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Hi, There is a tool designed precisely for this. It is called fsl_motion_outliers and will check your motion corrected data looking for points in time where there is an unusual amount of residual intensity change (after motion correction). Any outliers with respect to this are then identified and a confound matrix created that you can use in FEAT to effectively remove any changes associated with these timepoints. Note that this is different from deleting volumes as (i) it does not require adjusting the other model EVs, and importantly, (ii) it correctly accounts for any changes in signal and autocorrelation on either side of the "lost" timepoint(s) as well as adjusting the degrees of freedom correctly. To use it you just run fsl_motion_outliers on the original (unfiltered and not motion corrected) data for each subject/session individually. In each case it will create a confound matrix which you add into the analysis for this subject using the "Add additional confound EV(s)" button on the "Stats" tab in FEAT. And that's it! Hope this sorts your problem out. All the best, Mark On 21 May 2009, at 10:44, Klara Mareckova wrote: > Hello, > > do you happen to know if there is a relatively easy way in FSL how to > idicate which slices and particular time series should be cut off from > the analysis? > > I've analyzed the data for 50 subjects but found that they were moving > a lot and therefore even if I would set quite lenient criteria and > exclude everybody who moved more than 2 mm, I would end up with only > 29 subjects. This is way too much and that is why I was thinking about > cutting off the slices with the biggest movement (fslsplit &fslmerge). > However, if I do this a problem with the time series comes out. Is > there an easy way how to take care about this or do I have to go to > each particular subject's design, exclude the particular time series > and rerun the whole analysis? > > Do you also happen to have some guidelines about the exclusion > criteria for motion correction? In some articles about adult > participants I've seen exclusion criteria 1mm or 1degree but this > seems to be too strict for my subjects. > > Many thanks for your help. > > Klara >