IMAging GEnetics for MENtal Disorders
Coordinator: Andreas MEYER-LINDENBERG
Project Number: 602450
EC contribution: € 6,000,000.00
Project website: under construction
Mental disorders are leading causes of disability, absence from work and premature retirement in Europe. While magnetic resonance imaging (MRI) facilities are broadly available and a vast research literature exists, few neuroimaging applications have reached clinical practice in psychiatry. A major problem is that mental illnesses are currently diagnosed as discrete entities defined clinically.
Instead, recent results show that mental disorders are best understood as quantitative alterations in neural systems relevant across traditional diagnostic boundaries that reflect individual, genetic and environmental risk factors. In the IMAGEMEND consortium, we aim to discover these systems to identify the patient characteristics most relevant for treatment, derive biomarkers and decision rules from this systems-level dimensional account, and systematically validate biomarker panels in patient, high-risk and epidemiological samples to produce automated imaging-based diagnostic and predictive tests tailored for wide distribution throughout Europe in standard clinical settings.
Focusing on schizophrenia, bipolar disorder and attention deficit-hyperactivity disorder, we have assembled Europe’s largest dataset combining neuroimaging, genetic, environmental, cognitive and clinical information on approximately 13000 participants, and have recruited international replication datasets of more than 30000 people. This unique resource will be processed using a new generation of multivariate statistical analysis to optimize existing imaging technology for the benefit of patients.
We will also develop new imaging technology to enable the direct imaging-based therapeutic modification of neural circuits through rapid real-time MRI. Our deliverables will promote personalized treatment through more accurate patient stratification, allow diagnoses at the pre-symptomatic stage for early intervention and prevention, and improve prediction of treatment response and disease progression.