Thresholding of statistical maps in functional neuroimaging using the false discovery rate
Thresholding of statistical maps in functional neuroimaging using the false discovery rate
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DOI：
10.1006/nimg.2001.1037
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发表时间：
2002-04-01
期刊：
影响因子：
5.7
通讯作者：
Nichols, T
中科院分区：
文献类型：
--
作者：
Genovese, CR;Lazar, NA;Nichols, T
从神经成像数据中找到客观有效的体素统计阈值一直是一个长期存在的问题。在对图像中的每个体素执行至少一个测试的情况下，需要对阈值进行一些校正以控制错误率，但是用于多个假设测试的标准过程（例如，Bonferroni）往往不够敏感，在这种情况下是有用的。本文介绍了神经科学文献中控制错误发现率（FDR）的统计方法。最近的统计学理论工作表明，FDR控制程序将是有效的神经影像学数据的分析。这些程序同时对所有体素检验统计量进行操作，以确定哪些检验应被视为具有统计学显著性。该程序的创新之处在于，它们控制了被错误拒绝的被拒绝假设的预期比例。我们证明了这种方法使用模拟和功能磁共振成像数据从两个简单的实验。(C)2002 Elsevier Science（美国）。
Finding objective and effective thresholds for voxel-wise statistics derived from neuroimaging data has been a long-standing problem. With at least one test performed for every voxel in an image, some correction of the thresholds is needed to control the error rates, but standard procedures for multiple hypothesis testing (e.g., Bonferroni) tend to not be sensitive enough to be useful in this context. This paper introduces to the neuroscience literature statistical procedures for controlling the false discovery rate (FDR). Recent theoretical work in statistics suggests that FDR-controlling procedures will be effective for the analysis of neuroimaging data. These procedures operate simultaneously on all voxelwise test statistics to determine which tests should be considered statistically significant. The innovation of the procedures is that they control the expected proportion of the rejected hypotheses that are falsely rejected. We demonstrate this approach using both simulations and functional magnetic resonance imaging data from two simple experiments. (C) 2002 Elsevier Science (USA).