Could you please help me to clarify the key steps implemented into the Monte Carlo approach for multiple comparisons correction? As far as I understood, it consists of running many permutations for fitting the measurements of different ROI sizes, which are being selected randomly according to predefined p-value. The process stops when optimal cluster is found and as a result we have the clusters of difference with clusterwise p-value and 90% C.I.... Am I wrong?