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.

中文翻译：

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在神经影像数据的疾病关联研究中使用半参数分层混合模型进行效应大小估计


在使用神经影像数据的疾病关联研究中，评估个体关联的生物学或临床意义不仅需要检测大脑的疾病相关区域，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，用于神经影像数据空间依赖性下基于体素的推理。具体来说，我们采用具有隐藏马尔可夫随机场结构的分层混合模型来合并体素之间的空间依赖性。提出了效应大小分布的非参数规范，以灵活估计潜在的效应大小分布。仿真实验表明，与朴素估计方法相比，所提出的方法可以大大减少具有最大观察到的关联性的所选体素的效应大小估计中的选择偏差。提供了对阿尔茨海默病研究的神经影像数据的应用。