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Contributing to fMRIPrep

Welcome to the fMRIPrep repository! We're excited you're here and want to contribute.

Imposter's syndrome disclaimer1: We want your help. No, really.

There may be a little voice inside your head that is telling you that you're not ready to be an open-source contributor; that your skills aren't nearly good enough to contribute. What could you possibly offer a project like this one?

We assure you - the little voice in your head is wrong. If you can write code at all, you can contribute code to open-source. Contributing to open-source projects is a fantastic way to advance one's coding skills. Writing perfect code isn't the measure of a good developer (that would disqualify all of us!); it's trying to create something, making mistakes, and learning from those mistakes. That's how we all improve, and we are happy to help others learn.

Being an open-source contributor doesn't just mean writing code, either. You can help out by writing documentation, tests, or even giving feedback about the project (and yes - that includes giving feedback about the contribution process). Some of these contributions may be the most valuable to the project as a whole, because you're coming to the project with fresh eyes, so you can see the errors and assumptions that seasoned contributors have glossed over.

Driving principles

fMRIPrep is built around three overarching principles:

  1. Robustness - The pipeline adapts the preprocessing steps depending on the input dataset and should provide results as good as possible independently of scanner make, scanning parameters or presence of additional correction scans (such as fieldmaps).
  2. Ease of use - Thanks to dependence on the BIDS standard, manual parameter input is reduced to a minimum, allowing the pipeline to run in an automatic fashion.
  3. "Glass box" philosophy - Automation should not mean that one should not visually inspect the results or understand the methods. Thus, fMRIPrep provides visual reports for each subject, detailing the accuracy of the most important processing steps. This, combined with the documentation, can help researchers to understand the process and decide which subjects should be kept for the group level analysis.

These principles distill some design and organizational foundations:

  1. The tool only and fully supports BIDS and BIDS-Derivatives for the input and output data.
  2. The tool is packaged as a fully-compliant BIDS-App, not just in its user interface, but also in the continuous integration, testing and delivery.
  3. The tool is rigorously restricted to functional MRI preprocessing, including (but not limited to): input/output metadata assessment, head-motion correction, susceptibility-distortion correction, co-registration with anatomical data, spatial normalization to neuroimaging templates, noise components and other confounds extraction, etc. In other words, the tool does not deal with time filtering*, smoothing*, modeling, or connectivity extraction.
  4. The tool is agnostic to subsequent analysis, i.e., any software supporting BIDS-Derivatives for its inputs should be amenable to fit GLMs, extract time-series for connectivity analyses, etc.
  5. The tool is thoroughly and transparently documented (including the generation of individual reports that can be used as scaffolds for understanding the underpinnings and design decisions of the tool).
  6. The tool is community-driven, with a very open concept of contribution that is always credited with authorship offers when writing relevant papers.

* if you specify --use-aroma, filtering of the ICA-AROMA noise components and smoothing of the data will be applied to the bold file containing the phrase desc-smoothAROMAnonaggr_bold. Please see the fMRIPrep ICA-AROMA documentation for details.

Practical guide to submitting your contribution

These guidelines are designed to make it as easy as possible to get involved. If you have any questions that aren't discussed below, please let us know by opening an issue!

Before you start, you'll need to set up a free GitHub account and sign in. Here are some instructions.

Already know what you're looking for in this guide? Jump to the following sections:

Joining the conversation

fMRIPrep is maintained by a growing group of enthusiastic developers— and we're excited to have you join! Most of our discussions will take place on open issues.

We also encourage users to report any difficulties they encounter on NeuroStars, a community platform for discussing neuroimaging.

We actively monitor both spaces and look forward to hearing from you in either venue!

Contributing through GitHub

git is a really useful tool for version control. GitHub sits on top of git and supports collaborative and distributed working.

If you're not yet familiar with git, there are lots of great resources to help you git started! Some of our favorites include the git Handbook and the Software Carpentry introduction to git.

On GitHub, You'll use Markdown to chat in issues and pull requests. You can think of Markdown as a few little symbols around your text that will allow GitHub to render the text with a little bit of formatting. For example, you could write words as bold (**bold**), or in italics (*italics*), or as a link ([link]( to another webpage.

GitHub has a really helpful page for getting started with writing and formatting Markdown on GitHub.

Understanding issues

Every project on GitHub uses issues slightly differently.

The following outlines how the fMRIPrep developers think about these tools.

