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Quality control in functional MRI studies with MRIQC and fMRIPrep

The implementation of adequate quality assessment (QA) and quality control (QC) protocols within the magnetic resonance imaging (MRI) research workflow is resource- and time-consuming and even more so is their execution. As a result, QA/QC practices highly vary across laboratories and “MRI schools”,...

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Autores principales: Provins, Céline, MacNicol, Eilidh, Seeley, Saren H., Hagmann, Patric, Esteban, Oscar
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10406249/
https://www.ncbi.nlm.nih.gov/pubmed/37555175
http://dx.doi.org/10.3389/fnimg.2022.1073734
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author Provins, Céline
MacNicol, Eilidh
Seeley, Saren H.
Hagmann, Patric
Esteban, Oscar
author_facet Provins, Céline
MacNicol, Eilidh
Seeley, Saren H.
Hagmann, Patric
Esteban, Oscar
author_sort Provins, Céline
collection PubMed
description The implementation of adequate quality assessment (QA) and quality control (QC) protocols within the magnetic resonance imaging (MRI) research workflow is resource- and time-consuming and even more so is their execution. As a result, QA/QC practices highly vary across laboratories and “MRI schools”, ranging from highly specialized knowledge spots to environments where QA/QC is considered overly onerous and costly despite evidence showing that below-standard data increase the false positive and false negative rates of the final results. Here, we demonstrate a protocol based on the visual assessment of images one-by-one with reports generated by MRIQC and fMRIPrep, for the QC of data in functional (blood-oxygen dependent-level; BOLD) MRI analyses. We particularize the proposed, open-ended scope of application to whole-brain voxel-wise analyses of BOLD to correspondingly enumerate and define the exclusion criteria applied at the QC checkpoints. We apply our protocol on a composite dataset (n = 181 subjects) drawn from open fMRI studies, resulting in the exclusion of 97% of the data (176 subjects). This high exclusion rate was expected because subjects were selected to showcase artifacts. We describe the artifacts and defects more commonly found in the dataset that justified exclusion. We moreover release all the materials we generated in this assessment and document all the QC decisions with the expectation of contributing to the standardization of these procedures and engaging in the discussion of QA/QC by the community.
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spelling pubmed-104062492023-08-08 Quality control in functional MRI studies with MRIQC and fMRIPrep Provins, Céline MacNicol, Eilidh Seeley, Saren H. Hagmann, Patric Esteban, Oscar Front Neuroimaging Neuroimaging The implementation of adequate quality assessment (QA) and quality control (QC) protocols within the magnetic resonance imaging (MRI) research workflow is resource- and time-consuming and even more so is their execution. As a result, QA/QC practices highly vary across laboratories and “MRI schools”, ranging from highly specialized knowledge spots to environments where QA/QC is considered overly onerous and costly despite evidence showing that below-standard data increase the false positive and false negative rates of the final results. Here, we demonstrate a protocol based on the visual assessment of images one-by-one with reports generated by MRIQC and fMRIPrep, for the QC of data in functional (blood-oxygen dependent-level; BOLD) MRI analyses. We particularize the proposed, open-ended scope of application to whole-brain voxel-wise analyses of BOLD to correspondingly enumerate and define the exclusion criteria applied at the QC checkpoints. We apply our protocol on a composite dataset (n = 181 subjects) drawn from open fMRI studies, resulting in the exclusion of 97% of the data (176 subjects). This high exclusion rate was expected because subjects were selected to showcase artifacts. We describe the artifacts and defects more commonly found in the dataset that justified exclusion. We moreover release all the materials we generated in this assessment and document all the QC decisions with the expectation of contributing to the standardization of these procedures and engaging in the discussion of QA/QC by the community. Frontiers Media S.A. 2023-01-12 /pmc/articles/PMC10406249/ /pubmed/37555175 http://dx.doi.org/10.3389/fnimg.2022.1073734 Text en Copyright © 2023 Provins, MacNicol, Seeley, Hagmann and Esteban. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroimaging
Provins, Céline
MacNicol, Eilidh
Seeley, Saren H.
Hagmann, Patric
Esteban, Oscar
Quality control in functional MRI studies with MRIQC and fMRIPrep
title Quality control in functional MRI studies with MRIQC and fMRIPrep
title_full Quality control in functional MRI studies with MRIQC and fMRIPrep
title_fullStr Quality control in functional MRI studies with MRIQC and fMRIPrep
title_full_unstemmed Quality control in functional MRI studies with MRIQC and fMRIPrep
title_short Quality control in functional MRI studies with MRIQC and fMRIPrep
title_sort quality control in functional mri studies with mriqc and fmriprep
topic Neuroimaging
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10406249/
https://www.ncbi.nlm.nih.gov/pubmed/37555175
http://dx.doi.org/10.3389/fnimg.2022.1073734
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