New methods for multiple testing in permutation inference for the general linear model
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Permutation methods are commonly used to test significance of regressors of interest in general linear models (GLMs) for functional (image) data sets, in particular for neuroimaging applications as they rely on mild assumptions. Permutation inference for GLMs typically consists of three parts: choosing a relevant test statistic, computing pointwise permutation tests and applying a multiple testing correction. We propose new multiple testing methods as an alternative to the commonly used maximum value of test statistics across the image which improve in terms of power and robustness and allow to identify the regions of potential rejection via a graphical output. The proposed methods rely on sorting the permuted functional test statistics based on pointwise rank measures. We developed the R package GET which can be used for computation of the proposed procedures.
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