Hi,
I am running through an analysis with afni_proc.py and getting a problem with one subject that I run. The afni_proc.py command is the following:

afni_proc.py -subj_id sub10_ffa \
-dsets FFArun*+orig.HEAD \
-blocks despike tshift align tlrc volreg blur mask scale regress \
-copy_anat anat+orig \
-align_opts_aea -cost lpc+ZZ \
-tlrc_base TT_N27+tlrc \
-volreg_align_to MIN_OUTLIER \
-volreg_align_e2a \
-volreg_tlrc_warp \
-blur_size 4.0 \
-regress_local_times \
-regress_stim_times sub10_overstim/FFArun*.txt \
-regress_stim_labels Face House \
-regress_basis 'BLOCK(16,1)' \
-regress_opts_3dD \
-gltsym 'SYM: Face -House' \
-glt_label 1 F-H \
-regress_est_blur_errts \
-execute

And the text output is:

subject ID : sub10_ffa
AFNI version : AFNI_18.3.15
AFNI package : linux_ubuntu_16_64
TRs removed (per run) : 0
num stim classes provided : 2
final anatomy dset : anat_final.sub10_ffa+tlrc.HEAD
final stats dset : stats.sub10_ffa+tlrc.HEAD
final voxel resolution : 3.000000 3.000000 3.000000

motion limit : 0.3
num TRs above mot limit : 0
average motion (per TR) : 0.0362701
max motion displacement : 2.59561
outlier limit : 0.1
average outlier frac (TR) : 0.000903081
num TRs above out limit : 0

num runs found : 2
num TRs per run : 172 172
TRs total : 344
degrees of freedom used : 16
degrees of freedom left : 328

num regs of interest : 2
num TRs per stim : 75 75
ave mot per sresp : 0.044615 0.040188

TSNR average : 167.225
global correlation (GCOR) : 0.0387909
anat/EPI mask Dice coef : 0.920801
maximum F-stat (masked) : 34.8551
blur estimates (ACF) : 0.726891 3.24651 11.4694
blur estimates (FWHM) : 0 0 0

I checked the QC file generated by proc.py, the result is the attached image 1, it’s abnormal compared with other subjects. It was normal to use the same script to analyze the data of other subjects.I have checked everything I can think of and cannot seem to find where this error is coming from.

I also added “-regress_censor_motion 0.2, -regress_censor_outliers 0.1, -regress_motion_per_run, -regress_apply_mot_types demean deriv” to the script.This subject’s data result did not do well either,the result is the attached image 2.

Thank you very much for any assistance,

Dan