https://github.com/SIMEXP/fmriprep-denoise-benchmark

Benchmark denoising strategies available from fmriprep.
https://github.com/SIMEXP/fmriprep-denoise-benchmark

Last synced: 11 months ago
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Benchmark denoising strategies available from fmriprep.

• Host: GitHub
• URL: https://github.com/SIMEXP/fmriprep-denoise-benchmark
• Owner: SIMEXP
• License: mit
• Created: 2021-09-15T20:07:08.000Z (over 4 years ago)
• Default Branch: main
• Last Pushed: 2024-01-23T21:27:37.000Z (about 2 years ago)
• Last Synced: 2025-04-13T19:59:52.195Z (12 months ago)
• Language: Python
• Size: 117 MB
• Stars: 16
• Watchers: 4
• Forks: 8
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • Changelog: CHANGELOG.md
  • License: LICENSE
  • Citation: CITATION.cff

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README

          
# Benchmark denoising strategies on fMRIPrep output

[![DOI](https://neurolibre.org/papers/10.55458/neurolibre.00012/status.svg)](https://doi.org/10.55458/neurolibre.00012)

The project is a continuation of [load_confounds](https://github.com/SIMEXP/load_confounds).

The aim is to evaluate the impact of denoising strategy on functional connectivity data, using output processed by fMRIPrep LTS in a reproducible workflow.

**Preprint of the manuscript is now on [biorxiv](https://www.biorxiv.org/content/10.1101/2023.04.18.537240).

The reporducible Jupyter Book preprint is on [NeuroLibre](https://neurolibre.org/papers/10.55458/neurolibre.00012).**

## Recommandations for those who thought this project is a software

Bad news, this is not a software but a research project. 

It's more similar to your regular data science project. 

In other words, the code in this repository reflects the research done for the manuscript, and is not suitable for production level application.

Some useful part of the code has been extracted and further reviewed within SIMEXP lab for deplyment on generic fmriprep derivatives as docker images.

 - *time series and connectome workflow*: [`giga_connectome`](https://github.com/SIMEXP/giga_connectome).

 - *motion quality control metrics*: [`giga_auto_qc`](https://github.com/SIMEXP/giga_auto_qc).

## Quick start

```bash

git clone --recurse-submodules https://github.com/SIMEXP/fmriprep-denoise-benchmark.git

cd fmriprep-denoise-benchmark

virtualenv env

source env/bin/activate

pip install -r binder/requirements.txt

pip install .

make data

make book

```

## Dataset structure

- `binder/` contains files to configure for neurolibre and/or binder hub.

- `content/` is the source of the JupyterBook.

- `data/` is reserved to store data for running analysis.

  To build the book, one will need all the metrics from the study.

  The metrics are here:

  [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.7764979.svg)](https://doi.org/10.5281/zenodo.7764979)

  The data will be automatically downloaded to `content/notebooks/data`.

  You can by pass this step through accessing the Neurolibre preprint [![DOI](https://neurolibre.org/papers/10.55458/neurolibre.00012/status.svg)](https://doi.org/10.55458/neurolibre.00012)!

- Custom code is located in `fmriprep_denoise/`. This project is installable.

- Preprocessing SLURM scripts, and scripts for creating figure for manuscript are in `scripts/`.