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Does anyone have any comment on why prewhitening would not be recommended for seed analysis?
We are using the Thousand Functional Connectome for our pre-processing and it uses feat_model for orthogonilization and regression
of WM,CSF,WB and motion time-series. It also then calls film_gls with the parameters below.

Are there pros/cons to this method vs. simply using fsl_glm, which is what I have done in the past. I think the results
end up being pretty different, so it seems important. If someone could elaborate on exactly what this film_gls call is doing I would appreciate it,
is it simply prewhitening?

## 7. Get residuals
echo "Running film to get residuals"
film_gls -rn ${nuisance_dir}/stats -noest -sa -ms 5 ${func_dir}/${rest}_pp.nii.gz ${nuisance_dir}/nuisance.mat ${minVal}


Chris

On Thu, Mar 22, 2012 at 2:13 PM, Christian F. Beckmann <[log in to unmask]> wrote:
Hi

running all this through FEAT is really not the best option - while it could be tweaked to work, I think you should instead run this via fsl_glm and randomise. You can then continue to use res4D
hth
Christian

On 22 Mar 2012, at 10:35, Dr. David Watson wrote:

> Currently I am clear as far as extracting the Res4D image normalising this (with the appropriate brain mask) and extracting the seed time series for the VOI. I have checked these stages and everything looks okay. It is when I put the normalised Res4D image and the selected ts as an EV back into FEAT that I'm confused. What should be on and off in the settings. For example in Pre-stats I do not select Motion Correction or Slice Timing but do select BET brain extraction; In Stats I have Pre-whitening selected, put in the ts as an EV but have Motion Parameters deselected; I leave Post Stats at their defaults. Does this sound a reasonable setup?
>
> David