goal (Wiltschko et al, 2015).…”

Hierarchical frequency clusters in adaptive networks of phase oscillators

“…This phenomenon is reported for an adaptive network of Morris-Lecar bursting neurons with spike-timing-dependent plasticity rule 21 . In addition, the role of hierarchy and modularity in brain networks has been discussed recently [22][23][24][25][26] . Both features therefore seem to play a key role for real-world neural networks.…”

Estimation of Global and Local Complexities of Brain Networks: A Random Walks Approach

“…For instance, random walks are the basis of Infomap (Rosvall & Bergstrom, 2008), a popular method for detecting community structure in complex networks. Past studies of anatomical and functional brain connectivity have found interlinked communities that form a partly decomposable modular architecture (Ashourvan, Telesford, Verstynen, Vettel, & Bassett, 2019; Meunier, Lambiotte, Fornito, Ersche, & Bullmore, 2009). Such architectures are hallmarks of complex systems and are thought to be of fundamental importance for understanding mental processing and cognition (Bola & Borchardt, 2016).…”

“…In this work we selected sample entropy since it can quantify the amount of regularity and unpredictability of fluctuations in a time series (Richman & Moorman, 2000). This is important because of the presence of communities in brain networks (Ashourvan et al, 2019; Meunier et al, 2009), which will result in repetitive patterns of nodes in the time series of the random walker’s movement (Fortunato & Hric, 2016).…”