Simultaneous multifactor Bayesian analysis (SiMBA) of PET time activity curve data

• Published in: NeuroImage (Orlando, Fla.) Vol. 256; p. 119195
• Main Authors: Matheson, Granville J., Ogden, R. Todd
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01-08-2022; Elsevier Limited; Elsevier
• Subjects: Bayes Theorem; Bayesian analysis; Blood; Computational neuroscience; Estimates; Humans; Hypothesis testing; Image Processing, Computer-Assisted - methods; Medicin och hälsovetenskap; Neuroimaging; Neurological diseases; Parameter estimation; Pharmacokinetics; Positron emission tomography; Positron-Emission Tomography - methods; Statistical analysis
• ISSN: 1053-8119; 1095-9572; 1095-9572
• DOI: 10.1016/j.neuroimage.2022.119195

•SiMBA is a pharmacokinetic modelling approach for Positron Emission Tomography data.•It is a multivariate hierarchical implementation of the two-tissue compartment model.•This approach exploits similarities between individuals, regions and parameters.•SiMBA improves quantitative accuracy, but also precision of statistical inferences.
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.