Ikoma Yoko, Yasuno Fumihiko, Ito Hiroshi, Suhara Tetsuya, Ota Miho, Toyama Hinako, Fujimura Yota, Takano Akihiro, Maeda Jun, Zhang Ming-Rong, Nakao Ryuji, Suzuki Kazutoshi
Brain Imaging Project, National Institute of Radiological Sciences, Chiba, Japan.
J Cereb Blood Flow Metab. 2007 Jan;27(1):173-84. doi: 10.1038/sj.jcbfm.9600325. Epub 2006 May 10.
[(11)C]DAA1106 is a potent and selective ligand for the peripheral benzodiazepine receptor (PBR) with high affinity. It has been reported that the density of PBR is related to brain damage, so a reliable tracer method for the evaluation of PBR would be of use. We evaluated a quantification method of [(11)C]DAA1106 binding in simulated data and human brain data. In the simulation study, the reliability of parameters estimated from the nonlinear least-squares (NLS) method, graphical analysis (GA), and multilinear analysis (MA) was evaluated. In GA, variation of the estimated distribution volume (DV) was small. However, DV was underestimated as noise increased. In MA, bias was smaller, and variation of the estimated DV was larger than in GA. In NLS, although variation was larger than in GA, it was small enough in regions of interest analysis, and not only DV but also binding potential (BP), determined from the k(3)/k(4) without any constraint, could be estimated. The variation of BP estimated with NLS became larger as k(3) or k(4) became smaller. In human studies with normal volunteers, regions of interest were drawn on several brain regions, BP was calculated by NLS, and DV was also estimated by NLS, GA, and MA. As a result, DVs estimated with each method were well correlated. However, there was no correlation between BP with NLS and DV with NLS, GA, and MA, because of the variation of K(1)/k(2) between individuals. In conclusion, BP is estimated most reliably by NLS with the two-tissue compartment model.
[(11)C]DAA1106是一种对周围型苯二氮䓬受体（PBR）具有高亲和力的强效选择性配体。据报道，PBR的密度与脑损伤有关，因此一种可靠的用于评估PBR的示踪方法将很有用。我们在模拟数据和人脑数据中评估了[(11)C]DAA1106结合的定量方法。在模拟研究中，评估了从非线性最小二乘法（NLS）、图形分析（GA）和多线性分析（MA）估计的参数的可靠性。在GA中，估计分布容积（DV）的变化较小。然而，随着噪声增加，DV被低估。在MA中，偏差较小，估计DV的变化比GA中的大。在NLS中，尽管变化比GA中的大，但在感兴趣区域分析中足够小，并且不仅可以估计DV，还可以估计在没有任何约束的情况下由k(3)/k(4)确定的结合潜力（BP）。随着k(3)或k(4)变小，用NLS估计的BP的变化变大。在对正常志愿者的人体研究中，在几个脑区绘制感兴趣区域，通过NLS计算BP，并通过NLS、GA和MA估计DV。结果，用每种方法估计的DV具有良好的相关性。然而，由于个体之间K(1)/k(2)的变化，NLS法的BP与NLS、GA和MA法的DV之间没有相关性。总之，使用双组织室模型通过NLS最可靠地估计BP。
