Hello,

I have been having issues with the alignment for some of my data (resting state rsfMRI to T1). It appears that this occurs once every 11 subjects, which is not ideal. A colleague has checked with SPM12 and found no issues on their end. Checking with afni (pb03....volreg file) and QC (attached the files: ve2a.jpg and ve2a_2.jpg for visual), it shows that the problem occurs after tcat and despike, and during the volume registration step.

This is my script:

afni_proc.py -subj_id Subj_5SS                          \
    -blocks despike tshift align tlrc volreg blur mask regress  \
    -copy_anat /data2/open_data/ADNI/ADNI3///Subj_5SS/*Accel*.nii.gz                    \
    -dsets /data2/open_data/ADNI/ADNI3///Subj_5SS/*MRI_E*.nii.gz                  \
    -tcat_remove_first_trs 2                            \
    -align_opts_aea -cost lpc+ZZ                        \
    -tlrc_base MNI_avg152T1+tlrc                        \
    -tlrc_NL_warp                                       \
    -volreg_align_to MIN_OUTLIER                        \
    -volreg_align_e2a                                   \
    -volreg_tlrc_warp                                   \ 
    -mask_epi_anat yes                                  \
    -volreg_warp_dxyz 3                                 \
    -regress_motion_per_run                             \
    -regress_anaticor_fast                              \
    -regress_censor_motion 0.2                          \
    -regress_censor_outliers 0.05                       \
    -regress_apply_mot_types demean deriv               \
    -regress_est_blur_errts                             \
    -regress_run_clustsim no                            \
    -execute

I've tried changing "-volreg_align_to MIN_OUTLIER" to third or last and deleting "-volreg_align_e2a" (If I recall correctly, this causes afni to align a2e?); it did not make the final outcome any better.

I've also tried adding " -volreg_allin_auto_stuff -autoweight -warp shift_rotate \" after " -tlrc_NL_warp " when the program recommended I used autoweight.

Below is the @ss_review_basic output of this particular example:

subject ID                : Subj_5SS
AFNI version              : AFNI_19.2.01
AFNI package              : linux_ubuntu_16_64
TR                        : 3.0
TRs removed (per run)     : 2
num stim classes provided : 0
final anatomy dset        : anat_final.Subj_5SS+tlrc.HEAD
final stats dset          : NO_STATS
final errts dset          : errts.Subj_5SS.fanaticor+tlrc.HEAD
final voxel resolution    : 3.000000	3.000000	3.000000

motion limit              : 0.2
num TRs above mot limit   : 33
average motion (per TR)   : 0.145185
average censored motion   : 0.120185
max motion displacement   : 0.659715
max censored displacement : 0.617789
outlier limit             : 0.05
average outlier frac (TR) : 0.0078753
num TRs above out limit   : 10

num runs found            : 1
num TRs per run           : 198
num TRs per run (applied) : 136
num TRs per run (censored): 62
fraction censored per run : 0.313131
TRs total (uncensored)    : 198
TRs total                 : 136
degrees of freedom used   : 17
degrees of freedom left   : 119

TRs censored              : 62
censor fraction           : 0.313131
num regs of interest      : 0

TSNR average              : 237.344
global correlation (GCOR) : 0.0574289
anat/EPI mask Dice coef   : 0.796392
blur estimates (ACF)      : 0.664923 3.90318 12.9759
blur estimates (FWHM)     : 0 0 0

I initially suspected that it may be because these brains have severe cortical atrophy, but only about half of the misaligned data were controls. I also have a couple brains that have slight misalignments (not as severe as the attached photos, but still concerning enough). Any suggestion is greatly appreciated. Thank you.



Edited 2 time(s). Last edit at 07/29/2019 02:09AM by Nshin96.