Characterization of Neurodevelopmental Disease Trajectories using Richly Annotated Sequences of Graphs (RICHGRAPH)

• Applicants: Professor Dr. Klaus Maier-Hein; Professorin Dr. Heike Tost
• Subject Area: Human Cognitive and Systems Neuroscience
• Term: from 2016 to 2021
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 290781790

Mental disorders are the main cause of suicide, disability, and early retirement in Europe. While recent developments in magnetic resonance imaging have opened unprecedented windows into the developing brain, the full potential of computational methods to analyse the longitudinal and multi-modal imaging information at hand has not yet been exploited. After psychiatric neuroimaging research has long been focusing on dedicated regions in the brain (local analysis), the emerging field of connectomics has enabled a characterization of topological properties (global analysis) by establishing graphs as a representation of the brain. Current techniques, however, require the available multi-modal information to be reduced to a simple set of edges that capture the relationship between different brain regions in a single weight and thus fail to exploit the manifold MRI-derived quantitative parameters as potentially complementary sources of information. They are also unable to model network dynamics. Inspired by major progress in the field of social network analysis, this project will develop the next generation of connectomics techniques based on rich graphs that enable holistic processing of heterogeneous data from multiple sources over time. Advanced machine learning techniques applied to sequences of rich graphs will allow analyses and prediction of neurodevelopmental trajectories based on both local tissue characteristics and global network features. Comprehensive validation studies with large cohorts of patients and controls that include longitudinal imaging, genetic, environmental, and behavioral data will be performed with the long-term goal of (1) establishing novel multi-modal and longitudinal imaging biomarkers that represent pathological changes before the appearance of clinical symptoms, (2) linking brain imaging traits to gene variants and (3) advancing our understanding of the underlying biological processes to pave the way for development of new treatments.

• DFG Programme: Research Grants