Many key findings in neuroimaging studies involve similarities between brain maps, but statistical methods used to measure these findings have varied. Current state-of-the-art methods involve comparing observed group-level brain maps (after averaging intensities at each image location across multiple subjects) against spatial null models of these group-level maps. However, these methods typically make strong and potentially unrealistic statistical assumptions, such as covariance stationarity. To address these issues, in this article we propose using subject-level data and a classical permutation testing framework to test and assess similarities between brain maps. Our method is comparable to traditional permutation tests in that it involves randomly permuting subjects to generate a null distribution of intermodal correspondence statistics, which we compare to an observed statistic to estimate a p-value. We apply and compare our method in simulated and real neuroimaging data from the Philadelphia Neurodevelopmental Cohort. We show that our method performs well for detecting relationships between modalities known to be strongly related (cortical thickness and sulcal depth), and it is conservative when an association would not be expected (cortical thickness and activation on the n-back working memory task). Notably, our method is the most flexible and reliable for localizing intermodal relationships within subregions of the brain and allows for generalizable statistical inference.

中文翻译：

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一种简单的基于排列的多式联运对应测试

神经影像学研究中的许多关键发现都涉及大脑图谱之间的相似性，但用于衡量这些发现的统计方法却有所不同。当前最先进的方法涉及将观察到的组级脑图（在对多个受试者的每个图像位置的强度进行平均之后）与这些组级图的空间零模型进行比较。然而，这些方法通常会做出强有力且可能不切实际的统计假设，例如协方差平稳性。为了解决这些问题，在本文中，我们建议使用主题级数据和经典的排列测试框架来测试和评估大脑图之间的相似性。我们的方法与传统的排列测试相当，因为它涉及随机排列受试者以生成联运对应统计数据的零分布，我们将其与观察到的统计数据进行比较以估计 p值。我们在费城神经发育队列的模拟和真实神经影像数据中应用并比较我们的方法。我们表明，我们的方法在检测已知强相关的模态之间的关系（皮质厚度和脑沟深度）方面表现良好，并且当预计不会出现关联时（皮质厚度和 n 后工作记忆任务的激活），该方法是保守的。值得注意的是，我们的方法对于定位大脑子区域内的多式联运关系是最灵活和最可靠的，并且允许进行可概括的统计推断。