AFNI version info (afni -ver):
Precompiled binary linux_ubuntu_16_64: Sep 29 2023 (Version AFNI_23.2.12 'Marcus Didius Severus Julianus')
I have 3 stimuli files, the third is "NAN" (trials in which subjects did no respond).
For some subjects this file is empty, so I enter -1:-1 (I have 4 runs)
(for AM1 -regress_basis dmUBLOCK ** had an error)
afni_proc.py -subj_id $f.task.DM.blur6
-script $mypath/$f/afni/proc.$f.task.DM.blur6.new.MNI -scr_overwrite
-blocks despike tshift align tlrc volreg mask combine blur scale regress
-despike_opts_3dDes -localedit
-tshift_interp -wsinc9
-radial_correlate_blocks tcat volreg
-copy_anat $mypath/$f/afni/anatSS.T1W.nii
-anat_follower anat_w_skull anat $mypath/$f/afni/t1+orig
-anat_has_skull no
-dsets_me_echo $mypath/$f/epi_run?_face_echo_001+orig.HEAD
-dsets_me_echo $mypath/$f/epi_run?_face_echo_002+orig.HEAD
-dsets_me_echo $mypath/$f/epi_run?_face_echo_003+orig.HEAD
-tcat_remove_first_trs 5
-echo_times 12 31.7 51.4
-reg_echo 2
-mask_epi_anat yes
-align_opts_aea -giant_move
-align_opts_aea -cost lpc+ZZ -check_flip
-volreg_align_to MIN_OUTLIER -volreg_align_e2a
-volreg_warp_final_interp wsinc5
-volreg_tlrc_warp
-blur_to_fwhm -blur_size 6
-blur_in_mask yes
-tlrc_base MNI152_2009_template_SSW.nii.gz
-tlrc_NL_warp
-tlrc_NL_warped_dsets $mypath/$f/afni/anatQQ.T1W.nii $mypath/$f/afni/anatQQ.T1W.aff12.1D $mypath/$f/afni/anatQQ.T1W_WARP.nii
-combine_method 'm_tedana'
-combine_opts_tedana --fittype curvefit --tedpca aic --lowmem --n-threads 4
-regress_stim_times $prtpath/$f.faces.rose.ER.txt $prtpath/$f.faces.lav.ER.txt $prtpath/$f.faces.ER_NAN.txt
-regress_stim_labels rose lav nan
-regress_stim_types AM1
-regress_basis dmUBLOCK
-regress_opts_3dD -jobs 4
-gltsym 'SYM: 0.5rose +0.5lav'
-glt_label 1 bothodors
-gltsym 'SYM: rose -lav'
-glt_label 2 roseVSlav
-gltsym 'SYM: lav -rose'
-glt_label 3 lavVSrose
-test_stim_files no
-regress_opts_reml -GOFORIT 4 -allzero_OK
-regress_motion_per_run
-regress_apply_mot_types demean deriv
-regress_censor_motion 0.5
-regress_censor_outliers 0.05
-regress_3dD_stop
-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
-html_review_style pythonic
output.proc
foreach run ( 01 02 03 04 )
3dTstat -prefix rm.mean_r01 pb05.KAL734.task.DM.blur6.r01.blur+tlrc
++ 3dTstat: AFNI version=AFNI_23.2.12 (Sep 29 2023) [64-bit]
++ Authored by: KR Hammett & RW Cox
++ Output dataset ./rm.mean_r01+tlrc.BRIK
3dcalc -a pb05.KAL734.task.DM.blur6.r01.blur+tlrc -b rm.mean_r01+tlrc -c mask_epi_extents+tlrc -expr c * min(200, a/b*100)*step(a)*step(b) -prefix pb06.KAL734.task.DM.blur6.r01.scale
++ 3dcalc: AFNI version=AFNI_23.2.12 (Sep 29 2023) [64-bit]
++ Authored by: A cast of thousands
++ Output dataset ./pb06.KAL734.task.DM.blur6.r01.scale+tlrc.BRIK
end
3dTstat -prefix rm.mean_r02 pb05.KAL734.task.DM.blur6.r02.blur+tlrc
++ 3dTstat: AFNI version=AFNI_23.2.12 (Sep 29 2023) [64-bit]
++ Authored by: KR Hammett & RW Cox
++ Output dataset ./rm.mean_r02+tlrc.BRIK
3dcalc -a pb05.KAL734.task.DM.blur6.r02.blur+tlrc -b rm.mean_r02+tlrc -c mask_epi_extents+tlrc -expr c * min(200, a/b*100)*step(a)*step(b) -prefix pb06.KAL734.task.DM.blur6.r02.scale
++ 3dcalc: AFNI version=AFNI_23.2.12 (Sep 29 2023) [64-bit]
++ Authored by: A cast of thousands
++ Output dataset ./pb06.KAL734.task.DM.blur6.r02.scale+tlrc.BRIK
end
3dTstat -prefix rm.mean_r03 pb05.KAL734.task.DM.blur6.r03.blur+tlrc
++ 3dTstat: AFNI version=AFNI_23.2.12 (Sep 29 2023) [64-bit]
++ Authored by: KR Hammett & RW Cox
++ Output dataset ./rm.mean_r03+tlrc.BRIK
