Hi there,

In the past I ran proc_py on my data, where I smoothed/blurred the the data. Now I'd like to go back and create new stats files based on the unsmoothed timesseries versions of the data for ultimate MVPA analysis. What is the best way to go about this without rerunning all the preprocessing steps?

I know that the pb04 files (in my study at least) are the unsmoothed/blurred data. I'm guessing pb03m volreg files might include the unsmoothed timeseries.
However my preprocessing blocks in proc_py (-blocks tshift despike align volreg blur mask regress \) include both mask and regress blocks that operate on the pb04 input file before 3dDeconvolve. (I do not use the -regress_apply_mask option, though, so I don't think the mask is applied in regression.)

I want to make sure I am not loosing any necessary pre-processing steps besides the blur if I were to use 3dDeconvole and/or REML on the pb03 files. For reference, I will include my current proc py script so you can see the steps/options i use.
(i.e. I would like the stats files to be analogous to those I already have, except for the fact that they are derived from the unsmoothed timeseries.)

afni_proc.py -subj_id ${subj} \
	-script proc_${subj}_MD_012820.sh \
	-out_dir ${top_dir}${subj}/${subj}.${group_id}.preprocessed_MD_012820 \
	-dsets ${funct_dir}${subj}_ses-1_task-MD_run-0*.HEAD \
	-blocks tshift despike align volreg blur mask regress \
	-copy_anat ${struct_dir}outputBrainExtractionBrain+orig \
	-anat_has_skull no\
	-tcat_remove_first_trs 0\
	-tshift_opts_ts -verbose -tpattern @${top_dir}SliceTimes_perRow.txt	\
	-align_opts_aea -giant_move\
	-volreg_align_e2a\
	-volreg_align_to MIN_OUTLIER\
	-blur_size 4\
	-mask_apply anat\
	-regress_anaticor\
	-regress_reml_exec\
	-regress_opts_reml\
    -GOFORIT 12\
	    -regress_stim_times\
	    ${stim_times_dir}SR_enc_target_remembered.txt\
		${stim_times_dir}SR_enc_target_forgotten.txt\
	    ${stim_times_dir}SR_enc_lure_corrReject.txt\
	    ${stim_times_dir}SR_enc_lure_falseAlarm.txt\
	    ${stim_times_dir}SR_TH.txt\
		${stim_times_dir}SR_TM.txt\
		${stim_times_dir}SR_lCR_high.txt\
		${stim_times_dir}SR_lCR_low.txt\
		${stim_times_dir}SR_lFA_high.txt\
		${stim_times_dir}SR_lFA_low.txt\
		${stim_times_dir}OR_enc_target_remembered.txt\
		${stim_times_dir}OR_enc_target_forgotten.txt\
	    ${stim_times_dir}OR_enc_lure_corrReject.txt\
	    ${stim_times_dir}OR_enc_lure_falseAlarm.txt\
	    ${stim_times_dir}OR_TH.txt\
		${stim_times_dir}OR_TM.txt\
		${stim_times_dir}OR_lCR_high.txt\
		${stim_times_dir}OR_lCR_low.txt\
		${stim_times_dir}OR_lFA_high.txt\
		${stim_times_dir}OR_lFA_low.txt\
		${stim_times_dir}NoResponse.txt\
	-regress_stim_labels SR_enc_TH SR_enc_TM SR_enc_LCR SR_enc_LFA SR_TH SR_TM SR_LCR_high SR_LCR_low SR_LFA_high SR_LFA_low OR_enc_TH OR_enc_TM OR_enc_LCR OR_enc_LFA OR_TH OR_TM OR_LCR_high OR_LCR_low OR_LFA_high OR_LFA_low NoResponse\
	-regress_basis 'GAM'\
	-regress_local_times\
	-regress_run_clustsim no\
	-regress_est_blur_epits\
	-regress_est_blur_errts\
	-regress_censor_outliers 0.1\
	-regress_censor_motion 0.3\
        -regress_apply_mot_types demean deriv\
	-regress_motion_per_run\
	-regress_opts_3dD\
        -GOFORIT 12\
	-allzero_OK\
	-num_glt 1\
	-gltsym 'SYM: +SR_enc_TH +SR_enc_TM +SR_enc_LCR +SR_enc_LFA +SR_TH +SR_TM +SR_LCR_high +SR_LCR_low +SR_LFA_high +SR_LFA_low +OR_enc_TH +OR_enc_TM +OR_enc_LCR +OR_enc_LFA +OR_TH +OR_TM +OR_LCR_high +OR_LCR_low +OR_LFA_high +OR_LFA_low +NoResponse' -glt_label 4 'All Modeled Conditions vs Baseline'\
	-jobs 4\

Thank you so much in advance,

Jessie