The scientific goal of our research is to understand the organization of the human brain. We develop new methods and apply them for mapping the regional organization of the human brain into cortical areas as well as the topography of large-scale, distributed networks recruited by cognitive, socio-affective or sensorimotor tasks. We characterize the variability of brain structure, function and connectivity in large cohorts and their relation to age, gender and behavioral measurements. Using machine-learning approaches we train predictive models for inference on phenotypical characteristics of new, individual subjects from brain imaging data. We aim to translate this technology into clinical applications. Therefore, we implement and evaluate novel machine-learning approaches for improved diagnosis, stratification and outcome prediction at the level of individual patients based on neuroimaging and wearable technology. Furthermore, we want to enable research through open science. So an important focus of our work is to provide the developed tools and results to the scientific public through free software-tools and open data sets to encourage broad use. Our work is very close connected with the INM-7 at the research center Jülich.