A cluster mass permutation test with contextual enhancement for fMRI activation detection
A cluster mass permutation test with contextual enhancement for fMRI activation detection
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DOI：
10.1016/j.neuroimage.2006.03.058
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发表时间：
2006-08
期刊：
影响因子：
5.7
通讯作者：
L. Tillikainen;E. Salli;A. Korvenoja;H. Aronen
中科院分区：
文献类型：
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作者：
L. Tillikainen;E. Salli;A. Korvenoja;H. Aronen
基于高斯随机场 (GRF) 的方法通常用于统计推断和控制神经影像中的族错误率 (FWE)。它们要求误差场是底层连续多元高斯随机场的合理晶格近似，并且具有可微且可逆的空间自相关函数。排列检验根据数据估计检验统计量的分布，并针对 FWE 自动调整。在这里，我们提出了一种新的分析程序，即具有上下文增强的簇质量排列测试（CMPCE），并将其与 GRF 进行比较。在 CMPCE 中，首先对数据进行预白化以消除时间自相关。使用测量的空数据和包含模拟激活的空数据比较 CMPCE 和 GRF 的 FWE 率、聚类检测概率和描绘精度。我们还将这两种方法应用于功能磁共振成像实验，其中使用了对右手的触觉体感刺激。当使用空数据分析 FWE 时，CMPCE 和 GRF 给出的 FWE 均显着高于标称显着性水平 0.05（CMPCE 高达 0.12，GRF 高达 0.18），表明预白化、运动校正或高通滤波部分失败。在模拟激活数据中，对于相同的簇检测概率水平，CMPCE 给出的错误分类体素比 GRF 少。另一方面，基于 GRF 的方法的最大簇检测概率更高。两种方法在触觉功能磁共振成像数据中给出了相似的定性结果。 CMPCE 似乎是一种很有前途的 fMRI 分析方法，特别是在需要高描绘精度的情况下。
Gaussian random field (GRF)-based methods are commonly used for statistical inference and to control the family-wise error rate (FWE) in neuroimaging. They require that the error fields are reasonable lattice approximations to an underlying continuous multivariate Gaussian random field and have differentiable and invertible spatial autocorrelation function. Permutation test estimates the distribution of the test statistic from the data and adjusts automatically for the FWE. Here we present a new analysis procedure, the cluster mass permutation test with contextual enhancement (CMPCE), and compare it to GRF. In CMPCE, the data are first pre-whitened to remove temporal autocorrelations. The FWE rates, the cluster detection probability and delineation accuracy of CMPCE and GRF were compared using measured null data and null data containing simulated activations. We also applied both methods to an fMRI experiment where tactile somatosensory stimulation into the right hand was used. When analyzing the FWE using null data, both CMPCE and GRF gave significantly higher FWEs (CMPCE up to 0.12, GRF up to 0.18) than the nominal significance level 0.05, indicating that the pre-whitening, motion correction or high-pass filtering partially failed. In the simulated activation data, CMPCE gave less falsely classified voxels for the same cluster detection probability level than GRF. The maximal cluster detection probability was on the other hand higher in the GRF-based method. Both methods gave qualitatively similar results in the tactile fMRI data. CMPCE seems to be a promising fMRI analysis method, especially if high delineation accuracy is required.