{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T00:59:24Z","timestamp":1775869164424,"version":"3.50.1"},"update-to":[{"DOI":"10.1371\/journal.pcbi.1011942","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2024,3,28]],"date-time":"2024-03-28T00:00:00Z","timestamp":1711584000000}}],"reference-count":49,"publisher":"Public Library of Science (PLoS)","issue":"3","license":[{"start":{"date-parts":[[2024,3,18]],"date-time":"2024-03-18T00:00:00Z","timestamp":1710720000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100019217","name":"Institut de Valorisation des Donn\u00e9es","doi-asserted-by":"publisher","award":["PRF3"],"award-info":[{"award-number":["PRF3"]}],"id":[{"id":"10.13039\/501100019217","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100015569","name":"Consortium canadien en neurod\u00e9g\u00e9n\u00e9rescence associ\u00e9e au vieillissement","doi-asserted-by":"publisher","award":["Team 9 \u201cdiscovering new biomarkers\u201d"],"award-info":[{"award-number":["Team 9 \u201cdiscovering new biomarkers\u201d"]}],"id":[{"id":"10.13039\/100015569","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100021783","name":"Courtois Foundation","doi-asserted-by":"publisher","award":["Neuromod"],"award-info":[{"award-number":["Neuromod"]}],"id":[{"id":"10.13039\/501100021783","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100012950","name":"Institut national de recherche en informatique et en automatique","doi-asserted-by":"publisher","award":["Neuromind"],"award-info":[{"award-number":["Neuromind"]}],"id":[{"id":"10.13039\/100012950","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100019217","name":"Institut de Valorisation des Donn\u00e9es","doi-asserted-by":"publisher","award":["postdoctoral research funding"],"award-info":[{"award-number":["postdoctoral research funding"]}],"id":[{"id":"10.13039\/501100019217","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100019217","name":"Institut de Valorisation des Donn\u00e9es","doi-asserted-by":"publisher","award":["postdoctoral research funding"],"award-info":[{"award-number":["postdoctoral research funding"]}],"id":[{"id":"10.13039\/501100019217","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000055","name":"National Institute on Deafness and Other Communication Disorders","doi-asserted-by":"publisher","award":["5T32DC000038"],"award-info":[{"award-number":["5T32DC000038"]}],"id":[{"id":"10.13039\/100000055","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000009","name":"Foundation for the National Institutes of Health","doi-asserted-by":"publisher","award":["5R24MH117179"],"award-info":[{"award-number":["5R24MH117179"]}],"id":[{"id":"10.13039\/100000009","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100021783","name":"Courtois Foundation","doi-asserted-by":"publisher","award":["Neuromod"],"award-info":[{"award-number":["Neuromod"]}],"id":[{"id":"10.13039\/501100021783","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000156","name":"Fonds de Recherche du Qu\u00e9bec - Sant\u00e9","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000156","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Digital Alliance Canada"}],"content-domain":{"domain":["www.ploscompbiol.org"],"crossmark-restriction":false},"short-container-title":["PLoS Comput Biol"],"abstract":"<jats:p>\n                    Reducing contributions from non-neuronal sources is a crucial step in functional magnetic resonance imaging (fMRI) connectivity analyses. Many viable strategies for denoising fMRI are used in the literature, and practitioners rely on denoising benchmarks for guidance in the selection of an appropriate choice for their study. However, fMRI denoising software is an ever-evolving field, and the benchmarks can quickly become obsolete as the techniques or implementations change. In this work, we present a denoising benchmark featuring a range of denoising strategies, datasets and evaluation metrics for connectivity analyses, based on the popular fMRIprep software. The benchmark prototypes an implementation of a reproducible framework, where the provided Jupyter Book enables readers to reproduce or modify the figures on the Neurolibre