Modular and Hierarchically Modular Organization of Brain Networks

TLDR

Brain networks exhibit modular community structure, with modules defined as densely interconnected groups of nodes sparsely linked to other modules, often reflecting anatomically or functionally related regions, and this modularity is hierarchical across multiple scales, conferring robustness, adaptivity, and evolvability. The authors aim to review mathematical concepts for quantitatively assessing hierarchical modularity in brain networks. They summarize recent studies that analyze structural and functional brain networks derived from human neuroimaging data to investigate modularity.

Abstract

Brain networks are increasingly understood as one of a large class of information processing systems that share important organizational principles in common, including the property of a modular community structure. A module is topologically defined as a subset of highly inter-connected nodes which are relatively sparsely connected to nodes in other modules. In brain networks, topological modules are often made up of anatomically neighboring and/or functionally related cortical regions, and inter-modular connections tend to be relatively long distance. Moreover, brain networks and many other complex systems demonstrate the property of hierarchical modularity, or modularity on several topological scales: within each module there will be a set of sub-modules, and within each sub-module a set of sub-sub-modules, etc. There are several general advantages to modular and hierarchically modular network organization, including greater robustness, adaptivity, and evolvability of network function. In this context, we review some of the mathematical concepts available for quantitative analysis of (hierarchical) modularity in brain networks and we summarize some of the recent work investigating modularity of structural and functional brain networks derived from analysis of human neuroimaging data.