We will identify clusters in behavioral symptoms and neurobiological patterns by referring to the neuronal networks using structural and functional resting-state and task-based fMRI.

In order to study individual trajectories of the development of the brain and its networks during adolescence and young adulthood, the influence of genetic, epigenetic and environmental factors is examined. Further risks and resilience factors for the development of the networks are of interest.  

Biomarkers classifying clusters in behavioral symptoms and neurobiological patterns are to be developed in order to predict, at the age of 14, the development of dysfunctional network properties and associated psychopathology in young adulthood. A prediction model is to be created that takes into account all available data modalities, including functional and structural MRI, NODDI/DTI, clinical, behavioral, and neuropsychological information as well as genetic, environmental and epigenetic data. From the available predictors the ones are to be selected which are at low-threshold and cost-effective in order to be applicable in daily clinical practice.  

Results of the STRATIFY study will be validated on the longitudinal cohort of the IMAGEN project. Based on the assumption that mental disorders represent extreme manifestations of normally distributed quantitative behavioral traits (Plomin et al., 2009), this study tests whether the  properties of brain networks at the extreme end of the IMAGEN spectrum are comparable to those of from the clinical samples.