Dear all, I have been following the excellent replies on this mailing list with interest and now I need some of your wisdom. I have run a searchlight variant in a large brain region (leave-one-run-out & binary SVM) and have 10,000 permutations (labels in both training data and validation data are shuffled; the procedure is otherwise identical to the true labels). I'm only interested in whether there is significant information in the ROI as a whole, on the single-subject level. Unfortunately, my p-values don't pass FDR correction in all subjects (all have plenty of p<0.001 though). Instead, I read Nichols pointers on FWE control using the maximum statistic (www.fil.ion.ucl.ac.uk/spm/doc/papers/NicholsHayasaka.pdf), so I extracted the peak decoding accuracy from both the true label run and from all the permutations (across all search volumes). I now get a beautiful null distribution histogram for each subject and, voila, in all subjects the corresponding p-value is <0.05 (computed as (1 + the number of permuted max values > true max value)/(1 + the total number of permutations)). I was hoping this would be a super rigorous approach to multiple comparison control. What do you think? Best regards, M
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