CASIA OpenIR  > 脑网络组研究
A method for building a genome-connectome bipartite graph model
Yu, Qingbao1; Chen, Jiayu1; Du, Yuhui1,2; Sui, Jing1,3,4,5; Damaraju, Eswar1; Turner, Jessica A.6; van Erp, Theo G. M.7; Macciardi, Fabio7; Belger, Aysenil8; Ford, Judith M.9,10; McEwen, Sarah11; Mathalon, Daniel H.9,10; Mueller, Bryon A.12; Preda, Adrian7; Vaidya, Jatin13; Pearlson, Godfrey D.14,15,16; Calhoun, Vince D.1,16,17
Source PublicationJOURNAL OF NEUROSCIENCE METHODS
ISSN0165-0270
2019-05-15
Volume320Pages:64-71
Corresponding AuthorChen, Jiayu(jchen@mrn.org) ; Calhoun, Vince D.(vcalhoun@unm.edu)
AbstractIt 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.
KeywordfMRI FNC SNPs Bipartite graph
DOI10.1016/j.jneumeth.2019.03.011
WOS KeywordFUNCTIONAL NETWORK CONNECTIVITY ; RESTING-STATE FMRI ; BRAIN NETWORKS ; MULTICENTER FMRI ; HEALTHY CONTROLS ; GENETIC-CONTROL ; SCHIZOPHRENIA ; HERITABILITY ; PARCELLATION ; ACTIVATION
Indexed BySCI
Language英语
Funding ProjectNatural Science Foundation of Shanxi[2016021077] ; Chinese NSF[61703253] ; Chinese NSF[61773380] ; Chinese NSF[81471367] ; Brain Initiative of Beijing City[Z181100001518005] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDBS03040100] ; National Center for Research Resources at the National Institutes of Health[NIH 1 U24 RR025736-01] ; National Center for Research Resources at the National Institutes of Health[NIH 1 U24 RR021992] ; National Science Foundation (NSF)[1631838] ; National Science Foundation (NSF)[1618551] ; National Science Foundation (NSF)[1539067] ; National Institutes of Health (NIH)[R37MH43775] ; National Institutes of Health (NIH)[ROlEB000840] ; National Institutes of Health (NIH)[RO1REB020407] ; National Institutes of Health (NIH)[1R01DA040487] ; National Institutes of Health (NIH)[1R01EB006841] ; National Institutes of Health (NIH)[R01 EB005846] ; National Institutes of Health (NIH)[P20GM103472/5P20RR021938] ; 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]
Funding OrganizationNational 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
WOS Research AreaBiochemistry & Molecular Biology ; Neurosciences & Neurology
WOS SubjectBiochemical Research Methods ; Neurosciences
WOS IDWOS:000466260400008
PublisherELSEVIER SCIENCE BV
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/24592
Collection脑网络组研究
Corresponding AuthorChen, Jiayu; Calhoun, Vince D.
Affiliation1.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
11.Univ Calif Los Angeles, Dept Psychiat & Biobehav Sci, Los Angeles, CA 90095 USA
12.Univ Minnesota, Dept Psychiat, Minneapolis, MN 55454 USA
13.Univ Iowa, Dept Psychiat, Iowa City, IA 52242 USA
14.Olin Neuropsychiat Res Ctr, Hartford, CT 06106 USA
15.Yale Univ, Dept Neurosci, New Haven, CT 06520 USA
16.Yale Univ, Dept Psychiat, New Haven, CT 06520 USA
17.Univ New Mexico, Dept Elect & Comp Engn, Albuquerque, NM 87016 USA
Recommended Citation
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.
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