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Weighted Stochastic Block Models of the Human Connectome across the Life Span
Faskowitz, Joshua1,2; Yan, Xiaoran3; Zuo, Xi-Nian4,5,6; Sporns, Olaf1,2,3
2018-08-29
Source PublicationSCIENTIFIC REPORTS
ISSN2045-2322
Volume8Pages:16
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.

DOI10.1038/s41598-018-31202-1
Language英语
Funding OrganizationNational Institutes of Health ; National Science Foundation Graduate Research Fellowship ; National Basic Research Program ; National Natural Science Foundation of China ; Beijing Municipal Science & Technology Commission ; National R&D Infrastructure and Facility Development Program of China -
Funding ProjectNational 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] ; Beijing Municipal Science & Technology Commission[Z171100000117012] ; National R&D Infrastructure and Facility Development Program of China -
WOS Research AreaScience & Technology - Other Topics
WOS SubjectMultidisciplinary Sciences
WOS IDWOS:000443003800004
PublisherNATURE PUBLISHING GROUP
WOS KeywordHuman Cerebral-cortex ; Age-related-changes ; Diffusion Mri Data ; Brain Networks ; Functional Connectivity ; Spherical Deconvolution ; Structural Connectivity ; Community Detection ; Cortical Thickness ; Sex-differences
Citation statistics
Document Type期刊论文
Identifierhttp://ir.psych.ac.cn/handle/311026/26899
Collection中国科学院行为科学重点实验室
Corresponding AuthorSporns, Olaf
Affiliation1.Indiana Univ, Program Neurosci, Bloomington, IN 47405 USA
2.Indiana Univ, Dept Psychol & Brain Sci, Bloomington, IN 47405 USA
3.Indiana Univ, Indiana Univ Network Sci Inst, Bloomington, IN 47405 USA
4.Inst Psychol, CAS Key Lab Behav Sci, Beijing, Peoples R China
5.Inst Psychol, Res Ctr Lifespan Dev Mind & Brain CLIMB, Beijing, Peoples R China
6.Nanning Normal Univ, Key Lab Brain & Educ Sci, Nanning 530001, Guangxi, Peoples R China
Recommended Citation
GB/T 7714
Faskowitz, Joshua,Yan, Xiaoran,Zuo, Xi-Nian,et al. Weighted Stochastic Block Models of the Human Connectome across the Life Span[J]. SCIENTIFIC REPORTS,2018,8:16.
APA Faskowitz, Joshua,Yan, Xiaoran,Zuo, Xi-Nian,&Sporns, Olaf.(2018).Weighted Stochastic Block Models of the Human Connectome across the Life Span.SCIENTIFIC REPORTS,8,16.
MLA Faskowitz, Joshua,et al."Weighted Stochastic Block Models of the Human Connectome across the Life Span".SCIENTIFIC REPORTS 8(2018):16.
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