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Dataflows for the masses


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:

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:


Please use the following zenodo citation when using this App.


This App has the following command line arguments:

usage: [-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
                        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 should be 
			analyzed. Multiple participants can be specified with a space separated list.
                        Target in MNI space. Best to use the MindBoggle template - 
  --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)[] inside a docker container.

reprozip trace --dir /outputs/trace python /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)


  • 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