Benchmark denoising strategies on fMRIPrep output

The project is a continuation of 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 this project is now on biorxiv.

Quick start

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 building the JupyterBook. To build the book, one will need all the metrics from the study. The metrics are here:

• Custom code is located in fmriprep_denoise/. This project is installable.

• Preprocessing SLURM scripts are in script/

Poster and presentations

The results will be presented at QBIN science day 2023 as a flash talk, and OHBM 2023 Montreal as a poster 🎉.

The preliminary results were be presented at OHBM 2022 as a poster.