Dear Steve -fsl users

Thank you very much for the information about randomise..Still i need some
more help in order to explain the results that i have after running
randomise with 8 controls and 14 Parkinsons subjects. I used a c threshold
of 1 and the max amount of permutations (more than 300.000) as the randomise
tool suggested for the best accuracy in my case.More specific : 

randomise -i flow_all_subjets.nii.gz -o sienar -m
/usr/share/fsl/etc/standard/avg152T1_edges -d design.mat -t design.con -n
319770 -V -c 1

Contrast1: Parkinsons>Controls
Contrast2:Controls> Parkinsons
Contrast3:Parkinsons mean
Contrast4:Controls mean

I uploaded the results of the randomise test and i would really appreciate
if you could find some time and have a look at them. Ref.number: 942183

Firstly i checked the max_tstats 1&2 (in fslview dynamic range 0.949:1) and
there weren't any significant voxels there. Then i checked the maxc_tstats
1&2 (in range 0.949:1) for clusters but i also didn't see any significant
clusters. That means that there is no difference between the controls and
the patients?

As you advised me i checked after that the tstat1 and tstat2 which were
filtered also in range [0.5:3] as it was in your example even though i
didn't understand exactly why i should use this filtering range and not for
instance [0.1:5].What's the meaning of filtering our tstats 1&2 data in a
specific range? What about the intensities of these tstats?The bigger their
magnitude the more the evidence of atrophy/growth between the Patients and
the Controls in this area??By reducing their range with fslview what do we
succeed?

There are evidence in both tstat1 and tstat2 about atrophy (the value of
which, -positive or negative- will be evaluated with tstats3&4). So, correct
me if i'm wrong, we need both tstat1 and tstat2 in order to see all the
areas where there's atrophy between Patients/Controls.If we check only
tstat1 or tstat2 then we miss a lot of information about atrophy/growth
areas, right?

How these statistics (tstat1&2)show evidence of atrophy/growth, while
maxc_tstats and max_tstats don't give us that information? It's because
tstats shows only evidence of atrophy while maxc_tstats and max_tstats shows
the significance of these evidence?

And the final step is to check tstat3 and tstat4 in order to find out
whether each atrophy that was detected with tstat1 and tstat2 is positive or
negative.. In this step i didn't know what range i should use in order to
filter with fslview the data, so i used randomly the range [0.5:3] (the
range that was in tstats1&2).But then i didn't know which were the evidence
in tstat3&4 that shows positive/negative values for the atrophy...Is it the
intensities?positive intensities in tstat3 shows atrophy in Parkinsons
patients and negative once shows growth, while positive intensities in
tstat4 shows atrophy in Controls and negative once shows growth on them?
What about the magnitude of the intensities in tstats 3&4? the bigger the
absolute value of the intensity in a voxel the bigger the state of
atrophy/growth in this voxel? 

And finally since tstats are raw data, before randomisation, that means that
we don't have any idea about the significance of the evidence that we see
with these data..right?So why do we use them to extract information about
atrophy? It's only some basic evidence,but we don't know if it's important
evidence, only p-values will show us if these evidence are of importance...

I'm sorry for asking again so many questions but i don't really know how to
explain the results that i had after running randomise and i'm not sure if
these results that i already uploaded for your consideration shows some
statistical significant evidence about atrophy or growth in my groups of
study (Parkinsonians -Controls), so i would really need your opinion on that.

thanks a lot again for your time and effort.
Antonios-Constantine Thanellas