archives@tulane.edu / With the rapid development of precision medicine across almost all areas of medicine, the Research Domain Criteria (RDoC) project has been initiated to develop data-driven matrices toward precision medicine for mental disorder by integrating multilevel information including genomics, molecules, circuits, and behaviors. This thesis, under the guidance of the RDoC framework, aims to gain a more complete understanding of the role of oscillatory behavior and network connectivity in normal/abnormal brain functioning and cognitive development. Two specific topics were involved: 1. Understand the complex mechanism for mental disorder through multiomics data; 2. Study the development of FC from childhood to adulthood using multi-paradigm brain images. We intend to identify new and reliable biomarkers for the purpose of precise diagnosis and can potentially provide an enormous impetus for drug discovery through the comparison of normal and abnormal brains and the investigation of dynamic changes. This thesis proposes several new analytic graphical models (directed and undirected) to assess brain functional connectivity (FC), each targeting a specific problem in the biomedical applications: the psi-learning method to resolve the high dimensionality for networks on voxel level, the latent Gaussian copula model for mix data distributions, the joint Bayesian incorporating estimation to address heterogeneities in undirected graphical models; the psi-LiNGAM and BiLiNGAM for the situations of small sample size and heterogeneities in directed acyclic graphs, respectively. The proposed methods are validated through a series of simulation studies and large genomic and neuroimaging datasets, where they confirm results from previous studies and lead to new biological insights. In addition, we put extra efforts on promoting reproducible research and make the proposed methods widely available to the scientific community by the release of free and open-source codes. / 1 / Aiying Zhang