AFNI program: afni_proc.py

Output of `-help`

=========================================================================== afni_proc.py - generate a tcsh script for an AFNI process stream Purpose: This program is meant to create single subject processing scripts for task, resting state or surface-based analyses. The processing scripts are written in the tcsh language. The typical goal is to create volumes of aligned resopnse magnitudes (stimulus beta weights) to use as input for a group analysis. Inputs (only EPI is required): - anatomical dataset - EPI time series datasets - stimulus timing files - processing and design decisions: e.g. TRs to delete, blur size, censoring options, basis functions Main outputs (many datasets are created): - for task-based analysis: stats dataset (and anat_final) - for resting-state analysis: errts datasets ("cleaned up" EPI) Basic script outline: - copy all inputs to new 'results' directory - process data: e.g. despike,tshift/align/tlrc/volreg/blur/scale/regress - leave all (well, most) results there, so user can review processing - create @ss_review scripts to help user with basic quality control The exact processing steps are controlled by the user, including which main processing blocks to use, and their order. See the 'DEFAULTS' section for a description of the default options for each block. The output script (when executed) would create a results directory, copy input files into it, and perform all processing there. So the user can delete the results directory and modify/re-run the script at their whim. Note that the user need not actually run the output script. The user should feel free to modify the script for their own evil purposes, or to just compare the processing steps with those in their own scripts. Also, even if a user is writing their own processing scripts, it is a good idea to get some independent confirmation of the processing, such as by using afni_proc.py to compare the results on occasion. The text interface can be accessed via the -ask_me option. It invokes a question & answer session, during which this program sets user options on the fly. The user may elect to enter some of the options on the command line, even if using -ask_me. See "-ask_me EXAMPLES", below. ** However, -ask_me has not been touched in many years. I suggest starting with the 'modern' examples (for task/rest/surface), or by using the uber_subject.py GUI (graphical user interface) to generate an initial afni_proc.py command script. See uber_subject.py -help (or just start the GUI) for details. ================================================== SECTIONS: order of sections in the "afni_proc.py -help" output program introduction : (above) basic overview of afni_proc.py PROCESSING BLOCKS : list of possible processing blocks DEFAULTS : basic default operations, per block EXAMPLES : various examples of running this program NOTE sections : details on various topics RESTING STATE NOTE, TIMING FILE NOTE, MASKING NOTE, ANAT/EPI ALIGNMENT CASES NOTE, ANAT/EPI ALIGNMENT CORRECTIONS NOTE, WARP TO TLRC NOTE, RETROICOR NOTE, RUNS OF DIFFERENT LENGTHS NOTE, SCRIPT EXECUTION NOTE OPTIONS : desriptions of all program options informational : options to get quick info and quit general execution : options not specific to a processing block block options : specific to blocks, in default block order ================================================== PROCESSING BLOCKS (of the output script): The output script will go through the following steps, unless the user specifies otherwise. automatic blocks (the tcsh script will always perform these): setup : check subject arg, set run list, create output dir, and copy stim files tcat : copy input datasets and remove unwanted initial TRs default blocks (the user may skip these, or alter their order): tshift : slice timing alignment on volumes (default is -time 0) volreg : volume registration (default to third volume) blur : blur each volume (default is 4mm fwhm) mask : create a 'brain' mask from the EPI data (dilate 1 voxel) scale : scale each run mean to 100, for each voxel (max of 200) regress : regression analysis (default is GAM, peak 1, with motion params) optional blocks (the default is to _not_ apply these blocks) align : align EPI anat anatomy (via align_epi_anat.py) despike : truncate spikes in each voxel's time series empty : placeholder for some user command (uses 3dTcat as sample) ricor : RETROICOR - removal of cardiac/respiratory regressors tlrc : warp anat to standard space ================================================== DEFAULTS: basic defaults for each block (blocks listed in default order) A : denotes automatic block that is not a 'processing' option D : denotes a default processing block (others must be requested) A setup: - use 'SUBJ' for the subject id (option: -subj_id SUBJ) - create a t-shell script called 'proc_subj' (option: -script proc_subj) - use results directory 'SUBJ.results' (option: -out_dir SUBJ.results) A tcat: - do not remove any of the first TRs despike: - NOTE: by default, this block is _not_ used - automasking is not done (requires -despike_mask) ricor: - NOTE: by default, this block is _not_ used - polort based on twice the actual run length - solver is OLSQ, not REML - do not remove any first TRs from the regressors D tshift: - align slices to the beginning of the TR - use quintic interpolation for time series resampling (option: -tshift_interp -quintic) align: - align the anatomy to match the EPI (also required for the option of aligning EPI to anat) tlrc: - use TT_N27+tlrc as the base (-tlrc_base TT_N27+tlrc) - no additional suffix (-tlrc_suffix NONE) D volreg: - align to third volume of first run, -zpad 1 (option: -volreg_align_to third) (option: -volreg_zpad 1) - use cubic interpolation for volume resampling (option: -volreg_interp -cubic) - apply motion params as regressors across all runs at once - do not align EPI to anat - do not warp to standard space D blur: - blur data using a 4 mm FWHM filter with 3dmerge (option: -blur_filter -1blur_fwhm) (option: -blur_size 4) (option: -blur_in_mask no) D mask: - create a union of masks from 3dAutomask on each run - not applied in regression without -regress_apply_mask - if possible, create a subject anatomy mask - if possible, create a group anatomy mask (tlrc base) D scale: - scale each voxel to mean of 100, clip values at 200 D regress: - use GAM regressor for each stim (option: -regress_basis) - compute the baseline polynomial degree, based on run length (e.g. option: -regress_polort 2) - do not censor large motion - output fit time series - output ideal curves for GAM/BLOCK regressors - output iresp curves for non-GAM/non-BLOCK regressors empty: - do nothing (just copy the data using 3dTcat) ================================================== EXAMPLES (options can be provided in any order): 1. Minimum use. Provide datasets and stim files (or stim_times files). Note that a dataset suffix (e.g. HEAD) must be used with wildcards, so that datasets are not applied twice. In this case, a stim_file with many columns is given, where the script to changes it to stim_times files. afni_proc.py -dsets epiRT*.HEAD \ -regress_stim_files stims.1D or without any wildcard, the .HEAD suffix is not needed: afni_proc.py -dsets epiRT_r1+orig epiRT_r2+orig epiRT_r3+orig \ -regress_stim_files stims.1D ************************************************************** * New and improved! Examples that apply to AFNI_data4. * * (were quickly OLD and OBSOLETE, as we now use AFNI_data6) * ************************************************************** The following examples can be run from the AFNI_data4 directory, and are examples of how one might process the data for subject sb23. 2. Very simple. Use all defaults, except remove 3 TRs and use basis function BLOCK(30,1). The default basis function is GAM. afni_proc.py -subj_id sb23.e2.simple \ -dsets sb23/epi_r??+orig.HEAD \ -tcat_remove_first_trs 3 \ -regress_stim_times sb23/stim_files/blk_times.*.1D \ -regress_basis 'BLOCK(30,1)' 3. The current class example. This may change of course. Copy the anatomy into the results directory, register EPI data to the last TR, specify stimulus labels, compute blur estimates, and provide GLT options directly to 3dDeconvolve. The GLTs will be ignored after this, as they take up too many lines. afni_proc.py -subj_id sb23.blk \ -dsets sb23/epi_r??+orig.HEAD \ -copy_anat sb23/sb23_mpra+orig \ -tcat_remove_first_trs 3 \ -volreg_align_to last \ -regress_stim_times sb23/stim_files/blk_times.*.1D \ -regress_stim_labels tneg tpos tneu eneg epos \ eneu fneg fpos fneu \ -regress_basis 'BLOCK(30,1)' \ -regress_opts_3dD \ -gltsym 'SYM: +eneg -fneg' \ -glt_label 1 eneg_vs_fneg \ -gltsym 'SYM: 0.5*fneg 0.5*fpos -1.0*fneu' \ -glt_label 2 face_contrast \ -gltsym 'SYM: tpos epos fpos -tneg -eneg -fneg'\ -glt_label 3 pos_vs_neg \ -regress_est_blur_epits \ -regress_est_blur_errts 4. Similar to the class example, but specify the processing blocks, adding despike and tlrc, and removing tshift. Note that the tlrc block is to run @auto_tlrc on the anat. Ignore the GLTs. afni_proc.py -subj_id sb23.e4.blocks \ -dsets sb23/epi_r??+orig.HEAD \ -blocks despike volreg blur mask scale regress tlrc\ -copy_anat sb23/sb23_mpra+orig \ -tcat_remove_first_trs 3 \ -regress_stim_times sb23/stim_files/blk_times.*.1D \ -regress_stim_labels tneg tpos tneu eneg epos \ eneu fneg fpos fneu \ -regress_basis 'BLOCK(30,1)' \ -regress_est_blur_epits \ -regress_est_blur_errts 5a. RETROICOR example a, resting state data. Assuming the class data is for resting-state and that we have the appropriate slice-based regressors from RetroTS.m, apply the despike and ricor processing blocks. Note that '-do_block' is used to add non-default blocks into their default positions. Here the 'despike' and 'ricor' processing blocks would come before 'tshift'. Remove 3 TRs from the ricor regressors to match the EPI data. Also, since degrees of freedom are not such a worry, regress the motion parameters per-run (each run gets a separate set of 6 regressors). The regression will use 81 basic regressors (all of "no interest"), with 13 retroicor regressors being removed during pre-processing: 27 baseline regressors ( 3 per run * 9 runs) 54 motion regressors ( 6 per run * 9 runs) To example #3, add -do_block, -ricor_* and -regress_motion_per_run. afni_proc.py -subj_id sb23.e5a.ricor \ -dsets sb23/epi_r??+orig.HEAD \ -do_block despike ricor \ -tcat_remove_first_trs 3 \ -ricor_regs_nfirst 3 \ -ricor_regs sb23/RICOR/r*.slibase.1D \ -regress_motion_per_run If tshift, blurring and masking are not desired, consider replacing the -do_block option with an explicit list of blocks: -blocks despike ricor volreg regress 5b. RETROICOR example b, while running a normal regression. Add the ricor regressors to a normal regression-based processing stream. Apply the RETROICOR regressors across runs (so using 13 concatenated regressors, not 13*9). Note that concatenation is normally done with the motion regressors too. To example #3, add -do_block and three -ricor options. afni_proc.py -subj_id sb23.e5b.ricor \ -dsets sb23/epi_r??+orig.HEAD \ -do_block despike ricor \ -copy_anat sb23/sb23_mpra+orig \ -tcat_remove_first_trs 3 \ -ricor_regs_nfirst 3 \ -ricor_regs sb23/RICOR/r*.slibase.1D \ -ricor_regress_method 'across-runs' \ -volreg_align_to last \ -regress_stim_times sb23/stim_files/blk_times.*.1D \ -regress_stim_labels tneg tpos tneu eneg epos \ eneu fneg fpos fneu \ -regress_basis 'BLOCK(30,1)' \ -regress_est_blur_epits \ -regress_est_blur_errts Also consider adding -regress_bandpass. 5c. RETROICOR example c (modern): censoring and bandpass filtering. This is an example of how we might currently suggest analyzing resting state data. If no RICOR regressors exist, see example 9 (or just remove any ricor options). Censoring due to motion has long been considered appropriate in BOLD FMRI analysis, but is less common for those doing bandpass filtering in RC FMRI because the FFT requires one to either break the time axis (evil) or to replace the censored data with something probably inapproprate. Instead, it is slow (no FFT, but maybe SFT :) but effective to regress frequencies within the regression model, where censored is simple. Note: bandpassing in the face of RETROICOR processing is questionable. There is no strong opinion on it (at least within our group). To skip bandpassing, remove the -regress_bandpass option line. Also, align EPI to anat and warp to standard space. afni_proc.py -subj_id sb23.e5a.ricor \ -dsets sb23/epi_r??+orig.HEAD \ -blocks despike ricor tshift align tlrc \ volreg blur mask regress \ -tcat_remove_first_trs 3 \ -ricor_regs_nfirst 3 \ -ricor_regs sb23/RICOR/r*.slibase.1D \ -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 6. A modern example. GOOD TO CONSIDER. Align the EPI to the anatomy. Also, process in standard space. For alignment in either direction, add the 'align' block, which aligns the anatomy to the EPI. To then align the EPI to the anat, apply -volreg_align_e2a, where that transform (inverse) is applied along with the motion alignment. On top of that, complete the processing in standard space by running @auto_tlrc on the anat (via the 'tlrc' block) and applying the same transformation to the EPI via -volreg_tlrc_warp. Again, the EPI transformation is applied along with the motion alignment. So add the 2 processing blocks and 2 extra volreg warps to #3 via '-do_block align tlrc', '-volreg_align_e2a', '-volreg_tlrc_warp'. As an added bonus, censor TR pairs where the Euclidean Norm of the motion derivative exceeds 1.0. afni_proc.py -subj_id sb23.e6.align \ -dsets sb23/epi_r??+orig.HEAD \ -do_block align tlrc \ -copy_anat sb23/sb23_mpra+orig \ -tcat_remove_first_trs 3 \ -volreg_align_to last \ -volreg_align_e2a \ -volreg_tlrc_warp \ -regress_stim_times sb23/stim_files/blk_times.*.1D \ -regress_stim_labels tneg tpos tneu eneg epos \ eneu fneg fpos fneu \ -regress_basis 'BLOCK(30,1)' \ -regress_censor_motion 0.3 \ -regress_opts_3dD \ -gltsym 'SYM: +eneg -fneg' \ -glt_label 1 eneg_vs_fneg \ -regress_est_blur_epits \ -regress_est_blur_errts To process in orig space, remove -volreg_tlrc_warp. To apply manual tlrc transformation, use -volreg_tlrc_adwarp. To process as anat aligned to EPI, remove -volreg_align_e2a. 7. Similar to 6, but get a little more esoteric. a. Blur only within the brain, as far as an automask can tell. So add -blur_in_automask to blur only within an automatic mask created internally by 3dBlurInMask (akin to 3dAutomask). b. Let the basis functions vary. For some reason, we expect the BOLD responses to the telephone classes to vary across the brain. So we have decided to use TENT functions there. Since the TR is 3.0s and we might expect up to a 45 second BOLD response curve, use 'TENT(0,45,16)' for those first 3 out of 9 basis functions. This means using -regress_basis_multi instead of -regress_basis, and specifying all 9 basis functions appropriately. c. Use amplitude modulation. We expect responses to email stimuli to vary proportionally with the number of punctuation characters used in the message (in certain brain regions). So we will use those values as auxiliary parameters 3dDeconvolve by marrying the parameters to the stim times (using 1dMarry). Use -regress_stim_types to specify that the epos/eneg/eneu stim classes should be passed to 3dDeconvolve using -stim_times_AM2. d. Not only censor motion, but censor TRs when more than 10% of the automasked brain are outliers. So add -regress_censor_outliers. e. Include both de-meaned and derivatives of motion parameters in the regression. So add '-regress_apply_mot_types demean deriv'. f. Output baseline parameters so we can see the effect of motion. So add -bout under option -regress_opts_3dD. g. Save on RAM by computing the fitts only after 3dDeconvolve. So add -regress_compute_fitts. h. Speed things up. Have 3dDeconvolve use 4 CPUs and skip the single subject 3dClustSim execution. So add '-jobs 4' to the -regress_opts_3dD option and add '-regress_run_clustsim no'. afni_proc.py -subj_id sb23.e7.esoteric \ -dsets sb23/epi_r??+orig.HEAD \ -do_block align tlrc \ -copy_anat sb23/sb23_mpra+orig \ -tcat_remove_first_trs 3 \ -volreg_align_to last \ -volreg_align_e2a \ -volreg_tlrc_warp \ -blur_in_automask \ -regress_stim_times sb23/stim_files/blk_times.