Pernet C R, Latinus M, Nichols T E, Rousselet G A
Centre for Clinical Brain Sciences, Neuroimaging Sciences, University of Edinburgh, Edinburgh, UK.
Institut de Neurosciences de la Timone UMR 7289, Aix Marseille Université, CNRS, 13385 Marseille, France.
J Neurosci Methods. 2015 Jul 30;250:85-93. doi: 10.1016/j.jneumeth.2014.08.003. Epub 2014 Aug 13.
In recent years, analyses of event related potentials/fields have moved from the selection of a few components and peaks to a mass-univariate approach in which the whole data space is analyzed. Such extensive testing increases the number of false positives and correction for multiple comparisons is needed.
Here we review all cluster-based correction for multiple comparison methods (cluster-height, cluster-size, cluster-mass, and threshold free cluster enhancement - TFCE), in conjunction with two computational approaches (permutation and bootstrap).
Data driven Monte-Carlo simulations comparing two conditions within subjects (two sample Student's t-test) showed that, on average, all cluster-based methods using permutation or bootstrap alike control well the family-wise error rate (FWER), with a few caveats.
(i) A minimum of 800 iterations are necessary to obtain stable results; (ii) below 50 trials, bootstrap methods are too conservative; (iii) for low critical family-wise error rates (e.g. p=1%), permutations can be too liberal; (iv) TFCE controls best the type 1 error rate with an attenuated extent parameter (i.e. power<1).
近年来，事件相关电位/场的分析已从选择少数成分和峰值转向对整个数据空间进行分析的大规模单变量方法。这种广泛的测试增加了假阳性的数量，因此需要对多重比较进行校正。
在此，我们回顾了所有基于聚类的多重比较校正方法（聚类高度、聚类大小、聚类质量和无阈值聚类增强 - TFCE），并结合两种计算方法（置换和自抽样）。
在被试内比较两种条件的数据驱动蒙特卡罗模拟（双样本学生t检验）表明，平均而言，所有使用置换或自抽样的基于聚类的方法都能很好地控制家族性错误率（FWER），但有一些注意事项。
（i）至少需要800次迭代才能获得稳定的结果；（ii）在50次试验以下，自抽样方法过于保守；（iii）对于低临界家族性错误率（例如p = 1%），置换可能过于宽松；（iv）TFCE通过衰减范围参数（即功效<1）能最好地控制I型错误率。
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