Separating dopamine D and D receptor sources of [C]-(+)-PHNO binding potential: Independent component analysis of competitive binding.

Development of medications selective for dopamine D or D receptors is an active area of research in numerous neuropsychiatric disorders including addiction and Parkinson's disease. The positron emission tomography (PET) radiotracer [C]-(+)-PHNO, an agonist that binds with high affinity to both D and D receptors, has been used to estimate relative receptor subtype occupancy by drugs based on a priori knowledge of regional variation in the expression of D and D receptors. The objective of this work was to use a data-driven independent component analysis (ICA) of receptor blocking scans to separate D-and D-related signal in [C]-(+)-PHNO binding data in order to improve the precision of subtype specific measurements of binding and occupancy. Eight healthy volunteers underwent [C]-(+)-PHNO PET scans at baseline and at two time points following administration of the D-preferring antagonist ABT-728 (150-1000 ​mg). Parametric binding potential (BP) images were analyzed as four-dimensional image series using ICA to extract two independent sources of variation in [C]-(+)-PHNO BP. Spatial source maps for each component were consistent with respective regional patterns of D-and D-related binding. ICA-derived occupancy estimates from each component were similar to D-and D-specific occupancy estimated from a region-based approach (intraclass correlation coefficients ​> ​0.95). ICA-derived estimates of D receptor occupancy improved quality of fit to a single site binding model. Furthermore, ICA-derived estimates of the regional fraction of [C]-(+)-PHNO binding related to D receptors was generated for each subject and values showed good agreement with region-based model estimates and prior literature values. In summary, ICA successfully separated D-and D-related components of the [C]-(+)-PHNO binding signal, establishing this approach as a powerful data-driven method to quantify distinct biological features from PET data composed of mixed data sources.