*.1D \ -regress_stim_types times times times \ AM2 AM2 AM2 \ times times times \ -regress_stim_labels tneg tpos tneu \ eneg epos eneu \ fneg fpos fneu \ -regress_basis_multi \ 'BLOCK(30,1)' 'TENT(0,45,16)' 'BLOCK(30,1)' \ 'BLOCK(30,1)' 'TENT(0,45,16)' 'BLOCK(30,1)' \ 'BLOCK(30,1)' 'TENT(0,45,16)' 'BLOCK(30,1)' \ -regress_apply_mot_types demean deriv \ -regress_censor_motion 0.3 \ -regress_censor_outliers 0.1 \ -regress_compute_fitts \ -regress_opts_3dD \ -bout \ -gltsym 'SYM: +eneg -fneg' \ -glt_label 1 eneg_vs_fneg \ -jobs 4 \ -regress_run_clustsim no \ -regress_est_blur_epits \ -regress_est_blur_errts 8. Based on subject FT under AFNI_data6. Add -surf_spec and -surf_anat to provide the required spec and surface volume datasets. The surface volume will be aligned to the current anatomy in the processing script. Two spec files (lh and rh) are provided, one for each hemisphere (via wildcard). Also, specify a (resulting) 6 mm FWHM blur via -blur_size. This does not add a blur, but specifies a resulting blur level. So 6 mm can be given directly for correction for multiple comparisons on the surface. Censor per-TR motion above 0.3 mm. Note that no -regress_est_blur_errts option is given, since that applies to the volume only (and since the 6 mm blur is a resulting blur level, so the estimates are not needed). The -blocks option is provided, but it is the same as the default for surface-based analysis, so is not really needed here. Note that the 'surf' block is added and the 'mask' block is removed from the volume-based defaults. important options: -blocks : includes surf, but no mask (default blocks for surf, so not needed) -surf_anat : volumed aligned with surface -surf_spec : spec file(s) for surface This example is intended to be run from AFNI_data6/FT_analysis. It is provided with the class data in file s03.ap.surface. afni_proc.py -subj_id FT.surf \ -blocks tshift align volreg surf blur scale regress \ -copy_anat FT/FT_anat+orig \ -dsets FT/FT_epi_r?+orig.HEAD \ -surf_anat FT/SUMA/FTmb_SurfVol+orig \ -surf_spec FT/SUMA/FTmb_?h.spec \ -tcat_remove_first_trs 2 \ -volreg_align_to third \ -volreg_align_e2a \ -blur_size 6 \ -regress_stim_times FT/AV1_vis.txt FT/AV2_aud.txt \ -regress_stim_labels vis aud \ -regress_basis 'BLOCK(20,1)' \ -regress_censor_motion 0.3 \ -regress_opts_3dD \ -jobs 2 \ -gltsym 'SYM: vis -aud' -glt_label 1 V-A 9. Resting state analysis (modern): censoring and bandpass filtering. This is our suggested way to do pre-processing for resting state analysis, under the assumption that no cardio/physio recordings were made (see example 5 for cardio files). Censoring due to motion has long been considered appropriate in BOLD FMRI analysis, but is less common for those doing bandpass filtering in RC FMRI because the FFT requires one to either break the time axis (evil) or to replace the censored data with something probably inapproprate. Instead, it is slow (no FFT, but maybe SFT :) but effective to regress frequencies within the regression model, where censored is simple. inputs: anat, EPI output: errts dataset (to be used for correlation) special processing: - despike, as another way to reduce motion effect (see block despike) - censor motion TRs at the same time as bandpassing data (see -regress_censor_motion, -regress_bandpass) - regress motion parameters AND derivatives (see -regress_apply_mot_types) Note: for resting state data, a more strict threshold may be a good idea, since motion artifacts should play a bigger role than in a task-based analysis. So the typical suggestion of motion censoring at 0.3 for task based analysis has been changed to 0.2 for this resting state example, and censoring of outliers has also been added. Outliers are typically due to motion, and may capture motion in some cases where the motion parameters do not, because motion is not generally a whole-brain-between-TRs event. Note: if regressing out regions of interest, either create the ROI time series before the blur step, or remove blur from the list of blocks (and apply any desired blur after the regression). afni_proc.py -subj_id subj123 \ -dsets epi_run1+orig.HEAD \ -copy_anat anat+orig \ -blocks despike tshift align tlrc volreg blur mask regress \ -tcat_remove_first_trs 3 \ -volreg_align_e2a \ -volreg_tlrc_warp \ -regress_censor_motion 0.2 \ -regress_censor_outliers 0.1 \ -regress_bandpass 0.01 0.1 \ -regress_apply_mot_types demean deriv \ -regress_run_clustsim no \ -regress_est_blur_errts 9b. Resting state analysis with ANATICOR. Like example #9, but also regress out the signal from locally averaged white matter. The only change is adding the option -regress_anaticor. Note that -regress_anaticor implies options -mask_segment_anat and -mask_segment_erode. afni_proc.py -subj_id subj123 \ -dsets epi_run1+orig.HEAD \ -copy_anat anat+orig \ -blocks despike tshift align tlrc volreg blur mask regress \ -tcat_remove_first_trs 3 \ -volreg_align_e2a \ -volreg_tlrc_warp \ -regress_anaticor \ -regress_censor_motion 0.2 \ -regress_censor_outliers 0.1 \ -regress_bandpass 0.01 0.1 \ -regress_apply_mot_types demean deriv \ -regress_run_clustsim no \ -regress_est_blur_errts 10. Resting state analysis, with tissue-based regressors. Like example #9, but also regress eroded white matter and CSF averages. The WMe and CSFe signals come from the Classes dataset, created by 3dSeg via the -mask_segment_anat option. afni_proc.py -subj_id subj123 \ -dsets epi_run1+orig.HEAD \ -copy_anat anat+orig \ -blocks despike align tlrc volreg blur mask regress \ -tcat_remove_first_trs 3 \ -volreg_align_e2a \ -volreg_tlrc_warp \ -mask_segment_anat yes \ -regress_censor_motion 0.2 \ -regress_censor_outliers 0.1 \ -regress_bandpass 0.01 0.1 \ -regress_apply_mot_types demean deriv \ -regress_ROI WMe CSFe \ -regress_run_clustsim no \ -regress_est_blur_errts -------------------------------------------------- -ask_me EXAMPLES: a1. Apply -ask_me in the most basic form, with no other options. afni_proc.py -ask_me a2. Supply input datasets. afni_proc.py -ask_me -dsets ED/ED_r*.HEAD a3. Same as a2, but supply the datasets in expanded form. No suffix (.HEAD) is needed when wildcards are not used. afni_proc.py -ask_me \ -dsets ED/ED_r01+orig ED/ED_r02+orig \ ED/ED_r03+orig ED/ED_r04+orig \ ED/ED_r05+orig ED/ED_r06+orig \ ED/ED_r07+orig ED/ED_r08+orig \ ED/ED_r09+orig ED/ED_r10+orig a4. Supply datasets, stim_times files and labels. afni_proc.py -ask_me \ -dsets ED/ED_r*.HEAD \ -regress_stim_times misc_files/stim_times.*.1D \ -regress_stim_labels ToolMovie HumanMovie \ ToolPoint HumanPoint ================================================== Many NOTE sections: ================================================== -------------------------------------------------- RESTING STATE NOTE: Resting state data should be processed with physio recordings (for typical single-echo EPI data). Without such recordings, bandpassing is currently considered as the default. Comment on bandpassing: Bandpassing is the norm right now. However most TRs may be too long for this process to be able to remove the desired components of no interest. Perhaps bandpassing will eventually go away. But it is the norm right now. Also, there is a danger with bandpassing and censoring in that subjects with a lot of motion may run out of degrees of freedom (for baseline, censoring, bandpassing and removal of other signals of no interest). Many papers have been published where a lot of censoring was done, followed up by bandpassing. It is likely that many subjects ended up with negative degrees of freedom, making the resulting signals useless (or worse, misleading garbage). But without keeping track of it, researchers may not even know. In afni_proc.py, this is all done in a single regression model (removal of noise and baseline signals, bandpassing and censoring). If some subject were to lose too many TRs due to censoring, this step would fail, as it should. There are 3 main steps (generate ricor regs, pre-process, group analysis): step 0: If physio recordings were made, generate slice-based regressors using RetroTS. Such regressors can be used by afni_proc.py via the 'ricor' processing block. RetroTS is Ziad Saad's MATLAB routine to convert the 2 time series into 13 slice-based regressors. RetroTS requires the signal processing toolkit for MATLAB. step 1: analyze with afni_proc.py Consider these afni_proc.py -help examples: 5b. case of ricor and no bandpassing 5c. ricor and bandpassing and full registration 9. no ricor, but with bandpassing 10. also with tissue-based regressors soon: with WMeLocal (local white-matter, eroded) - ANATICOR extra motion regs via motion simulated time series (either locally or not) processing blocks: despike (shrink large spikes in time series) ricor (if applicable, remove the RetroTS regressors) tshift (correct for slice timing) volreg (align anat and EPI together, and to standard template) blur (apply desired FWHM blur to EPI data) regress (polort, motion, mot deriv, bandpass, censor) (depending on chosen options) soon: ANATICOR/WMeLocal extra motion regressors (via motion simulation) ==> "result" is errts dataset, "cleaned" of known noise sources step 2: correlation analysis, hopefully with 3dGroupInCorr The inputs to this stage are the single subject errts datasets. Ignoring 3dGroupInCorr, the basic steps in a correlation analysis (and corresponding programs) are as follows. This may be helpful for understanding the process, even when using 3dGroupInCorr. a. choose a seed voxel (or many) and maybe a seed radius for each subject: b. compute time series from seed (3dmaskave or 3dROIstats) c. generate correlation map from seed TS (3dTcorr1D (or 3dDeconvolve or 3dfim+)) d. normalize R->"Z-score" via Fisher's z-transform (3dcalc -expr atanh) e. perform group test, maybe with covariates (3dttest++: 1-sample, 2-sample or paired) To play around with a single subject via InstaCorr: a. start afni (maybe show images of both anat and EPI) b. start InstaCorr plugin from menu at top right of afni's Define Overlay panel c. Setup Icorr: c1. choose errts dataset (no Start,End; no Blur (already done in pre-processing)) c2. Automask -> No; choose mask dataset: full_mask c3. turn off Bandpassing (already done, if desired) d. in image window, show correlations d1. go to seed location, right-click, InstaCorr Set OR d1. hold ctrl-shift, hold left mouse button, drag e. have endless fun To use 3dGroupInCorr: a. run 3dSetupGroupIncorr with mask, labels, subject datasets (run once per group of subjects), e.g. 3dSetupGroupInCorr \ -labels subj.ID.list.txt \ -prefix sic.GROUP \ -mask EPI_mask+tlrc \ errts_subj1+tlrc \ errts_subj2+tlrc \ errts_subj3+tlrc \ ... \ errts_subjN+tlrc ==> sic.GROUP.grpincorr.niml (and .grpincorr.data) b. run 3dGroupInCorr on 1 or 2 sic.GROUP datasets, e.g. Here are steps for running 3dGroupInCorr via the afni GUI. To deal with computers that have multiple users, consider specifying some NIML port block that others are not using. Here we use port 2 (-npb 2), just to choose one. b1. start afni: afni -niml -npb 2 b2. start 3dGroupInCorr 3dGroupInCorr -npb 2 \ -setA sic.horses.grpincorr.niml \ -setB sic.moths.grpincorr.niml \ -labelA horses -labelB moths \ -covaries my.covariates.txt \ -center SAME -donocov -seedrad 5 b3. play with right-click -> InstaCorr Set or hold ctrl-shift/hold left mouse and drag slowly b4. maybe save any useful dataset via Define Datamode -> SaveAs OLay (and give a useful name) b'. alternative, generate result dataset in batch mode, by adding -batch and some parameters to the 3dGIC command e.g. -batch XYZAVE GIC.HvsM.PFC 4 55 26 In such a case, afni is not needed at all. The resulting GIC.HvsM.PFC+tlrc dataset would be written out without any need to start the afni GUI. This works well since seed coordinates for group tests are generally known in advance. See the -batch option under "3dGroupInCorr -help" for many details and options. c. threshold/clusterize resulting datasets, just as with a task analysis (afni GUI, 3dclust, or 3dmerge) -------------------------------------------------- TIMING FILE NOTE: One issue that the user must be sure of is the timing of the stimulus files (whether -regress_stim_files or -regress_stim_times is used). The 'tcat' step will remove the number of pre-steady-state TRs that the user specifies (defaulting to 0). The stimulus files, provided by the user, must match datasets that have had such TRs removed (i.e. the stim files should start _after_ steady state has been reached). -------------------------------------------------- MASKING NOTE: The default operation of afni_proc.py has changed (as of 24 Mar, 2009). Prior to that date, the default was to apply the 'epi' mask. As of 17 Jun 2009, only the 'extents' mask is, if appropriate. --- There may be 4 masks created by default, 3 for user evaluation and all for possible application to the EPI data (though it may not be recommended). The 4th mask (extents) is a special one that will be applied at volreg when appropriate, unless the user specifies otherwise. If the user chooses to apply one of the masks to the EPI regression (again, not necessarily recommended), it is done via the option -mask_apply while providing the given mask type (epi, anat, group or extents). --> To apply a mask during regression, use -mask_apply. Mask descriptions (afni_proc.py name, dataset name, short description): 1. epi ("full_mask") : EPI Automask An EPI mask dataset will be created by running '3dAutomask -dilate 1' on the EPI data after blurring. The 3dAutomask command is executed per run, after which the masks are combined via a union operation. 2. anat ("mask_anat.$subj") : anatomical skull-stripped mask If possible, a subject anatomy mask will be created. This anatomical mask will be created from the appropriate skull-stripped anatomy, resampled to match the EPI (that is output by 3dvolreg) and changed into a binary mask. This requires either the 'align' block or a tlrc anatomy (from the 'tlrc' block, or just copied via '-copy_anat'). Basically, it requires afni_proc.py to know of a skull-stripped anatomical dataset. By default, if both the anat and EPI masks exist, the overlap between them will be computed for evaluation. 3. group ("mask_group") : skull-stripped @auto_tlrc base If possible, a group mask will be created. This requires the 'tlrc' block, from which the @auto_tlrc -base dataset is chosen as the group anatomy. It also requires '-volreg_warp_epi' so that the EPI is in standard space. The group anatomy is then resampled to match the EPI and changed into a binary mask. 4. extents ("mask_extents") : mask based on warped EPI extents In the case of transforming the EPI volumes to match the anatomical volume (via either -volreg_align_e2a or -volreg_tlrc_warp), an extents mask will be created. This is to avoid a motion artifact that arises when transforming from a smaller volume (EPI) to a larger one (anat). ** Danger Will Robinson! ** This EPI extents mask is considered necessary because the align/warp transformation that is applied on top of the volreg alignment transform (applied at once), meaning the transformation from the EPI grid to the anatomy grid will vary per TR. The effect of this is seen at the edge voxels (extent edge), where a time series could be zero for many of the TRs, but have valid data for the rest of them. If this timing just happens to correlate with any regressor, the result could be a strong "activation" for that regressor, but which would be just a motion based artifact. What makes this particularly bad is that if it does happen, it tends to happen for *a cluster* of many voxels at once, possibly an entire slice. Such an effect is compounded by any additional blur. The result can be an entire cluster of false activation, large enough to survive multiple comparison corrections. Thanks to Laura Thomas and Brian Bones for finding this artifact. --> To deal with this, a time series of all 1s is created on the original EPI grid space. Then for each run it is warped with to the same list of transformations that is applied to the EPI data in the volreg step (volreg xform and either alignment to anat or warp to standard space). The result is a time series of extents of each original volume within the new grid. These volumes are then intersected over all TRs of all runs. The final mask is the set of voxels that have valid data at every TR of every run. Yay. 5. Classes and Classes_resam: GM, WM, CSF class masks from 3dSeg By default, unless the user requests otherwise (-mask_segment_anat no), and if anat_final is skull-stripped, then 3dSeg will be used to segment the anatomy into gray matter, white matter and CSF classes. A dataset named Classes is the result of running 3dSeg, which is then resampled to match the EPI and named Classes_resam. If the user wanted to, this dataset could be used for regression of said tissue classes (or eroded