Hi,
I am running TBSS on a single group of participants using a behavioral
measure for each participant as a regressor. I de-meaned the behavioral
scores in my design matrix (shown below) and set up two contrasts:
/NumWaves 2
/NumPoints 40
/PPheights 1.000000e+00 4.950000e+01
/Matrix
1.000000e+00 1.599000e+01
1.000000e+00 8.990000e+00
1.000000e+00 -1.001000e+01
1.000000e+00 1.174000e+01
1.000000e+00 9.740000e+00
1.000000e+00 1.174000e+01
1.000000e+00 -8.760000e+00
1.000000e+00 2.124000e+01
1.000000e+00 1.874000e+01
1.000000e+00 3.024000e+01
1.000000e+00 -5.560000e+00
1.000000e+00 7.740000e+00
1.000000e+00 1.874000e+01
1.000000e+00 2.024000e+01
1.000000e+00 1.999000e+01
1.000000e+00 -5.260000e+00
1.000000e+00 -1.926000e+01
1.000000e+00 -1.126000e+01
1.000000e+00 -1.526000e+01
1.000000e+00 -7.260000e+00
1.000000e+00 -8.260000e+00
1.000000e+00 -9.260000e+00
1.000000e+00 -1.026000e+01
1.000000e+00 -6.260000e+00
1.000000e+00 -1.076000e+01
1.000000e+00 -1.526000e+01
1.000000e+00 -1.226000e+01
1.000000e+00 -1.751000e+01
1.000000e+00 -4.260000e+00
1.000000e+00 -2.600000e-01
1.000000e+00 -1.526000e+01
1.000000e+00 3.740000e+00
1.000000e+00 2.974000e+01
1.000000e+00 -5.760000e+00
1.000000e+00 1.174000e+01
1.000000e+00 -1.526000e+01
1.000000e+00 -7.260000e+00
1.000000e+00 4.740000e+00
1.000000e+00 -9.260000e+00
1.000000e+00 -1.526000e+01
/ContrastName1 group mean
/ContrastName2 ISP score
/NumWaves 2
/NumContrasts 2
/PPheights 1.000125e+00 4.950000e+01
/RequiredEffect 0.679 2.420
/Matrix
1.000000e+00 0.000000e+00
0.000000e+00 1.000000e+00
I used the following inputs to run randomise
randomise -i all_FA_skeletonised -o tbss -m mean_FA_skeleton_mask -d
design.mat -t design.con -n 500 --T2 -D -V
Randomise has been running for a few hours now but it is still computing 2nd
permutation. Is it normal for randomise to be this slow when using
behavioral regressors? I know that there were some bugs in the previous
version of FSL where randomise would get stuck on a given permutation, so I
installed the newest version (4.1.5) to correct for that.
Sincerely,
Anastasia
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