Hi
Thanks for the hint. I am not exactly sure, though, how to run the analysis with PALM properly.
My design is a form of repated measures one-way ANOVA, but at the moment I am not interested in differences among levels, but the group mean of each level. Therefore, I am not sure how to specify the design matrix. I would like to follow the “recipe” on the randomise website (https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/Randomise/UserGuide#Repeated_measures_ANOVA) 1 factor 4 levels, but I am not sure how I can get the mean for each level with the contrast mentioned on the website.
Ultimately, I would like to have one image for group mean of each level. Is there another way to run several one sample t tests? Or I should treat my data as if they were seperate “modalities” in PALM?
Do I understand correctly that once the design matrix is specified, all I need to do to correct over multiple contrasts is to add the option "-corrcon”?
I would appreciate any feedback on this!
Best, Blazej
On 13 Apr 2017, at 12:47, Matthew Webster <[log in to unmask]> wrote:
> Hello,
> If you want to properly correct over multiple contrasts, then it’s probably best to use PALM ( https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/PALM ) instead of randomise.
>
> Hope this helps,
> Kind Regards
> Matthew
>
>> On 13 Apr 2017, at 11:20, Blazej Baczkowski <[log in to unmask]> wrote:
>>
>> Hello everyone,
>>
>> I would like to find a single common threshold for multiple statistical images (one sample t test, output from randomise) when correcting for multiple comparisons. I would be happy to hear your opinion on how to do it best. At the moment I am considering two options, but maybe there are better ones.
>>
>> 1) FDR
>> Is it possible for the FSl fdr command to find a threshold when the nifti image contains several volumes (concatenated *vox_p_tstat* maps from randomise)? When testing, it works, but I would like to confirm that the threshold I receive is indeed computed using all p values from several volumes in the nifti.
>>
>> Would it also make sense to use uncorrected TFCE image (*_tfce_p_tstat*) to preserve some spatial contingency among voxels?
>>
>> 2) TFCE and max value across images
>> I was wondering whether one could modify the randomise command such that TFCE p values are corrected based on the maximum TFCE value across all voxels and all images in each permutation providing a common distribution of max values for all images?
>>
>> Many thanks in advance!!
>> Best, Blazej
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