Abstract
The human brain can be described as a complex network of anatomical connections between distinct areas, referred to as the human connectome. Fundamental characteristics of connectome organization can be revealed using the tools of network science and graph theory. Of particular interest is the network’s community structure, commonly identified by modularity maximization, where communities are conceptualized as densely intra-connected and sparsely inter-connected. Here we adopt a generative modeling approach called weighted stochastic block models (WSBM) that can describe a wider range of community structure topologies by explicitly considering patterned interactions between communities. We apply this method to the study of changes in the human connectome that occur across the life span (between 6–85 years old). We find that WSBM communities exhibit greater hemispheric symmetry and are spatially less compact than those derived from modularity maximization. We identify several network blocks that exhibit significant linear and non-linear changes across age, with the most significant changes involving subregions of prefrontal cortex. Overall, we show that the WSBM generative modeling approach can be an effective tool for describing types of community structure in brain networks that go beyond modularity.
Highlights
The human brain forms a complex network of anatomically interconnected neurons and brain regions, the connectome[1] that can be modeled and analyzed with the tools of network science and graph theory[2]
We apply a weighted variant of the stochastic block model, called the Weighted Stochastic Block Model, or WSBM22,23,42, to whole-brain anatomical networks extracted from diffusion imaging and tractography data acquired across a major portion of the human life span
We ran the process on 100 additional independent runs at k = 10 to create a new frequency prior and observed that the resulting community structure had a 0.80 normalized mutual information (NMI) to the obtained weighted stochastic block models (WSBM) consensus model
Summary
The human brain forms a complex network of anatomically interconnected neurons and brain regions, the connectome[1] that can be modeled and analyzed with the tools of network science and graph theory[2]. We employ the stochastic block modeling framework to analyze, cross-sectionally, how brain networks, and the community structure of these networks, are modulated across the human life span. The white matter architecture that supports connections between these distinct cortical regions develops at variable rates[29,30,31] To characterize these changes in brain networks across the human life span several recent studies have applied tools of complex network analysis[7,32,33,34,35]. SBMs offer great flexibility as the way in which communities are defined transcends the narrower definition inherent in classical modularity maximization Despite their methodological advantages, SBMs have only recently been applied to the analysis of brain networks[16,38,39,40,41]. We discuss the patterns of change we detected in this study in the context of previous work reporting on modularity and age-dependent changes in functional connectivity
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