Thanks Doug. After using stage#1 of the longitudinal 2-stage model to
create cross-sectional measuers of e.g. thickness change from tp1 to tp2
(e.g. thickness-pc1), I then did analyses in QDEC on these measures to see
where in the brain such changes are

   - different from zero (within each group); and
   - different between groups (for every pair of groups in the current qdec
   table, e.g. F vs P)

For thickness-pc1 it all went fine, but for area-pc1 analyses, I get this
error right after starting the MonteCarlo Null-Z correction: "Completed
loading of analyzed data. fwhm.dat: 8.654965, rounded to 9. ERROR:
CSDread(): could not open
/usr/local/freesurfer/average/mult-comp-cor/fsaverage/rh/cortex/fwhm9/abs/th13/mc-z.csd".
This happens regardless of the threshold used (e.g. 0.01 or 0.05)
In case it's relevant, for the thickness analyses (whose correction worked
fine), the fwhm.dat was "10.190419, rounded to 11". I am using FS 5.3.0
(Ubuntu).

Thanks!
Tudor



On 17 June 2014 09:29, Douglas Greve <gr...@nmr.mgh.harvard.edu> wrote:

>
> It only needs to be accounted for when you compare across all 3 groups, in
> which case you'd have to use mri_glmfit
> doug
>
>
>
> On 6/17/14 12:07 AM, Tudor Popescu wrote:
>
>  Hi Doug,
>
> Thanks... Presumably I'd just leave each possible pair of groups at a time
> in the qdec table, and delete rows corresponding to subjects of the
> remaining group? And then do stats on, e.g. "long.thickness-rate" to
> compare rate of change between the current pair of groups?
> Doesn't variability across all 3 groups somehow have to be accounted for?
>
>  Tudor
>
>
> On 16 June 2014 20:38, Douglas Greve <gr...@nmr.mgh.harvard.edu> wrote:
>
>>
>> Yes, you could do each separately.
>> doug
>>
>>
>>
>> On 6/16/14 8:10 PM, Tudor Popescu wrote:
>>
>>   Dear FS list,
>>
>> I have a data set with 3 groups (2 treatments, 1 control), each with
>> equally-spaced time-points (pre and post structural scan). I've done the 3
>> longitudinal pre-processing steps, and stage#1 of the two-stage model, and
>> I would prefer to run stage#2 (cross-sectional analysis of the difference)
>> with QDEC as opposed to with mri_glmfit.
>>
>> I know that QDEC is meant for 2 groups, but I see that designs with 4 or
>> 6 groups can be analysed with QDEC (as per this
>> <https://surfer.nmr.mgh.harvard.edu/fswiki/FsgdExamples> FSGD examples
>> page) whereas designs with 3 groups cannot be. It seems to me that an even
>> number of groups is QDEC-able while an odd number isn't, but is there any
>> workaround so that I can still use QDEC? Perhaps if I only do pairwise
>> comparisons one at a time, i.e. treatment1 vs control and treatment2 vs
>> control?
>>
>> Many thanks!
>>  Tudor
>>
>>
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