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Hi - what Mark says makes a lot of sense. I would just add a couple of minor points: - You should definitely make sure you're using FSL 4.1 as it has more liberal masking than 4.0 (for these very reasons). - Not sure what Vince is thinking as the spatial smoothing is effectively done at the same stage in FSL and SPM, AFAIK. - But yes, reducing the masking threshold will definitely make the masking more liberal. Doing this is fine. Cheers. On 14 May 2009, at 08:49, Mark Jenkinson wrote: > Hi Stefan, > > The problem has no easy answer I'm afraid. > It isn't something that we've really come across very much, although > we have been thinking about ways of better incorporating this in > future releases now. > > There are three potential things that you could do. > > One is to do what David suggests and just make the masks bigger (e.g. > by lowering the %brain/background threshold) but this may include > voxels that do not contain useful signal and hence bias your results, > although with such a high N, this might not be too bad. > > The second thing to do is to include some voxel-specific regressors > which model out the effects of the missing data points. You would > also need to increase the masks to get these voxels included, and > you would need one regressor per number of subjects that you wanted > to reinstate. For example, to include all voxels where one subject > only was missing you would need one voxel-wise regressor which > contained zeros for all timepoints except a single one where there > was missing data. This doesn't have to be the same subject in each > voxel - this is the advantage of the voxel-wise regressors - so that > you would have certain voxels containing an all zero regressor (if > they had data for all timepoints) and others with a single one in the > appropriate slot corresponding to the subject which was missing > *for that voxel*. Note that timepoints = subject index in the above. > This is a nicer mathematical solution but is practically a little > difficult > to do and if you wanted to include voxels where two subjects were > missing then you'd need another voxel-wise regressor and so on. > Therefore if you wanted to include voxels where you had N=300 > or 299 or 298 or ... 270, then you'd need to make 30 voxel-wise > regressors and include them all which is cumbersome and may > slow the analysis down a fair bit. > > The third solution would be to use the outlier detection mechanism > to do the work for you. This again requires enlarging the masks for > each subject (to include all the voxels you want to - and it may be > better to do this by replacing the mask files in reg_standard directly > with a common mask that you want). Once you've done this you > would need to replace values in the cope images (again in > reg_standard) > which were previously outside the mask (and hence would currently > be zero) with values which are clearly outliers. You should be able > to work out a sensible outlier value by looking at your current > analyses and seeing how much the valid copes vary. If you do this > the outlier detection should hopefully identify these subjects as > outliers in those voxels and hence effectively remove them from > the analysis. I must say that I haven't tried this solution (or in > fact > the previous one) but in principle it should work. > > Sorry I haven't got a nicer and easier solution for you. > > All the best, > Mark > > > On 14 May 2009, at 04:10, David V. Smith wrote: > >> Hi Stefan, >> >> Which version of FSL are you using? I believe 4.0 was a bit too >> conservative when generating the masks (see previous posts on this >> matter), but the newer versions don’t really have a problem with >> this. >> >> But, regardless of what version you’re using, once a voxel is gone, >> it’s gone for everyone. So, even if you have just one bad mask in >> your dataset, it will mess up the others. >> >> I think your approach to overcoming this (i.e., lowering the %brain/ >> background threshold) is fine. Just make sure you do it for everyone. >> >> Hope this helps, >> David >> >> >> -- >> David V. Smith >> Graduate Student, Huettel Lab >> Center for Cognitive Neuroscience >> Duke University >> Box 90999 >> Durham, NC 27708 >> Lab phone: (919) 668-3635 >> Lab website: http://www.duke.edu/web/mind/level2/faculty/huettel/ >> >> From: FSL - FMRIB's Software Library [mailto:[log in to unmask]] On >> Behalf Of Stefan Ehrlich >> Sent: Wednesday, May 13, 2009 7:52 PM >> To: [log in to unmask] >> Subject: [FSL] problems due to concatenated masks in GFEAT of a >> very large study - 3rd posting - no answer? >> >> Dear FSL'ers, >> >> >> >> I had posted that earlier. This is the 3rd posting. It would be >> *very* helpful if someone could give me some advice on that. Please >> also let me also know If the problem is not described clearly >> enough or if there is no easy answer. >> >> >> >> I am running FEAT on a large fMRI dataset (n > 300). Each subject >> has 3 runs consisting of 16 blocks of a memory paradigm. After >> hundreds of hours of computing I have processed all first- and >> second level-analyses (from here on referred to as cross-runs). I >> have checked the registrations and individual as well as cross-run >> activation-maps (retrieval versus fixation) for a subset of >> subjects and all seems fine. As a next step I ran a “Single-Group >> Average” over all 300 cross-runs just to get an impression of the >> overall activation patterns. The results were in line with previous >> studies but in the top slices of the brain (horizontal slices) as >> well as in a rim covering the parts of the brain closest to the >> skull there was no activation whatsoever. I found out that this was >> probably due to the mask.nii of the gfeat. This group mask did not >> include the top slices and the outer rim . >> >> Subsequently I went back and checked all cross-run mask.nii and >> identified a very few masks which were missing several top slices >> (due to bad positioning in the scanner, I guess). After deleting >> these subjects from the overall analysis my final results and the >> “Single-Group Average” mask looks much better. However, the outer >> rim is still missing. >> >> It seems like FEAT concatenates all cross-run masks and does not >> include voxels which have a missing value in any single mask. Vince >> Calhoun told me on the phone that this might be due to the fact >> that FSL smoothes relatively late in the processing stream (in >> contrast to SPM). Concequently I went back and changed the brain/ >> background threshold (brain_thresh) from 10 to 1 and rerun a few >> subjects which had slightly impaired cross-run masks. With the new >> threshold more voxels get included. Do you think that is an >> approbriate approach? Has anybody experienced that problem before? >> Are there other solutions? >> >> >> >> Thank you so much for your thoughts! >> >> >> >> Stefan >> >> >> >> > --------------------------------------------------------------------------- Stephen M. Smith, Professor of Biomedical Engineering Associate Director, Oxford University FMRIB Centre FMRIB, JR Hospital, Headington, Oxford OX3 9DU, UK +44 (0) 1865 222726 (fax 222717) [log in to unmask] http://www.fmrib.ox.ac.uk/~steve ---------------------------------------------------------------------------