versions). --- masking, continued... Note that it may still not be a good idea to apply any of the masks to the regression, as it might then be necessary to intersect such masks across all subjects, though applying the 'group' mask might be reasonable. ** Why has the default been changed? It seems much better not to mask the regression data in the single-subject analysis at all, send _all_ of the results to group space, and apply an anatomically-based mask there. That could be computed from the @auto_tlrc reference dataset or from the union of skull-stripped subject anatomies. Since subjects have varying degrees of signal dropout in valid brain areas of the EPI data, the resulting EPI intersection mask that would be required in group space may exclude edge regions that are otherwise desirable. Also, it is helpful to see if much 'activation' appears outside the brain. This could be due to scanner or interpolation artifacts, and is useful to note, rather than to simply mask out and never see. Rather than letting 3dAutomask decide which brain areas should not be considered valid, create a mask based on the anatomy _after_ the results have been warped to a standard group space. Then perhaps dilate the mask by one voxel. Example #11 from '3dcalc -help' shows how one might dilate. ** Note that the EPI data can now be warped to standard space at the volreg step. In that case, it might be appropriate to mask the EPI data based on the Talairach template, such as what is used for -base in @auto_tlrc. This can be done via '-mask_apply group'. --- ** For those who have processed some of their data with the older method: Note that this change should not be harmful to those who have processed data with older versions of afni_proc.py, as it only adds non-zero voxel values to the output datasets. If some subjects were analyzed with the older version, the processing steps should not need to change. It is still necessary to apply an intersection mask across subjects in group space. It might be okay to create the intersection mask from only those subjects which were masked in the regression, however one might say that biases the voxel choices toward those subjects, though maybe that does not matter. Any voxels used would still be across all subjects. --- A mask dataset is necessary when computing blur estimates from the epi and errts datasets. Also, since it is nice to simply see what the mask looks like, its creation has been left in by default. The '-regress_no_mask' option is now unnecessary. --- Note that if no mask were applied in the 'scaling' step, large percent changes could result. Because large values would be a detriment to the numerical resolution of the scaled short data, the default is to truncate scaled values at 200 (percent), which should not occur in the brain. -------------------------------------------------- ANAT/EPI ALIGNMENT CASES NOTE: This outlines the effects of alignment options, to help decide what options seem appropriate for various cases. 1. EPI to EPI alignment (the volreg block) Alignment of the EPI data to a single volume is based on the 3 options -volreg_align_to, -volreg_base_dset and -volreg_base_ind, where the first option is by far the most commonly used. The logic of EPI alignment in afni_proc.py is: a. if -volreg_base_dset is given, align to that (this volume is copied locally as the dataset ext_align_epi) b. otherwise, use the -volreg_align_to or -volreg_base_ind volume The typical case is to align the EPI to one of the volumes used in pre-processing (where the dataset is provided by -dsets and where the particular TR is not removed by -tcat_remove_first_trs). If the base volume is the first or third (TR 0 or 2) from the first run, or is the last TR of the last run, then -volreg_align_to can be used. To specify a TR that is not one of the 3 just stated (first, third or last), -volreg_base_ind can be used. To specify a volume that is NOT one of those used in pre-processing (such as a pre-steady state volume that will be excluded by the option -tcat_remove_first_trs), use -volreg_base_dset. 2. anat to EPI alignment cases (the align block) This is specific to the 'align' processing block, where the anatomy is aligned to the EPI. The focus is on which EPI volume the anat gets aligned to. Whether this transformation is inverted in the volreg block (to instead align the EPI to the anat via -volreg_align_e2a) is an independent consideration. The logic of which volume the anatomy gets aligned to is as follows: a. if -align_epi_ext_dset is given, use that for anat alignment b. otherwise, if -volreg_base_dset, use that c. otherwise, use the EPI base from the EPI alignment choice To restate this: the anatomy gets aligned to the same volume the EPI gets aligned to *unless* -align_epi_ext_dset is given, in which case that volume is used. The entire purpose of -align_epi_ext_dset is for the case where the user might want to align the anat to a different volume than what is used for the EPI (e.g. align anat to a pre-steady state TR but the EPI to a steady state one). Output: The result of the align block is an 'anat_al' dataset. This will be in alignment with the EPI base (or -align_epi_ext_dset). In the default case of anat -> EPI alignment, the aligned anatomy is actually useful going forward, and is so named 'anat_al_keep'. Additionally, if the -volreg_align_e2a option is used (thus aligning the EPI to the original anat), then the aligned anat dataset is no longer very useful, and is so named 'anat_al_junk'. However, unless an anat+tlrc dataset was copied in for use in -volreg_tlrc_adwarp, the skull-striped anat (anat_ss) becomes the current one going forward. That is identical to the original anat, except that it went through the skull-stripping step in align_epi_anat.py. At that point (e2a case) the pb*.volreg.* datasets are aligned with the original anat or the skull-stripped original anat (and possibly in Talairach space, if the -volreg_tlrc_warp or _adwarp option was applied). Checking the results: The pb*.volreg.* volumes should be aligned with the anat. If -volreg_align_e2a was used, it will be with the original anat. If not, then it will be with anat_al_keep. Note that at the end of the regress block, whichever anatomical dataset is deemed "in alignment" with the stats dataset will be copied to anat_final.$subj. So compare the volreg EPI with the final anatomical dataset. -------------------------------------------------- ANAT/EPI ALIGNMENT CORRECTIONS NOTE: Aligning the anatomy and EPI is sometimes difficult, particularly depending on the contrast of the EPI data (between tissue types). If the alignment fails to do a good job, it may be necessary to run align_epi_anat.py in a separate location, find options that help it to succeed, and then apply those options to re-process the data with afni_proc.py. 1. If the anat and EPI base do not start off fairly close in alignment, the -giant_move option may be needed for align_epi_anat.py. Pass this option to AEA.py via the afni_proc.py option -align_opts_aea: afni_proc.py ... -align_opts_aea -giant_move 2. The default cost function used by align_epi_anat.py is lpc (local Pearson correlation). If this cost function does not work (probably due to poor or unusual EPI contrast), then consider cost functions such as lpa (absolute lpc), lpc+ (lpc plus fractions of other cost functions) or lpc+ZZ (approximate with lpc+, but finish with pure lpc). The lpa and lpc+ZZ cost functions are common alternatives. The -giant_move option may be necessary independently. Examples of some helpful options: -align_opts_aea -cost lpa -align_opts_aea -giant_move -align_opts_aea -cost lpc+ZZ -giant_move -align_opts_aea -cost lpc+ZZ -giant_move -resample off 3. Testing alignment with align_epi_anat.py directly. When having alignment problems, it may be more efficient to copy the anat and EPI alignment base to a new directory, figure out a good cost function or other options, and then apply them in a new afni_proc.py command. For testing purposes, it helps to test many cost functions at once. Besides the cost specified by -cost, other cost functions can be applied via -multi_cost. This is efficient, since all of the other processing does not need to be repeated. For example: align_epi_anat.py -anat2epi \ -anat subj99_anat+orig \ -epi pb01.subj99.r01.tshift+orig \ -epi_base 0 -volreg off -tshift off \ -giant_move \ -cost lpc -multi_cost lpa lpc+ZZ mi That adds -giant_move, and uses the basic lpc cost function along with 3 additional cost functions (lpa, lpc+ZZ, mi). The result is 4 new anatomies aligned to the EPI, 1 per cost function: subj99_anat_al+orig - cost func lpc (see -cost opt) subj99_anat_al_lpa+orig - cost func lpa (additional) subj99_anat_al_lpc+ZZ+orig - cost func lpc+ZZ (additional) subj99_anat_al_mi+orig - cost func mi (additional) Also, if part of the dataset gets clipped in the case of -giant_move, consider the align_epi_anat.py option '-resample off'. -------------------------------------------------- WARP TO TLRC NOTE: afni_proc.py can now apply a +tlrc transformation to the EPI data as part of the volreg step via the option '-volreg_tlrc_warp'. Note that it can also align the EPI and anatomy at the volreg step via '-volreg_align_e2a'. Manual Talairach transformations can also be applied, but separately, after volreg. See '-volreg_tlrc_adwarp'. This tlrc transformation is recommended for many reasons, though some are not yet implemented. Advantages include: - single interpolation of the EPI data Done separately, volume registration, EPI to anat alignment and/or the +tlrc transformation interpolate the EPI data 2 or 3 times. By combining these transformations into a single one, there is no resampling penalty for the alignment or the warp to standard space. Thanks to D Glen for the steps used in align_epi_anat.py. - EPI time series become directly comparable across subjects Since the volreg output is now in standard space, there is already voxel correspondence across subjects with the EPI data. - group masks and/or atlases can be applied to the EPI data without additional warping It becomes trivial to extract average time series data over ROIs from standard atlases, say. This could even be done automatically with afni_proc.py, as part of the single-subject processing stream (not yet implemented). One would have afni_proc.py extract average time series (or maybe principle components) from all the ROIs in a dataset and apply them as regressors of interest or of no interest. - with 3dBlurToFWHM, using an AlphaSim look-up table might be possible Since the blur and data grid could both be isotropic and integral, and since the transformation could depend on a known anatomy (such as the N27 Colin brain or icbm_452), it would be easy to create a look-up table of AlphaSim results (so users would not actually need to run it). The known numbers would correspond to a cluster size (each for a given, common voxel-wise threshold). This correction could then be applied automatically. Again, not yet implemented... - no interpolation of statistics If the user wishes to include statistics as part of the group analysis (e.g. using 3dMEMA.R), this warping becomes more needed. Warping to standard space *after* statistics are generated is not terribly valid. -------------------------------------------------- RETROICOR NOTE: ** Cardiac and respiratory regressors must be created from an external source, such as the RetroTS.m matlab program written by Z Saad. The input to that would be the 2+ signals. The output would be a single file per run, containing 13 or more regressors for each slice. That set of output files would be applied here in afni_proc.py. Removal of cardiac and respiratory regressors can be done using the 'ricor' processing block. By default, this would be done after 'despike', but before any other processing block. These card/resp signals would be regressed out of the MRI data in the 'ricor' block, after which processing would continue normally. In the final 'regress' block, regressors for slice 0 would be applied (to correctly account for the degrees of freedom and also to remove residual effects). --> This is now only true when using '-regress_apply_ricor yes'. The default as of 30 Jan 2012 is to not include them in the final regression (since degrees of freedom are really not important for a subsequent correlation analysis). Users have the option of removing the signal "per-run" or "across-runs". Example R1: 7 runs of data, 13 card/resp regressors, process "per-run" Since the 13 regressors are processed per run, the regressors can have different magnitudes each run. So the 'regress' block will actually get 91 extra regressors (13 regressors times 7 runs each). Example R2: process "across-run" In this case the regressors are catenated across runs when they are removed from the data. The major difference between this and "per-run" is that now only 1 best fit magnitude is applied per regressor (not the best for each run). So there would be only the 13 catenated regressors for slice 0 added to the 'regress' block. Those analyzing resting-state data might prefer the per-run method, as it would remove more variance and degrees of freedom might not be as valuable. Those analyzing a normal signal model might prefer doing it across-runs, giving up only 13 degrees of freedom, and helping not to over-model the data. ** The minimum options would be specifying the 'ricor' block (preferably after despike), along with -ricor_regs and -ricor_regress_method. Example R3: afni_proc.py option usage: Provide additional options to afni_proc.py to apply the despike and ricor blocks (which will be the first 2 blocks by default), with each regressor named 'slibase*.1D' going across all runs, and where the first 3 TRs are removed from each run (matching -tcat_remove_first_trs, most likely). -do_block despike ricor -ricor_regs slibase*.1D -ricor_regress_method across-runs -ricor_regs_nfirst 3 -------------------------------------------------- RUNS OF DIFFERENT LENGTHS NOTE: In the case that the EPI datasets are not all of the same length, here are some issues that may come up, listed by relevant option: -volreg_align_to OK, as of version 1.49. -ricor_regress_method OK, as of version 3.05. -regress_polort Probably no big deal. If this option is not used, then the degree of polynomial used for the baseline will come from the first run. Only 1 polort may be applied. -regress_est_blur_epits OK, as of version 1.49. * -regress_use_stim_files This may fail, as make_stim_times.py is not currently prepared to handle runs of different lengths. -regress_censor_motion OK, as of version 2.14 * probably will be fixed (please let me know of interest) -------------------------------------------------- SCRIPT EXECUTION NOTE: The suggested way to run the output processing SCRIPT is via... a) if you use tcsh: tcsh -xef SCRIPT |& tee output.SCRIPT b) if you use bash: tcsh -xef SCRIPT 2>&1 | tee output.SCRIPT c) if you use tcsh and the script is executable, maybe use one of: ./SCRIPT |& tee output.SCRIPT ./SCRIPT 2>&1 | tee output.SCRIPT Consider usage 'a' for example: tcsh -xef SCRIPT |& tee output.SCRIPT That command means to invoke a new tcsh with the -xef options (so that commands echo to the screen before they are executed, exit the script upon any error, do not process the ~/.cshrc file) and have it process the SCRIPT file, piping all output to the 'tee' program, which will duplicate output back to the screen, as well as to the given output file. parsing the command: tcsh -xef SCRIPT |& tee output.SCRIPT a. tcsh The script itself is written in tcsh syntax and must be run that way. It does not mean the user must use tcsh. Note uses 'a' and 'b'. There tcsh is specified by the user. The usage in 'c' applies tcsh implicitly, because the SCRIPT itself specifies tcsh at the top. b. tcsh -xef The -xef options are applied to tcsh and have the following effects: -x : echo commands to screen before executing them -e : exit (terminate) the processing on any errors -f : do not process user's ~/.cshrc file The -x option is very useful so one see not just output from the programs, but the actual commands that produce the output. It makes following the output much easier. The -e option tells the shell to terminate on any error. This is useful for multiple reasons. First, it allows the user to easily see the failing command and error message. Second, it would be confusing and useless to have the script try to continue, without all of the needed data. The -f option tells the shell not to process the user's ~/.cshrc (or ~/.tcshrc) file. The main reason for including this is because of the -x option. If there were any errors in the user's ~/.cshrc file and -x option were used, they would terminate the shell before the script even started, probably leaving the user confused. c. tcsh -xef SCRIPT The T-shell is invoked as described above, executing the contents of the specified text file (called 'SCRIPT', for example) as if the user had typed the included commands in their terminal window. d. |& These symbols are for piping the output of one program to the input of another. Many people know how to do 'afni_proc.py -help | less' (or maybe '| more'). This script will output a lot of text, and we want to get a copy of that into a text file (see below). Piping with '|' captures only stdout (standard output), and would not capture errors and warnings that appear. Piping with '|&' captures both stdout and stderr (standard error). The user may not be able to tell any difference between those file streams on the screen, but since programs write to both, we want to capture both. e. tee output.SCRIPT Where do we want to send this captured stdout and stderr text? Send it to the 'tee' program. Like a plumber's tee, the 'tee' program splits the data (not water) stream off into 2 directions. Here, one direction that tee sends the output is back to the screen, so the user can still see what is happening. The other direction is to the user-specified text file. In this example it would be 'output.SCRIPT'. With this use of 'tee', all screen output will be duplicated in that text file. ================================================== OPTIONS: (information options, general options, block options) (block options are ordered by block) ------------ informational/terminal options ------------ -help : show this help -hist : show the module history -requires_afni_version : show AFNI date required by processing script Many updates to afni_proc.py are accompanied by corresponding updates to other AFNI programs. So if the processing script is created on one computer but executed on another (with an older version of AFNI), confusing failures could result. The required date is adjusted whenever updates are made that rely on new features of some other program. If the processing script checks the AFNI version, the AFNI package must be as current as the date output via this option. Checks are controlled by the option '-check_afni_version'. The checking method compares the output of: afni_proc.py -requires_afni_version against the most recent date in afni_history: afni_history -past_entries 1 See also '-check_afni_version'. -show_valid_opts : show all valid options (brief format) -ver : show the version number ------------ general execution and setup options ------------ -anat_has_skull yes/no : specify whether the anatomy has a skull e.g. -anat_has_skull no Use this option to block any skull-stripping operations, likely either in the align or tlrc processing blocks. -ask_me : ask the user about the basic options to apply When this option is used, the program will ask the user how they wish to set the basic options. The intention is to give the user a feel for what options to apply (without using -ask_me). -bash : show example execution command in bash form After the script file is created, this program suggests how to run it (piping stdout/stderr through 'tee'). If the user is running the bash shell, this option will suggest the 'bash' form of a command to execute the newly created script. example of tcsh form for execution: tcsh -x proc.ED.8.glt |& tee output.proc.ED.8.glt example of bash form for execution: tcsh -x proc.ED.8.glt 2>&1 | tee output.proc.ED.8.glt Please see "man bash" or "man tee" for more information. -blocks BLOCK1 ... : specify the processing blocks to apply e.g. -blocks volreg blur scale regress e.g. -blocks despike tshift align volreg blur scale regress default: tshift volreg blur mask scale regress The user may apply this option to specify which processing blocks are to be included in the output script. The order of the blocks may be varied, and blocks may be skipped. See also '-do_block' (e.g. '-do_block despike'). -check_afni_version yes/no : check that AFNI is current enough e.g. -check_afni_version no default: yes Check that the version of AFNI is recent enough for processing of the afni_proc.py script. For the version check, the output of: afni_proc.py -requires_afni_version is tested against the most recent date in afni_history: afni_history -past_entries 1 In the case that newer features in other programs might not be needed by the given afni_proc.py script (depending on the options), the user is left with this option to ignore the AFNI version check. Please see 'afni_history -help' or 'afni -ver' for more information. See also '-requires_afni_version'. -check_results_dir yes/no : check whether dir exists before proceeding e.g. -check_results_dir no default: yes By default, if the results directory already exists, the script will terminate before doing any processing. Set this option to 'no' to remove that check. -check_setup_errors yes/no : terminate on setup errors e.g. -check_setup_errors yes default: no Have the script check $status after each command in the setup processing block. It is preferable to run the script using the -e option to tcsh (as suggested), but maybe the user does not wish to do so. -copy_anat ANAT : copy the ANAT dataset to the results dir e.g. -copy_anat Elvis/mprage+orig This will apply 3dcopy to copy the anatomical dataset(s) to the results directory. Note that if a +view is not given, 3dcopy will attempt to copy +acpc and +tlrc datasets, also. See also '3dcopy -help'. -copy_files file1 ... : copy file1, etc. into the results directory e.g. -copy_files glt_AvsB.txt glt_BvsC.1D glt_eat_cheese.txt e.g. -copy_files contrasts/glt_*.txt This option allows the user to copy some list of files into the results directory. This would happen before the tcat block, so such files may be used for other commands in the script (such as contrast files in 3dDeconvolve, via -regress_opts_3dD). -do_block BLOCK_NAME ...: add extra blocks in their default positions e.g. -do_block despike ricor e.g. -do_block align With this option, any 'optional block' can be applied in its default position. This includes the following blocks, along with their default positions: despike : first (between tcat and tshift) ricor : just after despike (else first) align : before tlrc, before volreg tlrc : after align, before volreg empty : NO DEFAULT, cannot be applied via -do_block Any block not included in -blocks can be added via this option (except for 'empty'). See also '-blocks', as well as the "PROCESSING BLOCKS" section of the -help output. -dsets dset1 dset2 ... : (REQUIRED) specify EPI run datasets e.g. -dsets Elvis_run1+orig Elvis_run2+orig Elvis_run3+orig e.g. -dsets Elvis_run*.HEAD The user must specify the list of EPI run datasets to analyze. When the runs are processed, they will be written to start with run 1, regardless of whether the input runs were just 6, 7 and 21. Note that when using a wildcard it is essential for the EPI datasets to be alphabetical, as that is how the shell will list them on the command line. For instance, epi_run1+orig through epi_run11+orig is not alphabetical. If they were specified via wildcard their order would end up as run1 run10 run11 run2 ... Note also that when using a wildcard it is essential to specify the datasets suffix, so that the shell doesn't put both the .BRIK and .HEAD filenames on the command line (which would make it twice as many runs of data). -execute : execute the created processing script If this option is applied, not only will the processing script be created, but it will then be executed in the "suggested" manner, such as via: tcsh -xef proc.sb23 |& tee output.proc.sb23 Note that it will actually use the bash format of the command, since the system command (C and therefore python) uses /bin/sh. tcsh -xef proc.sb23 2>&1 | tee output.proc.sb23 -gen_epi_review SCRIPT_NAME : specify script for EPI review e.g. -gen_epi_review review_orig_EPI.txt By default, the proc script calls gen_epi_review.py on the original EPI data (from the tcat step, so only missing pre-SS TRs). This creates a "drive afni" script that the user can run to quickly scan that EPI data for apparent issues. Without this option, the script will be called @epi_review.$subj, where $subj is the subject ID. The script starts afni, loads the first EPI run and starts scanning through time (effectively hitting 'v' in the graph window). The user can press <enter> in the prompting terminal window to go to each successive run. Note that the user has full control over afni, aside from a new run being loaded whey they hit <enter>. Recall that the <space> key (applied in the graph window) can terminate the 'v' (video mode). See 'gen_epi_review.py -help' for details. See also 'no_epi_review', to disable this feature. -no_epi_review This option is used to prevent writing a gen_epi_review.py command in the processing script (i.e. do not create a script to review the EPI data). The only clear reason to want this option is if gen_epi_review.py fails for some reason. It should not hurt to create that little text file (@epi_review.$subj, by default). See also '-gen_epi_review'. -keep_rm_files : do not have script delete rm.* files at end e.g. -keep_rm_files The output script may generate temporary files in a block, which would be given names with prefix 'rm.'. By default, those files are deleted at the end of the script. This option blocks that deletion. -move_preproc_files : move preprocessing files to preproc.data dir At the end of the output script, create a 'preproc.data' directory, and move most of the files there (dfile, outcount, pb*, rm*). See also -remove_preproc_files. -no_proc_command : do not print afni_proc.py command in script e.g. -no_proc_command If this option is applied, the command used to generate the output script will be stored at the end of the script. -out_dir DIR : specify the output directory for the script e.g. -out_dir ED_results default: SUBJ.results The AFNI processing script will create this directory and perform all processing in it. -outlier_count yes/no : should we count outliers with 3dToutcount? e.g. -outlier_count no default: yes By default, outlier fractions are computed per TR with 3dToutcount. To disable outlier counting, apply this option with parameter 'no'. This is a yes/no option, meaning those are the only valid inputs. Note that -outlier_count must be 'yes' in order to censor outliers with -regress_censor_outliers. See "3dToutcount -help" for more details. See also -regress_censor_outliers. -outlier_legendre yes/no : use Legendre polynomials in 3dToutcount? e.g. -outlier_legendre no default: yes By default the -legendre option is passed to 3dToutcount. Along with using better behaved polynomials, it also allows them to be higher than 3rd order (if desired). See "3dToutcount -help" for more details. -outlier_polort POLORT : specify polynomial baseline for 3dToutcount e.g. -outlier_polort 3 default: same degree that 3dDeconvolve would use: 1+time/150 Outlier counts come after detrending the data, where the degree of the polynomial trend defaults to the same that 3dDeconvolve would use. This option will override the default. See "3dToutcount -help" for more details. See "3dDeconvolve -help" for more details. See also '-regress_polort' and '-outlier_legendre'. -remove_preproc_files : delete pre-processed data At the end of the output script, delete the intermediate data (to save disk space). Delete dfile*, outcount*, pb* and rm*. See also -move_preproc_files. -script SCRIPT_NAME : specify the name of the resulting script e.g. -script ED.process.script default: proc_subj The output of this program is a script file. This option can be used to specify the name of that file. See also -scr_overwrite, -subj_id. -scr_overwrite : overwrite any existing script e.g. -scr_overwrite If the output script file already exists, it will be overwritten only if the user applies this option. See also -script. -sep_char CHAR : apply as separation character in filenames e.g. -sep_char _ default: . The separation character is used in many output filenames, such as the default '.' in: pb04.Nancy.r07.scale+orig.BRIK If (for some crazy reason) an underscore (_) character would be preferable, the result would be: pb04_Nancy_r07_scale+orig.BRIK If "-sep_char _" is applied, so is -subj_curly. See also -subj_curly. -subj_curly : apply $subj as ${subj} The subject ID is used in dataset names is typically used without curly brackets (i.e. $subj). If something is done where this would result in errors (e.g. "-sep_char _"), the curly brackets might be useful to delimit the variable (i.e. ${subj}). Note that this option is automatically applied in the case of "-sep_char _". See also -sep_char. -subj_id SUBJECT_ID : specify the subject ID for the script e.g. -subj_id elvis default: SUBJ The subject ID is used in dataset names and in the output directory name (unless -out_dir is used). This option allows the user to apply an appropriate naming convention. -test_for_dsets yes/no : test for existence of input datasets e.g. -test_for_dsets no default: yes This options controls whether afni_proc.py check for the existence of input datasets. In general, they must exist when afni_proc.py is run, in order to get run information (TR, #TRs, #runs, etc). -test_stim_files yes/no : evaluate stim_files for appropriateness? e.g. -test_stim_files no default: yes This options controls whether afni_proc.py evaluates the stim_files for validity. By default, the program will do so. Input files are one of local stim_times, global stim_times or 1D formats. Options -regress_stim_files and -regress_extra_stim_files imply 1D format for input files. Otherwise, -regress_stim_times is assumed to imply local stim_times format (-regress_global_times implies global stim_times format). Checks include: 1D : # rows equals total reps local times : # rows equal # runs : times must be >= 0.0 : times per run (per row) are unique : times cannot exceed run time global times : file must be either 1 row or 1 column : times must be >= 0.0 : times must be unique : times cannot exceed total duration of all runs This option provides the ability to disable this test. See "1d_tool.py -help" for details on '-look_like_*' options. See also -regress_stim_files, -regress_extra_stim_files, -regress_stim_times, -regress_local_times, -regress_global_times. -verb LEVEL : specify the verbosity of this script e.g. -verb 2 default: 1 Print out extra information during execution. -write_3dD_prefix PREFIX : specify prefix for outputs from 3dd_script e.g. -write_3dD_prefix basis.tent. default: test. If a separate 3dDeconvolve command script is generated via the option -write_3dD_script, then the given PREFIX will be used for relevant output files. in the script. See also -write_3dD_script. -write_3dD_script SCRIPT : specify SCRIPT only for 3dDeconvolve command e.g. -write_3dD_script run.3dd.tent This option is intended to be used with the EXACT same afni_proc.py command (aside from any -write_3dD_* options). The purpose is to generate a corresponding 3dDeconvolve command script which could be run in the same results directory. Alternatively, little things could be changed that would only affect the 3dDeconvolve command in the new script, such as the basis function(s). The new script should include a prefix to distinguish output files from those created by the original proc script. See also -write_3dD_prefix. ------------ block options (in default block order) ------------ These options pertain to individual processing blocks. Each option starts with the block name. -tcat_preSS_warn_limit LIMIT : TR #0 outlier limit to warn of pre-SS e.g. -tcat_preSS_warn_limit 0.7 default: 0.4 Outlier fractions are computed across TRs in the tcat processing block. If TR #0 has a large fraction, it might suggest that pre- steady state TRs have been included in the analysis. If the detected fraction exceeds this limit, a warning will be stored (and output by the @ss_review_basic script). The special case of limit = 0.0 implies no check will be done. -tcat_remove_first_trs NUM : specify how many TRs to remove from runs e.g. -tcat_remove_first_trs 3 default: 0 Since it takes several seconds for the magnetization to reach a steady state (at the beginning of each run), the initial TRs of each run may have values that are significantly greater than the later ones. This option is used to specify how many TRs to remove from the beginning of every run. -tcat_remove_last_trs NUM : specify TRs to remove from run ends e.g. -tcat_remove_last_trs 10 default: 0 For when the user wants a simple way to shorten each run. See also -ricor_regs_rm_nlast. -despike_mask : allow Automasking in 3dDespike By default, -nomask is applied to 3dDespike. Since anatomical masks will probably not be contained within the Automask operation of 3dDespike (which uses methods akin to '3dAutomask -dilate 4'), it is left up to the user to speed up this operation via masking. Note that the only case in which this should be done is when applying the EPI mask to the regression. Please see '3dDespike -help' and '3dAutomask -help' for more information. -despike_opts_3dDes OPTS... : specify additional options for 3dDespike e.g. -despike_opts_3dDes -nomask -ignore 2 By default, 3dDespike is used with only -prefix and -nomask (unless -despike_mask is applied). Any other options must be applied via -despike_opts_3dDes. Note that the despike block is not applied by default. To apply despike in the processing script, use either '-do_block despike' or '-blocks ... despike ...'