When I should apply retroicor?

AFNI Message Board
1

Hi experts,

I would like to know when I should apply retroicor in the preprocessing of fMRI data (before and after which steps).
Is it correct to apply it after the motion correction and before the slice timing?

Thank you very much in advance.
Marina.

2

Hi, Marina-

It has to be an eeeearly step, probably before anything except despiking. Please see the afni_proc.py help, which makes several comments and has examples of incorporating it.

Some highlights from afni_proc.py help:


RETROICOR NOTE: ~2~

    ** Cardiac and respiratory regressors must be created from an external
       source, such as the RetroTS.m matlab program written by Z Saad.  The
       input to that would be the 2+ signals.  The output would be a single
       file per run, containing 13 or more regressors for each slice.  That
       set of output files would be applied here in afni_proc.py.

    Removal of cardiac and respiratory regressors can be done using the 'ricor'
    processing block.  By default, this would be done after 'despike', but
    before any other processing block.

    These card/resp signals would be regressed out of the MRI data in the
    'ricor' block, after which processing would continue normally. In the final
    'regress' block, regressors for slice 0 would be applied (to correctly
    account for the degrees of freedom and also to remove residual effects).
        --> This is now only true when using '-regress_apply_ricor yes'.
            The default as of 30 Jan 2012 is to not include them in the final
            regression (since degrees of freedom are really not important for a
            subsequent correlation analysis).

...

And also from the afni_proc.py help, a full command example incorporating retroicor (= ricor block included):


Example 5c. RETROICOR (modern): censor and band pass. ~2~

           This is an example of how we might currently suggest analyzing
           resting state data.  If no RICOR regressors exist, see example 9
           (or just remove any ricor options).

           Censoring due to motion has long been considered appropriate in
           BOLD FMRI analysis, but is less common for those doing bandpass
           filtering in RS FMRI because the FFT requires one to either break
           the time axis (evil) or to replace the censored data with something
           probably inappropriate.

           Instead, it is slow (no FFT, but maybe SFT :) but effective to
           regress frequencies within the regression model, where censoring
           is simple.

           Note: band passing in the face of RETROICOR is questionable.  It may
                 be questionable in general.  To skip bandpassing, remove the
                 -regress_bandpass option line.

           Also, align EPI to anat and warp to standard space.

                afni_proc.py -subj_id sb23.e5a.ricor            \
                        -dsets sb23/epi_r??+orig.HEAD           \
                        -blocks despike ricor tshift align tlrc \
                                volreg blur mask regress        \
                        -copy_anat sb23/sb23_mpra+orig          \
                        -tcat_remove_first_trs 3                \
                        -ricor_regs_nfirst 3                    \
                        -ricor_regs sb23/RICOR/r*.slibase.1D    \
                        -volreg_align_e2a                       \
                        -volreg_tlrc_warp                       \
                        -blur_size 6                            \
                        -regress_motion_per_run                 \
                        -regress_censor_motion 0.2              \
                        -regress_bandpass 0.01 0.1              \
                        -regress_apply_mot_types demean deriv   \
                        -regress_run_clustsim no                \
                        -regress_est_blur_epits                 \
                        -regress_est_blur_errts

–pt

1 Like
3

Thank you very much for the help, it was very useful.

Best wishes,
Marina.