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Usage Notes

Warning

As of fMRIPrep 1.0.12, the software includes a tracking system to report usage statistics and errors. Users can opt-out using the --notrack command line argument.

Execution and the BIDS format

The fMRIPrep workflow takes as principal input the path of the dataset that is to be processed. The input dataset is required to be in valid :abbr:`BIDS (Brain Imaging Data Structure)` format, and it must include at least one T1w structural image and (unless disabled with a flag) a BOLD series. We highly recommend that you validate your dataset with the free, online BIDS Validator.

The exact command to run fMRIPRep depends on the Installation_ method. The common parts of the command follow the BIDS-Apps definition. Example:

fmriprep data/bids_root/ out/ participant -w work/

Further information about BIDS and BIDS-Apps can be found at the NiPreps portal.

Command-Line Arguments

.. argparse::
   :ref: fmriprep.cli.parser._build_parser
   :prog: fmriprep
   :nodefault:
   :nodefaultconst:

The command-line interface of the docker wrapper

.. argparse::
   :ref: fmriprep_docker.get_parser
   :prog: fmriprep-docker
   :nodefault:
   :nodefaultconst:

Limitations and reasons not to use fMRIPrep

  1. Very narrow :abbr:`FoV (field-of-view)` images oftentimes do not contain enough information for standard image registration methods to work correctly. Also, problems may arise when extracting the brain from these data. fMRIPrep supports pre-aligned BOLD series, and accepting pre-computed derivatives such as brain masks is a target of future effort.
  2. fMRIPrep may also underperform for particular populations (e.g., infants) and non-human brains, although appropriate templates can be provided to overcome the issue.
  3. The "EPInorm" approach is currently not supported, although we plan to implement this feature (see #620).
  4. If you really want unlimited flexibility (which is obviously a double-edged sword).
  5. If you want students to suffer through implementing each step for didactic purposes, or to learn shell-scripting or Python along the way.
  6. If you are trying to reproduce some in-house lab pipeline.

(Reasons 4-6 were kindly provided by S. Nastase in his open review of our pre-print).

The FreeSurfer license

fMRIPRep uses FreeSurfer tools, which require a license to run.

To obtain a FreeSurfer license, simply register for free at https://surfer.nmr.mgh.harvard.edu/registration.html.

When using manually-prepared environments or singularity, FreeSurfer will search for a license key file first using the $FS_LICENSE environment variable and then in the default path to the license key file ($FREESURFER_HOME/license.txt). If using the --cleanenv flag and $FS_LICENSE is set, use --fs-license-file $FS_LICENSE to pass the license file location to fMRIPRep.

It is possible to run the docker container pointing the image to a local path where a valid license file is stored. For example, if the license is stored in the $HOME/.licenses/freesurfer/license.txt file on the host system:

$ docker run -ti --rm \
    -v $HOME/fullds005:/data:ro \
    -v $HOME/dockerout:/out \
    -v $HOME/.licenses/freesurfer/license.txt:/opt/freesurfer/license.txt \
    nipreps/fmriprep:latest \
    /data /out/out \
    participant \
    --ignore fieldmaps

Using FreeSurfer can also be enabled when using fmriprep-docker:

$ fmriprep-docker --fs-license-file $HOME/.licenses/freesurfer/license.txt \
    /path/to/data/dir /path/to/output/dir participant
RUNNING: docker run --rm -it -v /path/to/data/dir:/data:ro \
    -v /home/user/.licenses/freesurfer/license.txt:/opt/freesurfer/license.txt \
    -v /path/to_output/dir:/out nipreps/fmriprep:1.0.0 \
    /data /out participant
...

If the environment variable $FS_LICENSE is set in the host system, then it will automatically used by fmriprep-docker. For instance, the following would be equivalent to the latest example:

$ export FS_LICENSE=$HOME/.licenses/freesurfer/license.txt
$ fmriprep-docker /path/to/data/dir /path/to/output/dir participant
RUNNING: docker run --rm -it -v /path/to/data/dir:/data:ro \
    -v /home/user/.licenses/freesurfer/license.txt:/opt/freesurfer/license.txt \
    -v /path/to_output/dir:/out nipreps/fmriprep:1.0.0 \
    /data /out participant
...