. Please see '3dDespike -help' for more information. See also '-do_blocks', '-blocks', '-despike_mask'. -ricor_datum DATUM : specify output data type from ricor block e.g. -ricor_datum float By default, if the input is unscaled shorts, the output will be unscaled shorts. Otherwise the output will be floats. The user may override this default with the -ricor_datum option. Currently only 'short' and 'float' are valid parameters. Note that 3dREMLfit only outputs floats at the moment. Recall that the down-side of float data is that it takes twice the disk space, compared with shorts (scaled or unscaled). Please see '3dREMLfit -help' for more information. -ricor_polort POLORT : set the polynomial degree for 3dREMLfit e.g. -ricor_polort 4 default: 1 + floor(run_length / 75.0) The default polynomial degree to apply during the 'ricor' block is similar to that of the 'regress' block, but is based on twice the run length (and so should be almost twice as large). This is to account for motion, since volreg has typically not happened yet. Use -ricor_polort to override the default. -ricor_regress_method METHOD : process per-run or across-runs e.g. -ricor_regress_method across-runs default: NONE: this option is required for a 'ricor' block * valid METHOD parameters: per-run, across-runs The cardiac and respiratory signals can be regressed out of each run separately, or out of all runs at once. The user must choose the method, there is no default. See "RETROICOR NOTE" for more details about the methods. -ricor_regress_solver METHOD : regress using OLSQ or REML e.g. -ricor_regress_solver REML default: OLSQ * valid METHOD parameters: OLSQ, REML Use this option to specify the regression method for removing the cardiac and respiratory signals. The default method is ordinary least squares, removing the "best fit" of the card/resp signals from the data (also subject to the polort baseline). To apply the REML (REstricted Maximum Likelihood) method, use this option. Note that 3dREMLfit is used for the regression in either case, particularly since the regressors are slice-based (they are different for each slice). Please see '3dREMLfit -help' for more information. -ricor_regs REG1 REG2 ... : specify ricor regressors (1 per run) e.g. -ricor_regs slibase*.1D This option is required with a 'ricor' processing block. The expected format of the regressor files for RETROICOR processing is one file per run, where each file contains a set of regressors per slice. If there are 5 runs and 27 slices, and if there are 13 regressors per slice, then there should be 5 files input, each with 351 (=27*13) columns. This format is based on the output of RetroTS.m, included in the AFNI distribution (as part of the matlab package), by Z Saad. -ricor_regs_nfirst NFIRST : ignore the first regressor timepoints e.g. -ricor_regs_nfirst 2 default: 0 This option is similar to -tcat_remove_first_trs. It is used to remove the first few TRs from the -ricor_regs regressor files. Since it is likely that the number of TRs in the ricor regressor files matches the number of TRs in the original input dataset (via -dsets), it is likely that -ricor_regs_nfirst should match -tcat_remove_first_trs. See also '-tcat_remove_first_trs', '-ricor_regs', '-dsets'. -ricor_regs_rm_nlast NUM : remove the last NUM TRs from each regressor e.g. -ricor_regs_rm_nlast 10 default: 0 For when the user wants a simple way to shorten each run. See also -tcat_remove_last_trs. -tshift_align_to TSHIFT OP : specify 3dTshift alignment option e.g. -tshift_align_to -slice 14 default: -tzero 0 By default, each time series is aligned to the beginning of the TR. This option allows the users to change the alignment, and applies the option parameters directly to the 3dTshift command in the output script. It is likely that the user will use either '-slice SLICE_NUM' or '-tzero ZERO_TIME'. Note that when aligning to an offset other than the beginning of the TR, and when applying the -regress_stim_files option, then it may be necessary to also apply -regress_stim_times_offset, to offset timing for stimuli to later within each TR. Please see '3dTshift -help' for more information. See also '-regress_stim_times_offset'. -tshift_interp METHOD : specify the interpolation method for tshift e.g. -tshift_interp -Fourier e.g. -tshift_interp -cubic default -quintic Please see '3dTshift -help' for more information. -tshift_opts_ts OPTS ... : specify extra options for 3dTshift e.g. -tshift_opts_ts -tpattern alt+z This option allows the user to add extra options to the 3dTshift command. Note that only one -tshift_opts_ts should be applied, which may be used for multiple 3dTshift options. Please see '3dTshift -help' for more information. -tlrc_anat : run @auto_tlrc on '-copy_anat' dataset e.g. -tlrc_anat Run @auto_tlrc on the anatomical dataset provided by '-copy_anat'. By default, warp the anat to align with TT_N27+tlrc, unless the '-tlrc_base' option is given. The -copy_anat option specifies which anatomy to transform. ** Note, use of this option has the same effect as application of the 'tlrc' block. Please see '@auto_tlrc -help' for more information. See also -copy_anat, -tlrc_base, -tlrc_no_ss and the 'tlrc' block. -tlrc_base BASE_DSET : run "@auto_tlrc -base BASE_DSET" e.g. -tlrc_base TT_icbm452+tlrc default: -tlrc_base TT_N27+tlrc This option is used to supply an alternate -base dataset for @auto_tlrc. Otherwise, TT_N27+tlrc will be used. Note that the default operation of @auto_tlrc is to "skull strip" the input dataset. If this is not appropriate, consider also the '-tlrc_no_ss' option. Please see '@auto_tlrc -help' for more information. See also -tlrc_anat, -tlrc_no_ss. -tlrc_opts_at OPTS ... : add additional options to @auto_tlrc e.g. -tlrc_opts_at -OK_maxite This option is used to add user-specified options to @auto_tlrc, specifically those afni_proc.py is not otherwise set to handle. Please see '@auto_tlrc -help' for more information. -tlrc_no_ss : add the -no_ss option to @auto_tlrc e.g. -tlrc_no_ss This option is used to tell @auto_tlrc not to perform the skull strip operation. Please see '@auto_tlrc -help' for more information. -tlrc_rmode RMODE : apply RMODE resampling in @auto_tlrc e.g. -tlrc_rmode NN This option is used to apply '-rmode RMODE' in @auto_tlrc. Please see '@auto_tlrc -help' for more information. -tlrc_suffix SUFFIX : apply SUFFIX to result of @auto_tlrc e.g. -tlrc_suffix auto_tlrc This option is used to apply '-suffix SUFFIX' in @auto_tlrc. Please see '@auto_tlrc -help' for more information. -align_epi_ext_dset DSET : specify dset/brick for align_epi_anat EPI e.g. -align_epi_ext_dset subj10/epi_r01+orig'[0]' This option allows the user to specify an external volume for the EPI base used in align_epi_anat.py in the align block. The user should apply sub-brick selection if the dataset has more than one volume. This volume would be used for both the -epi and the -epi_base options in align_epi_anat.py. The user might want to align to an EPI volume that is not in the processing stream in the case where there is not sufficient EPI contrast left after the magnetization has reached a steady state. Perhaps volume 0 has sufficient contrast for alignment, but is not appropriate for analysis. In such a case, the user may elect to align to volume 0, while excluding it from the analysis as part of the first volumes removed in -tcat_remove_first_trs. e.g. -dsets subj10/epi_r*_orig.HEAD -tcat_remove_first_trs 3 -align_epi_ext_dset subj10/epi_r01+orig'[0]' -volreg_align_to first Note that even if the anatomy were acquired after the EPI, the user might still want to align the anat to the beginning of some run, and align all the EPIs to a time point close to that. Since the anat and EPI are being forcibly aligned, it does not make such a big difference whether the EPI base is close in time to the anat acquisition. Note that this option does not affect the EPI registration base. Note that without this option, the volreg base dataset (whether one of the processed TRs or not) will be applied for anatomical alignment, assuming the align block is applied. See also -volreg_base_dset. Please see "align_epi_anat.py -help" for more information. -align_opts_aea OPTS ... : specify extra options for align_epi_anat.py e.g. -align_opts_aea -cost lpc+ZZ e.g. -align_opts_aea -Allineate_opts -source_automask+4 e.g. -align_opts_aea -giant_move -AddEdge -epi_strip 3dAutomask This option allows the user to add extra options to the alignment command, align_epi_anat.py. Note that only one -align_opts_aea option should be given, with possibly many parameters to be passed on to align_epi_anat.py. Note the second example. In order to pass '-source_automask+4' to 3dAllineate, one must pass '-Allineate_opts -source_automask+4' to align_epi_anat.py. Please see "align_epi_anat.py -help" for more information. Please see "3dAllineate -help" for more information. -align_epi_strip_method METHOD : specify EPI skull strip method in AEA e.g. -align_epi_strip_method 3dAutomask default: 3dSkullStrip When align_epi_anat.py is used to align the EPI and anatomy, it uses 3dSkullStrip to remove non-brain tissue from the EPI dataset. This option can be used to specify which method to use, one of 3dSkullStrip, 3dAutomask or None. This option assumes the 'align' processing block is used. Please see "align_epi_anat.py -help" for more information. Please see "3dSkullStrip -help" for more information. Please see "3dAutomask -help" for more information. -volreg_align_e2a : align EPI to anatomy at volreg step This option is used to align the EPI data to match the anatomy. It is done by applying the inverse of the anatomy to EPI alignment matrix to the EPI data at the volreg step. The 'align' processing block is required. At the 'align' block, the anatomy is aligned to the EPI data. When applying the '-volreg_align_e2a' option, the inverse of that a2e transformation (so now e2a) is instead applied to the EPI data. Note that this e2a transformation is catenated with the volume registration transformations, so that the EPI data is still only resampled the one time. If the user requests -volreg_tlrc_warp, the +tlrc transformation will also be applied at that step in a single transformation. See also the 'align' block and '-volreg_tlrc_warp'. -volreg_align_to POSN : specify the base position for volume reg e.g. -volreg_align_to last default: third This option takes 'first', 'third' or 'last' as a parameter. It specifies whether the EPI volumes are registered to the first or third volume (of the first run) or the last volume (of the last run). The choice of 'first' or 'third' should correspond to when the anatomy was acquired before the EPI data. The choice of 'last' should correspond to when the anatomy was acquired after the EPI data. The default of 'third' was chosen to go a little farther into the steady state data. Note that this is done after removing any volumes in the initial tcat operation. Please see '3dvolreg -help' for more information. See also -tcat_remove_first_trs, -volreg_base_ind and -volreg_base_dset. -volreg_base_dset DSET : specify dset/sub-brick for volreg base e.g. -volreg_base_dset subj10/vreg_base+orig'[4]' This option allows the user to specify an external dataset for the volreg base. The user should apply sub-brick selection if the dataset has more than one volume. Note that unless -align_epi_ext_dset is also applied, this volume will be used for anatomical to EPI alignment (assuming that is being done at all). See also -align_epi_ext_dset, -volreg_align_to and -volreg_base_ind. -volreg_base_ind RUN SUB : specify run/sub-brick indices for base e.g. -volreg_base_ind 10 123 default: 0 0 This option allows the user to specify exactly which dataset and sub-brick to use as the base registration image. Note that the SUB index applies AFTER the removal of pre-steady state images. * The RUN number is 1-based, matching the run list in the output shell script. The SUB index is 0-based, matching the sub-brick of EPI time series #RUN. Yes, one is 1-based, the other is 0-based. Life is hard. The user can apply only one of the -volreg_align_to and -volreg_base_ind options. See also -volreg_align_to, -tcat_remove_first_trs and -volreg_base_dset. -volreg_compute_tsnr yes/no : compute TSNR datasets from volreg output e.g. -volreg_compute_tsnr yes default: no Use this option to compute a temporal signal to noise (TSNR) dataset at the end of the volreg block. Both the signal and noise datasets are from the run 1 output, where the "signal" is the mean and the "noise" is the detrended time series. TSNR = average(signal) / stdev(noise) See also -regress_compute_tsnr. -volreg_interp METHOD : specify the interpolation method for volreg e.g. -volreg_interp -quintic e.g. -volreg_interp -Fourier default -cubic Please see '3dvolreg -help' for more information. -volreg_opts_vr OPTS ... : specify extra options for 3dvolreg e.g. -volreg_opts_vr -twopass e.g. -volreg_opts_vr -noclip -nomaxdisp This option allows the user to add extra options to the 3dvolreg command. Note that only one -volreg_opts_vr should be applied, which may be used for multiple 3dvolreg options. Please see '3dvolreg -help' for more information. -volreg_no_extent_mask : do not create and apply extents mask default: apply extents mask This option says not to create or apply the extents mask. The extents mask: When EPI data is transformed to the anatomical grid in either orig or tlrc space (i.e. if -volreg_align_e2a or -volreg_tlrc_warp is applied), then the complete EPI volume will only cover part of the resulting volume space. Worse than that, the coverage will vary over time, as motion will alter the final transformation (remember that volreg, EPI->anat and ->tlrc transformations are all combined, to prevent multiple resampling steps). The result is that edge voxels will sometimes have valid data and sometimes not. The extents mask is made from an all-1 dataset that is warped with the same per-TR transformations as the EPI data. The intersection of the result is the extents mask, so that every voxel in the extents mask has data at every time point. Voxels that are not are missing data from some or all TRs. It is called the extents mask because it defines the 'bounding box' of valid EPI data. It is not quite a tiled box though, as motion changes the location slightly, per TR. See also -volreg_align_e2a, -volreg_tlrc_warp. See also the 'extents' mask, in the "MASKING NOTE" section above. -volreg_regress_per_run : regress motion parameters from each run === This option has been replaced by -regress_motion_per_run. === -volreg_tlrc_adwarp : warp EPI to +tlrc space at end of volreg step default: stay in +orig space With this option, the EPI data will be warped to standard space (via adwarp) at the end of the volreg processing block. Further processing through regression will be done in standard space. This option is useful for applying a manual Talairach transform, which does not work with -volreg_tlrc_warp. To apply one from @auto_tlrc, -volreg_tlrc_warp is recommended. The resulting voxel grid is the minimum dimension, truncated to 3 significant bits. See -volreg_warp_dxyz for details. Note: this step requires a transformed anatomy, which can come from the -tlrc_anat option or from -copy_anat importing an existing one. Please see 'WARP TO TLRC NOTE' above, for additional details. See also -volreg_tlrc_warp, -volreg_warp_dxyz, -tlrc_anat, -copy_anat. -volreg_tlrc_warp : warp EPI to +tlrc space at volreg step default: stay in +orig space With this option, the EPI data will be warped to standard space in the volreg processing block. All further processing through regression will be done in standard space. Warping is done with volreg to apply both the volreg and tlrc transformations in a single step (so a single interpolation of the EPI data). The volreg transformations (for each volume) are stored and multiplied by the +tlrc transformation, while the volume registered EPI data is promptly ignored. The volreg/tlrc transformation is then applied as a single warp to the unregistered data. Note that this is only possible when using @auto_tlrc, not the 12 piece manual transformation. See -volreg_tlrc_adwarp for applying a manual transformation. The resulting voxel grid is the minimum dimension, truncated to 3 significant bits. See -volreg_warp_dxyz for details. Note: this step requires a transformed anatomy, which can come from the -tlrc_anat option or from -copy_anat importing an existing one. Please see 'WARP TO TLRC NOTE' above, for additional details. See also -volreg_tlrc_adwarp, -volreg_warp_dxyz, -tlrc_anat, -copy_anat. -volreg_warp_dxyz DXYZ : grid dimensions for _align_e2a or _tlrc_warp e.g. -volreg_warp_dxyz 3.5 default: min dim truncated to 3 significant bits (see description, below) This option allows the user to specify the grid size for output datasets from the -volreg_tlrc_warp and -volreg_align_e2a options. In either case, the output grid will be isotropic voxels (cubes). By default, DXYZ is the minimum input dimension, truncated to 3 significant bits (for integers, starts affecting them at 9, as 9 requires 4 bits to represent). Some examples: ---------------------------- (integer range, so >= 4) 8.00 ... 