More Specific Signal Detection in Functional Magnetic Resonance Imaging by False Discovery Rate Control for Hierarchically Structured Systems of Hypotheses.
More Specific Signal Detection in Functional Magnetic Resonance Imaging by False Discovery Rate Control for Hierarchically Structured Systems of Hypotheses.
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DOI：
10.1371/journal.pone.0149016
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发表时间：
2016
期刊：
影响因子：
3.7
通讯作者：
Dickhaus T
中科院分区：
文献类型：
--
作者：
Schildknecht K;Tabelow K;Dickhaus T
功能磁共振成像（fMRI）中的信号检测固有地涉及到测试大量假设的问题。解决这种多样性的流行策略是控制错误发现率（FDR）。在这项工作中，我们考虑的情况下，先验知识是可用于分区的所有假设的集合成不相交的子集或家庭，e。例如，在一个实施例中，通过关于某些感兴趣区域的功能性的先验知识。如果真零假设的比例在不同的家庭之间不同，这种结构信息可以用来增加统计功效。我们提出了一个两阶段的多重检验程序，首先排除那些家庭的分析，没有强有力的证据，包含真正的替代品。我们在测试的第一阶段显示了对家庭错误率的控制。然后，在第二阶段，我们继续测试每个非排除家庭内的假设，并在第二阶段获得每个家庭内的FDR的渐近控制。我们的主要数学结果是，这两个阶段的战略意味着渐近控制的FDR相对于所有的假设。在模拟中，我们证明了这种新的程序相比，在高度不平衡的家庭的情况下，既定的程序增加的权力。最后，我们应用所提出的方法模拟和真实的fMRI数据。
Signal detection in functional magnetic resonance imaging (fMRI) inherently involves the problem of testing a large number of hypotheses. A popular strategy to address this multiplicity is the control of the false discovery rate (FDR). In this work we consider the case where prior knowledge is available to partition the set of all hypotheses into disjoint subsets or families, e. g., by a-priori knowledge on the functionality of certain regions of interest. If the proportion of true null hypotheses differs between families, this structural information can be used to increase statistical power. We propose a two-stage multiple test procedure which first excludes those families from the analysis for which there is no strong evidence for containing true alternatives. We show control of the family-wise error rate at this first stage of testing. Then, at the second stage, we proceed to test the hypotheses within each non-excluded family and obtain asymptotic control of the FDR within each family at this second stage. Our main mathematical result is that this two-stage strategy implies asymptotic control of the FDR with respect to all hypotheses. In simulations we demonstrate the increased power of this new procedure in comparison with established procedures in situations with highly unbalanced families. Finally, we apply the proposed method to simulated and to real fMRI data.