Nonstationary cluster-size inference with random field and permutation methods
Nonstationary cluster-size inference with random field and permutation methods
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DOI：
10.1016/j.neuroimage.2004.01.041
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发表时间：
2004-06-01
期刊：
影响因子：
5.7
通讯作者：
Nichols, TE
中科院分区：
文献类型：
--
作者：
Hayasaka, S;Phan, KL;Nichols, TE
由于它们对空间扩展信号的敏感性增加，聚类大小测试被广泛用于检测大脑图像中的变化和激活。然而，当图像是非平稳的，集群大小分布取决于局部平滑。聚类在平滑区域中往往较大，导致误报增加，而在粗糙区域中，聚类往往较小，导致灵敏度降低。Worsley等人提出了一种根据图像的局部粗糙度调整聚类大小的随机场理论（RFT）方法[Worsley，K. J.，2002.非平稳FWHM及其对fMRI数据统计推断的影响。发表于2002年6月2日至6日在日本仙台举行的第八届人脑功能映射国际会议。可在NeuroImage 16（2）779-780中的CD-ROM上获得;脑图8（1999）98]。在本文中，我们在置换测试框架中实现了这种方法，该框架需要很少的假设，已知是精确的[J. Cereb.血流代谢16（1996）7]并且是稳健的[Neurolmage 20（2003）2343]。我们比较了我们的方法固定排列，固定RIFT，和非固定R方法。使用模拟数据，我们发现，我们的排列测试表现良好，我的设置检查，而非平稳RFT测试表现良好，只有在高DF下的平滑图像。我们还发现，固定RFT测试成为反保守下的非平稳，而非平稳RIFT和置换测试仍然有效下的非平稳。在一个真实的PET数据集上，我们发现，虽然非平稳测试由于平滑度估计的变化而降低了灵敏度，但这些测试对于粗糙区域中的聚类具有比平稳聚类大小测试更好的灵敏度。我们包括一个详细的和统一的描述沃斯利非平稳RIFT集群大小测试。(C)2004年爱思唯尔公司All rights reserved.
Because of their increased sensitivity to spatially extended signals, cluster-size tests are widely used to detect changes and activations in brain images. However, when images are nonstationary, the cluster-size distribution varies depending on local smoothness. Clusters tend to be large in smooth regions, resulting in increased false positives, while in rough regions, clusters tend to be small, resulting in decrease sensitivity. Worsley et al. proposed a random field theory (RFT) method that adjusts cluster sizes according to local roughness of images [Worsley, K.J., 2002. Nonstationary FWHM and its effect on statistical inference of fMRI data. Presented at the 8th International Conference on Functional Mapping of the Human Brain, June 2-6, 2002, Sendai, Japan. Available on CD-ROM in NeuroImage 16 (2) 779-780; Hum. Brain Mapp. 8 (1999) 98]. In this paper, we implement this method in a permutation test framework, which requires very few assumptions, is known to be exact [J. Cereb. Blood Flow Metab. 16 (1996) 7] and is robust [Neurolmage 20 (2003) 2343]. We compared our method to stationary permutation, stationary RIFT, and nonstationary R methods. Using simulated data, we found that our permutation test performs well under;my setting examined, whereas the nonstationary RFT test performs well only for smooth images under high df. We also found that the stationary RFT test becomes anticonservative under nonstationarity, while both nonstationary RIFT and permutation tests remain valid under nonstationarity. On a real PET data set we found that, though the nonstationary tests have reduced sensitivity due to smoothness estimation variability, these tests have better sensitivity for clusters in rough regions compared to stationary cluster-size tests. We include a detailed and consolidated description of Worsley nonstationary RIFT cluster-size lest. (C) 2004 Elsevier Inc. All rights reserved.