Reusing precomputed derivatives

Reusing a previous, partial execution of fMRIPrep

fMRIPrep will pick up where it left off a previous execution, so long as the work directory points to the same location, and this directory has not been changed/manipulated.

Using a previous run of FreeSurfer

fMRIPrep will automatically reuse previous runs of FreeSurfer if a subject directory named freesurfer/ is found in the output directory (<output_dir>/freesurfer). Reconstructions for each participant will be checked for completeness, and any missing components will be recomputed. You can use the --fs-subjects-dir flag to specify a different location to save FreeSurfer outputs. If precomputed results are found, they will be reused.

The anatomical fast-track

Starting with version 20.1.0, fMRIPrep has a command-line argument (--anat-derivatives <PATH>) to indicate a path from which the preprocessed information derived from the T1w, T2w (if present) and FLAIR (if present) images. This feature was envisioned to help process very large multi-session datasets where the anatomical images can be averaged (i.e., anatomy is not expected to vary substantially across sessions). An example where this kind of processing would be useful is My Connectome, a dataset that contains 107 sessions for a single-subject. Most of these sessions contain anatomical information which, given the design of the dataset, can be averaged across sessions as no substantial changes should happen. In other words, the anatomical information of the dataset can be considered as cross-sectional. Before version 20.1.0, preprocessing this dataset would be hard for two limitations:

  • if the dataset were to be processed in just one enormous job (be it in a commercial Cloud or :abbr:`HPC (high-performance computing)` resources), the amount of data to be processed surely would exceed the time limitations per job (and/or related issues, such as restarting from where it left before); or
  • if the processing were split in sessions, then fMRIPrep would attempt to re-process the anatomical information for every session.

Because processing this emerging type of datasets (densely sampled neuroimaging) was impractical with fMRIPrep, the option --anat-derivatives will shortcut the whole anatomical processing.

!DANGER!

Using the anatomical fast-track (the --anat-derivatives argument) has important side-effects that risk the reproducibility and reliability of fMRIPrep. This flag breaks fMRIPrep's internal tracing of provenance, and it trusts whatever input fMRIPrep is given (so long it is BIDS-Derivatives compliant and contains all the necessary files).

When reporting results obtained with --anat-derivatives, please make sure you highlight this particular deviation from fMRIPrep, and clearly describe the alternative preprocessing of anatomical data.

Attention!

When the intention is to combine the anatomical fast-track with some advanced options that involve standard spaces (e.g., --use-aroma or --cifti-output), please make sure you include the MNI152NLin6Asym space to the --output-spaces list in the first invocation of fMRIPrep (or sMRIPrep) from which the results are to be reused. Otherwise, a warning message indicating that fMRIPrep's expectations were not met will be issued, and the pre-computed anatomical derivatives will not be reused.

Troubleshooting

Logs and crashfiles are outputted into the <output dir>/fmriprep/sub-<participant_label>/log directory. Information on how to customize and understand these files can be found on the nipype debugging page.

Support and communication. The documentation of this project is found here: http://fmriprep.readthedocs.org/en/latest/.

All bugs, concerns and enhancement requests for this software can be submitted here: https://github.com/nipreps/fmriprep/issues.

If you have a problem or would like to ask a question about how to use fMRIPRep, please submit a question to NeuroStars.org with an fmriprep tag. NeuroStars.org is a platform similar to StackOverflow but dedicated to neuroinformatics.

Previous questions about fMRIPRep are available here: http://neurostars.org/tags/fmriprep/

To participate in the fMRIPRep development-related discussions please use the following mailing list: http://mail.python.org/mailman/listinfo/neuroimaging Please add [fmriprep] to the subject line when posting on the mailing list.