Department of Biostatistics, Nagoya University Graduate School of Medicine, Nagoya 466-0003, Japan.
Faculty of Medicine, Saga University, Saga 849-8501, Japan.
Comput Math Methods Med. 2020 Dec 9;2020:7482403. doi: 10.1155/2020/7482403. eCollection 2020.
In disease-association studies using neuroimaging data, evaluating the biological or clinical significance of individual associations requires not only detection of disease-associated areas of the brain but also estimation of the magnitudes of the associations or effect sizes for individual brain areas. In this paper, we propose a model-based framework for voxel-based inferences under spatial dependency in neuroimaging data. Specifically, we employ hierarchical mixture models with a hidden Markov random field structure to incorporate the spatial dependency between voxels. A nonparametric specification is proposed for the effect size distribution to flexibly estimate the underlying effect size distribution. Simulation experiments demonstrate that compared with a naive estimation method, the proposed methods can substantially reduce the selection bias in the effect size estimates of the selected voxels with the greatest observed associations. An application to neuroimaging data from an Alzheimer's disease study is provided.
在使用神经影像学数据进行疾病关联研究中，评估个体关联的生物学或临床意义不仅需要检测与疾病相关的大脑区域，还需要估计个体大脑区域的关联程度或效应大小。在本文中，我们提出了一种基于模型的框架，用于在神经影像学数据中的体素水平上进行空间相关性下的推断。具体来说，我们采用具有隐马尔可夫随机场结构的层次混合模型来整合体素之间的空间相关性。我们提出了一种非参数效应大小分布的指定方法，以灵活地估计潜在的效应大小分布。模拟实验表明，与一种简单的估计方法相比，所提出的方法可以显著减少对具有最大观察相关性的选定体素的效应大小估计中的选择偏差。我们还提供了一个应用于阿尔茨海默病研究中的神经影像学数据的示例。
