Department of Psychiatry, Columbia University, New York, NY 10032, USA; Department of Biostatistics, Columbia University Mailman School of Public Health, New York, NY 10032, USA.
Department of Psychiatry, Columbia University, New York, NY 10032, USA; Department of Biostatistics, Columbia University Mailman School of Public Health, New York, NY 10032, USA.
Neuroimage. 2022 Aug 1;256:119195. doi: 10.1016/j.neuroimage.2022.119195. Epub 2022 Apr 19.
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 [C]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.
正电子发射断层扫描（PET）是一种用于研究精神和神经疾病神经化学病理生理学的重要体内成像方法。然而，其成本高且参与者会受到辐射暴露，因此无法采用大样本量。因此，PET 成像的主要缺点是其缺乏研究临床相关研究问题的能力。在这里，我们介绍了一种新的 PET 定量和分析方法，称为 SiMBA，它通过利用个体之间和个体内部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 [C]WAY100635 数据中，SiMBA 极大地提高了统计功效和效应大小估计的一致性，而不会影响假阳性率。这种方法利用分层、多因素、多变量贝叶斯建模，有效地在整个数据集之间借用力量，以提高稳定性和对测量误差的鲁棒性。这样做可以提高参数可识别性和估计，而不会牺牲模型的可解释性。这需要增加计算开销，但是相对于收集 PET 数据所需的时间而言，这实际上可以忽略不计。这种方法有可能使以前由于实际限制而无法研究的临床相关假设成为可能。此外，由于这种方法不需要比传统分析所需的额外信息，因此可以重新检查以前已经花费大量费用收集的数据。在 PET 图像数据质量、示踪剂开发或数据共享方面没有重大进展的情况下，PET 成像在可以研究的研究假设范围内受到根本限制。这种方法，尤其是结合 PET 成像社区最近采取的措施来接受数据共享，将使 PET 神经影像学的研究可能性和临床相关性得到极大提高。
