The human brain works as a whole network composed of multiple interacting modules. Recently using diffusion tensor imaging, functional magnetic resonance imaging and graph theory, the organizational principles of human brain intrinsic architecture such as the existence of network hubs and small-worldness, have been testified. However, the human brain structure-function relationship has been always a major challenge in the field of computational neuroscience. Recently, the macaque brain network analysis showed that, the anatomical distance in the brain network architecture played a crucial role. A single-parameter random graph model based on the exponential distance rule predicts numerous topological features of the cortical network. Since the anatomical distance was so crucial in the animal brain network architecture, how does it play in the human brain network? The project proposed a novel analytic stratergy to integrate both human brain structural and functional information based on the individual variability,namely the predictive connectivity distance. This approach uses the correlation between the locally structural or functional information to predict whole brain functional connectivity vertex-wise across different brains, not only giving the distribution of the connectivity distance within the high-resolution human brain network, but also defining a new type of vertex-wise brain network in humans in vivo. Investigation of whether the connectivity distance distribution obeys the exponential law as in monkey, would help to elucidate the human brain structure-function relationship. The application studies of this method would also contribute to the elucidation of pathologies of neuropsychiatry diseases.