Detecting signals in FMRI data using powerful FDR procedures
Detecting signals in FMRI data using powerful FDR procedures
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使用强大的 FDR 程序检测 FMRI 数据中的信号
DOI：
10.4310/sii.2008.v1.n1.a3
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发表时间：
2008
影响因子：
0.8
通讯作者：
N. Cressie
中科院分区：
文献类型：
--
作者：
M. Pavlicova;T. Santner;N. Cressie
功能性磁共振成像（FMRI）彻底改变了将物理刺激与局部脑活动联系起来的研究。在处理功能磁共振成像数据的挑战中，它们是有噪声的，它们表现出空间相关性，并且它们通常很大，包含成千上万的信息体素。错误发现率（FDR）的概念对如何执行强大的多假设检验以检测如此大的多变量数据中的信号产生了很大的影响。在功能磁共振成像数据的空间依赖性需要特别注意，因为，如果忽略，它可能会导致失去控制的大小以及恶化的权力FDR程序。本文提出将体素检验统计量变换到小波空间，在小波空间系数近似不相关。我们证明，通过一系列的实验，在小波空间中增强的P值自适应阈值（EPAT）的FDR程序，保持控制的大小的多重测试程序，并提供了显着增加的功率的FDR程序，直接应用于地图（空间依赖）的测试统计。EPAT方法，这里开发的功能磁共振成像数据，是通用的，可以应用于其他相关的数据设置。
Functional magnetic resonance imaging (FMRI) has revolutionized the study of linking physical stimuli with localized brain activity. Among the challenges of working with FMRI data, they are noisy, they exhibit spatial correlation, and they are usually large containing tens of thousands of voxels of information. The notion of False Discovery Rate (FDR) has made a great impact on how to perform powerful multiple hypothesis tests to detect signals in such large multivariate data. The spatial dependence in FMRI data requires special care since, if ignored, it can lead to a loss of control of size as well as a deterioration in power of FDR procedures. This article advocates transforming the voxelwise test statistics to wavelet space, where the coefficients are approximately uncorrelated. We demonstrate, through a series of experiments, that an FDR procedure in wavelet space enhanced by P -value adaptive thresholding (EPAT), maintains control of the size of the multiple-testing procedure and offers substantially increased power over an FDR procedure that is applied directly to the map of (spatially dependent) test statistics. The EPAT methodology, developed here for FMRI data, is generic and can be applied in other dependent data settings.