期刊
JOURNAL OF NEUROSCIENCE METHODS
卷 250, 期 -, 页码 85-93出版社
ELSEVIER
DOI: 10.1016/j.jneumeth.2014.08.003
关键词
ERP; Family-wise error rate; Multiple comparison correction; Cluster-based statistics; Threshold free cluster enhancement; Monte-Carlo simulations
资金
- BBSRC [BB/K014218/1, BB/K01420X/1]
- BBSRC [BB/K01420X/1, BB/K014218/1] Funding Source: UKRI
- Biotechnology and Biological Sciences Research Council [BB/K014218/1, BB/K01420X/1] Funding Source: researchfish
- Wellcome Trust [100309/A/12/Z] Funding Source: researchfish
Background: 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. Method: 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). Results: 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. Conclusions: (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). Crown Copyright (C) 2014 Published by Elsevier B.V.
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