In regression-based analyses of group-level neuroimage data researchers typically fit a series of marginal general linear models to image outcomes at each spatially-referenced pixel. Spatial regularization of effects of interest is usually induced indirectly by applying spatial smoothing to the data during preprocessing. While this procedure often works well, resulting inference can be poorly calibrated. Spatial modeling of effects of interest leads to more powerful analyses, however the number of locations in a typical neuroimage can preclude standard computation with explicitly spatial models. Here we contribute a Bayesian spatial regression model for group-level neuroimaging analyses. We induce regularization of spatially varying regression coefficient functions through Gaussian process priors. When combined with a simple nonstationary model for the error process, our prior hierarchy can lead to more data-adaptive smoothing than standard methods. We achieve computational tractability through Vecchia approximation of our prior which, critically, can be constructed for a wide class of spatial correlation functions and results in prior models that retain full spatial rank. We outline several ways to work with our model in practice and compare performance against standard vertex-wise analyses. Finally we illustrate our method in an analysis of cortical surface fMRI task contrast data from a large cohort of children enrolled in the Adolescent Brain Cognitive Development study.

翻译：在基于回归的组级神经影像数据分析中，研究者通常对每个空间参考像素处的图像结果拟合一系列边际一般线性模型。感兴趣效应的空间正则化通常通过在预处理阶段对数据施加空间平滑来间接实现。尽管该流程通常效果良好，但其推断结果可能校准不佳。对感兴趣效应的空间建模能够实现更强大的分析，然而典型神经影像中位置数量众多，可能阻碍基于显式空间模型的标准计算。本文提出了一种用于组级神经影像分析的贝叶斯空间回归模型。我们通过高斯过程先验对空间变化的回归系数函数施加正则化。当与简单的非平稳误差过程模型结合时，我们的先验层次结构能够比标准方法实现更适应数据的平滑处理。我们通过Vecchia近似实现计算可行性——关键在于该近似可针对广泛的空间相关函数构建，且能产生保持完全空间秩的先验模型。我们概述了在实践中使用该模型的若干方式，并将其性能与标准顶点级分析方法进行比较。最后，我们基于青少年大脑认知发展研究中大规模儿童队列的皮层表面fMRI任务对比数据，展示了该方法的应用。