Brain functional & structural patterns predicting outcome in cannabis-induced psychosis

• Applicant: Professor Dr. Joseph Kambeitz
• Subject Area: Biological Psychiatry ; Clinical Psychiatry, Psychotherapy, Child and Adolescent Psychiatry ; Human Cognitive and Systems Neuroscience
• Term: from 2016 to 2024
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 313508422

Psychotic disorders reside among the leading factors of global disease burden and are associated with poor functional outcome in psychiatric patients. Due to the limited response to established treatments in a large subset of patients, recent research focusses on an approach of early-recognition and prevention. The aim is to identify subjects with a high-risk to develop psychosis in the future and to avoid their functional decline as well as the onset of a full psychotic disorder. Cannabis has been identified as one of the major risk factors for developing psychosis. However the biological basis of the relationship between cannabis and psychosis risk is only poorly understood. Studies indicate that the acute intoxication with cannabis induces symptoms that resemble the clinical picture of acute primary psychosis (PP). Initially it has been proclaimed that symptoms of this cannabis-induced psychosis (CIP) remit spontaneously following abstinence. However recent studies show that up to 50 % of all patients with CIP continue to develop a permanent form of a psychotic disorder. Even though patients with CIP represent the population associated with the highest estimated risk to develop a long-lasting form of psychosis, they have been widely neglected in research of psychosis. The aim of the present proposal is to set up a longitudinal, multimodal neuroimaging study of patients with CIP, including molecular, structural and functional neuroimaging at baseline as well as a follow up examination to record the clinical outcome of patients after 9 months. It is planned to apply multivariate pattern analysis (MVPA) to identify brain patterns in neuroimaging data that relate to clinical and neurocognitive variables. This multimodal and multivariate approach is designed to allow for the first time (1) identification of cerebral functional and structural abnormalities associated with CIP, thus shedding light on the underlying pathophysiological processes and (2) generation of statistical models that allow the prediction of the clinical outcome in patients with CIP. In this way the potential of neuroimaging-based models as a clinical tool to identify subjects with high-risk for psychosis will be evaluated, potentially setting the path for personalized and efficient treatment interventions for subjects with psychosis risk due to CIP.

• DFG Programme: Research Grants