Investigating Dopaminergic Abnormalities in Psychosis with Normative Modelling and Multisite Molecular Neuroimaging

Abstract

Molecular neuroimaging techniques, like PET and SPECT, offer invaluable insights into the brain’s in-vivo biology and its dysfunction in neuropsychiatric patients. However, the transition of molecular neuroimaging into diagnostics and precision medicine has been limited to a few clinical applications, hindered by issues like practical feasibility and high costs. In this study, we explore the use of normative modelling (NM) for molecular neuroimaging to identify individual patient deviations from a reference cohort of subjects. NM potentially addresses challenges such as small sample sizes and diverse acquisition protocols that are typical of molecular neuroimaging studies. We applied NM to two PET radiotracers targeting the dopaminergic system ([11C]-(+)-PHNO and [18F]FDOPA) to create a normative model to reference groups of controls. The models were subsequently utilized on various independent cohorts of patients experiencing psychosis. These cohorts were characterized by differing disease stages, treatment responses, and the presence or absence of matched controls. Our results showed that patients exhibited a higher degree of extreme deviations (∼3-fold increase) than controls, although this pattern was heterogeneous, with minimal overlap in extreme deviations topology (max 20%). We also confirmed the value of striatal [18F]FDOPA signal to predict treatment response (striatal AUC ROC: 0.77-0.83). Methodologically, we highlighted the importance of data harmonization before data aggregation. In conclusion, normative modelling can be effectively applied to molecular neuroimaging after proper harmonization, enabling insights into disease mechanisms and advancing precision medicine. The method is valuable in understanding the heterogeneity of patient populations and can contribute to maximising cost efficiency in studies aimed at comparing cases and controls.