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Dear Mike et al. If I may add to Cyril's answer: There is no statistical ground for fixing arbitrary the cluster size threshold to k=10. Imagine you resampled you original data to either 2x2x2 mm3 or 3x3x3 mm3 voxel resolution, keep all else equal and assume this is minimally affecting the underneath signal (which is true). Then for the same activation pattern (from you original data) you would consider as "interesting" a cluster of 80mm3 or 270mm3 volume depending on how you resampled your data!!! AFAIK this "p<.001 uncorrected and k=10" approach was used for *display purposes only* in order to remove from the picture any "too small to even consider" cluster of voxels and to make the image look prettier. What you actually need to do is: - take a voxel level cluster forming threshold, say p=.001 (or lower to be on the safe side) - derive the minimal cluster size (in voxels) for a cluster to be significant, say at FWE p<.05. For the latter you can use this little bit of code: https://github.com/CyclotronResearchCentre/SPM_ClusterSizeThreshold Note though that the results are valid only when smoothness is assumed to be homogeneous all over the brain volume, i.e. when the cluster extent threshold is the same across the brain! Best, Chris Le 11/07/2016 à 14:01 : >> Actually I have a puzzle. If an activation blob is identified via >> 0.001, k=10, uncorrected in my fMRI experiment, > not all those blobs are correct - now you can only keep the ones with > cluster size or height FWE p<=0.05 (or FDR, but that doesn't control > type 1 error rate per se)