Separating dopamine D₂ and D₃ receptor sources of [¹¹C]-(+)-PHNO binding potential: Independent component analysis of...

Development of medications selective for dopamine D₂ or D₃ receptors is an active area of research in numerous neuropsychiatric disorders including addiction and Parkinson's disease. The positron emission tomography (PET) radiotracer [¹¹C]-(+)-PHNO, an agonist that binds with high affinity to both D₂ and D₃ receptors, has been used to estimate relative receptor subtype occupancy by drugs based on a priori knowledge of regional variation in the expression of D₂ and D₃ receptors. The objective of this work was to use a data-driven independent component analysis (ICA) of receptor blocking scans to separate D₂-and D₃-related signal in [¹¹C]-(+)-PHNO binding data in order to improve the precision of subtype specific measurements of binding and occupancy. Eight healthy volunteers underwent [¹¹C]-(+)-PHNO PET scans at baseline and at two time points following administration of the D₃-preferring antagonist ABT-728 (150-1000 ​mg). Parametric binding potential (BP_ND) images were analyzed as four-dimensional image series using ICA to extract two independent sources of variation in [¹¹C]-(+)-PHNO BP_ND. Spatial source maps for each component were consistent with respective regional patterns of D₂-and D₃-related binding. ICA-derived occupancy estimates from each component were similar to D₂-and D₃-specific occupancy estimated from a region-based approach (intraclass correlation coefficients ​> ​0.95). ICA-derived estimates of D₃ receptor occupancy improved quality of fit to a single site binding model. Furthermore, ICA-derived estimates of the regional fraction of [¹¹C]-(+)-PHNO binding related to D₃ 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 D₂-and D₃-related components of the [¹¹C]-(+)-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.