Hayasaka Satoru, Nichols Thomas E
Department of Biostatistics, The University of Michigan, Ann Arbor, MI 48109, USA.
Neuroimage. 2003 Dec;20(4):2343-56. doi: 10.1016/j.neuroimage.2003.08.003.
Cluster size tests used in analyses of brain images can have more sensitivity compared to intensity based tests. The random field (RF) theory has been widely used in implementation of such tests, however the behavior of such tests is not well understood, especially when the RF assumptions are in doubt. In this paper, we carried out a simulation study of cluster size tests under varying smoothness, thresholds, and degrees of freedom, comparing RF performance to that of the permutation test, which is known to be exact. For Gaussian images, we find that the RF methods are generally conservative, especially for low smoothness and low threshold. For t images, the RF tests are found to be conservative at lower thresholds and do not perform well unless the threshold is high and images are sufficiently smooth. The permutation test performs well for any settings though the discreteness in cluster size must be accounted for. We make specific recommendations on when permutation tests are to be preferred to RF tests.
用于脑图像分析的聚类大小检验相比于基于强度的检验可能具有更高的灵敏度。随机场(RF)理论已广泛应用于此类检验的实施,然而此类检验的行为尚未得到很好的理解,尤其是当RF假设存疑时。在本文中,我们在不同的平滑度、阈值和自由度条件下对聚类大小检验进行了模拟研究,将RF方法的性能与已知精确的置换检验进行比较。对于高斯图像,我们发现RF方法通常较为保守,特别是对于低平滑度和低阈值的情况。对于t图像,发现RF检验在较低阈值时较为保守,除非阈值较高且图像足够平滑,否则表现不佳。置换检验在任何设置下都表现良好,不过必须考虑聚类大小的离散性。我们针对何时应优先选择置换检验而非RF检验提出了具体建议。