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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
发表期刊SCIENTIFIC REPORTS
ISSN2045-2322
卷号8页码:16
摘要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
语种英语
项目资助者National 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 - "Fundamental Science Data Sharing Platform" ; Guangxi Bagui Honor Scholarship Program ; Lilly Endowment, Inc., through Indiana University Pervasive Technology Institute ; Indiana METACyt Initiative ; Lilly Endowment, Inc.
资助项目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] ; Beijing Municipal Science & Technology Commission[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.
WOS研究方向Science & Technology - Other Topics
WOS类目Multidisciplinary Sciences
WOS记录号WOS:000443003800004
出版者NATURE PUBLISHING GROUP
关键词[WOS]HUMAN CEREBRAL-CORTEX ; AGE-RELATED-CHANGES ; DIFFUSION MRI DATA ; BRAIN NETWORKS ; FUNCTIONAL CONNECTIVITY ; SPHERICAL DECONVOLUTION ; STRUCTURAL CONNECTIVITY ; COMMUNITY DETECTION ; CORTICAL THICKNESS ; SEX-DIFFERENCES
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文献类型期刊论文
条目标识符http://ir.psych.ac.cn/handle/311026/26899
专题中国科学院行为科学重点实验室
通讯作者Sporns, Olaf
作者单位1.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
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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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