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https://github.com/bids-apps/afni_proc

prototype AFNI bids app implmenting participant level preprocessing with afni_proc.py
https://github.com/bids-apps/afni_proc

bids bidsap mri preprocessing

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prototype AFNI bids app implmenting participant level preprocessing with afni_proc.py

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README 

        
## 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:



```bash

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](https://afni.nimh.nih.gov/pub/dist/doc/program_help/afni_proc.py.html).



### How to report errors



Specific issues with this BIDS App should be reported on its

[issues page](https://github.com/nih-fmrif/afni_proc_BIDS_app/issues). AFNI

issues should be posted to the

[AFNI Message Board](https://afni.nimh.nih.gov/afni/community/board/list.php?1)



### 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- 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):



```bash

    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.