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Hi, Thanks for your answer. I tried randomise in another sample of 80 subjects. As you said, I indeed found a significantly lower FA in females than in males, using the --T2 option and TFCE. Nevertheless, when I look at age effects in a sample, I don't manage to find any effect, even when I lower my p value to 0.75. I am doing: randomise -i all_FA_skeletonised -o tbss -m mean_FA_skeleton_mask -d design.mat -t design.con -n 500 -D --T2 -V tbss_fill tbss_tfce_corrp_tstat4 0.75 mean_FA tbss_fillfslview mean_FA -b 0,0.6 mean_FA_skeleton -l Green -b 0.2,0.7 tbss_fill -l Red-Yellow fslview mean_FA -b 0,0.6 mean_FA_skeleton -l Green -b 0.2,0.7 tbss_fill -l Red-Yellow It really questions me about the sensitivity of TBSS analyses if anyone get an idea ? Thanks, Josselin --------------------------------------------------------------------------- Hi Josselin, I am using randomise as well. It is quite sensitive even with 8 female v.s. 8 male DTI data. I found regions survived from multiple comparison correction by T2 option. But we did not find significant regions when we only look age effect. From our experience, gender matters for DTI data. If you have half sample size of female and male, try to separate them and use randomise. Or you might did something wrong with the design.mat. Hope this is helpful. You can also use matlab or other program and look at your data. Get a sense of it. You can use GLM to analysis TBSS processed DTI skeleton data, especially you have quite large sample size (see Dr. Smith 2006 TBSS paper). Cheers, Yingying =========================================================== Yingying Wang, Graduate Student, Biomedical Engineering, University of Cincinnati. Pediatric Neuroimaging Research Consortium, Cincinnati Children's Hospital Medical Center. MLC 5033, 3333 Burnet Avenue, Cincinnati, OH 45229-3039, United States. O: 513-636-3495 C: 513-833-7448 Email: [log in to unmask] Blog: http://wang2yg.blogspot.com Homepage: http://homepages.uc.edu/~wang2yg ===========================================================