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Dear All, since I'm experiencing some motion problems with some runs, I was searching the forum for a method to account for sudden head movement, that may not been detected or accounted for with McFlirt. I stumbled over fsl_motion_outliers and it looks like this is what I was looking for. Reading the posts I got confused and just wanted to check to avoid misunderstanding. In a post Mark Jenkins says: ********************** On Thu, May 21, 2009 at 6:07 AM, Mark Jenkinson <[log in to unmask]> wrote: 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! ************ So there is one part saying: "and will check your motion corrected data" and below is written "To use it you just run fsl_motion_outliers on the original (unfiltered and not motion corrected)" And this seems contradictory to me... I guess the below one is correct and one should run it on the 'virgin' swapped functional one. Is that correct? Also, is there any other literature on fsl_motion_outliers (I read that a paper is in process)... Thanks, Dorit