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Hi David, I would do this a different way - First, take all the dr_stage2_subject000* files, which are already 4D with one volume for each component. You should have 60 of these (20 subs * 3 runs). Average the volumes together using fslmaths that correspond to your three runs (e.g., fslmaths dr_stage2_subject0001 -add dr_stage2_subject0002 -add dr_stage2_subject0003 -div 3 dr_stage2_subjectNum1_avg3runs) Then, use fslroi to extract your component of interest from this new 4D file containing the average PE images for each component across your three runs. e.g. fslroi dr_stage2_subjectNum1_avg3runs dr_stage2_subjectNum1_avg3runs_ic0014 14 1 (depending on whether your label of ic0014 counts from 0 as in the labeled output of dual regression, or refers to the number in the report.html, in which case subtract one from that number in the command) Then feed the dr_stage2_subjectNum1_avg3runs_ic0014 image into your randomise step. This way you can also extract other averaged components easily in the future from your single averaged volume for each participant using fslroi. Just my two cents On Fri, Sep 16, 2011 at 7:44 PM, David Soto <[log in to unmask]> wrote: > Hello, my interest is in IC14- given that each subject has 3 runs (and there > 20 subjects) > the dr_stage2_ic0014.nii file contains 60 volumes......... > following David pointers I am thinking of doing the following > 1) do fslsplit dr_stage2_ic0014 myIC > 2) in order to get the averages for each subject I would do: > fslmaths myIC1 -add myIC2 -add myIC3 -div 3 myICsubj1 > ... > fslmaths myIC4 -add myIC5 -add myIC6 -div 3 myICsubj2 > ... and so on, up to myICsubj20 > > 3) feed these into randomise, and including > > single-group average with additional covariate > does this make sense? > Cheers > > > > On Fri, Sep 16, 2011 at 9:39 PM, Benjamin Kay <[log in to unmask]> wrote: >> >> fslmerge and fslsplit >> >> On Friday, September 16, 2011 16:33:05 you wrote: >> > Thanks David, yes it makes sense....my question is how can i average the >> > dr_stage2_subject >> > files that are generated for each run, into a single dr_stage2 for each >> > subject with the ICs as the 4th dimension......and how can I then split >> > them in ICs to run randomise in the component that am interested.... >> > hope this is not much of a hassle.. >> > Cheers >> > >> > On Fri, Sep 16, 2011 at 8:25 PM, David V. Smith >> <[log in to unmask]>wrote: >> > > Hi David, >> > > >> > > Multiple runs complicates this a bit because it's not clear how to set >> > > up >> > > a set of models that would correspond to analogous levels 1, 2, and 3 >> > > in >> > > FEAT. >> > > >> > > At the end of the day, I think you'll need one row for each subject >> > > (e.g., the average of their neural and behavioral data). I have had to >> > > do something similar when dealing with multiple runs and dual >> > > regression >> > > ( >> > > >> > > https://www.jiscmail.ac.uk/cgi-bin/webadmin?A2=ind1010&L=FSL&D=0&1=FSL&9= >> > > >> > > A&J=on&d=No+Match%3BMatch%3BMatches&z=4&P=206089<https://www.jiscmail.ac. >> > > >> > > uk/cgi-bin/webadmin?A2=ind1010&L=FSL&D=0&1=FSL&9=A&J=on&d=No+Match;Match; >> > > Matches&z=4&P=206089>). For this, I ended up averaging the appropriate >> > > DR >> > > output files (producing one file for each subject) and then running >> > > randomise on the output that I created. When it's set up like this, I >> > > think you'll see that the description on the "single-group average >> > > with >> > > additional covariate" web page is better suited for your application. >> > > >> > > Make sense? >> > > >> > > Cheers, >> > > David >> > > >> > > >> > > >> > > On Sep 16, 2011, at 2:55 PM, David Soto wrote: >> > > >> > > Hi David, to follow up on your suggestion of using .mat & .con files >> > > for >> > > the single-Group Average with Additional Covariate in the >> > > dual_regression......in my study each subject has 3 runs, so the >> > > filelist >> > > that introduced for ICA and then dual-regression is >> > > subj1run1 >> > > subj1run2 >> > > subj1run3 >> > > subj2run1 >> > > subj2run2 >> > > subj2run3 >> > > ...and so on >> > > >> > > so am guessing that to specify for dual regression the additional >> > > covariate (namely the Stroop effect in RT) I should specify it for >> > > each >> > > run, namely subj1run1 STROOP >> > > subj1run2 STROOP >> > > subj1run3 STROOP >> > > subj2run1 STROOP >> > > subj2run2 STROOP >> > > subj2run3 STROOP >> > > ...and so on >> > > >> > > just to confirm am understanding this? >> > > >> > > Thanks a lot, David > > -- Graeme C. Schwindt, HBSc MD/PhD Student University of Toronto [log in to unmask]