Dear Ami,
Yes, indeed in addition to switching on the highpass filter in the pre-stats. In the full model setup of the stats tab there is an option to switch on 'apply temporal filtering' for every EV you define.
I think the filtering of the confound EVs (i.e. WM and CSF) is done automatically when you switch on the highpass filtering in pre-stats (see http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FEAT/FAQ ).
Best,
Raimon
-----Original Message-----
From: FSL - FMRIB's Software Library [mailto:[log in to unmask]] On Behalf Of Ami Tsuchida
Sent: maandag 19 oktober 2015 18:33
To: [log in to unmask]
Subject: Re: [FSL] ICA-AROMA and PPI in Feat
Thank you Raimon,
Just to clarify, you mean that in addition to turning on the high pass filter under pre-stat, there is an option to apply temporal filtering to my mean time series regressors for seed, WM, CSF in the full model set-up? If so could you tell me where I can do this? Or should I apply the temporal filtering manually before entering them as the regressors in my first-level model? Could you give me an example command line if that's the case?
Thank you for your help!
Ami
On Oct 19, 2015, at 4:58 AM, Pruim, R.H.R. (Raimon) <[log in to unmask]> wrote:
> Hi Ami,
>
> 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!
>
> Best,
> Raimon
>
>
>
>
>
> ________________________________________
> 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
>
> Hello,
>
> 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 don't need to add them anymore, am I correct?
>
> Thank you!
>
> Ami Tsuchida
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