Advanced brain imaging techniques make it possible to measure individuals' structural connectomes in large cohort studies non-invasively. The structural connectome is initially shaped by genetics and subsequently refined by the environment. It is extremely interesting to study structural connectomes and their relationships to environmental factors or human traits, which motivates the routine collection of high-resolution connectomes in large human studies (e.g., the Human Connectome Project and UK Biobank). However, there is a fundamental gap between the state of the art in image acquisition and the tools available to reconstruct and analyze the connectome data, due to the complexity of data. This lecture aims at introducing our recent effort in the thread. More specifically, we will introduce a population-based structural connectome (PSC) mapping framework to reproducibly extract binary networks, weighted networks, and streamline-based brain connectomes. In addition, various novel methods of analyzing the outputs of PSC will be introduced. We will also introduce a user-friendly software package that implements the PSC and the analysis methods.