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Hi, I have a question about correcting for motion in resting-state fmri data. We collect 180 volumes (TR=2s) of fmri data during a resting-state. The data is first processed using the pre-stats tab under FEAT. The parameters we use are: Motion Correction (mcflirt) B0 unwarping Slice-timing correction Bet brain extraction Spatial smoothing FWHM = 6 mm Intensity Normalisation Highpass temporal filtering plus, Melodic Here's the problem: MCFLIRT seems to do a good job when there is gradual movement over the course of the scan. However, if there is a sudden movement (say, the subject coughed or jerked their arm), MCLFIRT does not seem to be able to correct it. To make matters worse, there are often motion spikes that show up in the timecourse of non-motion components. We've tried filtering out some of the worst melodic components, but that doesn't seem to effect components that have been (for lack of a better term) corrupted by motion. The best results we've had have been from splitting the 4D volume, removing the volumes with the most motion and merging the remaining volumes back together. Even though we don't have a block design, I'm still wary of doing this, especially if there is a better way. Here's my question: Is there any way to do a more robust elimination of motion, other than the parameters I've mentioned above? Also, should deleting volumes be avoided at all costs or is it okay to remove a few with bad motion? Thanks. -Pete