I have collect some resting state data and wish to to do a connectivity analysis seed regions.
I have pre-processed the data using afni_proc.py using physiology data (heart and Resp) collected during the scan
(the rest.slibase.1D was generated using RetroTS.m)

afni_proc.py -subj_id MJ0044rest_physio \
-dsets MJ0044rest+orig.HEAD \
-blocks despike ricor align tlrc volreg blur mask regress \
-copy_anat MJ0044_spgr1+orig \
-tcat_remove_first_trs 3 \
-ricor_regs_nfirst 3 \
-ricor_regs ./rest.slibase.1D \
-volreg_align_to last \
-volreg_align_e2a \
-volreg_tlrc_warp \
-blur_size 6 \
-regress_motion_per_run \
-regress_censor_motion 0.2 \
-regress_bandpass 0.01 0.1 \
-regress_apply_mot_types demean deriv \
-regress_run_clustsim no \
-regress_est_blur_errts



following Gang Chen's simple correction analysis, I modified the 3dDeconvolve, in the script generated by afni_proc.py, to include a -cbucket


set subj = MJ0044rest_physio

3dDeconvolve -input pb04.$subj.r*.blur+tlrc.HEAD \
-censor motion_${subj}_censor.1D \
-ortvec bandpass_rall.1D bandpass \
-polort 4 -jobs 12 \
-num_stimts 12 \
-stim_file 1 mot_demean.r01.1D'[0]' -stim_base 1 -stim_label 1 roll_01 \
-stim_file 2 mot_demean.r01.1D'[1]' -stim_base 2 -stim_label 2 pitch_01 \
-stim_file 3 mot_demean.r01.1D'[2]' -stim_base 3 -stim_label 3 yaw_01 \
-stim_file 4 mot_demean.r01.1D'[3]' -stim_base 4 -stim_label 4 dS_01 \
-stim_file 5 mot_demean.r01.1D'[4]' -stim_base 5 -stim_label 5 dL_01 \
-stim_file 6 mot_demean.r01.1D'[5]' -stim_base 6 -stim_label 6 dP_01 \
-stim_file 7 mot_deriv.r01.1D'[0]' -stim_base 7 -stim_label 7 roll_02 \
-stim_file 8 mot_deriv.r01.1D'[1]' -stim_base 8 -stim_label 8 pitch_02 \
-stim_file 9 mot_deriv.r01.1D'[2]' -stim_base 9 -stim_label 9 yaw_02 \
-stim_file 10 mot_deriv.r01.1D'[3]' -stim_base 10 -stim_label 10 dS_02 \
-stim_file 11 mot_deriv.r01.1D'[4]' -stim_base 11 -stim_label 11 dL_02 \
-stim_file 12 mot_deriv.r01.1D'[5]' -stim_base 12 -stim_label 12 dP_02 \
-fout -tout -x1D X.xmat.1D -xjpeg X.jpg \
-x1D_uncensored X.nocensor.xmat.1D \
-fitts fitts.$subj \
-errts errts.${subj} \
-bucket stats.$subj -cbucket cstats.$subj


the next step involves 3dSynthesize,
what should columns I use for the -select param?


set subj = MJ0044rest_physio
3dSynthesize -cbucket cstats.{$subj}+tlrc -matrix X.xmat.1D -select \
-prefix ${subj}.EffectsofNoInterest


my X.xmat.1D contains the following column headers:

