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>> some point? I was thinking of applying a low-pass filter prior to running >> the ICA for our subjects, b/c many of these components are high-frequency >> and present primarily in the inferior regions where I know there are many >> arteries and veins. My guess is some kind of cardiac source. I notice that >> low-pass filtering is not generally done though? We currently do smoothing >> of 6-mm FWHM, and .01Hz high-pass filter. Thanks for any suggestions. > Low pass filtering is sometimes done before running ICA, I don't have a > strong clear opinion on whether this is a good thing or not. However if > you're just doing data cleanup before running FEAT, this would probably not > interact well with the autocorrelation modelling in FEAT. One problem with low pass filtering is that it can temporally blur very frequency artifacts/noise, or introduce ringing artifacts (depending on the shape/severity of the filter). I would think that using Melodic de-noising without low pass filtering would be a better solution >> 3)Also, is there a difference between fsl_regfilt and fsl_glm? It seems >> fsl_glm has more output options. Thanks. > You're right - there's some overlap - I would use whichever is more > convenient for your purposes. We were discussing this recently and started to ponder the idea of using a weighted or masked regression of Melodic component time series, so as to remove their effect only from voxels which loaded highly on that component (this would be to avoid loss of true signal change when a component is correlated with one or more explanatory variables). Any thoughts on doing that? -Tom Centre for Integrative Neuroscience & Neurodynamics School of Psychology and CLS University of Reading Ph. +44 (0)118 378 7530 [log in to unmask] http://www.personal.reading.ac.uk/~sxs07itj/index.html