[K9P0] Pipeline reproduction (AFNI - deriv)

[bclenet]

Softwares

• softwares : AFNI 18.2.15

Input data

derivatives (fMRIprep)

Additional context

• • preprocessing_comments : All of the preprocessing steps aside from smoothing, scaling, and scrubbing, are based on what we have inferred from the fmriprep documentation. Since we did not apply fmriprep ourselves, our report reflects our best estimate of which defaults and parameters were used for the preprocessed data that was given to us.

Subject level : AFNI's 3dDeconolve, AFNI's 3dREMLfit
Group level : AFNI's 3dMEMA, AFNI's 3dClustSim

List of tasks

Please tick the boxes below once the corresponding task is finished. 👍

• 👌 A maintainer of the project approved the issue, by assigning a 🏁status: ready for dev label to it.
• 🌳 Create a branch on your fork to start the reproduction.
• 🌅 Create a file team_{team_id}.py inside the narps_open/pipelines/ directory. You can use a file inside narps_open/pipelines/templates as a template if needed.
• 📥 Create a pull request as soon as you completed the previous task.
• 🧠 Write the code for the pipeline, using Nipype and the file architecture described in docs/pipelines.md.
• 📘 Make sure your code is documented enough.
• 🐍 Make sure your code is explicit and conforms with PEP8.
• 🔬 Create tests for your pipeline. You can use files in tests/pipelines/test_team_* as examples.
• 🔬 Make sure your code passes all the tests you created (see docs/testing.md).