Genovese Christopher R, Lazar Nicole A, Nichols Thomas
Department of Statistics, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA.
Neuroimage. 2002 Apr;15(4):870-8. doi: 10.1006/nimg.2001.1037.
Finding objective and effective thresholds for voxelwise 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.
为源自神经影像数据的体素统计找到客观有效的阈值一直是个长期存在的问题。由于要对图像中的每个体素至少进行一次测试，因此需要对阈值进行某种校正以控制错误率，但多重假设检验的标准程序（例如Bonferroni法）在这种情况下往往不够灵敏，无法发挥作用。本文向神经科学文献中引入了控制错误发现率（FDR）的统计程序。统计学领域近期的理论研究表明，控制FDR的程序对于神经影像数据分析将是有效的。这些程序同时对所有体素测试统计量进行操作，以确定哪些测试应被视为具有统计学显著性。这些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。我们使用模拟以及来自两个简单实验的功能磁共振成像数据来演示这种方法。
