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README.md

Prototype AFNI preprocessing app

Description

This is a prototype AFNI bids app implmenting participant level preprocessing with afni_proc.py. This pipeline is currently doing temporal alignment, nonlinear registration to standard space, bluring of 4 mm, masking, and scaling for all epis in the input bids dataset using the following afni proc command:

afni_proc.py -subj_id {subj_id} \
 -script proc.bids -scr_overwrite -out_dir {out_dir} \
-blocks tshift align tlrc volreg blur mask scale \
-copy_anat {anat_path} -tcat_remove_first_trs 0 \
-dsets {epi_paths} -align_opts_aea -cost lpc+ZZ -giant_move \
-tlrc_base MNI152_T1_2009c+tlrc -tlrc_NL_warp \
-volreg_align_to MIN_OUTLIER \
-volreg_align_e2a -volreg_tlrc_warp -blur_size 4.0 -bash

Documentation

Documenation for afni_proc.py is available here.

How to report errors

Specific issues with this BIDS App should be reported on its issues page. AFNI issues should be posted to the AFNI Message Board

Acknowledgements

Please cite the 1996 paper if you use AFNI: Cox RW (1996) AFNI: Software for analysis and visualization of functional magnetic resonance neuroimages. Comput Biomed Res 29(3):162–173

Usage

This App has the following command line arguments:

	usage: run.py [-h]
	              [--participant_label PARTICIPANT_LABEL [PARTICIPANT_LABEL ...]]
	              bids_dir output_dir

	Example BIDS App entry point script.

	positional arguments:
	  bids_dir              The directory with the input dataset formatted
	                        according to the BIDS standard.
	  output_dir            The directory where the output files should be stored.
	                        If you are running a group level analysis, this folder
	                        should be prepopulated with the results of
	                        the participant level analysis.
	  {participant}         Level of the analysis that will be performed. Multiple
                            participant level analyses can be run independently
                            (in parallel). Only "participant" is currently supported.

	optional arguments:
	  -h, --help            show this help message and exit
	  --participant_label PARTICIPANT_LABEL [PARTICIPANT_LABEL ...]
	                        The label(s) of the participant(s) that should be
	                        analyzed. The label corresponds to
	                        sub-<participant_label> from the BIDS spec (so it does
	                        not include "sub-"). If this parameter is not provided
	                        all subjects will be analyzed. Multiple participants
	                        can be specified with a space separated list.
	  --afni_proc AFNI_PROC
                            Optional: command string for afni proc. Parameters
                            that vary by subject should be encapsulated in curly
                            braces and must all be included {subj_id},
                            {out_dir}, {anat_path}, or {epi_paths}.-script option
                            is added automatically so don't add it to the command. The first
                            _T1w for each subject will currently be used as the
                            anat.All of the _bold will be used as the
                            functionals.Example:--afni_proc="-subj_id {subj_id} -scr_overwrite -out_dir {out_dir} -blocks tshift align tlrc volreg blur mask scale -copy_anat {anat_path} -tcat_remove_first_trs 0 -dsets {epi_paths} -align_opts_aea -cost lpc+ZZ -giant_move -tlrc_base MNI152_T1_2009c+tlrc -tlrc_NL_warp -volreg_align_to MIN_OUTLIER -volreg_align_e2a -volreg_tlrc_warp -blur_size 4.0 -bash"

To run it in participant level mode (for one participant):

docker run -i --rm \
	-v /Users/filo/data/ds005:/bids_dataset:ro \
	-v /Users/filo/outputs:/outputs \
	bids/example \
	/bids_dataset /outputs participant --participant_label 01

Special considerations

This is a very early prototype. More functionality is likely coming. Expect breaking changes.