reproducible preprint server (\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/neurolibre.org\/\" xlink:type=\"simple\">https:\/\/neurolibre.org\/<\/jats:ext-link>\n                    ). We demonstrate how such a reproducible benchmark can be used for continuous evaluation of research software, by comparing two versions of the fMRIprep. Most of the benchmark results were consistent with prior literature. Scrubbing, a technique which excludes time points with excessive motion, combined with global signal regression, is generally effective at noise removal. Scrubbing was generally effective, but is incompatible with statistical analyses requiring the continuous sampling of brain signal, for which a simpler strategy, using motion parameters, average activity in select brain compartments, and global signal regression, is preferred. Importantly, we found that certain denoising strategies behave inconsistently across datasets and\/or versions of fMRIPrep, or had a different behavior than in previously published benchmarks. This work will hopefully provide useful guidelines for the fMRIprep users community, and highlight the importance of continuous evaluation of research methods.\n                  <\/jats:p>","DOI":"10.1371\/journal.pcbi.1011942","type":"journal-article","created":{"date-parts":[[2024,3,18]],"date-time":"2024-03-18T13:56:32Z","timestamp":1710770192000},"page":"e1011942","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":27,"title":["Continuous evaluation of denoising strategies in resting-state fMRI connectivity using fMRIPrep and Nilearn"],"prefix":"10.1371","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4078-2038","authenticated-orcid":true,"given":"Hao-Ting","family":"Wang","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8888-1572","authenticated-orcid":true,"given":"Steven L.","family":"Meisler","sequence":"additional","affiliation":[]},{"given":"Hanad","family":"Sharmarke","sequence":"additional","affiliation":[]},{"given":"Natasha","family":"Clarke","sequence":"additional","affiliation":[]},{"given":"Nicolas","family":"Gensollen","sequence":"additional","affiliation":[]},{"given":"Christopher J.","family":"Markiewicz","sequence":"additional","affiliation":[]},{"given":"Fran\u00e7ois","family":"Paugam","sequence":"additional","affiliation":[]},{"given":"Bertrand","family":"Thirion","sequence":"additional","affiliation":[]},{"given":"Pierre","family":"Bellec","sequence":"additional","affiliation":[]}],"member":"340","published-online":{"date-parts":[[2024,3,18]]},"reference":[{"key":"pcbi.1011942.ref001","doi-asserted-by":"crossref","first-page":"4734","DOI":"10.1073\/pnas.0911855107","article-title":"Toward discovery science of human brain function","volume":"107","author":"BB Biswal","year":"2010","journal-title":"Proc Natl Acad Sci U S A"},{"key":"pcbi.1011942.ref002","first-page":"19","article-title":"Clinical applications of resting state functional connectivity","volume":"4","author":"MD Fox","year":"2010","journal-title":"Front Syst Neurosci"},{"key":"pcbi.1011942.ref003","first-page":"8","article-title":"Advances and pitfalls in the analysis and interpretation of resting-state FMRI data","volume":"4","author":"DM Cole","year":"2010","journal-title":"Front Syst Neurosci"},{"key":"pcbi.1011942.ref004","doi-asserted-by":"crossref","first-page":"623","DOI":"10.1016\/j.neuroimage.2011.12.063","article-title":"Impact of in-scanner head motion on multiple measures of functional connectivity: relevance for studies of neurodevelopment in youth.","volume":"60","author":"TD Satterthwaite","year":"2012","journal-title":"Neuroimage"},{"key":"pcbi.1011942.ref005","first-page":"11","article-title":"How to remove or control confounds in predictive models, with applications to brain biomarkers","author":"D Chyzhyk","year":"2022","journal-title":"Gigascience"},{"key":"pcbi.1011942.ref006","doi-asserted-by":"crossref","first-page":"174","DOI":"10.1016\/j.neuroimage.2017.03.020","article-title":"Benchmarking of participant-level confound regression strategies for the control of motion artifact in studies of functional connectivity.","volume":"154","author":"R