A parsimonious statistical method to detect groupwise differentially expressed functional connectivity networks.
A parsimonious statistical method to detect groupwise differentially expressed functional connectivity networks.
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DOI：
10.1002/hbm.23007
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发表时间：
2015-12
影响因子：
4.8
通讯作者：
Wang G
中科院分区：
文献类型：
--
作者：
Chen S;Kang J;Xing Y;Wang G
组水平的功能连接分析通常旨在检测具有不同临床或心理实验条件的亚组之间改变的连接模式，例如，比较病例和健康对照。我们提出了一种新的统计方法来检测差异表达的连接网络，具有显着提高的功率和较低的假阳性率。我们的方法的目标是捕获大脑区域数量有限的网络中最差异表达的连接（根据简约原则）。通过简约性，网络中的假阳性个体连通性边缘被有效地减少，而信息（差异表达）边缘被允许从彼此借用强度以增加网络的整体功率。我们根据组合图论为每个网络开发了一个测试统计量，并通过使用具有多重测试调整的排列测试来提供网络的p值（在弱意义上）。我们通过模拟研究和静息态功能磁共振成像病例对照研究，验证并比较了这种新方法与现有方法，包括错误发现率和基于网络的统计。结果表明，我们的方法可以识别差异表达的连接网络，而现有的方法是有限的。
Group-level functional connectivity analyses often aim to detect the altered connectivity patterns between subgroups with different clinical or psychological experimental conditions, for example, comparing cases and healthy controls. We present a new statistical method to detect differentially expressed connectivity networks with significantly improved power and lower false-positive rates. The goal of our method was to capture most differentially expressed connections within networks of constrained numbers of brain regions (by the rule of parsimony). By virtue of parsimony, the false-positive individual connectivity edges within a network are effectively reduced, whereas the informative (differentially expressed) edges are allowed to borrow strength from each other to increase the overall power of the network. We develop a test statistic for each network in light of combinatorics graph theory, and provide p-values for the networks (in the weak sense) by using permutation test with multiple-testing adjustment. We validate and compare this new approach with existing methods, including false discovery rate and network-based statistic, via simulation studies and a resting-state functional magnetic resonance imaging case–control study. The results indicate that our method can identify differentially expressed connectivity networks, whereas existing methods are limited.