  • Issues are individual pieces of work that need to be completed to move the project forward. A general guideline: if you find yourself tempted to write a great big issue that is difficult to describe as one unit of work, please consider splitting it into two or more issues.

    Issues are assigned labels which explain how they relate to the overall project's goals and immediate next steps.

Issue Labels

The current list of issue labels are here and include:

  • Good first issue These issues contain a task that is amenable to new contributors because it doesn't entail a steep learning curve.

    If you feel that you can contribute to one of these issues, we especially encourage you to do so!

  • Bug These issues point to problems in the project.

    If you find new a bug, please give as much detail as possible in your issue, including steps to recreate the error. If you experience the same bug as one already listed, please add any additional information that you have as a comment.

  • Feature These issues are asking for new features and improvements to be considered by the project.

    Please try to make sure that your requested feature is distinct from any others that have already been requested or implemented. If you find one that's similar but there are subtle differences, please reference the other request in your issue.

In order to define priorities and directions in the development roadmap, we have two sets of special labels:

Label Description
GitHub labels
GitHub labels
GitHub labels
Estimation of the downstream impact the proposed feature/bugfix will have.
GitHub labels
GitHub labels
GitHub labels
Estimation of effort required to implement the requested feature or fix the reported bug.

One way to understand these labels is to consider how they would apply to an imaginary issue. For example, if -- after a release -- a bug is identified that re-introduces a previously solved issue (i.e., its regresses the code outputs to some undesired behavior), we might assign it the following labels: GitHub labels GitHub labels GitHub labels. Its development priority would then be "high", since it is a low-effort, high-impact change.

Long-term goals may be labelled as a combination of: GitHub labels and GitHub labels or GitHub labels since they will have a high-impact on the code-base, but require a medium or high amount of effort. Of note, issues with the labels: GitHub labels or GitHub labels are less likely to be addressed because they are less likely to impact the code-base, or because they will require a very high activation energy to do so.

Making a change

We appreciate all contributions to fMRIPrep, but those accepted fastest will follow a workflow similar to the following:

  1. Comment on an existing issue or open a new issue referencing your addition.
    This allows other members of the fMRIPrep development team to confirm that you aren't overlapping with work that's currently underway and that everyone is on the same page with the goal of the work you're going to carry out.
    This blog is a nice explanation of why putting this work in up front is so useful to everyone involved.

  2. Fork the fMRIPrep repository to your profile.
    This is now your own unique copy of fMRIPrep. Changes here won't affect anyone else's work, so it's a safe space to explore edits to the code!

  3. Clone your forked fMRIPrep repository to your machine/computer.
    While you can edit files directly on github, sometimes the changes you want to make will be complex and you will want to use a text editor that you have installed on your local machine/computer. (One great text editor is vscode).

    In order to work on the code locally, you must clone your forked repository.

    To keep up with changes in the fMRIPrep repository, add the "upstream" fMRIPrep repository as a remote to your locally cloned repository.

    git remote add upstream

    Make sure to keep your fork up to date with the upstream repository.

    For example, to update your master branch on your local cloned repository:

    git fetch upstream
    git checkout master
    git merge upstream/master
  4. Create a new branch to develop and maintain the proposed code changes.
    For example:

    git fetch upstream  # Always start with an updated upstream
    git checkout -b fix/bug-1222 upstream/master

    Please consider using appropriate branch names as those listed below, and mind that some of them are special (e.g., doc/ and docs/):

    • fix/<some-identifier>: for bugfixes
    • enh/<feature-name>: for new features
    • doc/<some-identifier>: for documentation improvements. You should name all your documentation branches with the prefix doc/ or docs/ as that will preempt triggering the full battery of continuous integration tests.
  5. Make the changes you've discussed, following the fMRIPrep coding style guide.
    Try to keep the changes focused: it is generally easy to review changes that address one feature or bug at a time. It can also be helpful to test your changes locally, using a fMRIPrep development environment. Once you are satisfied with your local changes, add/commit/push them to the branch on your forked repository.

