correction for multiple comparison over many ROIs I would like to know how to correct a p value of a stiatistic when the same test was run over multiple regions of interest. The modality is MEG. 
So, I have 4 Regions Of Interests, and I ran the same cluster-based permutation test on the averaged timecourse of each ROI. I got more than 10 clusters, each with p &lt; 0.05, but how can I get the corrected p value considering there was more than one region? 

Any help will be much appreciated.
You have one p-values for each ROI timecourse (for the largest cluster) indicating whether your conditions are exchangeable. If you want to correct for the 4 ROIs you could use any adequate multiple comparison correction, from classical Bonferroni (which might be too conservative as the tests are likely not completly independent) to Benjamini-Hochberg style FDR correction (but applied to the cluster permutation test p-values).

This leaves us with the question which p-values count, one per ROI (the largest cluster, i.e. smallest p-value) or the p-values for all supra cluster-threshold clusters. I would think the former is more appropriate, as you already corrected for the multiple clusters.

That is, for the Bonferroni correction you would multiply your p-values by 4, (or identically, divide your critical alpha by 4).

This solution is incomplete, as I do not know how to get corrected p-values for the second largest cluster with FDR or Holm-Bonferroni.