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J Neurosci Methods. 2019 May 15;320:64-71. doi: 10.1016/j.jneumeth.2019.03.011. Epub 2019 Mar 19.

A method for building a genome-connectome bipartite graph model.

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The Mind Research Network, Albuquerque, NM, 87106, USA.
The Mind Research Network, Albuquerque, NM, 87106, USA. Electronic address:
The Mind Research Network, Albuquerque, NM, 87106, USA; School of Computer and Information Technology, Shanxi University, Taiyuan, 030006, China.
The Mind Research Network, Albuquerque, NM, 87106, USA; Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Science, Beijing, 100190, China; CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences in Beijing, 100049, China.
Department of Psychology, Georgia State University, GA, 30303, USA.
Department of Psychiatry and Human Behavior, School of Medicine, University of California, Irvine, CA, 92697, USA.
Department of Psychiatry, University of North Carolina, Chapel Hill, NC, 27514, USA.
Department of Psychiatry, University of California San Francisco, CA, 94143, USA; San Francisco VA Medical Center, San Francisco, CA, 94121, USA.
Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, CA, 90095, USA.
Department of Psychiatry, University of Minnesota, Minneapolis, MN, 55454, USA.
Department of Psychiatry, University of Iowa, IA, 52242, USA.
Olin Neuropsychiatry Research Center, Hartford, CT 06106, USA; Department of Neuroscience, Yale University, New Haven, CT 06520, USA; Department of Psychiatry, Yale University, New Haven, CT, 06520, USA.
The Mind Research Network, Albuquerque, NM, 87106, USA; Department of Psychiatry, Yale University, New Haven, CT, 06520, USA; Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM, 87016, USA. Electronic address:


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.


Bipartite graph; FNC; SNPs; fMRI

[Available on 2020-05-15]

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