Hi All,
I am currently preprocessing resting state fMRI data to conduct connectivity analyses outside of FSL (using GIMME). I am wanting to use FSL Motion Outliers for my motion correction, but I'm unsure how to employ this without a FEAT 1st level model. At this point my thoughts on the pipeline would be:
1. Run FSL Motion Outliers
2. Brain extraction
3. Prestats
a. McFlirt
b. Intensity Normalization
c. Spatial Smoothing (6mm FWHM)
d. High pass filter set to 0
e. Turn off temporal filtering
4. Apply Motion Outliers matrix using: fsl_glm -i filtered_func_data -d $<motion outliers matrix> --demean --out_res=censored_filtered_func_data
5. Segmentation of WM and CSF with FAST
6. Nuisance Regression
7. High-pass filtering
8. Standardize to MNI space
My questions surrounding this approach are: Is it necessary to demean when applying the motion outliers to the filtered_func_data, and if so, do I need to "re-add" the mean after this step (specifically before Nuisance Regression)? If I do need to add the mean back.. what mean should I be calculating? The mean from the original filtered_func_data, I assume?
Not sure if I'm on the right track here, so any help would be appreciated.
Thanks so much,
Rachel
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