A difference degree test for comparing brain networks
A difference degree test for comparing brain networks
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DOI：
10.1002/hbm.24718
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发表时间：
2019-07-26
影响因子：
4.8
通讯作者：
Guo, Ying
中科院分区：
文献类型：
--
作者：
Higgins, Ixavier A.;Kundu, Suprateek;Guo, Ying
最近，研究功能连接作为精神障碍生物标志物的方法不断涌现。典型的方法包括在每个边缘进行大规模单变量测试或比较网络指标以识别不同的拓扑特征。这些方法的局限性包括由于大量比较而导致统计功效较低，以及难以将网络中的整体差异归因于局部变化。我们提出了一种捕获差异度的方法，差异度是关联到差异网络中每个区域的边的数量。我们的差异度测试（DDT）是一个两步程序，用于识别与大量差异加权边缘（DWE）相关的大脑区域。首先，我们选择一个数据自适应阈值来识别 DWE，然后对每个大脑区域发生的 DWE 数量进行统计测试。我们通过生成一组适当的零网络来实现这一点，这些网络使用 Hirschberger-Qi-Steuer 算法在观察到的差异网络的一阶矩和二阶矩上进行匹配。这种表述允许将网络的真实拓扑与由相关性测量引起的有害拓扑分开，该相关性测量以与大脑功能无关的方式改变区域间连接。在模拟中，所提出的方法在检测差异连接的感兴趣区域方面优于竞争方法。将滴滴涕应用于重度抑郁症数据集可以识别默认模式网络中通常与这种反刍障碍有关的大脑区域。
Recently, there has been a proliferation of methods investigating functional connectivity as a biomarker for mental disorders. Typical approaches include massive univariate testing at each edge or comparisons of network metrics to identify differing topological features. Limitations of these methods include low statistical power due to the large number of comparisons and difficulty attributing overall differences in networks to local variation. We propose a method to capture the difference degree, which is the number of edges incident to each region in the difference network. Our difference degree test (DDT) is a two-step procedure for identifying brain regions incident to a significant number of differentially weighted edges (DWEs). First, we select a data-adaptive threshold which identifies the DWEs followed by a statistical test for the number of DWEs incident to each brain region. 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 algorithm. This formulation permits separation of the network's true topology from the nuisance topology induced by the correlation measure that alters interregional connectivity in ways unrelated to brain function. In simulations, the proposed approach outperforms competing methods in detecting differentially connected regions of interest. Application of DDT to a major depressive disorder dataset leads to the identification of brain regions in the default mode network commonly implicated in this ruminative disorder.