Thanks. I do not know if in the previous mail we well described our case.
I repropose my question in more explicative way.
In a first step we want assess the differences between 4 groups, considering a single structure (eg. right ROI). We have 4 groups (e.g. Young, Older, MCI, AD). This case is similar to the example reported on the site for Three Groups (One Factor/Three Levels), No Covariates. There you have put Older (OA), MCI, AD. Here we aim at add an other group which is Young (YA). Please could you show me the possible combination of mtx.files? Especially of group.effect and YA-OA+mci-ad.mtx?
In the second step we would evaluate the differences between 4 groups in more complex design, including more than a single structure (e.g. right and left ROI1, right and left ROI2, right and left ROI3, right and left ROI4).
Many thanks
Stefano
----Messaggio originale---- Da: "Douglas N Greve" greve@nmr.mgh.harvard.edu Data: 27-apr-2017 17.40 A: freesurfer@nmr.mgh.harvard.edu Ogg: Re: [Freesurfer] Questions on fsdg file using complex study design
On 04/27/2017 09:02 AM, stdp82@virgilio.it wrote:
Dear list,
we have several difficulty to understand how I should build fsdg file and we are worried to produce correct results.
On FS site, it is not available an example if we would look for differences among 4 different groups (A, B, C, D; 4 Groups - One Factor/Three Levels - No Covariates). The proposed design with 2 levels and 2 factors is no helpful in our case.
Mathematically, it is the same.
We have 4 groups (1 factor and 4 levels, no covariates) and we our hypothesis (in term of impaiment) is A<B<C<D.
This is not a valid linear contrast since it requires an AND operation (A<B AND B<C AND C<D). You have to use a conjunction analysis in which you create 3 contrasts then conjunct them (with mri_concat --conjunct)
Which should be the group.effect.mtx? How should we look to difference between Group1, Group2, Group3 and Group4? Should we opt for G1+G3 vs G2+G4 or G1+G2 vs G3+G4? Other option?
Please may you argue a rationale?
e.g. Looking your example on three Groups (One Factor/Three Levels), No Covariates, we wonder why if Normals==MCI AND Normals==AD, then it MUST be the case that MCI==AD?
We aim at assess differences by using more complex design (4x2x4), including 4 different structures, looking at right and left structures and having 4 levels (4 different groups).
How should we do, please?
Thank you very much.
Best regards
Stefano
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