Yes, of course. Details below:

+@SSwarper followed by afni_proc.py (will paste afni_proc.py command below). The issue is with the EPI to anatomical alignment.
+Image attached.
+20.0.19 (I know it's slightly older, but I'm using an analysis cluster and this is the newest available.)

In investigating this issue and another one I'm having with a different participant, I can't figure out at what step the alignment from the anatomical to the EPI is saved. I have an "anatSS.subj_al_keep+orig" dataset that is in good alignment with the EPI in original space, but then the anat_final and the .volreg EPI are not. I'm sure I'm missing something either in my afni_proc.py command or in my understanding of the output, but it seems like the anat_final is coming directly from the anatQQ from @SSwarper without the alignment to the EPI transformation. I'm pasting my afni_proc.py command below so maybe you can let me know if I am missing a step. Thank you!

afni_proc.py -subj_id $subj.$task \
-script $subj.$task.data_prep -scr_overwrite \
-out_dir $task_dir/imaging_data/subjects/$subj/$scan_dir \
-copy_anat $top_dir/anatSS.$subj.nii \
-anat_has_skull no \
-anat_follower anat_w_skull anat $top_dir/$subj.anat.uni.ac+orig \
-dsets pb00.$subj.$task.ac+orig.HEAD \
-blocks tcat despike ricor tshift align tlrc volreg blur mask scale regress \
-radial_correlate_blocks tcat volreg \
-tcat_remove_first_trs 3 \
-ricor_regs_nfirst 3 \
-ricor_regs $top_dir/$subj.$task.RVT.1D \
-align_opts_aea -check_flip -cmass cmass -align_centers yes -cost $cost \
-tlrc_base TT_N27_SSW.nii.gz \
-tlrc_NL_warp \
-tlrc_NL_warped_dsets $top_dir/anatQQ.$subj.nii \
$top_dir/anatQQ.$subj.aff12.1D \
$top_dir/anatQQ.{$subj}_WARP.nii \
-volreg_align_to MIN_OUTLIER \
-volreg_tlrc_warp \
-volreg_warp_dxyz 1.75 \
-mask_epi_anat yes \
-blur_size 6 \
-regress_apply_ricor yes \
-regress_stim_times \
$stim_dir/listen_p.1D \
$stim_dir/count.1D \
$stim_dir/respond_p.1D \
-regress_stim_labels \
listen count respond \
-regress_basis 'BLOCK(40,1)' \
-regress_opts_3dD \
-local_times \
-jobs 8 \
-regress_apply_mot_types demean deriv \
-regress_censor_motion 0.3 \
-regress_censor_outliers 0.05 \
-regress_reml_exec \
-regress_compute_fitts \
-regress_make_ideal_sum sum_ideal.1D \
-regress_est_blur_epits \
-regress_est_blur_errts \
-regress_run_clustsim no