3dcalc -a pb05.KAL734.task.DM.blur6.r03.blur+tlrc -b rm.mean_r03+tlrc -c mask_epi_extents+tlrc -expr c * min(200, a/b*100)*step(a)*step(b) -prefix pb06.KAL734.task.DM.blur6.r03.scale
++ 3dcalc: AFNI version=AFNI_23.2.12 (Sep 29 2023) [64-bit]
++ Authored by: A cast of thousands
++ Output dataset ./pb06.KAL734.task.DM.blur6.r03.scale+tlrc.BRIK
end
3dTstat -prefix rm.mean_r04 pb05.KAL734.task.DM.blur6.r04.blur+tlrc
++ 3dTstat: AFNI version=AFNI_23.2.12 (Sep 29 2023) [64-bit]
++ Authored by: KR Hammett & RW Cox
++ Output dataset ./rm.mean_r04+tlrc.BRIK
3dcalc -a pb05.KAL734.task.DM.blur6.r04.blur+tlrc -b rm.mean_r04+tlrc -c mask_epi_extents+tlrc -expr c * min(200, a/b*100)*step(a)*step(b) -prefix pb06.KAL734.task.DM.blur6.r04.scale
++ 3dcalc: AFNI version=AFNI_23.2.12 (Sep 29 2023) [64-bit]
++ Authored by: A cast of thousands
++ Output dataset ./pb06.KAL734.task.DM.blur6.r04.scale+tlrc.BRIK
end
1d_tool.py -infile dfile_rall.1D -set_nruns 4 -demean -write motion_demean.1D
1d_tool.py -infile dfile_rall.1D -set_nruns 4 -derivative -demean -write motion_deriv.1D
1d_tool.py -infile motion_demean.1D -set_nruns 4 -split_into_pad_runs mot_demean
1d_tool.py -infile motion_deriv.1D -set_nruns 4 -split_into_pad_runs mot_deriv
1d_tool.py -infile dfile_rall.1D -set_nruns 4 -show_censor_count -censor_prev_TR -censor_motion 0.5 motion_KAL734.task.DM.blur6
total number of censored TRs (simple form) = 0
1deval -a motion_KAL734.task.DM.blur6_censor.1D -b outcount_KAL734.task.DM.blur6_censor.1D -expr a*b
1d_tool.py -infile censor_KAL734.task.DM.blur6_combined_2.1D -show_trs_uncensored space
set ktrs = 1dcat out.keep_trs_rall.txt
3dDeconvolve -input pb06.KAL734.task.DM.blur6.r01.scale+tlrc.HEAD pb06.KAL734.task.DM.blur6.r02.scale+tlrc.HEAD pb06.KAL734.task.DM.blur6.r03.scale+tlrc.HEAD pb06.KAL734.task.DM.blur6.r04.scale+tlrc.HEAD -censor censor_KAL734.task.DM.blur6_combined_2.1D -ortvec mot_demean.r01.1D mot_demean_r01 -ortvec mot_demean.r02.1D mot_demean_r02 -ortvec mot_demean.r03.1D mot_demean_r03 -ortvec mot_demean.r04.1D mot_demean_r04 -ortvec mot_deriv.r01.1D mot_deriv_r01 -ortvec mot_deriv.r02.1D mot_deriv_r02 -ortvec mot_deriv.r03.1D mot_deriv_r03 -ortvec mot_deriv.r04.1D mot_deriv_r04 -polort 4 -num_stimts 3 -stim_times_AM1 1 stimuli/KAL734.faces.rose.ER.txt dmUBLOCK -stim_label 1 rose -stim_times_AM1 2 stimuli/KAL734.faces.lav.ER.txt dmUBLOCK -stim_label 2 lav -stim_times_AM1 3 stimuli/KAL734.faces.ER_NAN.txt dmUBLOCK -stim_label 3 nan -jobs 4 -gltsym SYM: 0.5*rose +0.5*lav -glt_label 1 bothodors -gltsym SYM: rose -lav -glt_label 2 roseVSlav -gltsym SYM: lav -rose -glt_label 3 lavVSrose -fout -tout -x1D X.xmat.1D -xjpeg X.jpg -x1D_uncensored X.nocensor.xmat.1D -errts errts.KAL734.task.DM.blur6 -x1D_stop -bucket stats.KAL734.task.DM.blur6
++ '-stim_times_AM1 1 stimuli/KAL734.faces.rose.ER.txt' has 1 auxiliary values per time point
++ '-stim_times_AM1 1': basis function model 'dmUBLOCK' uses 1 parameters,
out of the 1 found in timing file 'stimuli/KAL734.faces.rose.ER.txt'
++ '-stim_times_AM1 2 stimuli/KAL734.faces.lav.ER.txt' has 1 auxiliary values per time point
++ '-stim_times_AM1 2': basis function model 'dmUBLOCK' uses 1 parameters,
out of the 1 found in timing file 'stimuli/KAL734.faces.lav.ER.txt'
e[7m*+ WARNING:e[0m '-stim_times_AM1 3' didn't read any good times from file 'stimuli/KAL734.faces.ER_NAN.txt'
++ '-stim_times_AM1 3 stimuli/KAL734.faces.ER_NAN.txt' has 1 auxiliary values per time point
++ '-stim_times_AM1 3': basis function model 'dmUBLOCK' uses 1 parameters,
