Hi,
On 27 Jan 2009, at 21:16, Robert Kelly wrote:
> Thank you Steve, for a very quick, clear and precise answer to my
> question. I now get identical results with randomise.
>
> A follow-up question: If we desire a t-test comparison using some
> form of variance-normalized COPEs (to compensate for potential
> differences in voxel-wise variance across subjects, the way that
> comparing Z-stat images instead of COPEs does), would FLAME meet
> this objective?
Yes - that's the primary point of FLAME (as opposed to simple OLS,
which doesn't do any weighting).
> If so, after running FLAME is it possible to repeat the group
> comparison with randomise and obtain results (raw tstat images)
> identical to those of FLAME? If not, do you know of an easy way to
> accomplish this goal using FSL?
Not currently - randomise is currently just OLS-based - possibly in
the future.
> The reason I ask is that ultimately, we would like to
>
> A. Perform a standard, parametric (GRF-theory), cluster thresholding
> based on an unpaired t-test voxel-wise comparison of two groups of
> subjects (fMRI data); and
> B. Compare results with those obtained from randomise.
>
> I suppose we could stick to comparing FEAT-OLS output with randomise
> output, given that this is quite doable, but it would be nice if we
> could find an efficient way of performing the same analyses while
> adjusting for inter-subject differences in variance at each voxel.
Your understanding is correct - at present to make this comparison you
would need to use the OLS option in FEAT.
Cheers.
>
>
> Cheers,
> Robert
>
>
> At 04:13 AM 1/26/2009, you wrote:
>> 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
>>
>>
>> ---------------------------------------------------------------------------
>> 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)
>> [log in to unmask] http://www.fmrib.ox.ac.uk/~steve
>> ---------------------------------------------------------------------------
>
---------------------------------------------------------------------------
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)
[log in to unmask] http://www.fmrib.ox.ac.uk/~steve
---------------------------------------------------------------------------
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