  6. Submit a pull request.
    A member of the development team will review your changes to confirm that they can be merged into the main code base.
    Pull request titles should begin with a descriptive prefix (for example, ENH: Support for SB-reference in multi-band datasets):

    • ENH: enhancements or new features (example)
    • FIX: bug fixes (example)
    • TST: new or updated tests (example)
    • DOC: new or updated documentation (example)
    • STY: style changes (example)
    • REF: refactoring existing code (example)
    • CI: updates to continous integration infrastructure (example)
    • MAINT: general maintenance (example)
    • For works-in-progress, add the WIP tag in addition to the descriptive prefix. Pull-requests tagged with WIP: will not be merged until the tag is removed.
  7. Have your PR reviewed by the developers team, and update your changes accordingly in your branch.
    The reviewers will take special care in assisting you address their comments, as well as dealing with conflicts and other tricky situations that could emerge from distributed development.

fMRIPrep coding style guide

Whenever possible, instances of Nipype Nodes and Workflows should use the same names as the variables they are assigned to. This makes it easier to relate the content of the working directory to the code that generated it when debugging.

Workflow variables should end in _wf to indicate that they refer to Workflows and not Nodes. For instance, a workflow whose basename is myworkflow might be defined as follows:

from nipype.pipeline import engine as pe

myworkflow_wf = pe.Workflow(name='myworkflow_wf')

If a workflow is generated by a function, the name of the function should take the form init_<basename>_wf:

def init_myworkflow_wf(name='myworkflow_wf):
    workflow = pe.Workflow(name=name)
    return workflow

myworkflow_wf = init_workflow_wf(name='myworkflow_wf')

If multiple instances of the same workflow might be instantiated in the same namespace, the workflow names and variables should include either a numeric identifier or a one-word description, such as:

myworkflow0_wf = init_workflow_wf(name='myworkflow0_wf')
myworkflow1_wf = init_workflow_wf(name='myworkflow1_wf')

# or

myworkflow_lh_wf = init_workflow_wf(name='myworkflow_lh_wf')
myworkflow_rh_wf = init_workflow_wf(name='myworkflow_rh_wf')

Recognizing contributions

We welcome and recognize all contributions regardless their size, content or scope: from documentation to testing and code development. You can see a list of current developers and contributors in our zenodo file. Before every release, a new zenodo file will be generated. The update script will also sort creators and contributors by the relative size of their contributions, as provided by the git-line-summary utility distributed with the git-extras package. Last positions in both the creators and contributors list will be reserved to the project leaders. These special positions can be revised to add names by punctual request and revised for removal and update of ordering in an scheduled manner every two years. All the authors enlisted as creators participate in the revision of modifications.


Developers are members of a wonderful team driving the project. Names and contacts of all developers are included in the .maint/developers.json file Examples of steering activities that drive the project are: actively participating in the follow-up meetings, leading documentation sprints, helping in the design of the tool and definition of the roadmap, providing resources (in the broad sense, including funding), code-review, etc.


Contributors enlisted in the .maint/contributors.json file actively help or have previously helped the project in a broad sense: writing code, writing documentation, benchmarking modules of the tool, proposing new features, helping improve the scientific rigor of implementations, giving out support on the different communication channels (mattermost, NeuroStars, GitHub issues, etc.). If you are new to the project, don't forget to add your name and affiliation to the list of contributors there! Our Welcome Bot will send an automated message reminding this to first-time contributors. Before every release, unlisted contributors will be invited again to add their names to the file (just in case they missed the automated message from our Welcome Bot).

Contributors who have contributed at some point to the project but were required or they wished to disconnect from the project's updates and to drop-out from publications and other dissemination activities, are listed in the .maint/former.json file.


Anyone listed as a developer or a contributor can start the submission process of a manuscript as first author. To compose the author list, all the creators MUST be included (except for those people who opt to drop-out) and all the contributors MUST be invited to participate. First authorship(s) is (are) reserved for the authors that originated and kept the initiative of submission and wrote the manuscript. To generate the ordering of your paper, please run python .maint/ from the root of the repository, on the up-to-date upstream/master branch. Then, please modify this list and place your name first. All developers and contributors are pulled together in a unique list, and last authorships assigned. fMRIPrep and its community adheres to open science principles, such that a pre-print should be posted on an adequate archive service (e.g., ArXiv or BioRxiv) prior publication.


fMRIPrep is licensed under the Apache 2.0 license. By contributing to fMRIPrep, you acknowledge that any contributions will be licensed under the same terms.

Thank you!

You're awesome. 👋😃

— Based on contributing guidelines from the STEMMRoleModels project.


  1. The imposter syndrome disclaimer was originally written by Adrienne Lowe for a PyCon talk, and was adapted based on its use in the README file for the MetPy project.