Exploring resting-state brain functional connectivity of autism spectrum disorders (ASD) using functional magnetic resonance imaging (fMRI) data has become a popular topic over the past few years. The data in a standard brain template consist of over 170,000 voxel specific points in time for each human subject. Such an ultra-high dimensionality makes the voxel-level functional connectivity analysis (involving four billion voxel pairs) both statistically and computationally inefficient. In this work, we introduce a new framework to identify the functional brain network at the anatomical region level for each individual. We propose two pairwise tests to detect region dependence, and one multiple testing procedure to identify global structures of the network. The limiting null distribution of each test statistic is derived. It is also shown that the tests are rate optimal when the alternative block networks are sparse. The numerical studies show that the proposed tests are valid and powerful. We apply our method to a resting-state fMRI study on autism and identify patient-unique and control-unique hub regions. These findings are biologically meaningful and consistent with the existing literature.

中文翻译：

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脑解剖区域功能网络的高维测试

使用功能磁共振成像 (fMRI) 数据探索自闭症谱系障碍 (ASD) 的静息状态大脑功能连接已成为过去几年的热门话题。标准大脑模板中的数据由每个人类受试者的超过 170,000 个体素特定时间点组成。这种超高维度使得体素级功能连通性分析（涉及 40 亿个体素对）在统计和计算上都低效。在这项工作中，我们引入了一个新框架来识别每个人在解剖区域级别的功能性大脑网络。我们提出了两个成对测试来检测区域依赖性，以及一个多重测试程序来识别网络的全局结构。导出每个测试统计量的极限零分布。还表明，当替代块网络稀疏时，测试是速率最优的。数值研究表明，所提出的测试是有效和强大的。我们将我们的方法应用于自闭症的静息态 fMRI 研究，并确定患者独特和控制独特的枢纽区域。这些发现具有生物学意义并且与现有文献一致。