Subject: | | Group conjuction analysis across three experiments |
From: | | Atesh Koul <[log in to unmask]> |
Reply-To: | | [log in to unmask][log in to unmask], 10 Jul 2012 20:22:09 +05301779_iso-8859-1 Dear Krishna,
Thanks for your reply. My experiment has 2 contrasts A and B in one spm.mat, two other contrasts C and D in second spm.mat and similarly E and F in third spm.mat. All I need to do is a conjunction of A to F. Given this scenario, can you please elaborate a little more on the first approach of Random effects analysis.
Atesh
> Dear Atesh, > > The conjunction principle as in Nichols et al. paper is that the minimum t > value of all the contrasts will become the t value of the conjunction. > Hence the correct method of doing conjunction is to do a random effects > analysis and use the contrasts [1 0 0], [0 1 0] and [0 0 1] and do a > conjunction between them. The alternative option is to do three one-sample > t-tests, threshold them and do an imcalc to get a map where all the three > overlap. if you want to know how much is the overlap, example in contrast > A, the t-value may be 5 where as in contrast B, the t value may be 3. This > you can do by using MRIcron or slover by projecting blobs of different > colors e.g. red, blue, green and selecting the transparency of the colors, > you can get the overlap areas using the usual RGB combinations. In general > the Random effects analysis will be useful if you want to further probe > into the results of your experiment such as finding the difference between > contrasts A and B etc. > > The other methods like implicit masking should not be used for > conjunctions, because the t-value you get are from the main contrast > selected. Masking only filters out voxels not active in the second > contrast. > > HTH > >[log in to unmask] |
Date: | | Sun, 8 Jul 2012 11:46:46 +0530 |
Content-Type: | | text/plain |
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Dear all,
I have a fairly simple question but have got a bit confused with the
different options people have suggested on the mailing list. I want to do
a conjunction analysis and find regions which are common in three
experiments. In my results, I want a heat map that represents the level of
overlap between the three experiments. However, reading through some of
the approaches that people have suggested on the mailing list, I have come
up with more than one ways to do this. I would like to know which approach
is better and would give me correct results:
1. Inclusive masking: I select Results from one experiment, then use
inclusive masking using a thresholded t-map and see the regions common in
the two experiments. Then use this t-map to mask my result from third
experiment. (In this case however, I have found that the regions depend
slightly on which experiments' results you use first and the heat map is
not an indication of extent of overlap)
2. Use Imcalc to mask two t-maps and get the results. (In this case as
well, heat map is not an indication of extent of overlap).
3. Using single contrasts: I take only the single condition contrasts (use
a contrast vector 1 0 0 etc.) for all participants, take it to group level
for all conditions and experiments, then run a random effects analysis on
them (an approach I am not familiar with).
I would highly appreciate any help in this regard.
Atesh Koul
Graduate student,
National Brain Research Centre, India
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