标题： 基于微分度数检验的脑网络比较 显示英文标题

标题： A Differential Degree Test for Comparing Brain Networks

摘要： 最近，图论已成为表征大脑功能组织的一种流行方法。在大脑网络的图论分析中，一个重要目标是识别不同疾病类型或状态下网络差异。典型的方法包括对每条边进行大量的单变量测试，或者比较局部和/或全局网络度量以识别拓扑组织中的偏差。这些方法的一些局限性包括由于大量比较而导致统计功效较低，以及难以将网络的整体差异归因于大脑功能的局部变异。 我们提出了一种新的差分度数检验（DDT），用于识别在两个群体中具有大量不同权重边的大脑区域。该检验可以帮助通过展示显著改变的神经连接来检测与疾病相关的关键大脑位置。我们通过使用Hirschberger-Qi-Steuer（HQS）算法生成一组适当的零网络来实现这一点，这些零网络在观察到的差异网络的第一和第二矩上匹配。这种设定允许分离网络的真实拓扑结构与由相关度量引起的干扰拓扑结构，后者可能会以与大脑功能无关的方式驱动区域间连接。 模拟表明，所提出的方法在检测不同的连接感兴趣区域时通常优于竞争方法。此外，我们提出了一种数据自适应阈值选择程序，能够检测不同权重的边，并且被证明在控制误差率的情况下优于进行边对边比较的竞争方法。将我们的方法应用于重度抑郁症数据集，导致识别出默认模式网络中通常与这种沉思障碍相关的脑区。 显示英文摘要

摘要： Recently, graph theory has become a popular method for characterizing brain functional organization. One important goal in graph theoretical analysis of brain networks is to identify network differences across disease types or conditions. Typical approaches include massive univariate testing of each edge or comparisons of local and/or global network metrics to identify deviations in topological organization. Some limitations of these methods include low statistical power due to the large number of comparisons and difficulty attributing overall differences in networks to local variations in brain function. We propose a novel differential degree test (DDT) to identify brain regions incident to a large number of differentially weighted edges across two populations. The proposed test could help detect key brain locations involved in diseases by demonstrating significantly altered neural connections. We achieve this by generating an appropriate set of null networks which are matched on the first and second moments of the observed difference network using the Hirschberger-Qi-Steuer (HQS) algorithm. This formulation permits separation of the network's true topology from the nuisance topology which is induced by the correlation measure and may drive inter-regional connectivity in ways unrelated to the brain function. Simulations indicate that the proposed approach routinely outperforms competing methods in detecting differentially connected regions of interest. Furthermore, we propose a data-adaptive threshold selection procedure which is able to detect differentially weighted edges and is shown to outperform competing methods that perform edge-wise comparisons controlling for the error rate. An application of our method to a major depressive disorder dataset leads to the identification of brain regions in the default mode network commonly implicated in this ruminative disorder.