Modeling Covariate Effects in Group Independent Component Analysis with Applications to Functional Magnetic Resonance Imaging

Independent component analysis (ICA) is a powerful computational tool for separating independent source signals from their linear mixtures. ICA has been widely applied in neuroimaging studies to identify and characterize underlying brain functional networks. An important goal in such studies is to assess the effects of subjects' clinical and demographic covariates on the spatial distributions of the functional networks. Currently, covariate effects are not incorporated in existing group ICA decomposition methods. Hence, they can only be evaluated through ad-hoc approaches which may not be accurate in many cases. In this paper, we propose a hierarchical covariate ICA model that provides a formal statistical framework for estimating and testing covariate effects in ICA decomposition. A maximum likelihood method is proposed for estimating the covariate ICA model. We develop two expectation-maximization (EM) algorithms to obtain maximum likelihood estimates. The first is an exact EM algorithm, which has analytically tractable E-step and M-step. Additionally, we propose a subspace-based approximate EM, which can significantly reduce computational time while still retain high model-fitting accuracy. Furthermore, to test covariate effects on the functional networks, we develop a voxel-wise approximate inference procedure which eliminates the needs of computationally expensive covariance estimation. The performance of the proposed methods is evaluated via simulation studies. The application is illustrated through an fMRI study of Zen meditation.Comment: 36 pages, 5 figure

Essays on corporate governance and firm performance

This thesis contains two studies that examine the interaction between corporate governance and firm performance. In the first study, I examine whether board friendliness reduces crash risk. I measure friendliness by the Political Homophily Index (PHI), which captures the similarity of political orientations of managers and directors. We find that firms’ crash risk decreases in political homophily. The results are robust when we instrument the change in PHI by the change in local political homogeneity. Our results suggest that better alignment in political orientations facilitates information sharing, including information on bad outcomes in a timely manner. The effect is more pronounced when firms have stronger corporate governance mechanisms and directors have a stronger incentive to acquire information. In the second study, I examine how the use of relative performance evaluation (RPE) affects industry competition. Using data from the U.S. airline industry, we estimate a dynamic game of competition with heterogeneous firms in an oligopolistic market with the presence of RPE contracts. As is standard, RPE makes CEO compensation less sensitive to market conditions. Therefore, the CEO’s propensity to operate in a given market is determined by a trade-off that arises between the reduction in compensation based on market conditions and the gain from being compared to competing agents. The estimation results show that the use of RPE decreases a firm’s tendency to be active under bad market conditions by 10.1%. Conversely, the tendency to be active rises in good market conditions by 12.4%. These effects are stronger for firms with lower fixed operating costs

Subcloning, Expression, and Enzymatic Study of PRMT5

Protein arginine methyltransferases (PRMTs)mediate the transfer of methyl groups to arginine residues in histone and non-histone proteins. PRMT5 is an important member of PRMTs which symmetrically dimethylates arginine 8 in histone H3 (H3R8) and arginine 3 in histone H4 (H4R3). PRMT5 was reported to inhibit some tumor suppressors in leukemia and lymphoma cells and regulate p53 gene, through affecting the promoter of p53. Through methylation of H4R3, PRMT5 can recruit DNA-methyltransferase 3A (DNMT3A) which regulates gene transcription. All the above suggest that PRMT5 has an important function of suppressing cell apoptosis and is a potential anticancer target. Currently, the enzymatic activities of PRMT5 are not clearly understood. In our study, we improved the protein expression methodology and greatly enhanced the yield and quality of the recombinant PRMT5. In addition, mutagenesis and enzymatic studies implicate an interesting mechanism of PRMT5 activity regulation