Hi - yes, one big difference here - second-level analyses are fed by
lower-level COPEs and NOT Z-stat images. Hence to get identical
results (and indeed you should for the raw tstat images) you should
feed randomise from the COPEs from lower level. You don't need to do
the concatenation yourself - you can just use the "filtered_func_data"
from inside the .gfeat/cope?.feat higher-level FEAT analysis, which is
the standard-space concatenated lower-level COPE outputs.
Cheers.
On 26 Jan 2009, at 05:37, Robert Kelly wrote:
> 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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Stephen M. Smith, Professor of Biomedical Engineering
Associate Director, Oxford University FMRIB Centre
FMRIB, JR Hospital, Headington, Oxford OX3 9DU, UK
+44 (0) 1865 222726 (fax 222717)
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