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Benchmark denoising strategies on fMRIPrep output

Jupyter Book Badge

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.

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
make book

Dataset structure

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

  • content/ is the source of the JupyterBook.

  • inputs/ 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: DOI

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

  • Preprocessing SLURM scripts are in script/

Poster

The preliminary results will be presented at OHBM 2022 as a poster. Please find poster number WTh570.

Find the presenter at the virtual poster session on Wednesday, June 8, 2022, 8:30 PM GMT + 1.

At Glasgow, please contact the presenter on Twitter to schedule a time to chat, or come to see the presenter on Wednesday, June 22, 2022, 12:45 PM at the poster hall.

spoiler