期刊
SCIENTIFIC REPORTS
卷 8, 期 -, 页码 -出版社
NATURE PORTFOLIO
DOI: 10.1038/s41598-018-31202-1
关键词
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资金
- National Institutes of Health [R01 AT009036-01]
- National Science Foundation Graduate Research Fellowship [1342962]
- National Basic Research Program [2015CB351702]
- National Natural Science Foundation of China [81220108014]
- Beijing Municipal Science & Technology Commission [Z161100002616023, Z171100000117012]
- National R&D Infrastructure and Facility Development Program of China - Fundamental Science Data Sharing Platform [DKA2017-12-02-21]
- Guangxi Bagui Honor Scholarship Program
- Lilly Endowment, Inc., through Indiana University Pervasive Technology Institute
- Indiana METACyt Initiative
- Lilly Endowment, Inc.
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.
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