Hello,
We would like to compare results for a two-sample unpaired t-test performed
on the same data in two different ways: 1) using FEAT OLS and 2) using
randomise. However, I am puzzled by the observation that the tstat images
for the group comparisons, one image generated using FEAT OLS and one
using randomise, are not identical. Please explain what causes this
discrepancy.
Specifically, I performed the following
1. .feat directories from a first-level analysis on fMRI data were fed into
a higher-level FEAT analysis, selecting on the Stats tab “Mixed Effects: Simple
OLS” and selecting with the Model Setup Wizard “two groups, unpaired.”
2. Files from the .gfeat directory produced in the last step were fed to
randomise in an attempt to perform the identical two-group comparison, using
the command
“randomise -i TwoSamp4D -o TwoSampT -d design.mat -t design.con -m
mask –n 1000 –c 2.8689 –V”
where design.mat, design.con, and mask were taken from the .gfeat directory;
and TwoSamp4D was created by concatenating the standard-space
transformed zstat images from the lower-level, .feat directories (using flirt
with reg/standard, stats/zstat1.nii.gz, and reg/example_func2standard.mat).
3. Comparing TwoSampT_tstat1.nii.gz from randomise with
cope1.feat/stats/tstat1.nii.gz from the .gfeat directory reveals that the two t-
score maps are highly correlated (0.96 with fslcc), yet differ by far more than
can be accounted for by rounding error.
Being able to compare FEAT OLS and randomise is important to one of our
current studies, so it would be a great help to understand why the t-score
maps are not identical, and what, if anything can be done to make them
identical, so that we can then compare p-values obtained for clusters,
knowing that we are making a valid comparison.
Your insights on this topic would be much appreciated.
Robert Kelly
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