out of the 1 found in timing file 'stimuli/KAL734.faces.ER_NAN.txt'
++ 3dDeconvolve extending num_stimts from 3 to 51 due to -ortvec
++ 3dDeconvolve: AFNI version=AFNI_23.2.12 (Sep 29 2023) [64-bit]
++ Authored by: B. Douglas Ward, et al.
++ Auto-catenated input datasets treated as multiple imaging runs
++ Auto-catenated datasets start at: 0 461 922 1383
++ STAT automask has 111117 voxels (out of 545468 = 20.4%)
++ Skipping check for initial transients
++ Input polort=4; Longest run=553.2 s; Recommended minimum polort=4 ++ OK ++
++ -stim_times using TR=1.2 s for stimulus timing conversion
++ -stim_times using TR=1.2 s for any -iresp output datasets
++ [you can alter the -iresp TR via the -TR_times option]
++ ** -stim_times NOTE ** guessing GLOBAL times if 1 time per line; LOCAL otherwise
++ ** GUESSED ** -stim_times_AM1 1 using LOCAL times
++ ** GUESSED ** -stim_times_AM1 2 using LOCAL times
++ ** GUESSED ** -stim_times_AM1 3 using GLOBAL times
e[7m*+ WARNING:e[0m '-stim_times_AM1 3' (GLOBAL) has 4 times outside range 0 .. 2211.6 [PSFB syndrome]
+ dataset TR being used is 1.2 s -- unusable times follow
-1 -1 -1 -1
e[7m*+ WARNING:e[0m '-stim_times_AM1 3' (GLOBAL) has 4 runs and 4 early events; do you want LOCAL times?
e[7m*+ WARNING:e[0m !! '-stim_times_AM1 3' file 'stimuli/KAL734.faces.ER_NAN.txt' has no good stimulus time values
------------------------------------------------------------
GLT matrix from 'SYM: 0.5*rose +0.5*lav':
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
------------------------------------------------------------
GLT matrix from 'SYM: rose -lav':
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
------------------------------------------------------------
GLT matrix from 'SYM: lav -rose':
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
++ Number of time points: 1844 (before censor) ; 1843 (after)
+ Number of parameters: 71 [68 baseline ; 3 signal]
++ total shared memory needed = 41,455,568 bytes (about 41 million)
++ mmap() memory allocated: 41,455,568 bytes (about 41 million)
++ Memory required for output bricks = 41,455,568 bytes (about 41 million)
++ Wrote matrix image to file X.jpg
++ Wrote matrix values to file X.xmat.1D
++ ========= Things you can do with the matrix file =========
++ (a) Linear regression with ARMA(1,1) modeling of serial correlation:
3dREMLfit -matrix X.xmat.1D \
-input "pb06.KAL734.task.DM.blur6.r01.scale+tlrc.HEAD pb06.KAL734.task.DM.blur6.r02.scale+tlrc.HEAD pb06.KAL734.task.DM.blur6.r03.scale+tlrc.HEAD pb06.KAL734.task.DM.blur6.r04.scale+tlrc.HEAD" \
-fout -tout -Rbuck stats.KAL734.task.DM.blur6_REML -Rvar stats.KAL734.task.DM.blur6_REMLvar \
-Rerrts errts.KAL734.task.DM.blur6_REML -verb
++ N.B.: 3dREMLfit command above written to file stats.REML_cmd
++ (b) Visualization/analysis of the matrix via ExamineXmat.R
++ (c) Synthesis of sub-model datasets using 3dSynthesize
++ ==========================================================
++ Wrote matrix values to file X.nocensor.xmat.1D
e[7m*+ WARNING:e[0m -------------------------------------------------
e[7m*+ WARNING:e[0m Problems with the X matrix columns, listed below:
e[7m*+ WARNING:e[0m !! * Column 22 [nan#0] is all zeros
e[7m*+ WARNING:e[0m -------------------------------------------------
++ ----- Signal+Baseline matrix condition [X] (1843x71): 4.84069 ++ VERY GOOD ++
e[7m*+ WARNING:e[0m !! in Signal+Baseline matrix:
* Largest singular value=2.18464
* 1 singular value is less than cutoff=2.18464e-07
* Implies strong collinearity in the matrix columns!
