Schizophrenia (SZ) is a chronic and severe mental disorder that affects how a person thinks, feels, and behaves. It hasbeen proposed that this disorder is related to disrupted brain connectivity, which has been verified by many studies,but its underlying mechanism is still unclear. Recent advances have combined heterogeneous data including bothmedical images (e.g., fMRI) and genomic data (e.g., SNPs and DNA methylations), which give rise to a newperspective on SZ. In this paper, we aim to explore the associations between DNA methylations and various brainregions to shed light on the neuro-epigenetic interactions in the SZ disease. We proposed a joint Gaussian copulamodel, where we used the Gaussian copula model to address the data integration issue and the joint network estimationfor different conditions (case-control study). Unlike previous studies using methods such as CCA or ICA, the proposedmethod not only can provide the neuro-epigenetic interactions but also the brain connectivity, and methylation selfinteractionsall at the same time. The data we used were collected by the Mind Clinical Imaging Consortium (MCIC),which includes the fMRI image and the epigenetic information such as methylation levels. The data were from 183subjects, among them 79 SZ patients and 104 healthy controls. We have identified several hub brain regions and hubDNA methylations of the SZ patients and have also detected 10 methylation-brain ROI interactions for SZ. Ouranalysis results are shown to be both statistically and biologically significant.
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