Ciric","year":"2017","journal-title":"Neuroimage"},{"key":"pcbi.1011942.ref007","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1016\/j.neuroimage.2017.12.073","article-title":"An evaluation of the efficacy, reliability, and sensitivity of motion correction strategies for resting-state functional MRI.","volume":"171","author":"L Parkes","year":"2018","journal-title":"Neuroimage"},{"key":"pcbi.1011942.ref008","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1038\/s41586-020-2314-9","article-title":"Variability in the analysis of a single neuroimaging dataset by many teams","volume":"582","author":"R Botvinik-Nezer","year":"2020","journal-title":"Nature"},{"key":"pcbi.1011942.ref009","doi-asserted-by":"crossref","first-page":"119623","DOI":"10.1016\/j.neuroimage.2022.119623","article-title":"Open and reproducible neuroimaging: From study inception to publication","volume":"263","author":"G Niso","year":"2022","journal-title":"Neuroimage"},{"key":"pcbi.1011942.ref010","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1038\/s41592-018-0235-4","article-title":"fMRIPrep: a robust preprocessing pipeline for functional MRI","volume":"16","author":"O Esteban","year":"2019","journal-title":"Nat Methods"},{"key":"pcbi.1011942.ref011","doi-asserted-by":"crossref","first-page":"14","DOI":"10.3389\/fninf.2014.00014","article-title":"Machine learning for neuroimaging with scikit-learn","volume":"8","author":"A Abraham","year":"2014","journal-title":"Front Neuroinform"},{"key":"pcbi.1011942.ref012","doi-asserted-by":"crossref","first-page":"3362","DOI":"10.1002\/hbm.24603","article-title":"Exploring the impact of analysis software on task fMRI results","volume":"40","author":"A Bowring","year":"2019","journal-title":"Hum Brain Mapp"},{"key":"pcbi.1011942.ref013","article-title":"Moving beyond processing and analysis-related variation in neuroscience","author":"X Li","year":"2021","journal-title":"bioRxiv"},{"key":"pcbi.1011942.ref014","doi-asserted-by":"crossref","first-page":"e38234","DOI":"10.1371\/journal.pone.0038234","article-title":"The effects of FreeSurfer version, workstation type, and Macintosh operating system version on anatomical volume and cortical thickness measurements","volume":"7","author":"EHBM Gronenschild","year":"2012","journal-title":"PLoS One."},{"key":"pcbi.1011942.ref015","first-page":"10","article-title":"The OpenNeuro resource for sharing of neuroscience data","author":"CJ Markiewicz","year":"2021","journal-title":"Elife"},{"key":"pcbi.1011942.ref016","article-title":"MRI data of 3\u201312 year old children and adults during viewing of a short animated film.","author":"H Richardson","year":"2019","journal-title":"Openneuro"},{"key":"pcbi.1011942.ref017","article-title":"UCLA Consortium for Neuropsychiatric Phenomics LA5c Study","author":"R Bilder","year":"2020","journal-title":"Openneuro"},{"key":"pcbi.1011942.ref018","doi-asserted-by":"crossref","first-page":"e1005209","DOI":"10.1371\/journal.pcbi.1005209","article-title":"BIDS apps: Improving ease of use, accessibility, and reproducibility of neuroimaging data analysis methods.","volume":"13","author":"KJ Gorgolewski","year":"2017","journal-title":"PLoS Comput Biol"},{"key":"pcbi.1011942.ref019","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1109\/MCSE.2021.3059263","article-title":"Jupyter: Thinking and storytelling with code and data","volume":"23","author":"BE Granger","year":"2021","journal-title":"Comput Sci Eng"},{"key":"pcbi.1011942.ref020","author":"A Karakuzu","year":"2022","journal-title":"NeuroLibre: A preprint server for full-fledged reproducible neuroscience"},{"key":"pcbi.1011942.ref021","article-title":"The pandas development team. pandas-dev\/pandas: Pandas","year":"2023","journal-title":"Zenodo"},{"key":"pcbi.1011942.ref022","doi-asserted-by":"crossref","DOI":"10.25080\/Majora-92bf1922-00a","article-title":"Data Structures for Statistical Computing in Python. Proceedings of the 9th","author":"W. McKinney","year":"2010","journal-title":"Python in Science Conference. SciPy"},{"key":"pcbi.1011942.ref023","unstructured":"nilearn.interfaces.fmriprep.load_confounds. In: Nilearn [Internet]. [cited 14 Jul 2023]. Available: