Cluster-based computational methods for mass univariate analyses of event-related brain potentials/fields: A simulation study.
Cluster-based computational methods for mass univariate analyses of event-related brain potentials/fields: A simulation study.
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DOI：
10.1016/j.jneumeth.2014.08.003
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发表时间：
2015-07-30
影响因子：
3
通讯作者：
Rousselet GA
中科院分区：
文献类型：
--
作者：
Pernet CR;Latinus M;Nichols TE;Rousselet GA
近年来，事件相关电位/场的分析已经从选择几个分量和峰值转向了分析整个数据空间的质量-单变量方法。这种广泛的测试增加了误报的数量，需要对多个比较进行校正。在这里，我们回顾了所有基于簇的校正多种比较方法（簇高度，簇大小，簇质量和无阈值簇增强- TFCE），结合两种计算方法（排列和bootstrap）。数据驱动的蒙特卡罗模拟比较了受试者内的两种情况（两个样本学生t检验），结果表明，平均而言，所有使用排列或bootstrap的基于聚类的方法都能很好地控制家庭错误率（FWER），但有一些警告。(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，bootstrap方法过于保守；（iii）对于低临界家庭误差率（例如p = 1%），排列可能过于自由；（iv）当程度参数（即功率< 1）衰减时，TFCE对1型错误率的控制效果最好。
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).