++ Signal+Baseline matrix singular values:
7.45057e-09 0.0932324 0.122817 0.142649 0.147781
0.162865 0.171309 0.179042 0.20094 0.239918
0.248494 0.283939 0.319885 0.32489 0.384641
0.397615 0.406722 0.414376 0.429258 0.438557
0.444026 0.484973 0.489914 0.539253 0.584518
0.596907 0.619081 0.625392 0.635368 0.6468
0.718215 0.722545 0.763431 0.792993 0.813532
0.822937 0.858666 0.873303 0.875781 0.931853
0.983713 0.999919 1 1.00662 1.0097
1.03314 1.03391 1.03677 1.03925 1.05021
1.06713 1.08868 1.14315 1.14821 1.21153
1.23842 1.28128 1.32777 1.34256 1.38692
1.40086 1.40575 1.44349 1.49 1.50863
1.51177 1.64756 1.98519 2.06433 2.10418
2.18464
++ ----- Signal-only matrix condition [X] (1843x3): 1 ++ VERY GOOD ++
e[7m*+ WARNING:e[0m !! in Signal-only matrix:
* Largest singular value=1
* 1 singular value is less than cutoff=1e-07
* Implies strong collinearity in the matrix columns!
++ Signal-only matrix singular values:
0 1 1
++ ----- Baseline-only matrix condition [X] (1843x68): 4.81925 ++ VERY GOOD ++
++ ----- stim_base-only matrix condition [X] (1843x48): 3.51973 ++ VERY GOOD ++
++ ----- polort-only matrix condition [X] (1843x20): 1.00741 ++ VERY GOOD ++
++ 3dDeconvolve exits: -x1D_stop option was invoked
if ( 0 != 0 ) then
1d_tool.py -show_cormat_warnings -infile X.xmat.1D
tee out.cormat_warn.txt
-- no warnings for correlation matrix (cut = 0.400) --
1d_tool.py -show_df_info -infile X.xmat.1D
tee out.df_info.txt
initial DF : 1844 : 100.0%
DF used for regs of interest : 3 : 0.2%
DF used for censoring : 1 : 0.1%
DF used for polort : 20 : 1.1%
DF used for motion : 48 : 2.6%
total DF used : 72 : 3.9%
final DF : 1772 : 96.1%
tcsh -x stats.REML_cmd -GOFORIT 4 -allzero_OK
3dREMLfit -matrix X.xmat.1D -input pb06.KAL734.task.DM.blur6.r01.scale+tlrc.HEAD pb06.KAL734.task.DM.blur6.r02.scale+tlrc.HEAD pb06.KAL734.task.DM.blur6.r03.scale+tlrc.HEAD pb06.KAL734.task.DM.blur6.r04.scale+tlrc.HEAD -fout -tout -Rbuck stats.KAL734.task.DM.blur6_REML -Rvar stats.KAL734.task.DM.blur6_REMLvar -Rerrts errts.KAL734.task.DM.blur6_REML -verb -GOFORIT 4 -allzero_OK
++ 3dREMLfit: AFNI version=AFNI_23.2.12 (Sep 29 2023) [64-bit]
++ Authored by: RWCox
e[7m** FATAL ERROR:e[0m Unknown option '-allzero_OK'
** Program compile date = Sep 29 2023