https:\/\/nilearn.github.io\/stable\/modules\/generated\/nilearn.interfaces.fmriprep.load_confounds.html"},{"key":"pcbi.1011942.ref024","doi-asserted-by":"crossref","first-page":"9673","DOI":"10.1073\/pnas.0504136102","article-title":"The human brain is intrinsically organized into dynamic, anticorrelated functional networks","volume":"102","author":"MD Fox","year":"2005","journal-title":"Proc Natl Acad Sci U S A"},{"key":"pcbi.1011942.ref025","doi-asserted-by":"crossref","first-page":"2142","DOI":"10.1016\/j.neuroimage.2011.10.018","article-title":"Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion","volume":"59","author":"JD Power","year":"2012","journal-title":"Neuroimage"},{"key":"pcbi.1011942.ref026","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1016\/j.neuroimage.2007.04.042","article-title":"A component based noise correction method (CompCor) for BOLD and perfusion based fMRI","volume":"37","author":"Y Behzadi","year":"2007","journal-title":"Neuroimage"},{"key":"pcbi.1011942.ref027","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1016\/j.neuroimage.2015.02.064","article-title":"ICA-AROMA: A robust ICA-based strategy for removing motion artifacts from fMRI data.","volume":"112","author":"RHR Pruim","year":"2015","journal-title":"Neuroimage"},{"key":"pcbi.1011942.ref028","unstructured":"nilearn.interfaces.fmriprep.load_confounds_strategy. In: Nilearn [Internet]. [cited 14 Jul 2023]. Available: https:\/\/nilearn.github.io\/stable\/modules\/generated\/nilearn.interfaces.fmriprep.load_confounds_strategy.html"},{"key":"pcbi.1011942.ref029","doi-asserted-by":"crossref","first-page":"1568","DOI":"10.1038\/s41592-022-01681-2","article-title":"TemplateFlow: FAIR-sharing of multi-scale, multi-species brain models","volume":"19","author":"R Ciric","year":"2022","journal-title":"Nat Methods"},{"key":"pcbi.1011942.ref030","doi-asserted-by":"crossref","first-page":"3262","DOI":"10.21105\/joss.03262","article-title":"DataLad: distributed system for joint management of code, data, and their relationship","volume":"6","author":"Y Halchenko","year":"2021","journal-title":"J Open Source Softw"},{"key":"pcbi.1011942.ref031","author":"H-T Wang","year":"2023","journal-title":"A reproducible benchmark of resting-state fMRI denoising strategies using fMRIPrep and Nilearn"},{"key":"pcbi.1011942.ref032","doi-asserted-by":"crossref","first-page":"278","DOI":"10.1016\/j.neuroimage.2015.02.063","article-title":"Evaluation of ICA-AROMA and alternative strategies for motion artifact removal in resting state fMRI.","volume":"112","author":"RHR Pruim","year":"2015","journal-title":"Neuroimage"},{"key":"pcbi.1011942.ref033","article-title":"Benchmark denoising strategies on fMRIPrep output\u2014input data","author":"H-T Wang","year":"2022","journal-title":"Zenodo"},{"key":"pcbi.1011942.ref034","doi-asserted-by":"crossref","first-page":"536","DOI":"10.1016\/j.neuroimage.2014.10.044","article-title":"Recent progress and outstanding issues in motion correction in resting state fMRI","volume":"105","author":"JD Power","year":"2015","journal-title":"Neuroimage"},{"key":"pcbi.1011942.ref035","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1016\/j.neuroimage.2013.03.004","article-title":"A comprehensive assessment of regional variation in the impact of head micromovements on functional connectomics.","volume":"76","author":"C-G Yan","year":"2013","journal-title":"Neuroimage"},{"key":"pcbi.1011942.ref036","doi-asserted-by":"crossref","first-page":"1059","DOI":"10.1016\/j.neuroimage.2009.10.003","article-title":"Complex network measures of brain connectivity: uses and interpretations","volume":"52","author":"M Rubinov","year":"2010","journal-title":"Neuroimage"},{"key":"pcbi.1011942.ref037","unstructured":"Extracting signals from a brain parcellation. In: Nilearn [Internet]. [cited 14 Jul 2023]. Available: https:\/\/nilearn.github.io\/stable\/auto_examples\/03_connectivity\/plot_signal_extraction.html"},{"key":"pcbi.1011942.ref038","article-title":"XCP-D: A Robust Postprocessing Pipeline of fMRI data","author":"A Adebimpe","year":"2023","journal-title":"Zenodo"},{"key":"pcbi.1011942.ref039","doi-asserted-by":"crossref","first-page":"2727","DOI":"10.1002\/hbm.25829","article-title":"ENIGMA