paper.md

title	tags	authors	affiliations	date	bibliography
Benchmark denoising strategies on fMRIPrep outputs	fMRIPrep denoising fMRI	name affiliation orcid Hao-Ting Wang 1, 7 0000-0003-4078-2038 name affiliation orchid Steven L Meisler 2, 3 0000-0002-8888-1572 name affiliation orchid Hanad Sharmarke 1 name affiliation orchid François Paugam 4 name affiliation orchid Nicolas Gensollen 5 0000-0001-7199-9753 name affiliation orchid Bertrand Thirion 5 0000-0001-5018-7895 name affiliation orchid Christopher J Markiewicz 6 0000-0002-6533-164X name affiliation orchid Pierre Bellec 1, 7 0000-0002-9111-0699	name index Centre de recherche de l'Institut universitaire de gériatrie de Montréal (CRIUGM), Montréal, Québec, Canada 1 name index Harvard University, MA, USA 2 name index Massachusetts Institute of Technology, MA, USA 3 name index Computer Science and Operations Research Department, Université de Montréal, Montréal, Québec, Canada 4 name index Inria, CEA, Université Paris-Saclay, Paris, France 5 name index Department of Psychology, Stanford University, Stanford, United States 6 name index Psychology Department, Université de Montréal, Montréal, Québec, Canada 7	05 October 2021	paper.bib

Summary

Various fMRI denoising benchmark has been published in the past, however the impact of the denoising strategies has yet to be evaluated on the popular minimal preprocessing tool fMRIPrep [@fmriprep1]. The confound output of fMRIPrep presented the users with a range of possible nusianse regressors. While users are benifted from a wide selection of regressors, without understanding of the literature one can pick a combination of regressors that reintroduce noise to the signal. Current work aims to introduce an application programming interface (API) to standardise fMRIPrep confounds retrieval, and provide benchmarks of different strategies using functional connectivity generated from resting state data. The main tool is a part of nilearn [@nilearn].

Acknowledgements

The initial API was started by Hanad Sharmarke and Pierre Bellec. The implementation was completed by Hao-Ting Wang, Steven Meisler, François Paugam, and Pierre Bellec. Hao-Ting Wang migrated the code base to nilearn. Nicolas Gensollen and Bertrand Thirion reviewed the code migrated to nilearn. We thank Chris Markiewicz for feedbacks related to fMRIPrep.

Hao-Ting Wang and Pierre Bellec drafted the paper.

Please see the original repository for a full history of development and contributors.

References