fmriprep and afni_proc

Hi AFNI Experts,

I have some fmriprep preprocessed data which I am looking to complete first and second level GLM task analysis in AFNI. I have followed a couple other threads on the subject (e.g. here and here). I am using AFNI version 23.2.04 and looking to run afni_proc.py blocks "blur mask scale regress".

In fmriprep, the following preprocessing steps have been completed: (1) head motion correction, (2) susceptibility distortion correction, (3) BOLD-T1w coregistration, (4) registration to MNI152 template, (5) confound estimation.

Before running the afni_proc.py script, I have successfully:

  1. converted and concatenated fmriprep motion confounds from all runs to "dfile_rall.1D" (nframes x 6), ordered as roll, pitch, yaw (deg), dS, dL, dP (mm)

  2. converted additional confounds of interest from fmriprep confounds.tsv to "confounds.txt" where run confounds are zero padded so each run's confound has 1 column, and stacked across runs. For example: run01_global_signal, run02_global_signal (nfames x nconfounds)
    **where nframes are the number of time points across all runs (1258 + 1258)

  3. stored events timing as .1D files with local timing vectors

I am using the afni_proc.py command:

[truncated] 

++ have do_clean, cleaning up...

gen_epi_review.py -script @epi_review.101 -dsets pb00.101.r01.tcat+tlrc.HEAD pb00.101.r02.tcat+tlrc.HEAD
cat
cat out.ap_uvars.txt
afni_python_wrapper.py -eval data_file_to_json()
gen_ss_review_scripts.py -exit0 -init_uvars_json out.ap_uvars.json -write_uvars_json out.ss_review_uvars.json
** failed to find volreg dset, continuing...
** failed to guess final_anat (continuing)
** failed to guess align_anat (continuing)
** failed to guess volreg base dset (continuing)
** failed to guess align_anat (continuing)
++ writing ss review basic:          @ss_review_basic
** no final_anat, skipping align check...
** no volreg_dset, skipping align check...
++ writing ss review driver:         @ss_review_driver
++ writing ss review drive commands: @ss_review_driver_commands
rm -f rm.errts.unit+tlrc.BRIK rm.errts.unit+tlrc.HEAD rm.mask_r01+tlrc.BRIK.gz rm.mask_r01+tlrc.HEAD rm.mask_r02+tlrc.BRIK.gz rm.mask_r02+tlrc.HEAD rm.mean_r01+tlrc.BRIK rm.mean_r01+tlrc.HEAD rm.mean_r02+tlrc.BRIK rm.mean_r02+tlrc.HEAD rm.noise.all+tlrc.BRIK rm.noise.all+tlrc.HEAD rm.out.cen.r01.1D rm.out.cen.r02.1D rm.signal.all+tlrc.BRIK rm.signal.all+tlrc.HEAD
if ( -e @ss_review_basic ) then
./@ss_review_basic
tee out.ss_review.101.txt

subject ID                : 101
AFNI version              : AFNI_23.2.04
AFNI package              : linux_ubuntu_16_64
TR                        : 0.46
TRs removed (per run)     : 22
multiband level           : 1
slice timing pattern      : simult
num stim classes provided : 3
final stats dset          : stats.101_REML+tlrc.HEAD
orig voxel counts         : 61	73	61
orig voxel resolution     : 3.024000	3.024000	3.000000
orig volume center        : -0.720001	17.136002	18.000000
final voxel resolution    : 3.024000	3.024000	3.000000

motion limit              : 0.3
num TRs above mot limit   : 7
average motion (per TR)   : 0.084016
average censored motion   : 0.0826319
max motion displacement   : 3.87307
max censored displacement : 2.69611
outlier limit             : 0.1
average outlier frac (TR) : 0.00107248
num TRs above out limit   : 1

num runs found            : 2
num TRs per run           : 1258 1258
num TRs per run (applied) : 1258 1249
num TRs per run (censored): 0 9
fraction censored per run : 0 0.00715421
TRs total (uncensored)    : 2516
TRs total                 : 2507
degrees of freedom used   : 27
degrees of freedom left   : 2480
final DF fraction         : 0.985692

TRs censored              : 9
censor fraction           : 0.003577
num regs of interest      : 3
num TRs per stim (orig)   : 72 265 217
num TRs censored per stim : 0 0 0
fraction TRs censored     : 0.000 0.000 0.000
ave mot per sresp (orig)  : 0.078362 0.081972 0.077312
ave mot per sresp (cens)  : 0.078362 0.081972 0.077312

TSNR average              : 197.925
global correlation (GCOR) : 0.0732032
maximum F-stat (masked)   : 66.8671
blur estimates (ACF)      : 0.735122 4.22917 13.3962
blur estimates (FWHM)     : 0 0 0

apqc_make_tcsh.py -review_style pythonic -subj_dir . -uvar_json out.ss_review_uvars.json
+* WARN: no main dset (not template, anat_final nor vr_base)
++ Done making (executable) IC errts script: 
      run_instacorr_errts.tcsh
++ Done making (executable) GV errts script: 
      run_graphview_errts.tcsh
++ Done making (executable) IC pbrun script: 
      run_instacorr_pbrun.tcsh
++ Done making (executable) GV pbrun script: 
      run_graphview_pbrun.tcsh
++ APQC create: qc_00_vorig_anat
Traceback (most recent call last):
  File "/root/abin//apqc_make_tcsh.py", line 471, in <module>
    cmd      = lat.apqc_vorig_all( ap_ssdict, obase, "vorig", "anat", 
  File "/root/abin/afnipy/lib_apqc_tcsh.py", line 2171, in apqc_vorig_all
    with open(pbar_json, 'r') as fff:
FileNotFoundError: [Errno 2] No such file or directory: 'QC_101/media/qc_00_vorig_anat.pbar.json'

Thanks in advance for any advice.
Amy