There have been significant interests in the representation of structural or functional profiles for establishment of common architecture (structural/functional correspondences) across individuals and populations in the brain mapping field. However, due to the considerable variability of structural and functional architectures in brains, it is challenging for the earlier studies to jointly represent the connectome-scale profiles to establish a common cortical architecture which can comprehensively encode both brain structure and function. To address this challenge, in this dissertation, I developed four novel computational approaches to explore the common architecture of the brain from three different scales, including landmark level, local region level and network level, respectively. Experimental results based on the four approaches demonstrated that common architecture of the brain can be successfully identified by multimodal fusion at different scales. Those common architectures have both functional and structural consistency across the subjects and those common architectures will bring new insights to understand the brain architecture and its working mechanisms, which can be further used in many neuroimaging fields, e.g., brain disease diagnosis; treatment, and follow up.