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AFNI Class Notes 08/30/19:

afni_proc.py

 

Example:
set subj = CAJe_110
set top_dir = /mnt/nfs2/users/alans/CAJe/${subj}BRIKS
afni_proc.py -subj_id $subj \
-script proc.$subj -scr_overwrite \
-blocks despike tshift align tlrc volreg blur mask scale \
-despike_opts_3dDes -ignore 2 \
-copy_anat $top_dir/${subj}+orig \
-dsets $top_dir/${subj}MS+orig.HEAD \
-tcat_remove_first_trs 2 \
-tlrc_base MNI_avg152T1+tlrc \
-volreg_align_to MIN_OUTLIER \
-volreg_align_e2a \
-volreg_tlrc_warp \
-blur_in_automask \
-blur_to_fwhm -blur_size 6.0

Running afni_proc.py produces: proc.CAJe_110 (txt)

This can be run to create the needed files. Primarily:

 

File Description Blocks Notes
pb00.CAJe_110.r01.tcat+orig combined or cut original functional scan tcat auto: controlled above by
-tcat_remove_first_trs 2
pb01.CAJe_110.r01.despike+orig 3dDespiked file despike option: helps bad data muchly
pb02.CAJe_110.r01.tshift+orig 3dTshift tshift option: suggested for event based
pb03.CAJe_110.r01.volreg+tlrc Fulling aligned file align, tlrc, volreg required: This is a key step using align_epi_anat.py
pb04.CAJe_110.r01.blur+tlrc blurred file blur option: since we used -blur_in_automask -blur_size 4.0 this is blur "to"
pb05.CAJe_110.r01.scale+tlrc scaled scale option: preps for 3dREMLfit
CAJe_110_ns+tlrc anatomical underlay tlrc option: we are in tlrc not mni due to : -tlrc_base TT_N27+tlrc
outcount_rall.1D 3dToutcount file outcount auto: data quality signal. vital to observe to assess signal
dfile_rall.1D volreg residuals volreg This is the useful in regression later and data quality now.
out.mask_ae_overlap.txt outcome of alignments align Gives info of anat v. epi: %(A \ B) gives the epi outside or anat and if this is really high that is bad news.
out.mask_ae_dice.txt outcome of alignments align quick measure of overlap less detailed but can be of use.

 

plot your timeseries quality files with 1dplot outcount_rall.1D, etc.

You can also get an estimate of movement with SumMove