Developing Multi-Modal Neuroimaging Biomarkers for the Detection of Early-Stage Psychosis

The project will develop new predictive algorithms for mental health outcomes in young people at high-risk for Schizophrenia (ScZ) that will synthesize expertise from cognitive neuroimaging, clinical psychology and data-analytics. This framework will utilize a unique multi-modal neuroimaging data-set that has been collected as part of the MRC-funded Youth Mental Health Risk and Resilience (YouR) study. The YouR-study is the only study worldwide to employ Magnetoencephalography (MEG) in combination with Magnetic Resonance Imaging (MRI) and Magnetic Resonance Spectroscopy (MRS)-data to develop predictors for ScZ in at-risk individuals. Currently, n=150 participants meeting clinical high-risk (CHR) criteria have been comprehensively assessed with a multi-modal imaging battery in combination with follow-up data for up to three years. In addition, n=40 participants with affective disorders and substance abuse (CHR-negative) as well as n=50 controls have been recruited.

The project will employ cutting-edge machine learning techniques to identify multi-modal biomarkers for the prediction of psychosis in CHR-participants and related mental health outcomes. We expect that the resulting predictors for mental health outcomes in at-risk individuals are significantly more powerful than biomarkers from single modalities. Moreover, we expect to identify subgroups of CHR-participants based on multi-modal neuroimaging data that will reveal insights into pathophysiology processes underlying emerging psychosis that will likely be important for the development of novel psychological and pharmacological therapies.