Hello,
           Randomise always returns ( 1 - p ) values, but for the one-sample t-test on FA values, you are not testing if there is a difference between means, you are essentially testing if the mean is greater than 0 ( which it will be for FA values! ) and hence the large ( 1 - p ) values in your output. For the two-sample t-test on your real data, if you are getting significance across the whole skeleton, this can be a sign that there is a systematic offset/artefact in the data..

Many Regards

Matthew

> I am trying to use TBSS with "randomise" and have some problems understanding my results.
> Essentially I get significant differences over my entire FA-skeleton with a two sample t-test
> between my patient and control group (I don’t believe on such strong significant difference –
> the differences, if at all, should be very subtle.)
> 
> To better get a grip on what is going on, I decided to create some simulated FA-data 
> (not on skeletonised but the “raw” FA-data! )
> 
> 1. I chose one 3D-FA data set with good SNR and filtered it further to minimize variance
>    across all voxels (filename: FA_001)
> 2. generated 10 “subjects” with the same FA-data from 1.:
> 
>    fslmaths FA_001 –add 0 FA_002     (and so on to FA_010)
> 
> 3. merged all individual 3D-FA data into one 4D data set: 
> 
>    fslmerge –t all_FA `$FSLDIR/bin/imglob FA_*` 
> 
> 4. used the “randn” in fslmaths to add Gaussian noise to “all_FA” with SNR=100
> 
> fslmaths FA_001 –mul 100 –randn –div 100 all_FA_noise
> 
> 5. checked all_FA in fslview -> looks ok
> 6. created mean and mask from all_FA_noise:
>     mean_FA; mean_FA_mask
> 7. did a one sample t-test (mean)  
> 
> randomise –i all_FA_noise –m mean_FA_mask –o OneSampT_FA -1 –T   (5040 realizations)
> 
> I actually used randomize_parallel on a 4 core machine:
> 
> randomize_parallel –i all_FA_noise –m mean_FA_mask –o OneSampT_FA -1 –T
> (21 fragments of 240 permutations; 5040)
> 
> 8. results from 7. :
> 
> OneSampT_FA_tfce_corrp_tstat1
> OneSampT_FA_tfce_p_tstat1
> OneSampT_FA_tfce_tstat1
> 
> And here is where my confusion starts:
> 
> According to the FSL-manuals this should be a “test for activation” – so in my interpretation a test to check if
> there are no significant differences of the mean across all “timepoints” (or “subjects” in case of TBSS).
> Does the OnesampleT-test also produces (1-p) or p-values?
> 
> OneSampT_FA_tstat1 is supposed to contain the probability distribution for the one sample test, is this
> correct? The histogram looks ok and stretches in a asymmetric Rayleigh-like distribution from 0 to
> ca. 200. High values are in WM, low values are in GM and CSF. Essentially it looks like a noisy FA-map.
> So I assume this is not "demeaned" is it?
> 
> There is almost no virtual difference between “*_tfce_p_tstat1” and “*_tfce_corrp_tstat1”
> except that the first (are those the uncorrected p-values???) has a smooth histogram and
> the latter a “discrete” binarised histogram between 0…1 but almost all values are at 1.0 or very closed to it.
> They both look almost like the mean_FA_mask (all pixels are closed to 1 and there is no contrast)
> 
> Now if this is supposed to be (1-p) I am confused here. My data are virtually identical except for noise
> (but with a very high SNR=100). 
> That means I should find a large p for testing the difference of the mean between all data (timepoints), am I right?
> If “*_tfce_corrp_tstat1” is really (1-p)as the manual states, the opposite would be true for the simulated data – a strong differences between the mean of my data - which is impossible since they are generated from the same source
> with very high SNR.
> 
> Any help highly appreciated,
> Burkhard.
>