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Hi Anders, For these you use fslmaths: fslmaths t2.nii.gz -sub t1.nii.gz -div $deltaT slope.nii.gz Where $deltaT is the time interval between the two scans. Anderson Am Mittwoch, 10. April 2013 schrieb Anders Eklund : > When I try to calculate the beta weights using fsl_glm I get the error > message > > fdtr domain error > > for the subjects who only have 2 timepoints. I suppose that this is > because the degrees of freedom becomes zero, as I have 2 measurements and 2 > regressors. But I don't need the t-value, I only need the betas and those > should still be possible to calculate. I tried setting --dof=10 but still > the same error. > > cheers, > Anders > > > ________________________________________ > Från: FSL - FMRIB's Software Library [[log in to unmask] <javascript:;>] > för Anderson M. Winkler [[log in to unmask] <javascript:;>] > Skickat: den 9 april 2013 20:05 > Till: [log in to unmask] <javascript:;> > Ämne: [FSL] Test of linear effect with randomise, for repeated measurements > > Hi Anders, > > Yes, that would be it. Only 5 subjects is a problem, and I don't see an > easy solution other than recruiting more people. If you need some results > before deciding if it worth the effort and cost, maybe you could compute > the p-values parametrically and correct with FDR (RFT will likely fail with > such low df). This probably wouldn't be acceptable for publication, but > maybe it could give you some slack guidance if you see dramatic effects. > > All the best, > > Anderson > > > Am Dienstag, 9. April 2013 schrieb Anders Eklund : > Thanks, > > so in short calculate beta1 and beta2 for each subject and voxel, and then > permute volumes of beta2 with randomise? If I have 5 subjects, there will > however only be 32 possible permutations. > > cheers, > Anders > > ________________________________________ > Från: FSL - FMRIB's Software Library [[log in to unmask] <javascript:;>] > för Anderson M. Winkler [[log in to unmask] <javascript:;>] > Skickat: den 9 april 2013 13:32 > Till: [log in to unmask] <javascript:;> > Ämne: Re: [FSL] Test of linear effect with randomise, for repeated > measurements > > Hi Anders, > > I see two issues with this design: > - First, and easy to solve, is that you need to have an intercept per > subject. So replace EV1 with 5 EVs. > - Second, this is harder: the permutations can only be done under the > assumption that the covariance between the observations are all identical > within subject, which I think is a bit unlikely. Having this assumption not > met precludes using randomise for your design. > > Perhaps you could eschew this by computing the slope for each subject (use > fslmaths or, e.g., Octave/Matlab), then make a 4D file containing one > volume per subject (with the slopes), and compute a 1-sample t-test in > randomise -- it will then do a sign-flipping test, which is indeed > appropriate given that the positive and negative slopes are equally likely > under the null. > > All the best, > > Anderson > > > > 2013/4/8 Anders Eklund <[log in to unmask] <javascript:;><mailto: > [log in to unmask] <javascript:;>>> > Dear FSL experts, > > I would like to test longitudinal data for a linear (or possibly > quadratic) effect over time. Some subjects have been scanned two times, > some three times and some four times. A first level analysis has been > performed with a customized bash script, and the result for each subject > and timepoint is now taken to a second level analysis. I would like to > investigate if any parts of the brain are linearly affected as function of > the time after injury (rather than an ordinary t-test between different > timepoints). How do I setup this testing with randomise? > > Let's say I have data from 5 subjects. Subjects 1 and 2 were scanned four > times, subject 3 and 4 three times and subject 5 two times. > > I use two regressors in the design matrix, one for the mean and one for > the number of days between the injury and the scan. Each subject belongs to > one group. > > Group EV1 EV2 > 1 1 10 > 1 1 25 > 1 1 55 > 1 1 85 > 2 1 8 > 2 1 29 > 2 1 58 > 2 1 85 > 3 1 2 > 3 1 34 > 3 1 66 > 4 1 5 > 4 1 45 > 4 1 80 > 5 1 6 > 5 1 60 > > The design matrix is simply the two regressors, and design.grp is the > group column. > > I want to test if the linear effect is significant, so the t-contrast is 0 > 1. > > The input to randomise is one 4D file where the order of the volumes > matches the group column. > > Can anyone verify if this is correct? > > cheers, > Anders