Adjusting the effect of nonstationarity in cluster-based and TFCE inference

► In this paper adjusted cluster sizes are used in a permutation-testing framework for both cluster-based and threshold-free cluster enhancement (TFCE) inference and tested on both simulated and real data. ► We find TFCE inference is already fairly robust to nonstationarity in the data, while cluster-based inference requires an adjustment to ensure homogeneity. ► A group of possible multi-level adjustments are introduced and their results on simulated and real data are assessed using a new performance index. ► We also find that adjusting for local smoothness via a local estimation of a statistic's null distribution (from a first pass through the permutation testing) is more effective at removing nonstationarity than correction via local smoothness estimation.