Yale PET Center, Yale School of Medicine, New Haven, CT, USA.
Yale PET Center, Yale School of Medicine, New Haven, CT, USA; Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA.
Neuroimage. 2020 Jul 1;214:116762. doi: 10.1016/j.neuroimage.2020.116762. Epub 2020 Mar 19.
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.
开发对多巴胺 D 或 D 受体具有选择性的药物是许多神经精神疾病（包括成瘾和帕金森病）研究的活跃领域。正电子发射断层扫描（PET）示踪剂 [C]-(+)-PHNO 是一种与 D 和 D 受体具有高亲和力的激动剂，已被用于根据 D 和 D 受体表达的区域变化的先验知识来估计药物对受体亚型的相对占有率。这项工作的目的是使用受体阻断扫描的基于数据的独立成分分析（ICA）来分离 [C]-(+)-PHNO 结合数据中的 D 和 D 相关信号，以提高结合和占有率的亚型特异性测量的精度。八名健康志愿者在基线和给予 D 优先拮抗剂 ABT-728（150-1000mg）后两个时间点进行了 [C]-(+)-PHNO PET 扫描。使用 ICA 对参数绑定潜力（BP）图像进行了作为四维图像系列的分析，以提取 [C]-(+)-PHNO BP 中的两个独立变化源。每个分量的空间源图与各自的 D 和 D 相关结合的区域模式一致。从每个分量得出的 ICA 衍生的占有率估计值与从基于区域的方法估计的 D 和 D 特异性占有率相似（组内相关系数>0.95）。ICA 衍生的 D 受体占有率估计值改善了对单一位点结合模型的拟合质量。此外，为每个受试者生成了与 D 受体相关的 [C]-(+)-PHNO 结合的区域分数的 ICA 衍生估计值，并且这些值与基于区域的模型估计值和先前的文献值显示出良好的一致性。总之，ICA 成功地分离了 [C]-(+)-PHNO 结合信号的 D 和 D 相关分量，确立了这种方法是从由混合数据源组成的 PET 数据中定量分析不同生物学特征的有力数据驱动方法。
