Grant: Project Grant

Schizophrenia is a severe mental illness characterized by psychotic symptoms, such as hallucinations and delusions. A key challenge is the treatment response heterogeneity across patients: despite similar initial symptoms, patients often show divergent disease trajectories over time, and some symptoms persist in spite of treatment. Neuroleptic medication that targets either dopamine or acetylcholine receptors or both neuromodulatory systems is used to treat schizophrenia. However, there are no clinical tests to guide treatment and predict which individual patient will benefit from which type of pharmacological intervention. Consequently, treatment decisions rely on a trial-and-error process, which places a severe burden on patients. To overcome this impasse, this project introduces a neurocomputational approach for understanding the emergence of psychosis symptoms focusing on key pathologies of dopaminergic and cholinergic systems using reward and contextual learning paradigms combined with validated mathematical models of cortical computation. To examine the clinical validity of this approach, we propose a study with mixed cross-sectional and prospective design in first episode psychosis (FEP) patients. As this is a purely prospective study, we do not assign FEP patients to any treatment or diagnostic procedure. Instead, we use a cross-sectional comparison, contrasting two groups of FEP patients for whom the treating psychiatrist has decided that pharmacological intervention is necessary: Patients who respond (R+ group) and do not respond (R- group) to first-line neuroleptic medication will be recruited and assessed using noninvasive brain imaging between 96 hours before and 120 hours after medication is initiated. As this computational approach is a mechanistic model embedded in neurophysiology, it characterises the key cognitive and neurobiological causes of psychosis, in order to inform treatment intervention strategies.