[50GV] Pipeline reproduction (FSL, denoiser - deriv)

[bclenet]

Softwares

FSL 5.0.9
Custom code : denoiser package (https://github.com/arielletambini/denoiser).

Input data

derivatives (fMRIprep)

Additional context

• preprocessing_order : Since we used fmriprep pre-processed data, the only pre-processing our team carried out was denoising the fmriprep pre-processed data using the confounds files generated from fmriprep and a denoiser package (https://github.com/arielletambini/denoiser).
• noise_removal : Nuisance regression of 10 confounds from confounds.tsv file from fmriprep using a denoiser package (https://github.com/arielletambini/denoiser). We regressed out the following: White Matter, CSF, Framewise Displacement, stdDVARS, and 6 motion parameters (X, Y, Z, RotX, RotY, RotZ)
• spatial_smoothin : 5mm FWHM spatial smoothing carried out in FSL as part of FEAT analysis in the fmriprep MNI space preprocessed data.

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).