9.99 --> 8.0 ... 4.00 ... 4.99 --> 4.0 ---------------------------- (3 significant bits) 2.50 ... 2.99 --> 2.5 2.00 ... 2.49 --> 2.0 1.75 ... 1.99 --> 1.75 1.50 ... 1.74 --> 1.5 1.25 ... 1.49 --> 1.25 1.00 ... 1.24 --> 1.0 0.875 ... 0.99 --> 0.875 0.75 ... 0.874 --> 0.75 0.625 ... 0.74 --> 0.625 0.50 ... 0.624 --> 0.50 0.4375 ... 0.49 --> 0.4375 0.375 ... 0.4374 --> 0.375 ... -volreg_zpad N_SLICES : specify number of slices for -zpad e.g. -volreg_zpad 4 default: -volreg_zpad 1 This option allows the user to specify the number of slices applied via the -zpad option to 3dvolreg. -surf_anat ANAT_DSET : specify surface volume dataset e.g. -surf_anat SUMA/sb23_surf_SurfVol+orig This option is required in order to do surface-based analysis. This volumetric dataset should be the one used for generation of the surface (and therefore should be in perfect alignment). It may be output by the surface generation software. Unless specified by the user, the processing script will register this anatomy with the current anatomy. Use -surf_anat_aligned if the surf_anat is already aligned with the current experiment. Use '-surf_anat_has_skull no' if the surf_anat has already been skull stripped. Please see '@SUMA_AlignToExperiment -help' for more details. See also -surf_anat_aligned, -surf_anat_has_skull. See example #8 for typical usage. -surf_spec spec1 [spec2]: specify surface specificatin file(s) e.g. -surf_spec SUMA/sb23_?h_141_std.spec Use this option to provide either 1 or 2 spec files for surface analysis. Each file must have lh or rh in the name (to encode the hemisphere), and that can be their only difference. So if the files do not have such a naming pattern, they should probably be copied to new files that do. For example, consider the spec files included with the AFNI_data4 sample data: SUMA/sb23_lh_141_std.spec SUMA/sb23_rh_141_std.spec -surf_A surface_A : specify first surface for mapping e.g. -surf_A smoothwm default: -surf_A smoothwm This option allows the user to specify the first (usually inner) surface for use when mapping from the volume and for blurring. If the option is not given, the smoothwm surface will be assumed. -surf_B surface_B : specify second surface for mapping e.g. -surf_B pial default: -surf_B pial This option allows the user to specify the second (usually outer) surface for use when mapping from the volume (not for blurring). If the option is not given, the pial surface will be assumed. -surf_blur_fwhm FWHM : NO LONGER VALID Please use -blur_size, instead. -blur_filter FILTER : specify 3dmerge filter option e.g. -blur_filter -1blur_rms default: -1blur_fwhm This option allows the user to specify the filter option from 3dmerge. Note that only the filter option is set here, not the filter size. The two parts were separated so that users might generally worry only about the filter size. Please see '3dmerge -help' for more information. See also -blur_size. -blur_in_automask : apply 3dBlurInMask -automask This option forces use of 3dBlurInMask -automask, regardless of whether other masks exist and are being applied. Note that one would not want to apply -automask via -blur_opts_BIM, as that might result in failure because of multiple -mask options. Note that -blur_in_automask implies '-blur_in_mask yes'. Please see '3dBlurInMask -help' for more information. See also -blur_in_mask, -blur_opts_BIM. -blur_in_mask yes/no : specify whether to restrict blur to a mask e.g. -blur_in_mask yes default: no This option allows the user to specify whether to use 3dBlurInMask instead of 3dmerge for blurring. Note that the algorithms are a little different, and 3dmerge comes out a little more blurred. Note that 3dBlurInMask uses only FWHM kernel size units, so the -blur_filter should be either -1blur_fwhm or -FWHM. Please see '3dBlurInMask -help' for more information. Please see '3dmerge -help' for more information. See also -blur_filter. -blur_opts_BIM OPTS ... : specify extra options for 3dBlurInMask e.g. -blur_opts_BIM -automask This option allows the user to add extra options to the 3dBlurInMask command. Only one -blur_opts_BIM should be applied, which may be used for multiple 3dBlurInMask options. This option is only useful when '-blur_in_mask yes' is applied. Please see '3dBlurInMask -help' for more information. See also -blur_in_mask. -blur_opts_merge OPTS ... : specify extra options for 3dmerge e.g. -blur_opts_merge -2clip -20 50 This option allows the user to add extra options to the 3dmerge command. Note that only one -blur_opts_merge should be applied, which may be used for multiple 3dmerge options. Please see '3dmerge -help' for more information. -blur_size SIZE_MM : specify the size, in millimeters e.g. -blur_size 6.0 default: 4 This option allows the user to specify the size of the blur used by 3dmerge (or another applied smoothing program). It is applied as the 'bmm' parameter in the filter option (such as -1blur_fwhm) in 3dmerge. Note the relationship between blur sizes, as used in 3dmerge: sigma = 0.57735027 * rms = 0.42466090 * fwhm (implying fwhm = 1.359556 * rms) Programs 3dmerge and 3dBlurInMask apply -blur_size as an additional gaussian blur. Therefore smoothing estimates should be computed per subject for the correction for multiple comparisons. Programs 3dBlurToFWHM and SurfSmooth apply -blur_size as the resulting blur, and so do not requre blur estimation. Please see '3dmerge -help' for more information. Please see '3dBlurInMask -help' for more information. Please see '3dBlurToFWHM -help' for more information. Please see 'SurfSmooth -help' for more information. See also -blur_filter. -blur_to_fwhm : blur TO the blur size (not add a blur size) This option changes the program used to blur the data. Instead of using 3dmerge, this applies 3dBlurToFWHM. So instead of adding a blur of size -blur_size (with 3dmerge), the data is blurred TO the FWHM of the -blur_size. Note that 3dBlurToFWHM should be run with a mask. So either: o put the 'mask' block before the 'blur' block, or o use -blur_in_automask It is not appropriate to include non-brain in the blur estimate. Note that extra options can be added via -blur_opts_B2FW. Please see '3dBlurToFWHM -help' for more information. See also -blur_size, -blur_in_automask, -blur_opts_B2FW. -blur_opts_B2FW OPTS ... : specify extra options for 3dBlurToFWHM e.g. -blur_opts_B2FW -rate 0.2 -temper This allows the user to add extra options to the 3dBlurToFWHM command. Note that only one -blur_opts_B2FW should be applied, which may be used for multiple 3dBlurToFWHM options. Please see '3dBlurToFWHM -help' for more information. -mask_apply TYPE : specify which mask to apply in regression e.g. -mask_apply group If possible, masks will be made for the EPI data, the subject anatomy, the group anatomy and EPI warp extents. This option is used to specify which of those masks to apply to the regression. Valid choices: epi, anat, group, extents. A subject 'anat' mask will be created if the EPI anat anatomy are aligned, or if the EPI data is warped to standard space via the anat transformation. In any case, a skull-stripped anat will exist. A 'group' anat mask will be created if the 'tlrc' block is used (via the -block or -tlrc_anat options). In such a case, the anat template will be made into a binary mask. This option makes -regress_apply_mask obsolete. See "MASKING NOTE" and "DEFAULTS" for details. See also -blocks. -mask_dilate NUM_VOXELS : specify the automask dilation e.g. -mask_dilate 3 default: 1 By default, the masks generated from the EPI data are dilated by 1 step (voxel), via the -dilate option in 3dAutomask. With this option, the user may specify the dilation. Valid integers must be at least zero. Note that 3dAutomask dilation is a little different from the natural voxel-neighbor dilation. Please see '3dAutomask -help' for more information. See also -mask_type. -mask_rm_segsy Y/N : choose whether to delete the Segsy directory e.g. -mask_rm_segsy no default: yes This option is a companion to -mask_segment_anat. In the case of running 3dSeg to segment the anatomy, a resulting Segsy directory is created. Since the main result is a Classes dataset, and to save disk space, the Segsy directory is removed by default. Use this option to preserve it. See also -mask_segment_anat. -mask_segment_anat Y/N : choose whether to segment anatomy e.g. -mask_segment_anat yes default: no (if anat_final is skull-stripped) This option controls whether 3dSeg is run to segment the anatomical dataset. Such a segmentation would then be resampled to match the grid of the EPI data. When this is run, 3dSeg creates the Classes dataset, which is a composition mask of the GM/WM/CSF (gray matter, white matter and cerebral spinal fluid) regions. Then 3dresample is used to create Classes_resam, the same mask but at the resolution of the EPI. Such a dataset might have multiple uses, such as tissue-based regression. Note that for such a use, the ROI time series should come from the volreg data, before any blur. Please see '3dSeg -help' for more information See also -mask_rm_segsy. -mask_segment_erode Y/N e.g. -mask_segment_erode Yes default: yes (if -regress_ROI or -regress_anaticor) This option is a companion to -mask_segment_anat. Anatomical segmentation is used to create GM (gray matter), WM (white matter) and CSF masks. See also -mask_segment_anat, -regress_anaticor. -mask_test_overlap Y/N : choose whether to test anat/EPI mask overlap e.g. -mask_test_overlap No default: Yes If the subject anatomy and EPI masks are computed, then the default operation is to run 3dABoverlap to evaluate the overlap between the two masks. Output is saved in a text file. This option allows one to disable such functionality. Please see '3dABoverlap -help' for more information. -mask_type TYPE : specify 'union' or 'intersection' mask type e.g. -mask_type intersection default: union This option is used to specify whether the mask applied to the analysis is the union of masks from each run, or the intersection. The only valid values for TYPE are 'union' and 'intersection'. This is not how to specify whether a mask is created, that is done via the 'mask' block with the '-blocks' option. Please see '3dAutomask -help', '3dMean -help' or '3dcalc -help'. See also -mask_dilate, -blocks. -scale_max_val MAX : specify the maximum value for scaled data e.g. -scale_max_val 1000 default 200 The scale step multiples the time series for each voxel by a scalar so that the mean for that particular run is 100 (allowing interpretation of EPI values as a percentage of the mean). Values of 200 represent a 100% change above the mean, and so can probably be considered garbage (or the voxel can be considered non-brain). The output values are limited so as not to sacrifice the precision of the values of short datasets. Note that in a short (2-byte integer) dataset, a large range of values means bits of accuracy are lost for the representation. No max will be applied if MAX is <= 100. Please see 'DATASET TYPES' in the output of '3dcalc -help'. See also -scale_no_max. -scale_no_max : do not apply a limit to the scaled values The default limit for scaled data is 200. Use of this option will remove any limit from being applied. A limit on the scaled data is highly encouraged when working with 'short' integer data, especially when not applying a mask. See also -scale_max_val. -regress_3dD_stop : 3dDeconvolve should stop after X-matrix gen Use this option to tell 3dDeconvolve to stop after generating the X-matrix (via -x1D_stop). This is useful if the user only wishes to run the regression through 3dREMLfit. See also -regress_reml_exec. -regress_anaticor : generate errts using ANATICOR method Apply the ANATICOR method of HJ Jo, regressing out the WMeLocal time series, which varies across voxels. WMeLocal is the average time series from all voxels within 45 mm which are in the eroded white matter mask. The script will run the standard regression via 3dDeconvolve (or stop after setting up the X-matrix, if the user says to), and use that X-matrix, possibly censored, in 3dTfitter. The WMeLocal time series is applied along with the X-matrix to get the result. Note that other 4-D time series might be regressed out via the 3dTfitter step, as well. This option implies -mask_segment_anat and -mask_segment_erode. See also -mask_segment_anat, -mask_segment_erode, -regress_3dD_stop. -regress_apply_mask : apply the mask during scaling and regression By default, any created union mask is not applied to the analysis. Use this option to apply it. ** This option is essentially obsolete. Please consider -mask_apply as a preferable option to choose which mask to apply. See "MASKING NOTE" and "DEFAULTS" for details. See also -blocks, -mask_apply. -regress_apply_mot_types TYPE1 ... : specify motion regressors e.g. -regress_apply_mot_types basic e.g. -regress_apply_mot_types deriv e.g. -regress_apply_mot_types demean deriv default: demean By default, the motion parameters from 3dvolreg are applied in the regression, but after first removing the mean, per run. This is the application of the 'demean' regressors. This option gives the ability to choose a combination of: basic: dfile_rall.1D - the parameters straight from 3dvolreg (or an external motion file, see -regress_motion_file) demean: 'basic' params with the mean removed, per run deriv: per-run derivative of 'basic' params (de-meaned) ** Note that basic and demean cannot both be used, as they would cause multi-collinearity with the constant drift parameters. ** Note also that basic and demean will give the same results, except for the betas of the constant drift parameters (and subject to computational precision). ** A small side effect of de-meaning motion parameters is that the constant drift terms should evaluate to the mean baseline. See also -regress_motion_file, -regress_no_motion_demean, -regress_no_motion_deriv, -regress_no_motion. -regress_apply_ricor yes/no : apply ricor regs in final regression e.g. -regress_apply_ricor yes default: no This is from a change in the default behavior 30 Jan 2012. Prior to then, the 13 (?) ricor regressors from slice 0 would be applied in the final regression (mostly accounting for degrees of freedom). But since resting state analysis relies on a subsequent correlation analysis, it seems cleaner not to regress them (a second time). -regress_bandpass lowf highf : bandpass the frequency range e.g. -regress_bandpass 0.01 0.1 This option is intended for use in resting state analysis. Use this option to perform bandpass filtering during the linear regression. While such an operation is slow (much slower than the FFT using 3dBandpass), doing it during the regression allows one to perform (e.g. motion) censoring at the same time. This option has a similar effect to running 3dBandpass, e.g. the example of '-regress_bandpass 0.01 0.1' is akin to running: 3dBandpass -ort motion.1D -band 0.01 0.1 except that it is done in 3dDeconvolve using linear regression. And censoring is easy in the context of regression. Note that the Nyquist frequency is 0.5/TR. That means that if the TR were >= 5 seconds, there would be no frequencies within the band range of 0.01 to 0.1 to filter. So there is no point to such an operation. On the flip side, if the TR is 1.0 second or shorter, the range of 0.01 to 0.1 would remove about 80% of the degrees of freedom (since everything above 0.1 is filtered/removed, up through 0.5). This might result in a model that is overfit, where there are almost as many (or worse, more) regressors than time points to fit. So a 0.01 to 0.1 bandpass filter might make the most sense for a TR in [2.0, 3.0], or so. A different filter range would affect this, of course. See also -regress_censor_motion. -regress_basis BASIS : specify the regression basis function e.g. -regress_basis 'BLOCK(4,1)' e.g. -regress_basis 'BLOCK(5)' e.g. -regress_basis 'TENT(0,14,8)' default: GAM This option is used to set the basis function used by 3dDeconvolve in the regression step. This basis function will be applied to all user-supplied regressors (please let me know if there is need to apply different basis functions to different regressors). ** Note that use of dmBLOCK requires -stim_times_AM1 (or AM2). So consider option -regress_stim_types. ** If using -regress_stim_types 'file' for a particular regressor, the basis function will be ignored. In such a case, it is safest to use 'NONE' for the corresponding basis function. Please see '3dDeconvolve -help' for more information, or the link: http://afni.nimh.nih.gov/afni/doc/misc/3dDeconvolveSummer2004 See also -regress_basis_normall, -regress_stim_times, -regress_stim_types. -regress_basis_normall NORM : specify the magnitude of basis functions e.g. -regress_basis_normall 1.0 This option is used to set the '-basis_normall' parameter in 3dDeconvolve. It specifies the height of each basis function. For the example basis functions, -basis_normall is not recommended. Please see '3dDeconvolve -help' for more information. See also -regress_basis. -regress_censor_extern CENSOR.1D : supply an external censor file e.g. -regress_censor_extern censor_bad_trs.1D This option is used to provide an initial censor file, if there is some censoring that is desired beyond the automated motion and outlier censoring. Any additional censoring (motion or outliers) will be combined. See also -regress_censor_motion, -regress_censor_outliers. -regress_censor_motion LIMIT : censor TRs with excessive motion e.g. -regress_censor_motion 0.3 This option is used to censor TRs where the subject moved too much. "Too much" is decided by taking the derivative of the motion parameters (ignoring shifts between runs) and the sqrt(sum squares) per TR. If this Euclidean Norm exceeds the given LIMIT, the TR will be censored. This option will result in the creation of 3 censor files: motion_$subj_censor.1D motion_$subj_CENSORTR.txt motion_$subj_enorm.1D motion_$subj_censor.1D is a 0/1 columnar file to be applied to 3dDeconvolve via -censor. A row with a 1 means to include that TR, while a 0 means to exclude (censor) it. motion_$subj_CENSORTR.txt is a short text file listing censored TRs, suitable for use with the -CENSORTR option in 3dDeconvolve. The -censor option is the one applied however, so this file is not used, but may be preferable for users to have a quick peek at. motion_$subj_enorm.1D is the time series that the LIMIT is applied to in deciding which TRs to censor. It is the Euclidean norm of the derivatives of the motion parameters. Plotting this will give users a visual indication of why TRs were censored. By default, the TR prior to the large motion derivative will also be censored. To turn off that behavior, use -regress_censor_prev with parameter 'no'. If censoring the first few TRs from each run is also necessary, use -regress_censor_first_trs. Please see '1d_tool.py -help' for information on censoring motion. See also -regress_censor_prev and -regress_censor_first_trs. -regress_censor_first_trs N : censor the first N TRs in each run e.g. -regress_censor_first_trs 3 default: N = 0 If, for example, censoring the first 3 TRs per run is desired, a user might add "-CENSORTR '*:0-2'" to the -regress_opts_3dD option. However, when using -regress_censor_motion, these censoring options must be combined into one for 3dDeconvolve. The -regress_censor_first_trs censors those TRs along with any with large motion. See '-censor_first_trs' under '1d_tool.py -help' for details. See also '-regress_censor_motion'. -regress_censor_prev yes/no : censor TRs preceding large motion default: -regress_censor_prev yes Since motion spans two TRs, the derivative is not quite enough information to decide whether it is more appropriate to censor the earlier or later TR. To error on the safe side, many users choose to censor both. Use this option to specify whether to include the previous TR when censoring. By default this option is applied as 'yes'. Users may elect not not to censor the previous TRs by setting this to 'no'. See also -regress_censor_motion. -regress_censor_outliers LIMIT : censor TRs with excessive outliers e.g. -regress_censor_outliers 0.15 This option is used to censor TRs where too many voxels are flagged as outliers by 3dToutcount. LIMIT should be in [0.0, 1.0], as it is a limit on the fraction of masked voxels. '3dToutcount -automask -fraction' is used to output the fraction of (auto)masked voxels that are considered outliers at each TR. If the fraction of outlier voxels is greater than LIMIT for some TR, that TR is censored out. Depending on the scanner settings, early TRs might have somewhat higher intensities. This could lead to the first few TRs of each run being censored. To avoid censoring the first few TRs of each run, apply the -regress_skip_first_outliers option. Note that if motion is also being censored, the multiple censor files will be combined (multiplied) before 3dDeconvolve. See '3dToutcount -help' for more details. See also -regress_skip_first_outliers, -regress_censor_motion. -regress_compute_gcor yes/no : compute GCOR from unit errts e.g. -regress_compute_gcor no default: yes By default, the global correlation (GCOR) is computed from the masked residual time series (errts). GCOR can be thought of as the result of: A1. compute the correlations of each voxel with every other --> can be viewed as an NMASK x NMASK correlation matrix A2. compute GCOR: the average of the NMASK^2 values Since step A1 would take a lot of time and disk space, a more efficient computation is desirable: B0. compute USET: scale each voxel time series to unit length B1. compute GMU: the global mean of this unit dataset B2. compute a correlation volume (of each time series with GMU) B3. compute the average of this volume The actual computation is simplified even further, as steps B2 and B3 combine as the L2 norm of GMU. The result is: B2'. length(GMU)^2 (or the sum of squares of GMU) The steps B0, B1 and B2' are performed in the proc script. Note: This measure of global correlation is a single number in the range [0, 1] (not in [-1, 1] as some might expect). Note: computation of GCOR requires a residual dataset, an EPI mask, and a volume analysis (no surface at the moment). -regress_compute_tsnr yes/no : compute TSNR datasets from errts e.g. -regress_compute_tsnr no default: yes By default, a temporal signal to noise (TSNR) dataset is created at the end of the regress block. The "signal" is the mean of the all_runs dataset (input to 3dDeconvolve), and the "noise" is the errts dataset (residuals from 3dDeconvolve). The main difference between the TSNR datasets from the volreg and regress blocks is that the data in the regress block has been smoothed (plus it has been "completely" detrended, according to the regression model - this includes polort, motion and even stim responses). Use this option to prevent the TSNR dataset computation in the 'regress' block. TSNR = average(signal) / stdev(noise) See also -volreg_compute_tsnr. -regress_make_cbucket yes/no : add a -cbucket option to 3dDeconvolve default: 'no' Recall that the -bucket dataset (no 'c') contains beta weights and various statistics, but generally not including baseline terms (polort and motion). The -cbucket dataset (with a 'c') is a little different in that it contains: - ONLY betas (no t-stats, no F-stats, no contrasts) - ALL betas (including baseline terms) So it has one volume (beta) per regressor in the X-matrix. The use is generally for 3dSynthesize, to recreate time series datasets akin to the fitts, but where the user can request any set of parameters to be included (for example, the polort and the main 2 regressors of interest). Setting this to 'yes' will result in the -cbucket option being added to the 3dDeconvolve command. Please see '3dDeconvolve -help' for more details. -regress_motion_per_run : regress motion parameters from each run default: regress motion parameters catenated across runs By default, motion parameters from the volreg block are catenated across all runs, providing 6 (assuming 3dvolreg) regressors of no interest in the regression block. With -regress_motion_per_run, the motion parameters from each run are used as separate regressors, providing a total of (6 * nruns) regressors. This allows for the magnitudes of the regressors to vary over each run, rather than using a single (best) magnitude over all runs. So more motion-correlated variance can be accounted for, at the cost of the extra degrees of freedom (6*(nruns-1)). This option will apply to all motion regressors, including derivatives (if requested). ** This option was previously called -volreg_regress_per_run. ** -regress_skip_first_outliers NSKIP : ignore the first NSKIP TRs e.g. -regress_skip_first_outliers 4 default: 0 When using -regress_censor_outliers, any TR with too high of an outlier fraction will be censored. But depending on the scanner settings, early TRs might have somewhat higher intensities, leading to them possibly being inappropriately censored. To avoid censoring any the first few TRs of each run, apply the -regress_skip_first_outliers option. See also -regress_censor_outliers. -regress_compute_fitts : compute fitts via 3dcalc, not 3dDecon This option is to save memory during 3dDeconvolve, in the case where the user has requested both the fitts and errts datasets. Normally 3dDeconvolve is used to compute both the fitts and errts time series. But if memory gets tight, it is worth noting that these datasets are redundant, one can be computed from the other (given the all_runs dataset). all_runs = fitts + errts Using -regress_compute_fitts, -fitts is no longer applied in 3dD (though -errts is). Instead, note that an all_runs dataset is created just after 3dDeconvolve. After that step, the script will create fitts as (all_runs-errts) using 3dcalc. Note that computation of both errts and fitts datasets is required for this option to be applied. See also -regress_est_blur_errts, -regress_errts_prefix, -regress_fitts_prefix and -regress_no_fitts. -regress_cormat_warnings Y/N : specify whether to get cormat warnings e.g. -mask_cormat_warnings No default: Yes By default, '1d_tool.py -show_cormat_warnings' is run on the regression matrix. Any large, pairwise correlations are shown in text output (which is also saved to a text file). This option allows one to disable such functionality. Please see '1d_tool.py -help' for more details. -regress_est_blur_epits : estimate the smoothness of the EPI data This option specifies to run 3dFWHMx on each of the EPI datasets used for regression, the results of which are averaged. These blur values are saved to the file blur_est.$subj.1D, along with any similar output from errts. These blur estimates may be input to AlphaSim, for any multiple testing correction done for this subject. If AlphaSim is run at the group level, it is reasonable to average these estimates across all subjects (assuming they were scanned with the same protocol and at the same scanner). The mask block is required for this operation (without which the estimates are not reliable). Please see '3dFWHMx -help' for more information. See also -regress_est_blur_errts. -regress_est_blur_errts : estimate the smoothness of the errts This option specifies to run 3dFWHMx on the errts dataset, output from the regression (by 3dDeconvolve). These blur estimates may be input to AlphaSim, for any multiple testing correction done for this subject. If AlphaSim is run at the group level, it is reasonable to average these estimates across all subjects (assuming they were scanned with the same protocol and at the same scanner). Note that the errts blur estimates should be not only slightly more accurate than the epits blur estimates, but they should be slightly smaller, too (which is beneficial). The mask block is required for this operation (without which the estimates are not reliable). Please see '3dFWHMx -help' for more information. See also -regress_est_blur_epits. -regress_errts_prefix PREFIX : specify a prefix for the -errts option e.g. -regress_fitts_prefix errts This option is used to add a -errts option to 3dDeconvolve. As with -regress_fitts_prefix, only the PREFIX is specified, to which the subject ID will be added. Please see '3dDeconvolve -help' for more information. See also -regress_fitts_prefix. -regress_fitts_prefix PREFIX : specify a prefix for the -fitts option e.g. -regress_fitts_prefix model_fit default: fitts By default, the 3dDeconvolve command in the script will be given a '-fitts fitts' option. This option allows the user to change the prefix applied in the output script. The -regress_no_fitts option can be used to eliminate use of -fitts. Please see '3dDeconvolve -help' for more information. See also -regress_no_fitts. -regress_global_times : specify -stim_times as global times default: 3dDeconvolve figures it out, if it can By default, the 3dDeconvolve determines whether -stim_times files are local or global times by the first line of the file. If it contains at least 2 times (which include '*' characters), it is considered as local_times, otherwise as global_times. The -regress_global_times option is mostly added to be symmetric with -regress_local_times, as the only case where it would be needed is when there are other times in the first row, but the should still be viewed as global. See also -regress_local_times. -regress_local_times : specify -stim_times as local times default: 3dDeconvolve figures it out, if it can By default, the 3dDeconvolve determines whether -stim_times files are local or global times by the first line of the file. If it contains at least 2 times (which include '*' characters), it is considered as local_times, otherwise as global_times. In the case where the first run has only 1 stimulus (maybe even every run), the user would need to put an extra '*' after the first stimulus time. If the first run has no stimuli, then two would be needed ('* *'), but only for the first run. Since this may get confusing, being explicit by adding this option is a reasonable thing to do. See also -regress_global_times. -regress_iresp_prefix PREFIX : specify a prefix for the -iresp option e.g. -regress_iresp_prefix model_fit default: iresp This option allows the user to change the -iresp prefix applied in the 3dDeconvolve command of the output script. By default, the 3dDeconvolve command in the script will be given a set of '-iresp iresp' options, one per stimulus type, unless the regression basis function is GAM. In the case of GAM, the response form is assumed to be known, so there is no need for -iresp. The stimulus label will be appended to this prefix so that a sample 3dDeconvolve option might look one of these 2 examples: -iresp 7 iresp_stim07 -iresp 7 model_fit_donuts The -regress_no_iresp option can be used to eliminate use of -iresp. Please see '3dDeconvolve -help' for more information. See also -regress_no_iresp, -regress_basis. -regress_make_ideal_sum IDEAL.1D : create IDEAL.1D file from regressors e.g. -regress_make_ideal_sum ideal_all.1D By default, afni_proc.py will compute a 'sum_ideal.1D' file that is the sum of non-polort and non-motion regressors from the X-matrix. This -regress_make_ideal_sum option is used to specify the output file for that sum (if sum_idea.1D is not desired). Note that if there is nothing in the X-matrix except for polort and motion regressors, or if 1d_tool.py cannot tell what is in there (if there is no header information), then all columns will be used. Computing the sum