Simultaneous multifactor Bayesian analysis (SiMBA) of PET time activity curve data.
Simultaneous multifactor Bayesian analysis (SiMBA) of PET time activity curve data.
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DOI：
10.1016/j.neuroimage.2022.119195
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发表时间：
2022-08-01
期刊：
影响因子：
5.7
通讯作者：
Ogden, R. Todd
中科院分区：
文献类型：
--
作者：
Matheson, Granville J.;Ogden, R. Todd
正电子发射断层扫描（PET）是一种体内成像方法，对于研究精神和神经疾病的神经化学病理生理学至关重要。然而，其高成本和参与者暴露于辐射使得采用大样本量是不可行的。因此，PET 成像的主要缺点是缺乏研究临床相关研究问题的能力。在这里，我们介绍了一种称为 SiMBA 的 PET 定量和分析新方法，该方法通过利用个体之间以及个体内区域之间的相似性来提高 PET 分析的效率，从而有助于缓解这些问题。在模拟的[11C]WAY100635数据中，SiMBA在不影响误报率的情况下极大地提高了统计功效和效应大小估计的一致性。该方法利用分层、多因素、多变量贝叶斯建模来有效地借用整个数据集的力量，以提高对测量误差的稳定性和鲁棒性。这样做可以改进参数的可识别性和估计，而不会牺牲模型的可解释性。这是以增加计算开销为代价的，但相对于收集 PET 数据所需的时间来说，这实际上可以忽略不计。这种方法有可能使测试临床相关的假设成为可能，而在考虑到实际限制之前，这些假设是永远无法研究的。此外，由于该方法不需要传统分析所需的任何额外信息，因此可以重新检查先前已经花费巨大成本收集的数据。由于 PET 图像数据质量、放射性示踪剂开发或数据共享缺乏显着进步，PET 成像从根本上限制了可研究的研究假设范围。这种方法，特别是与 PET 成像界最近采取的数据共享措施相结合，将有可能大大提高 PET 神经影像的研究可能性和临床相关性。
Positron emission tomography (PET) is an in vivo imaging method essential for studying the neurochemical pathophysiology of psychiatric and neurological disease. However, its high cost and exposure of participants to radiation make it unfeasible to employ large sample sizes. The major shortcoming of PET imaging is therefore its lack of power for studying clinically-relevant research questions. Here, we introduce a new method for performing PET quantification and analysis called SiMBA, which helps to alleviate these issues by improving the efficiency of PET analysis by exploiting similarities between both individuals and regions within individuals. In simulated [11C]WAY100635 data, SiMBA greatly improves both statistical power and the consistency of effect size estimation without affecting the false positive rate. This approach makes use of hierarchical, multifactor, multivariate Bayesian modelling to effectively borrow strength across the whole dataset to improve stability and robustness to measurement error. In so doing, parameter identifiability and estimation are improved, without sacrificing model interpretability. This comes at the cost of increased computational overhead, however this is practically negligible relative to the time taken to collect PET data. This method has the potential to make it possible to test clinically-relevant hypotheses which could never be studied before given the practical constraints. Furthermore, because this method does not require any additional information over and above that required for traditional analysis, it makes it possible to re-examine data which has already previously been collected at great expense. In the absence of dramatic advancements in PET image data quality, radiotracer development, or data sharing, PET imaging has been fundamentally limited in the scope of research hypotheses which could be studied. This method, especially combined with the recent steps taken by the PET imaging community to embrace data sharing, will make it possible to greatly improve the research possibilities and clinical relevance of PET neuroimaging.
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