Hi Lucia, Please see below: On 7 August 2017 at 08:03, Li, Lucia M <[log in to unmask]> wrote: > Dear Anderson, > > > Thank you very much for the design & explanation. We are now setting up > the feat designs for the first tests (one for testing main effect of TIME > and the interaction, one for testing main effect of GROUP). > > > I just had a couple of questions I hope you may be able to clarify. > > > 1) In the excel file with the design you made previously, there is a > column marked "EB". Do I need to change the 'Group' column in the 'EVs' tab > of the GLM design to the same as this? (i.e. instead of the default list of > '1', I need to change it to '1 1 2 2 3 3 ... ')? Or is the EB column in the > file you sent only there to explain what you've done? > > > These are the exchangeability blocks, one per subject. For the time effects and the interactions, these would be entered into randomise with the option "-e design.grp". For the effect of group, in addition to "-e design.grp", you'd also include the option "--permuteBlocks". In PALM, this could go all in a single run with "-eb" instead of "-e": palm -i input.nii.gz -d design1.csv -d design2.csv -t contrasts1.csv -t contrasts2.csv -eb EB.csv -whole -within -corrcon [... other options ...] > 2) In C1 (main effect of time), the way the contrast is set up is to test > 't1>t2'. If I wanted to get 't2>t1', would I just enter in '-8 -8' (or in > our case '-20 -20' because we have 20 subjects) in that line on the > 'Contrasts & F tests' tab? > Yes, or just [-1 -1 0 0 ...] > > 3) In the file that you've sent, you've put 'F1' next to 'C1' and 'F2' > next to 'C2'. Am I right in thinking that what this means is to have 2 > F-tests, and for F1, check the box next to C1 only? And for the F2, check > the box next to C2 only? (i.e. I do not need to check the boxes for 'C1' > and 'C2' within the same F-test column) > > > There is no need to do F-tests, unless you really want them, since the "-corrcon" already corrects across all. If you really want the F-tests, F1 go with C1 only, F2 with C2 only. If you include negative contrasts, the F-tests remain unchanged. Hope this helps! All the best, Anderson > Many thanks again for your help and further clarification. > > > Kind regards, > > Lucia > ------------------------------ > *From:* FSL - FMRIB's Software Library <[log in to unmask]> on behalf of > Anderson M. Winkler <[log in to unmask]> > *Sent:* 05 August 2017 03:05:30 > *To:* [log in to unmask] > *Subject:* Re: [FSL] fMRI - 2 groups, 2 conditions > > Hi Lucia, > > Please see below: > > > On 3 August 2017 at 19:19, Lucia M Li <[log in to unmask]> wrote: > >> Dear FSL users & experts, >> >> I have 20 subjects, who were split into two intervention groups (n=8 >> drug, n=12 placebo), and underwent a task fMRI at two time points (pre and >> post intervention). >> >> I was hoping to test for whether there is a: >> - a main effect of intervention (the between subject factor) >> - a main effect of time (the within subject factor) >> - an interaction between the two factors >> >> > Can I suggest you change the way as the analysis is approached? The most > interesting effect is the interaction time by group, in that it will show > that the slopes over time differ when the subjects took the drug compared > when they did not. > > The main effect of time would collapse the two groups, but that isn't > interesting because one of the groups took the drug, whereas the other did > not. It's different than in an observational study where over time we may > be investigating the progression of a disorder. > > The main effect of group would collapse the two timepoints, but that isn't > interesting either because in the first timepoint nothing was administered > (either drug or placebo), such that there is no point in mixing the two. > > You can still do the analysis and test the interaction (definitely the one > you would want to report), and also the main effects of group and time, but > if the interaction is significant, then these main effects are further less > interesting, because the effect of one depends on the other, and > vice-versa. In any case, there is a worked out example at this > <https://www.jiscmail.ac.uk/cgi-bin/webadmin?A2=FSL;3cee56eb.1510> > earlier post. Note that there are two designs, one for the within-subject > effects and interaction, and another for the between-subject effects. > > If the allocation of subjects into the two groups was random, and you'd > like to show that there is no residual (incidental) difference between them > after the randomisation, a simple two-sample t-test using the baseline is > sufficient. > > Also, if the subjects were randomised into treatments, you can test only > the second timepoint, comparing the two groups, while including the first > timepoint (baseline) as a continuous, voxelwise nuisance regressor in the > design matrix, thus eliminating the need for a repeated measures design. > This would be assembled as a two-sample t-test with additional covariate, > following this > <https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/GLM#Two-Group_Difference_Adjusted_for_Covariate> > example from the GLM manual. > > Now trying to answer the questions below: > > >> I thought that the 2-way mixed effect ANOVA might be the way to do it ( >> https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/GLM#ANOVA:_2-groups. >> 2C_2-levels_per_subject_.282-way_Mixed_Effect_ANOVA.29) but am a little >> confused as to how exactly to set it up. >> >> In the example given: >> - does 'run' signify a factor with different levels or two runs done to >> acquire more data? >> > > 'Run' means timepoint. This is testing the main effect of time. It can > also be seen as a within-subject factor with two levels. It doesn't mean > the two runs were just for having more data (e.g., to improve power or SNR). > > >> - if the former, am I right in thinking that C1 (run effect) would give >> me the effect of the within subject factor? >> > > Yes, that's right. > > >> - am I right in thinking that C2 would give me the effect of the >> interaction between of the two factors? > > > Yes, C2 is for the interaction group by time (or group by run), which is > your most interesting contrast if you use this strategy, or if your > subjects were not randomised (in which case you wouldn't use the baseline > as nuisance). > > >> i.e. there is no contrast which would give me the effect of the between >> subjects factor? >> > > Exactly, in this design it isn't possible to test the main effect of > group. That requires a different design, that is in the spreadsheet in the > earlier post linked above. > > Hope this helps! > > All the best, > > Anderson > > > >> >> Many thanks in advance for your help! >> >> Kind regards, >> Lucia >> > >