Hao-TingJun 21, 2023
Our work “A reproducible benchmark of resting-state #fMRI denoising strategies using #fMRIPrep and @nilearn is now officially on the reproducible preprint service #NeuroLibre and @biorxivpreprint 🎉
https://neurolibre.org/papers/10.55458/neurolibre.00012
A reproducible benchmark of resting-state fMRI denoising strategies using fMRIPrep and Nilearn

Wang et al., (2023). A reproducible benchmark of resting-state fMRI denoising strategies using fMRIPrep and Nilearn. NeuroLibre Reproducible Preprints, 12, https://doi.org/10.55458/neurolibre.00012

NeuroLibre
Show threadHao-TingJun 21, 2023
We love #fmriprep, but the confound documentation is a bit long and difficult to navigate. It’s not a trivial job to get the right regressors implemented in the benchmarks done on non-fMRIPrep workflow.
Show threadHao-TingJun 21, 2023
To ensure a robust and reusable workflow, we implemented APIs in @nilearn to retrieve the confound regressors for denoising fMRIPrep data. You can see the example here to see how to use it in your analysis: https://nilearn.github.io/stable/auto_examples/03_connectivity/plot_signal_extraction.html
Extracting signals from a brain parcellation

Here we show how to extract signals from a brain parcellation and compute a correlation matrix. We also show the importance of defining good confounds signals: the first correlation matrix is compu...

Nilearn
Show threadHao-Ting
We created a fully reproducible denoising benchmark featuring a range of denoising strategies and evaluation metrics for connectivity analyses based on the classic paper Ciric 2017, built on the #fMRIPrep and @nilearn software packages.
Jun 21, 2023 at 2:50pmWeb
Show threadHao-TingJun 21, 2023
We apply this reproducible benchmark to investigate the robustness of the conclusions across two #OpenNeuro datasets and two LTS versions of #fMRIPrep.
Show threadHao-TingJun 21, 2023
The reproducible element didn’t stop on my local computing cluster! In fact I lost my work laptop during the process of making manuscript ready figures. Thanks to the magic of reproducibility practice, I cloned my repo and reinstated the whole work in a day.
Show threadHao-TingJun 21, 2023
You can experience this yourself (without losing your laptop and resetting your work environment) on #neurolibre 🎉 All the quality metrics data are available and you can check out all the figures in the manuscript, and all the alternative visualisations!
https://neurolibre.org/papers/10.55458/neurolibre.00012
A reproducible benchmark of resting-state fMRI denoising strategies using fMRIPrep and Nilearn

Wang et al., (2023). A reproducible benchmark of resting-state fMRI denoising strategies using fMRIPrep and Nilearn. NeuroLibre Reproducible Preprints, 12, https://doi.org/10.55458/neurolibre.00012

NeuroLibre
Show threadHao-TingJun 21, 2023
You can read the manuscript version on @biorxivpreprint https://www.biorxiv.org/content/10.1101/2023.04.18.537240v2
Thanks to you all on and off mastodon
@pierre_bellec @smeisler @effigies
@brainhackorg