Permutation-Based Inference for Function-on-Scalar Regression With an Application in PET Brain Imaging.
Permutation-Based Inference for Function-on-Scalar Regression With an Application in PET Brain Imaging.
复制标题
基于排列的标量函数回归推理及其在 PET 脑成像中的应用。
DOI：
10.1080/10485252.2023.2206926
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发表时间：
2023
影响因子：
1.2
通讯作者：
Ogden,RTodd
中科院分区：
文献类型：
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作者：
Shieh,Denise;Ogden,RTodd
通过使用正电子发射断层扫描(PET)成像，可以研究人类大脑中各种蛋白质的密度。我们在此报告一项关于5-羟色胺转运体(5-HTT)结合的研究数据。虽然PET成像数据分析最常见的是聚集到几个离散的TEA优先感兴趣区域的数据，在这项研究中，主要感兴趣的是沿着连续的解剖定义的束的许多位置所做的5-HTT结合潜力的测量，该束被选择跟踪5-羟色胺能轴突。我们的目标是描述沿着这条通路的结合模式，并确定这些模式在对照组和抑郁症患者之间有何不同。由于我们数据的性质，我们使用标量函数回归模型来优化我们的数据。对主效应(沿道位置；诊断组)及其相互作用的推断都是使用不需要分布假设的排列测试策略进行的。此外，为了研究同质性问题，我们实现了一种置换测试策略，该策略采用了从时间序列分析到函数数据设置的“块自举”方法。
The density of various proteins throughout the human brain can be studied through the use of positron emission tomography (PET) imaging. We report here on data from a study of serotonin transporter (5-HTT) binding. While PET imaging data analysis is most commonly performed on data that are aggregated into several discretea prioriregions of interest, in this study, primary interest is on measures of 5-HTT binding potential that are made at many locations along a continuous anatomically defined tract, one that was chosen to follow serotonergic axons. Our goal is to characterise the binding patterns along this tract and also to determine how such patterns differ between control subjects and depressed patients. Due to the nature of our data, we utilise function-on-scalar regression modelling to make optimal use of our data. Inference on both main effects (position along the tract; diagnostic group) and their interactions are made using permutation testing strategies that do not require distributional assumptions. Also, to investigate the question of homogeneity we implement a permutation testing strategy, which adapts a ‘block bootstrapping’ approach from time series analysis to the functional data setting.