IMPROVED ASSESSMENT OF SIGNIFICANT ACTIVATION IN FUNCTIONAL MAGNETIC-RESONANCE-IMAGING (FMRI) - USE OF A CLUSTER-SIZE THRESHOLD
IMPROVED ASSESSMENT OF SIGNIFICANT ACTIVATION IN FUNCTIONAL MAGNETIC-RESONANCE-IMAGING (FMRI) - USE OF A CLUSTER-SIZE THRESHOLD
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DOI：
10.1002/mrm.1910330508
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发表时间：
1995-05-01
影响因子：
3.3
通讯作者：
NOLL, DC
中科院分区：
文献类型：
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作者：
FORMAN, SD;COHEN, JD;NOLL, DC
典型的功能磁共振（fMRI）研究提出了多重统计比较的艰巨问题（即，在128 x 128图像中> 10，000）。为了防止误报，研究人员通常依赖于降低每像素误报概率。这种方法不可避免地会损失检测统计上显著的活动的能力。另一种方法，它依赖于假设，真正的神经活动的地区往往会刺激信号的变化在连续的像素，提出。如果知道此类集群大小的概率分布是每像素误报概率的函数，则可以独立使用集群大小阈值来拒绝误报。蒙特卡洛模拟和人类受试者的fMRI研究都被用来验证这种方法可以将统计能力提高五倍，而不仅仅是依赖于调整每个像素的假阳性概率。
The typical functional magnetic resonance (fMRI) study presents a formidable problem of multiple statistical comparisons (i.e, >10,000 in a 128 x 128 image). To protect against false positives, investigators have typically relied on decreasing the per pixel false positive probability. This approach incurs an inevitable loss of power to detect statistically significant activity. An alternative approach, which relies on the assumption that areas of true neural activity will tend to stimulate signal changes over contiguous pixels, is presented. If one knows the probability distribution of such cluster sizes as a function of per pixel false positive probability, one can use cluster-size thresholds independently to reject false positives. Both Monte Carlo simulations and fMRI studies of human subjects have been used to verify that this approach can improve statistical power by as much as fivefold over techniques that rely solely on adjusting per pixel false positive probabilities.