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Dear Nikos, sorry for bothering you with the following question: I would like to use non-stationary RFT for my VBM analysis but I got confused when I compare the results obtained by Dr. Hayasaka's NS toolbox with the results obtained by the normal spm_results_ui or Christian Gaser's non-stationarity correction implemented in the VBM5 toolbox. In a two-sample t test I found one significant cluster by using a primary threshold of p <0.01 and a FWE-corrected extent threshold of 0.05: 1) Normal stationary RFT shows me a corrected p of 0.021 (k=6066) for this cluster. 2) The NS toolbox calculates a corrected p value of 0.010 (k=6066, resels=5.316). It seems as if k refers to the stationary RFT results...? In this case, k refers to the number of voxels. In the NS toolbox the corrected p-values are calculated based on RESELs rather than the number of voxels, but k is displayed for reporting purpose. 3) Christian Gaser's toolbox produces a corrected p value of 0.009 (k=7019). Why is the p value so much lower compared to the NS toolbox...? The difference between the p-values is small (p=0.010 vs. p=0.009) but I see that the number of voxels (k) is different as well. If the primary threshold is the same in both analyses, I suspect there may be some differences between the statistic images from the NS toolbox and the VBM5 toolbox. Has the data been processed the same or differently in both analyses (template creation, normalization, segmentation, smoothing, etc)? Do you if there is any way to input the adjusted cluster sizes to the cluster labeling routine of AAL? The gin_cluster_plabels script of the AAL toolbox is similar to spm_list.m. I highlighted the line where I would make corrections but I am not sure...I don't know how to pass the non-uniformity adjusted XYZ to gin_det_plabels. I am not familiar with AAL, but the NS toolbox does not alter XYZ. In other words, it doesn't create the non-uniformity adjusted XYZ. Hope this helps, -Satoru Satoru Hayasaka PhD ---------- Assistant Professor, Public Health Sciences & Radiology Wake Forest University School of Medicine (ph) +1-336-716-8504 / (fax) +1-336-716-0798 (email) shayasak _at_ wfubmc _dot_ edu