Academic Journal
Multi-scale detection of hierarchical community architecture in structural and functional brain networks.
| العنوان: | Multi-scale detection of hierarchical community architecture in structural and functional brain networks. |
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| المؤلفون: | Arian Ashourvan, Qawi K Telesford, Timothy Verstynen, Jean M Vettel, Danielle S Bassett |
| المصدر: | PLoS ONE, Vol 14, Iss 5, p e0215520 (2019) |
| بيانات النشر: | Public Library of Science (PLoS), 2019. |
| سنة النشر: | 2019 |
| المجموعة: | LCC:Medicine LCC:Science |
| مصطلحات موضوعية: | Medicine, Science |
| الوصف: | 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. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 1932-6203 |
| Relation: | https://doaj.org/toc/1932-6203 |
| DOI: | 10.1371/journal.pone.0215520 |
| URL الوصول: | https://doaj.org/article/4048136b22934ce2a3a8a55e9b8e06ca |
| رقم الانضمام: | edsdoj.4048136b22934ce2a3a8a55e9b8e06ca |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 19326203 |
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| DOI: | 10.1371/journal.pone.0215520 |