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Dear FSLers, The results of VBM are actually nonstationary images, and thus we cannot directly use cluster-based inference. We need correct the inference for such nonstationary. Previously, I used nonstationary cluster extent correction based on RFT for multiple comparison correction for VBM results. Recently, I read the paper, titled "Adjusting the effect of nonstationarity in cluster-based and TFCE inference". In this study, Dr. Salimi-Khorshidi and colleagues find that adjusting for local smoothness via a separate resampling procedure is more effective at removing nonstationarity than an adjustment via a random field theory based smoothness estimator. Where could I use this method? In the FSL software, there is nonparametric randomise TFCE method. However, in the original TFCE paper (Threshold-free cluster enhancement: Addressing problems of smoothing, threshold dependence and localisation in cluster inference, 2009, neuroimage), I do not find the separate resampling procedure in this paper. What about the current version of FSL software? Has used the separate resampling procedure or not? Has used the method introduced in "Adjusting the effect of nonstationarity in cluster-based and TFCE inference"? If not, could I use the TFCE method for VBM's multiple comparison correction. Best, Feng