The analysis of brain imaging data has attracted much attention recently. A popular analysis is to discover a network representation of brain from the neuroimaging data, where each node denotes a brain region and each edge represents a functional association or structural connection between two brain regions. Motivated by the multi-subject and multi-collection settings in neuroimaging studies, in this paper, we consider brain network discovery under two novel settings: 1) unified setting: Given a collection of subjects, discover a single network that is good for all subjects. 2) contrasting setting: Given two collections of subjects, discover a single network that best discriminates two collections. We show that the existing formulation of graphical Lasso (GLasso) cannot address above problems properly. Two novel models, UGLasso (Unified Graphical Lasso) and CGLasso(Contrasting Graphical Lasso), are proposed to address these two problems respectively. We evaluate our methods on synthetic data and two realworld functional magnetic resonance imaging (fMRI) datasets. Empirical results demonstrate the effectiveness of the proposed methods.