A key challenge in this topic group "AI for Psychiatry" is the annotation of the labels (psychiatric disorders) for machine learning. Mapping the diagnostic label from a clinically defined nosology (e.g., the Diagnostic and Statistical Manual of Mental Disorders (DSM) or the International Classification of Diseases (ICD)) to varying biological measures has proven to be problematic. It assumes consistent biological relationships with broad constellations of symptoms that in practice do not show. As a consequence, two research approaches emerged. First is the implementation of transdiagnostic models structured around behavioral and neurobiological dimensions that transcend current diagnostic boundaries. Second is the identification of diagnostic subgroups to explain variation within diagnostic categories through the detection of behaviorally or biologically homogeneous subgroups. The approach of integrating different (e.g. behavioral and neurobiological) measures of a comprehensive set of cognitive domains has become increasingly influential in the field of psychiatry. In fact, the multimodal and multi-dimensional integration of neuroscientific measures is central to one of the flagship projects of the National Institute of Mental Health (NIMH), the Research Domain Criteria Project (RDoC), initiated in their Strategic Plan in 2008. In a nutshell, RDoC advocates that mental illness may be better reflected in considering the continuously scaled profile of function and dysfunction on a broad range of cognitive domains rather than by a list of predefined "core symptoms". In line with this, it is argued that in the long run genuine advances in the treatment of mental disorders may be reached by developing reliable and valid measures across a range of measurement modalities.