Hi Greg, Thank you very much for your detailed response!
I have now generated a Movement_Regressors.mat file with the correct 24 parameters (6 rigid body, 6 derivatives, 6 rigid body squared, 6 derivatives squared). As I didn't have code handy to run highpass filtering on the movement regressors, I based this file on the detrended movement regressors; the HCP's highpass filtering is described as "detrending-like", so this should (hopefully) give approximately the same result. I also noticed an error in my original highpass filtering of the fMRI data that accounts for part of the difference I was seeing; the correct sigma is 1389, not 1000 (cutoff = 2*TR*sigma, so a TR of 0.72s and cutoff of 2000s requires a sigma of 1389; I forgot to account for the TR in my original filtering). Between the two, I am able to get much closer to matching the HCP's denoised data. Regarding your second point, I think my setup is removing all of the variance in the movement parameters, but not in the noise ICs. fsl_glm, which is how I remove the movement parameters, appears to always do full regression. By default, though, fsl_regfilt performs partial regression of the specified noise ICs. But the difference regarding movement regression would definitely make a difference and seems to be one thing that can't be matched using FSL only. So yes, this was very informative! Thank you again for your help. -Ely _______________________________________________ HCP-Users mailing list HCP-Users@humanconnectome.org http://lists.humanconnectome.org/mailman/listinfo/hcp-users
