To me your pipeline seems correct! Just realize that the mean time series you derive are not highpass filtered. So, next to setting highpass filtering on in pre-stats, also make sure that you apply temporal filtering to your regressors when setting up your first-level model in the stats tab in Feat. Additional confound EVs for motion outliers should indeed not be necessary. Good luck!
Van: FSL - FMRIB's Software Library [[log in to unmask]] namens Ami Tsuchida [[log in to unmask]]
Verzonden: vrijdag 16 oktober 2015 18:54
Aan: [log in to unmask]
Onderwerp: [FSL] ICA-AROMA and PPI in Feat
I am trying to incorporate ICA-AROMA for my PPI analysis pipeline, and I just want to confirm my pipeline setup is good.
1) Run preprocessing in Feat with Motion correction, Spatial smoothing, Intensity normalization, and registration.
2) Run ICA AROMA with preprocess.feat as an input
3) Extract mean time series of PPI seed (+WM and CSF mask) from denoised_func_data
4) Run first-level Feat with Highpass filtering on but other pre stat features off, and in the stats set up a model for the PPI, and add WM and CSF regressors as nuisance variables, and do not add motion parameters
Previously I have included the output of fsl_motion_outliers as the additional confound EVs as well, but with ICA-AROMA I donít need to add them anymore, am I correct?