Dear FSL Users,
I have been reading documentation regarding Randomise and being that this is
my first analysis I had some questions. I had a comparison of 17 control
subjects to 17 patients using affine and nonlinear mapping. After looking
at my tstat1 images for different smoothing (s2 s3 etc) I used randomise to
do 5000 permutations of the data set(after selecting s2).
randomise -i GM_mod_merg_s2 -m GM_mask -o fslvbm -d design.mat -t design.con
-T -n 5000 -V
The output files are tfce_corrp_tstat and tfce_p_tstat. When I display
using corrp there are no results while I receive results when using p
(really 1-p)
fslview $FSLDIR/data/standard/MNI152_T1_2mm fslvbm_tfce_corrp_tstat1 -l
Red-Yellow -b 0.949,1
fslview $FSLDIR/data/standard/MNI152_T1_2mm fslvbm_tfce_p_tstat1 -l
Red-Yellow -b 0.949,1
I wanted to find out if it is okay to use the p_tstat1 file to show
significance in results or if we should always only use corrp_tstat? Is the
difference only that corrp ensures there are less false positives? Because
the ROI are very small areas I am worried that the corrp_tstat might be
sensitive enough to pick up these areas since they are clearly present in
p_tstat. Any help or insight would be much appreciated as I am still a bit
fuzzy on the difference between these two parameters. Also, is TFCE always
recommended or would cluster or voxelwise statistics be recommended if I am
not defining any ROI ahead of time. Thanks.
Sincerely,
Ajay Kurani
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