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Hi, So 120 subjects overall. I'd see little case for variance smoothing. I would consider using it with less than maybe about 15 subjects, but this isn't a strict cutoff. Others in the list may have a different opinion. All the best, Anderson On 22 July 2016 at 06:29, neuroimage analyst <[log in to unmask]> wrote: > Thank you Anderson for that quick reply. We have exactly 20 subject in 1 > group and around 100 in another. Does this makes the case for variance > smoothing? Regardless of our current problem, what should be a good rule of > thumb that one should follow for variance smoothing? > > I hope sombody could clear up our pcasl smoothing question. > > Thank you again for your time. > > Regards > > On Jul 21, 2016 10:21 PM, "Anderson M. Winkler" <[log in to unmask]> > wrote: > >> Hi, >> >> About smoothing pCASL: I leave for others to comment. >> >> About randomise: Assuming you followed the example from the manual >> <http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/GLM#Two-Group_Difference_.28Two-Sample_Unpaired_T-Test.29>, >> there is no need for -D as it makes in this case the design rank deficient >> (randomise may fix this internally but we should enter the design and >> options correctly anyway). >> >> How many subjects are used? If the sample size is relatively large (e.g., >> >20) variance smoothing isn't much needed. >> >> About -c: there is no strict rule but probably higher is better. Using >> 3.1 might be a good idea. >> >> All other options seem fine. >> >> All the best, >> >> Anderson >> >> >> On 22 July 2016 at 01:40, Neuroimage Analyst < >> [log in to unmask]> wrote: >> >>> Hello FSL Users, >>> >>> We processed our pCASL dataset using BASIL with the --spatial option. >>> >>> The original dataset had a resolution of 3.5x3.5x3.5 mm. >>> >>> All the subjects were then normalised to MNI Space and have a resolution >>> of 2 mm isotropic. >>> >>> Now, we wanted to perform group level analysis on two groups to see if >>> any regions have statistically significantly different CBF. >>> >>> So, we smoothed the (already smoothed) normalised perfusion map using >>> 4mm Gaussian Kernel (using the command: >>> fslmaths each_subject_perfusion -kernel gauss 1.69866 -fmean >>> each_subject_smoothed_perfusion_map) >>> >>> Then we used randomise to compare 2 groups and used 4mm variance >>> smoothing inside randomise. Here is the command: >>> >>> randomise -i my_4d_2grp_concat_files -o comp -d design.mat -t design.con >>> -D -m MNI_152_t1_2mm -v 4 -x -T -R --uncorrp >>> >>> and found significantly different regions consistent to our hypothesis. >>> >>> We were just wondering if our statistical analysis pipeline is right? >>> >>> In addition, if anybody could tell us how do we extract FWE corrected >>> cluster map from tstat* map obtained in the previous step. (Question really >>> is what threshold to input in -c option of randomise?) >>> >>> We really appreciate the time and suggestion to solve our problem. >>> >>> Thank you. >>> >>> Regards >>> >> >>