School of Mathematics and Statistics, University of New South Wales, Sydney, 2052, Australia.
ARC Centre of Excellence For Mathematical and Statistical Frontiers, ACEMS, Australia.
Phys Med Biol. 2021 May 20;66(11). doi: 10.1088/1361-6560/abfa37.
We describe an intuitive, easy to use method called PET-ABC that enables full Bayesian statistical inference from single subject dynamic PET data. The performance of PET-ABC was compared with weighted non-linear least squares (WNLS) in terms of reliability of kinetic parameter estimation and statistical power for model selection.Dynamic PET data based on 1-tissue and 2-tissue compartmental models were simulated with 2 noise models and 3 noise levels. PET-ABC was used to evaluate the reliability of parameter estimates under each condition. It was also used to perform model selection for a simulated noisy dataset composed of a mixture of 1- and 2-tissue compartment kinetics. Finally, PET-ABC was used to analyze a non-steady state dynamic [C] raclopride study performed on a fully conscious rat administered either 2 mg.kgamphetamine or saline 20 min after tracer injection.PET-ABC yielded posterior point estimates for model parameters with smaller variance than WNLS, as well as probability density functions indicating confidence intervals for those estimates. It successfully identified the superiority of a 2-tissue compartment model to fit the simulated mixed model data. For the drug challenge study, the post observation probability of striatal displacement of the PET signal was 0.9 for amphetamine and approximately 0 for saline, indicating a high probability of amphetamine-induced endogenous dopamine release in the striatum. PET-ABC also demonstrated superior statistical power to WNLS (0.87 versus 0.09) for selecting the correct model in a simulated ligand displacement study.PET-ABC is a simple and intuitive method that provides complete Bayesian statistical analysis of single subject dynamic PET data, including the extent to which model parameter estimates and model choice are supported by the data. Software for PET-ABC is freely available as part of thePETabcpackagehttps://github.com/cgrazian/PETabc.
我们描述了一种直观、易用的方法，称为 PET-ABC，它能够从单个对象的动态 PET 数据中进行全贝叶斯统计推断。在动力学参数估计的可靠性和模型选择的统计能力方面，比较了 PET-ABC 与加权非线性最小二乘法（WNLS）的性能。使用 2 种噪声模型和 3 个噪声水平模拟了基于 1 组织和 2 组织隔室模型的动态 PET 数据。使用 PET-ABC 评估了每种条件下参数估计的可靠性。还使用它对由 1 组织和 2 组织隔室动力学混合组成的模拟噪声数据集执行模型选择。最后，使用 PET-ABC 分析了在清醒大鼠上进行的非稳态[C]raclopride 研究，该大鼠在示踪剂注射后 20 分钟内接受了 2mg/kg 安非他命或生理盐水。PET-ABC 为模型参数提供了后验点估计，其方差小于 WNLS，并且还提供了这些估计的概率密度函数，表明了置信区间。它成功地确定了 2 组织隔室模型优于拟合模拟混合模型数据。对于药物挑战研究，PET 信号纹状体位移的后观察概率为安非他命的 0.9，生理盐水的约为 0，表明纹状体中安非他命诱导的内源性多巴胺释放的可能性很高。PET-ABC 还在模拟配体置换研究中表现出比 WNLS 更高的统计能力（0.87 与 0.09），用于选择正确的模型。PET-ABC 是一种简单直观的方法，它对单个对象的动态 PET 数据进行了完整的贝叶斯统计分析，包括模型参数估计和模型选择在多大程度上得到数据的支持。PET-ABC 的软件作为 PETabc 包的一部分免费提供https://github.com/cgrazian/PETabc。
