The field of neuroimaging dedicated to mapping connections in the brain is increasingly being recognized as key for understanding neurodevelopment and pathology. Networks of these connections are quantitatively represented using complex structures including matrices, functions, and graphs, which require specialized statistical techniques for estimation and inference about developmental and disorder-related changes. Unfortunately, classical statistical testing procedures are not well suited to high-dimensional testing problems. In the context of global or regional tests for differences in neuroimaging data, traditional analysis of variance (ANOVA) is not directly applicable without first summarizing the data into univariate or low-dimensional features, a process that may mask salient features of the high-dimensional distributions. In this work, we consider a general framework for two-sample testing of complex structures by studying generalized within- and between-group variances based on distances between complex and potentially high-dimensional observations. We derive an asymptotic approximation to the null distribution of the ANOVA test statistic, and conduct simulation studies with scalar and graph outcomes to study finite sample properties of the test. Finally, we apply our test to our motivating study of structural connectivity in autism spectrum disorder. This article is protected by copyright. All rights reserved.

中文翻译：

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基于距离的大脑连通性方差分析

致力于绘制大脑连接的神经成像领域越来越被认为是理解神经发育和病理学的关键。这些连接的网络使用包括矩阵、函数和图形在内的复杂结构进行定量表示，这需要专门的统计技术来估计和推断与发育和障碍相关的变化。不幸的是，经典的统计测试程序并不适合高维测试问题。在神经影像数据差异的全局或区域测试的背景下，传统的方差分析 (ANOVA) 在没有首先将数据汇总为单变量或低维特征的情况下不能直接适用，这一过程可能会掩盖高维的显着特征分布。在这项工作中，我们通过研究基于复杂和潜在高维观察之间的距离的广义组内和组间方差，考虑了复杂结构的双样本测试的一般框架。我们推导出 ANOVA 检验统计量的零分布的渐近近似值，并使用标量和图形结果进行模拟研究，以研究检验的有限样本属性。最后，我们将我们的测试应用于我们对自闭症谱系障碍结构连通性的激励研究。本文受版权保护。版权所有。我们推导出 ANOVA 检验统计量的零分布的渐近近似值，并使用标量和图形结果进行模拟研究，以研究检验的有限样本属性。最后，我们将我们的测试应用于我们对自闭症谱系障碍结构连通性的激励研究。本文受版权保护。版权所有。我们推导出 ANOVA 检验统计量的零分布的渐近近似值，并使用标量和图形结果进行模拟研究，以研究检验的有限样本属性。最后，我们将我们的测试应用于我们对自闭症谱系障碍结构连通性的激励研究。本文受版权保护。版权所有。