means adding a 1d_tool.py command to figure out which columns should be used in the sum (since mixing GAM, TENT, etc., makes it harder to tell up front), and a 3dTstat command to actually sum those columns of the 1D X-matrix (the X-matrix is output by 3dDeconvolve). Please see '3dDeconvolve -help', '1d_tool.py -help' and '3dTstat -help'. See also -regress_basis, -regress_no_ideal_sum. -regress_motion_file FILE.1D : use FILE.1D for motion parameters e.g. -regress_motion_file motion.1D Particularly if the user performs motion correction outside of afni_proc.py, they may wish to specify a motion parameter file other than dfile_rall.1D (the default generated in the volreg block). Note: such files no longer need to be copied via -copy_files. If the motion file is in a remote directory, include the path, e.g. -regress_motion_file ../subject17/data/motion.1D . -regress_no_fitts : do not supply -fitts to 3dDeconvolve e.g. -regress_no_fitts This option prevents the program from adding a -fitts option to the 3dDeconvolve command in the output script. See also -regress_fitts_prefix. -regress_no_ideal_sum : do not create sum_ideal.1D from regressors By default, afni_proc.py will compute a 'sum_ideal.1D' file that is the sum of non-polort and non-motion regressors from the X-matrix. This option prevents that step. See also -regress_make_ideal_sum. -regress_no_ideals : do not generate ideal response curves e.g. -regress_no_ideals By default, if the GAM or BLOCK basis function is used, ideal response curve files are generated for each stimulus type (from the output X matrix using '3dDeconvolve -x1D'). The names of the ideal response function files look like 'ideal_LABEL.1D', for each stimulus label, LABEL. This option is used to suppress generation of those files. See also -regress_basis, -regress_stim_labels. -regress_no_iresp : do not supply -iresp to 3dDeconvolve e.g. -regress_no_iresp This option prevents the program from adding a set of -iresp options to the 3dDeconvolve command in the output script. By default -iresp will be used unless the basis function is GAM. See also -regress_iresp_prefix, -regress_basis. -regress_no_mask : do not apply the mask in regression ** This is now the default, making the option unnecessary. This option prevents the program from applying the mask dataset in the scaling or regression steps. If the user does not want to apply a mask in the regression analysis, but wants the full_mask dataset for other reasons (such as computing blur estimates), this option can be used. See also -regress_est_blur_epits, -regress_est_blur_errts. -regress_no_motion : do not apply motion params in 3dDeconvolve e.g. -regress_no_motion This option prevents the program from adding the registration parameters (from volreg) to the 3dDeconvolve command. -regress_no_motion_demean : do not compute de-meaned motion parameters default: do compute them Even if they are not applied in the regression, the default is to compute de-meaned motion parameters. These may give the user a better idea of motion regressors, since their scale will not be affected by jumps across run breaks or multi-run drift. This option prevents the program from even computing such motion parameters. The only real reason to not do it is if there is some problem with the command. -regress_no_motion_deriv : do not compute motion parameter derivatives default: do compute them Even if they are not applied in the regression, the default is to compute motion parameter derivatives (and de-mean them). These can give the user a different idea about motion regressors, since the derivatives are a better indication of per-TR motion. Note that the 'enorm' file that is created (and optionally used for motion censoring) is basically made by collapsing (via the Euclidean Norm - the square root of the sum of the squares) these 6 derivative columns into one. This option prevents the program from even computing such motion parameters. The only real reason to not do it is if there is some problem with the command. See also -regress_censor_motion. -regress_opts_3dD OPTS ... : specify extra options for 3dDeconvolve e.g. -regress_opts_3dD -gltsym ../contr/contrast1.txt \ -glt_label 1 FACEvsDONUT \ -jobs 6 \ -GOFORIT 8 This option allows the user to add extra options to the 3dDeconvolve command. Note that only one -regress_opts_3dD should be applied, which may be used for multiple 3dDeconvolve options. Please see '3dDeconvolve -help' for more information, or the link: http://afni.nimh.nih.gov/afni/doc/misc/3dDeconvolveSummer2004 -regress_opts_reml OPTS ... : specify extra options for 3dREMLfit e.g. -regress_opts_reml \ -gltsym ../contr/contrast1.txt FACEvsDONUT \ -MAXa 0.92 This option allows the user to add extra options to the 3dREMLfit command. Note that only one -regress_opts_reml should be applied, which may be used for multiple 3dREMLfit options. Please see '3dREMLfit -help' for more information. -regress_polort DEGREE : specify the polynomial degree of baseline e.g. -regress_polort 2 default: 1 + floor(run_length / 150.0) 3dDeconvolve models the baseline for each run separately, using Legendre polynomials (by default). This option specifies the degree of polynomial. Note that this will create DEGREE * NRUNS regressors. The default is computed from the length of a run, in seconds, as shown above. For example, if each run were 320 seconds, then the default polort would be 3 (cubic). Please see '3dDeconvolve -help' for more information. -regress_reml_exec : execute 3dREMLfit, matching 3dDeconvolve cmd 3dDeconvolve automatically creates a 3dREMLfit command script to match the regression model of 3dDeconvolve. Via this option, the user can have that command executed. Note that the X-matrix used in 3dREMLfit is actually generated by 3dDeconvolve. The 3dDeconvolve command generates both the X-matrix and the 3dREMLfit command script, and so it must be run regardless of whether it actually performs the regression. To terminate 3dDeconvolve after creation of the X-matrix and 3dREMLfit command script, apply -regress_3dD_stop. See also -regress_3dD_stop. -regress_ROI R1 R2 ... : specify a list of mask averages to regress out e.g. -regress_ROI WMe e.g. -regress_ROI brain WMe CSF Use this option to regress out one more more known ROI averages. Currently known ROIs include: name description source dataset creation program ----- -------------- -------------- ---------------- brain EPI brain mask full_mask 3dAutomask CSF CSF mask_CSF_resam 3dSeg -> Classes CSFe CSF (eroded) mask_CSFe_resam 3dSeg -> Classes GM gray matter mask_GM_resam 3dSeg -> Classes GMe gray (eroded) mask_GMe_resam 3dSeg -> Classes WM white matter mask_WM_resam 3dSeg -> Classes WMe white (eroded) mask_WMe_resam 3dSeg -> Classes Note: use of this option requires the 'mask' processing block Note: use of any non-brain cases requires -mask_segment_anat. See also -mask_segment_anat. Please see '3dSeg -help' for motion information on the masks. -regress_RONI IND1 ... : specify a list of regressors of no interest e.g. -regress_RONI 1 17 22 Use this option flag regressors as ones of no interest, meaning they are applied to the baseline (for full-F) and the corresponding beta weights are not output (by default at least). The indices in the list should match those given to 3dDeconvolve. They start at 1 first with the main regressors, and then with any extra regressors (given via -regress_extra_stim_files). Note that these do not apply to motion regressors. The user is encouraged to check the 3dDeconvolve command in the processing script, to be sure they are applied correctly. -regress_stim_labels LAB1 ... : specify labels for stimulus classes e.g. -regress_stim_labels houses faces donuts default: stim01 stim02 stim03 ... This option is used to apply a label to each stimulus type. The number of labels should equal the number of files used in the -regress_stim_times option, or the total number of columns in the files used in the -regress_stim_files option. These labels will be applied as '-stim_label' in 3dDeconvolve. Please see '3dDeconvolve -help' for more information. See also -regress_stim_times, -regress_stim_labels. -regress_stim_times FILE1 ... : specify files used for -stim_times e.g. -regress_stim_times ED_stim_times*.1D e.g. -regress_stim_times times_A.1D times_B.1D times_C.1D 3dDeconvolve will be run using '-stim_times'. This option is used to specify the stimulus timing files to be applied, one file per stimulus type. The order of the files given on the command line will be the order given to 3dDeconvolve. Each of these timing files will be given along with the basis function specified by '-regress_basis'. The user must specify either -regress_stim_times or -regress_stim_files if regression is performed, but not both. Note the form of the files is one row per run. If there is at most one stimulus per run, please add a trailing '*'. Labels may be specified using the -regress_stim_labels option. These two examples of such files are for a 3-run experiment. In the second example, there is only 1 stimulus at all, occurring in run #2. e.g. 0 12.4 27.3 29 * 30 40 50 e.g. * 20 * * Please see '3dDeconvolve -help' for more information, or the link: http://afni.nimh.nih.gov/afni/doc/misc/3dDeconvolveSummer2004 See also -regress_stim_files, -regress_stim_labels, -regress_basis, -regress_basis_normall, -regress_polort. -regress_stim_files FILE1 ... : specify TR-locked stim files e.g. -regress_stim_times ED_stim_file*.1D e.g. -regress_stim_times stim_A.1D stim_B.1D stim_C.1D Without the -regress_use_stim_files option, 3dDeconvolve will be run using '-stim_times', not '-stim_file'. The user can still specify the 3dDeconvolve -stim_file files here, but they would then be converted to -stim_times files using the script, make_stim_times.py . It might be more educational for the user to run make_stim_times.py outside afni_proc.py (such as was done before example 2, above), or to create the timing files directly. Each given file can be for multiple stimulus classes, where one column is for one stim class, and each row represents a TR. So each file should have NUM_RUNS * NUM_TRS rows. The stim_times files will be labeled stim_times.NN.1D, where NN is the stimulus index. Note that if the stimuli were presented at a fixed time after the beginning of a TR, the user should consider the option, -regress_stim_times_offset, to apply that offset. --- If the -regress_use_stim_files option is provided, 3dDeconvolve will be run using each stim_file as a regressor. The order of the regressors should match the order of any labels, provided via the -regress_stim_labels option. Please see '3dDeconvolve -help' for more information, or the link: http://afni.nimh.nih.gov/afni/doc/misc/3dDeconvolveSummer2004 See also -regress_stim_times, -regress_stim_labels, -regress_basis, -regress_basis_normall, -regress_polort, -regress_stim_times_offset, -regress_use_stim_files. -regress_extra_stim_files FILE1 ... : specify extra stim files e.g. -regress_extra_stim_files resp.1D cardiac.1D e.g. -regress_extra_stim_files regs_of_no_int_*.1D Use this option to specify extra files to be applied with the -stim_file option in 3dDeconvolve (as opposed to the more usual -stim_times). These files will not be converted to stim_times. Corresponding labels can be given with -regress_extra_stim_labels. See also -regress_extra_stim_labels, -regress_ROI, -regress_RONI. -regress_extra_stim_labels LAB1 ... : specify extra stim file labels e.g. -regress_extra_stim_labels resp cardiac If -regress_extra_stim_files is given, the user may want to specify labels for those extra stimulus files. This option provides that mechanism. If this option is not given, default labels will be assigned (like stim17, for example). Note that the number of entries in this list should match the number of extra stim files. See also -regress_extra_stim_files. -regress_stim_times_offset OFFSET : add OFFSET to -stim_times files e.g. -regress_stim_times_offset 1.25 default: 0 If the -regress_stim_files option is used (so the script converts -stim_files to -stim_times before 3dDeconvolve), the user may want to add an offset to the times in the output timing files. For example, if -tshift_align_to is applied, and the user chooses to align volumes to the middle of the TR, it would be appropriate to add TR/2 to the times of the stim_times files. This OFFSET will be applied to the make_stim_times.py command in the output script. Please see 'make_stim_times.py -help' for more information. See also -regress_stim_files, -regress_use_stim_files, -tshift_align_to. -regress_stim_types TYPE1 TYPE2 ... : specify list of stim types e.g. -regress_stim_types times times AM2 AM2 times AM1 file e.g. -regress_stim_types AM2 default: times If amplitude, duration or individual modulation is desired with any of the stimulus timing files provided via -regress_stim_files, then this option should be used to specify one (if all of the types are the same) or a list of stimulus timing types. One can also use the type 'file' for the case of -stim_file, where the input is a 1D regressor instead of stimulus times. The types should be (possibly repeated) elements of the set: {times, AM1, AM2, IM}, where they indicate: times: a standard stimulus timing file (not married) ==> use -stim_times in 3dDeconvolve command AM1: have one or more married parameters ==> use -stim_times_AM1 in 3dDeconvolve command AM2: have one or more married parameters ==> use -stim_times_AM2 in 3dDeconvolve command IM: NO married parameters, but get beta for each stim ==> use -stim_times_IM in 3dDeconvolve command file: a 1D regressor, not a stimulus timing file ==> use -stim_file in 3dDeconvolve command Please see '3dDeconvolve -help' for more information. See also -regress_stim_times. See also example 7 (esoteric options). -regress_use_stim_files : use -stim_file in regression, not -stim_times The default operation of afni_proc.py is to convert TR-locked files for the 3dDeconvolve -stim_file option to timing files for the 3dDeconvolve -stim_times option. If the -regress_use_stim_times option is provided, then no such conversion will take place. This assumes the -regress_stim_files option is applied to provide such -stim_file files. This option has been renamed from '-regress_no_stim_times'. Please see '3dDeconvolve -help' for more information. See also -regress_stim_files, -regress_stim_times, -regress_stim_labels. --------------- 3dClustSim options ------------------ -regress_run_clustsim yes/no : add 3dClustSim attrs to stats dset e.g. -regress_run_clustsim no default: yes This option controls whether 3dClustSim will be executed after the regression analysis. Since the default is 'yes', the effective use of this option would be to turn off the operation. 3dClustSim is a more advanced version of AlphaSim, and generates a table of cluster sizes/alpha values that can be then stored in the stats dataset for a simple multiple comparison correction in the cluster interface of the afni GUI. The blur estimates and mask dataset are required, and so the option is only relevant in the context of blur estimation. Please see '3dClustSim -help' for more information. See also -regress_est_blur_epits, -regress_est_blur_epits and -regress_opts_CS. -regress_CS_NN LEVELS : specify NN levels for 3dClustSim command e.g. -regress_CS_NN 1 default: -regress_CS_NN 123 This option allows the user to specify which nearest neighbors to consider when clustering. Cluster results will be generated for each included NN level. Using multiple levels means being able to choose between those same levels when looking at the statistical results using the afni GUI. The LEVELS should be chosen from the set {1,2,3}, where the respective levels mean "shares a face", "shares an edge" and "shares a corner", respectively. Any non-empty subset can be used. They should be specified as is with 3dClustSim. So there are 7 valid subsets: 1, 2, 3, 12, 13, 23, and 123. Please see '3dClustSim -help' for details on its '-NN' option. -regress_opts_CS OPTS ... : specify extra options for 3dClustSim e.g. -regress_opts_CS -athr 0.05 0.01 0.005 0.001 This option allows the user to add extra options to the 3dClustSim command. Only 1 such option should be applied, though multiple options to 3dClustSim can be included. Please see '3dClustSim -help' for more information. See also -regress_run_clustsim. - R Reynolds Dec, 2006 thanks to Z Saad ===========================================================================

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AFNI program: afni_proc.py

Output of -help

Output of `-help`