A method for building a genome-connectome bipartite graph model

doi:10.1016/j.jneumeth.2019.03.011

	A method for building a genome-connectome bipartite graph model
	Yu, Qingbao 1; Chen, Jiayu 1; Du, Yuhui 1,2; Sui, Jing1,3,4,5 ; Damaraju, Eswar 1; Turner, Jessica A.6; van Erp, Theo G. M.7; Macciardi, Fabio 7; Belger, Aysenil 8; Ford, Judith M.9,10
刊名	JOURNAL OF NEUROSCIENCE METHODS
	2019-05-15
卷号	320 页码:64-71
关键词	fMRI FNC SNPs Bipartite graph
ISSN号	0165-0270
DOI	10.1016/j.jneumeth.2019.03.011
通讯作者	Chen, Jiayu(jchen@mrn.org) ; Calhoun, Vince D.(vcalhoun@unm.edu)
英文摘要	It has been widely shown that genomic factors influence both risk for schizophrenia and variation in functional brain connectivity. Moreover, schizophrenia is characterized by disrupted brain connectivity. In this work, we proposed a genome-connectome bipartite graph model to perform imaging genomic analysis. Functional network connectivity (FNC) was estimated after decomposing resting state functional magnetic resonance imaging data from both healthy controls (HC) and patients with schizophrenia (SZ) into spatial brain components using group independent component analysis (G-ICA). Then 83 FNC connections showing a group difference (HC vs SZ) were selected as fMRI nodes, and eighty-one schizophrenia-related single nucleotide polymorphisms (SNPs) were selected as genetic nodes respectively in the bipartite graph. Edges connecting pairs of genetic and fMRI nodes were defined based on the SNP-FNC associations across subjects evaluated by a general linear model. Results show that some SNP nodes in the bipartite graph have a high degree implying they are influential in modulating brain connectivity and may be more strongly associated with the risk of schizophrenia than other SNPs. A bi-clustering analysis detected a cluster with 15 SNPs interacting with 38 FNC connections, most of which were within or between somato-motor and visual brain areas. This suggests that the activity of these brain regions may be related to common SNPs and provides insights into the pathology of schizophrenia. The findings suggest that the SNP-FNC bipartite graph approach is a novel model to investigate genetic influences on functional brain connectivity in mental illness.
资助项目	National Institutes of Health (NIH)[P20GM103472/5P20RR021938] ; National Institutes of Health (NIH)[R01 EB005846] ; National Institutes of Health (NIH)[1R01EB006841] ; National Institutes of Health (NIH)[1R01DA040487] ; National Institutes of Health (NIH)[RO1REB020407] ; National Institutes of Health (NIH)[ROlEB000840] ; National Institutes of Health (NIH)[R37MH43775] ; National Science Foundation (NSF)[1539067] ; National Science Foundation (NSF)[1618551] ; National Science Foundation (NSF)[1631838] ; National Center for Research Resources at the National Institutes of Health[NIH 1 U24 RR021992] ; National Center for Research Resources at the National Institutes of Health[NIH 1 U24 RR025736-01] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDBS03040100] ; Brain Initiative of Beijing City[Z181100001518005] ; Chinese NSF[81471367] ; Chinese NSF[61773380] ; Chinese NSF[61703253] ; Natural Science Foundation of Shanxi[2016021077]
WOS关键词	FUNCTIONAL NETWORK CONNECTIVITY ; RESTING-STATE FMRI ; BRAIN NETWORKS ; MULTICENTER FMRI ; HEALTHY CONTROLS ; GENETIC-CONTROL ; SCHIZOPHRENIA ; HERITABILITY ; PARCELLATION ; ACTIVATION
WOS研究方向	Biochemistry & Molecular Biology ; Neurosciences & Neurology
语种	英语
出版者	ELSEVIER SCIENCE BV
WOS记录号	WOS:000466260400008
资助机构	National Institutes of Health (NIH) ; National Science Foundation (NSF) ; National Center for Research Resources at the National Institutes of Health ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Brain Initiative of Beijing City ; Chinese NSF ; Natural Science Foundation of Shanxi
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/24591]
专题	中国科学院自动化研究所
通讯作者	Chen, Jiayu; Calhoun, Vince D.
作者单位	1.Mind Res Network, 1101 Yale Blvd NE, Albuquerque, NM 87106 USA 2.Shanxi Univ, Sch Comp & Informat Technol, Taiyuan 030006, Shanxi, Peoples R China 3.Chinese Acad Sci, Brainnetome Ctr, Beijing 100190, Peoples R China 4.Chinese Acad Sci, Natl Lab Pattern Recognit, Inst Automat, Beijing 100190, Peoples R China 5.Univ Chinese Acad Sci Beijing, CAS Ctr Excellence Brain Sci & Intelligence Techn, Beijing 100049, Peoples R China 6.Georgia State Univ, Dept Psychol, Univ Plaza, Atlanta, GA 30303 USA 7.Univ Calif Irvine, Dept Psychiat & Human Behav, Sch Med, Irvine, CA 92697 USA 8.Univ N Carolina, Dept Psychiat, Chapel Hill, NC 27514 USA 9.Univ Calif San Francisco, Dept Psychiat, San Francisco, CA 94143 USA 10.San Francisco VA Med Ctr, San Francisco, CA 94121 USA
推荐引用方式 GB/T 7714	Yu, Qingbao,Chen, Jiayu,Du, Yuhui,et al. A method for building a genome-connectome bipartite graph model[J]. JOURNAL OF NEUROSCIENCE METHODS,2019,320:64-71.
APA	Yu, Qingbao.,Chen, Jiayu.,Du, Yuhui.,Sui, Jing.,Damaraju, Eswar.,...&Calhoun, Vince D..(2019).A method for building a genome-connectome bipartite graph model.JOURNAL OF NEUROSCIENCE METHODS,320,64-71.
MLA	Yu, Qingbao,et al."A method for building a genome-connectome bipartite graph model".JOURNAL OF NEUROSCIENCE METHODS 320(2019):64-71.