Building a bridge between neurosciences and mental health implies overcoming enormous challenges due to the complexity of the studied organ, the brain, and the overwhelming psychological and social universe where it interacts. As Huys et al. (2016) state, dealing with such complexities requires powerful techniques. Computational psychiatry combines multiple levels and types of analysis with multiple types of data in an effort to improve the understanding, prediction, and treatment of mental illness. Using two complementary approaches, data-based and theory-based, this project seeks to (i) increase the effectiveness of diagnostic systems from multiple clinical questionnaires, (ii) understand the transdiagnostic psychopathological dimensions from different levels of analysis (behavior, genetics, electrophysiology, brain imaging) and (iii) testing specific treatments and techniques for large populations through the use of big data.