HALFpipe: Interactive, reproducible, and efficient analysis for resting-state and task-based fMRI data","volume":"43","author":"L Waller","year":"2022","journal-title":"Hum Brain Mapp"},{"key":"pcbi.1011942.ref040","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1016\/j.neuroimage.2016.11.052","article-title":"Towards a consensus regarding global signal regression for resting state functional connectivity MRI.","volume":"154","author":"K Murphy","year":"2017","journal-title":"Neuroimage"},{"key":"pcbi.1011942.ref041","doi-asserted-by":"crossref","first-page":"116400","DOI":"10.1016\/j.neuroimage.2019.116400","article-title":"Correction of respiratory artifacts in MRI head motion estimates","volume":"208","author":"DA Fair","year":"2020","journal-title":"Neuroimage"},{"key":"pcbi.1011942.ref042","doi-asserted-by":"crossref","first-page":"116041","DOI":"10.1016\/j.neuroimage.2019.116041","article-title":"Distinctions among real and apparent respiratory motions in human fMRI data","volume":"201","author":"JD Power","year":"2019","journal-title":"Neuroimage"},{"key":"pcbi.1011942.ref043","doi-asserted-by":"crossref","first-page":"118907","DOI":"10.1016\/j.neuroimage.2022.118907","article-title":"Advancing motion denoising of multiband resting-state functional connectivity fMRI data.","volume":"249","author":"JC Williams","year":"2022","journal-title":"Neuroimage"},{"key":"pcbi.1011942.ref044","doi-asserted-by":"crossref","first-page":"288","DOI":"10.1093\/cercor\/bhu239","article-title":"Generation and Evaluation of a Cortical Area Parcellation from Resting-State Correlations","volume":"26","author":"EM Gordon","year":"2016","journal-title":"Cereb Cortex"},{"key":"pcbi.1011942.ref045","doi-asserted-by":"crossref","first-page":"3095","DOI":"10.1093\/cercor\/bhx179","article-title":"Local-Global Parcellation of the Human Cerebral Cortex from Intrinsic Functional Connectivity MRI","volume":"28","author":"A Schaefer","year":"2018","journal-title":"Cereb Cortex"},{"key":"pcbi.1011942.ref046","doi-asserted-by":"crossref","first-page":"3","DOI":"10.12688\/mniopenres.12767.2","article-title":"MIST: A multi-resolution parcellation of functional brain networks","volume":"1","author":"S Urchs","year":"2019","journal-title":"MNI Open Res"},{"key":"pcbi.1011942.ref047","doi-asserted-by":"crossref","first-page":"117126","DOI":"10.1016\/j.neuroimage.2020.117126","article-title":"Fine-grain atlases of functional modes for fMRI analysis","volume":"221","author":"K Dadi","year":"2020","journal-title":"Neuroimage"},{"key":"pcbi.1011942.ref048","doi-asserted-by":"crossref","first-page":"1092125","DOI":"10.3389\/fnins.2023.1092125","article-title":"Functional connectivity MRI quality control procedures in CONN.","volume":"17","author":"F Morfini","year":"2023","journal-title":"Front Neurosci"},{"key":"pcbi.1011942.ref049","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1111\/j.2517-6161.1995.tb02031.x","article-title":"Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing","author":"Y Benjamini","year":"1995","journal-title":"Journal of the Royal Statistical Society: Series B (Methodological)."}],"updated-by":[{"DOI":"10.1371\/journal.pcbi.1011942","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2024,3,28]],"date-time":"2024-03-28T00:00:00Z","timestamp":1711584000000}}],"container-title":["PLOS Computational Biology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pcbi.1011942","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,14]],"date-time":"2024-11-14T09:40:37Z","timestamp":1731577237000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pcbi.1011942"}},"subtitle":[],"editor":[{"given":"Catie","family":"Chang","sequence":"first","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2024,3,18]]},"references-count":49,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2024,3,18]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pcbi.1011942","relation":{"has-preprint":[{"id-type":"doi","id":"10.1101\/2023.04.18.537240","asserted-by":"object"}]},"ISSN":["1553-7358"],"issn-type":[{"value":"1553-7358","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,3,18]]}}}