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

Preprocess functional tasks in a BIDS dataset.
https://github.com/bids-apps/nipypelines

bids bidsapp

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Preprocess functional tasks in a BIDS dataset.

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README 

        
## Dataflows for the masses



### Description

This app allows preprocessing functional tasks in a BIDs dataset.



A preprocessing workflow for functional timeseries data.



This workflow makes use of ANTS, FreeSurfer, FSL, NiPy, and CompCor.



This workflow includes 2mm subcortical atlas and templates that are available from:



http://mindboggle.info/data.html



specifically the 2mm versions of:



- `Joint Fusion Atlas `_

- `MNI template `_



Requirements:

Current dataflow requires freesurfer to have been run on a participant and stored in /bids_dataset/derivatives/freesurfer.

The docker container allows you to mount your own freesurfer directory and provide the path to it using the `--subjects_dir` 

flag.



### How to report errors

Please create a new issue here: https://github.com/BIDS-Apps/nipypelines/issues/new



### Acknowledgements

Please use the following zenodo citation when using this App.



### Usage

This App has the following command line arguments:



```

usage: run.py [-h]

              [--participant_label PARTICIPANT_LABEL [PARTICIPANT_LABEL ...]]

              [-t TARGET_FILE] [--subjects_dir FSDIR]

              [--target_surfaces TARGET_SURFS [TARGET_SURFS ...]]

              [--vol_fwhm VOL_FWHM] [--surf_fwhm SURF_FWHM] [-l LOWPASS_FREQ]

              [-u HIGHPASS_FREQ] [-w WORK_DIR] [-p PLUGIN]

              [--plugin_args PLUGIN_ARGS]

              bids_dir output_dir {participant}



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 

                        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) using the same output_dir.



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 should be 

			analyzed. Multiple participants can be specified with a space separated list.

  -t TARGET_FILE, --target TARGET_FILE

                        Target in MNI space. Best to use the MindBoggle template - 

			OASIS-30_Atropos_template_in_MNI152_2mm.nii.gz

  --subjects_dir FSDIR  FreeSurfer subject directory

  --target_surfaces TARGET_SURFS [TARGET_SURFS ...]

                        FreeSurfer target surfaces (default ['fsaverage5'])

  --vol_fwhm VOL_FWHM   Spatial FWHM (default 6.0)

  --surf_fwhm SURF_FWHM

                        Spatial FWHM (default 15.0)

  -l LOWPASS_FREQ, --lowpass_freq LOWPASS_FREQ

                        Cutoff frequency for low pass filter (Hz) (default 0.1)

  -u HIGHPASS_FREQ, --highpass_freq HIGHPASS_FREQ

                        Cutoff frequency for high pass filter (Hz) (default 0.01)

  -w WORK_DIR, --work_dir WORK_DIR

                        Work directory

  -p PLUGIN, --plugin PLUGIN

                        Plugin to use

  --plugin_args PLUGIN_ARGS

                        Plugin arguments

```



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



```

    docker run -i --rm \

		-v /path/to/ds005:/bids_dataset \

		-v /path/to/outputs:/outputs \

		bids/nipypelines \

		/bids_dataset /outputs participant --participant_label 01

```



### Commercial use



The following **non-free** Debian packages are part of this BIDS App:



    non-free/science        fsl-5.0-core

    non-free/science        fsl-atlases



If you are considering commercial use of this App please consult the relevant licenses.



### Using reprozip to minimize container size



This app compresses the FreeSurfer and FSL routines used in the Dockerfile to minimize size. This was carried 

out using (reprozip)[https://vida-nyu.github.io/reprozip/] inside a docker container. 



`reprozip trace --dir /outputs/trace python run.py /bids_dataset /outputs participant --participant_label 01`



This generates a `config.yml` containing all the necessary files used in running the software. The necessary 

files for running the app is generated using the following snippet of code after running `reprozip trace`.



```

from yaml import read

import yaml as yl

import os

import json



with open('config.yml', 'rt') as fp:

    data = yl.load(fp)



paths1 = [val['path'] for val in data['inputs_outputs'] if all([key not in val['path'] for key in ('bids_dataset', 'scratch', 'outputs', '/run')])]

paths2 = [val for val in data['other_files'] if ('fsl' in val or 'opt' in val ) and ('miniconda' not in val and 'bids_dataset' not in val)]



paths = [val for val in paths1 if val not in paths2] + paths2

files = [val for val in paths if not os.path.isdir(val)]



os.system('tar zcf files.tgz %s' % ' '.join(files))



rel_env = dict([(k, v) for k, v in data['runs'][0]['environ'].items() if any([key in k.lower() or key in v.lower() for key in ('fsl', 'freesurfer')])])

info = dict(files=files, environ=rel_env)



with open('appinfo.json', 'wt') as fp:

    json.dump(info, fp, indent=2)

```



### TODO



  - [ ] Add revised version with Topup processing

  - [ ] Add CIFTI2 output using nibabel

  - [ ] Allow custom ROIs to be processed

  - [ ] Generate default seed-based connectomes 

  - [ ] Generate embedded maps