A method for building a genome-connectome bipartite graph model

Publication date: Available online 19 March 2019Source: Journal of Neuroscience MethodsAuthor(s): Qingbao Yu, Jiayu Chen, Yuhui Du, Jing Sui, Eswar Damaraju, Jessica A. Turner, Theo G.M. van Erp, Fabio Macciardi, Aysenil Belger, Judith M. Ford, Sarah McEwen, Daniel H. Mathalon, Bryon A. Mueller, Adrian Preda, Jatin Vaidya, Godfrey D. Pearlson, Vince D. CalhounAbstractIt 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 str...
Source: Journal of Neuroscience Methods - Category: Neuroscience Source Type: research