Separating dopamine D 2 and D 3 receptor sources of [ 11 C]-(+)-PHNO binding potential: Independent component analysis of competitive binding.

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 [11 C]-(+)-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 [11 C]-(+)-PHNO binding data in order to improve the precision of subtype specific measurements of binding and occupancy. Eight healthy volunteers underwent [11 C]-(+)-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 (BPND ) images were analyzed as four-dimensional image series using ICA to extract two independent sources of variation in [11 C]-(+)-PHNO BPND . 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 [11 C]-(+)-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 [11 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.