Hi Tom,
after you laid out the 2x2 ANOVA design in a post a few weeks ago, I tried it out and wonder what your opinion is on the following situation. I have scans from two groups (trained, non-trained) and two time points (pre-training, post-training). Although each subject has a pre- and a post-training scan, I have unequal numbers of subjects in my groups (10, 12). So when I prepare the difference map required to look at the effect of group, following your logic, I would first add the map of group A at time point 1 and group A at time point 2, then add map B at time 1 and map B at time 2. If I do that I get two files with different volume numbers as group A has only 10 subjects and group B has 12. When I now subtract these maps from each other, the result is a difference map that consists of 10 volumes.
I'm not quite sure I follow what you did, but what you should end up with after subtracting time1 from time2 is 22 volumes (1 for each subject). Essentially, for each subject, you subtract the two time points and get a difference image. Then you put those difference images into a two-sample t-test (10 in one group, 12 in the other group).
Which part of my data was omitted here? The last 2 subjects of group two? Is this good practice or should I try something else? Also, to complicate things, I have DTI data that I submitted to the tbss processing stream in FSL. So in addition to a map with data, I also need a mask file which I would make from the pooled group I guess. Can you let me know whether the FEAT GUI to set up the design and matrix files is the better option in my case?
Any comments are highly appreciated.
Maren
On Tue, May 15, 2012 at 6:00 PM, Thomas Nichols
<[log in to unmask]> wrote:
Dear Ciara,
Repeated measures, if you read the list, is quite tricky. Really the best thing is to eliminate any repeated measures and run separate analyses for each contrast of interest. I.e. If the design is
A1 A2 B1 B2
then compute the effects for each subject with fslmaths:
Main effect of A vs B: (A1+A2)-(B1+B2)
Main effect of 1 vs 2: (A1+B1)-(A2+B2)
Interaction: (A1-A2)-(B1-B2)
Then you'll have reduced your data to a set of 24 images which you can submit to a one-sample t-test.
Does this help?
-Tom
On Tue, May 15, 2012 at 7:16 PM, Ciara Greene
<[log in to unmask]> wrote:
Hi FSL users,
I'm a bit confused as to how to go about implementing a 2x2 repeated measures design in randomise.
I have resting state data from 24 subjects under 4 conditions - 2 factors, each with 2 levels - each of which was acquired in a separate run. I have conducted concat ICA on this data and now want to compare ICs across the different scanning conditions using dual regression. I understand that implementing repeated measures designs in randomise can be tricky; is there a way to extend the 1 factor/4 levels design described at http://www.fmrib.ox.ac.uk/fsl/randomise/index.html#Ex:RepeatMeas to examine main effects of factor 1 and factor 2 and their interaction?
Thanks for your help,
Ciara
__________________________________________________________
Thomas Nichols, PhD
Principal Research Fellow, Head of Neuroimaging Statistics
Department of Statistics & Warwick Manufacturing Group
University of Warwick, Coventry CV4 7AL, United Kingdom