# ColumnLabels = "Run#1Pol#0 ; Run#1Pol#1 ; Run#1Pol#2 ; Run#1Pol#3 ; Run#1Pol#4 ; roll_01#0 ; pitch_01#0 ; yaw_01#0 ; dS_01#0 ; dL_01#0 ; dP_01#0 ; roll_02#0 ; pitch_02#0 ; yaw_02#0 ; dS_02#0 ; dL_02#0 ; dP_02#0 ; bandpass[0]#0 ; bandpass[1]#0 ; bandpass[2]#0 ; bandpass[3]#0 ; bandpass[4]#0 ; bandpass[5]#0 ; bandpass[6]#0 ; bandpass[7]#0 ; bandpass[8]#0 ; bandpass[9]#0 ; bandpass[10]#0 ; bandpass[11]#0 ; bandpass[12]#0 ; bandpass[13]#0 ; bandpass[14]#0 ; bandpass[15]#0 ; bandpass[16]#0 ; bandpass[17]#0 ; bandpass[18]#0 ; bandpass[19]#0 ; bandpass[20]#0 ; bandpass[21]#0 ; bandpass[22]#0 ; bandpass[23]#0 ; bandpass[24]#0 ; bandpass[25]#0 ; bandpass[26]#0 ; bandpass[27]#0 ; bandpass[28]#0 ; bandpass[29]#0 ; bandpass[30]#0 ; bandpass[31]#0 ; bandpass[32]#0 ; bandpass[33]#0 ; bandpass[34]#0 ; bandpass[35]#0 ; bandpass[36]#0 ; bandpass[37]#0 ; bandpass[38]#0 ; bandpass[39]#0 ; bandpass[40]#0 ; bandpass[41]#0 ; bandpass[42]#0 ; bandpass[43]#0 ; bandpass[44]#0 ; bandpass[45]#0 ; bandpass[46]#0 ; bandpass[47]#0 ; bandpass[48]#0 ; bandpass[49]#0 ; bandpass[50]#0 ; bandpass[51]#0 ; bandpass[52]#0 ; bandpass[53]#0 ; bandpass[54]#0 ; bandpass[55]#0 ; bandpass[56]#0 ; bandpass[57]#0 ; bandpass[58]#0 ; bandpass[59]#0 ; bandpass[60]#0 ; bandpass[61]#0 ; bandpass[62]#0 ; bandpass[63]#0 ; bandpass[64]#0 ; bandpass[65]#0 ; bandpass[66]#0 ; bandpass[67]#0 ; bandpass[68]#0 ; bandpass[69]#0 ; bandpass[70]#0 ; bandpass[71]#0 ; bandpass[72]#0 ; bandpass[73]#0 ; bandpass[74]#0 ; bandpass[75]#0 ; bandpass[76]#0 ; bandpass[77]#0 ; bandpass[78]#0 ; bandpass[79]#0 ; bandpass[80]#0 ; bandpass[81]#0 ; bandpass[82]#0 ; bandpass[83]#0 ; bandpass[84]#0 ; bandpass[85]#0 ; bandpass[86]#0 ; bandpass[87]#0 ; bandpass[88]#0 ; bandpass[89]#0 ; bandpass[90]#0 ; bandpass[91]#0 ; bandpass[92]#0 ; bandpass[93]#0 ; bandpass[94]#0 ; bandpass[95]#0 ; bandpass[96]#0 ; bandpass[97]#0 ; bandpass[98]#0 ; bandpass[99]#0 ; bandpass[100]#0 ; bandpass[101]#0 ; bandpass[102]#0 ; bandpass[103]#0 ; bandpass[104]#0 ; bandpass[105]#0 ; bandpass[106]#0 ; bandpass[107]#0 ; bandpass[108]#0 ; bandpass[109]#0 ; bandpass[110]#0 ; bandpass[111]#0 ; bandpass[112]#0 ; bandpass[113]#0 ; bandpass[114]#0 ; bandpass[115]#0 ; bandpass[116]#0 ; bandpass[117]#0 ; bandpass[118]#0 ; bandpass[119]#0 ; bandpass[120]#0 ; bandpass[121]#0 ; bandpass[122]#0 ; bandpass[123]#0 ; bandpass[124]#0 ; bandpass[125]#0 ; bandpass[126]#0 ; bandpass[127]#0 ; bandpass[128]#0 ; bandpass[129]#0 ; bandpass[130]#0 ; bandpass[131]#0 ; bandpass[132]#0 ; bandpass[133]#0 ; bandpass[134]#0 ; bandpass[135]#0 ; bandpass[136]#0 ; bandpass[137]#0 ; bandpass[138]#0 ; bandpass[139]#0 ; bandpass[140]#0 ; bandpass[141]#0 ; bandpass[142]#0 ; bandpass[143]#0 ; bandpass[144]#0 ; bandpass[145]#0 ; bandpass[146]#0 ; bandpass[147]#0 ; bandpass[148]#0 ; bandpass[149]#0"