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Demonstrating quality control procedures for fMRI in DPABI

Quality control (QC) is an important stage for functional magnetic resonance imaging (fMRI) studies. The methods for fMRI QC vary in different fMRI preprocessing pipelines. The inflating sample size and number of scanning sites for fMRI studies further add to the difficulty and working load of the Q...

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Detalles Bibliográficos
Autores principales: Lu, Bin, Yan, Chao-Gan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9989208/
https://www.ncbi.nlm.nih.gov/pubmed/36895416
http://dx.doi.org/10.3389/fnins.2023.1069639
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author Lu, Bin
Yan, Chao-Gan
author_facet Lu, Bin
Yan, Chao-Gan
author_sort Lu, Bin
collection PubMed
description Quality control (QC) is an important stage for functional magnetic resonance imaging (fMRI) studies. The methods for fMRI QC vary in different fMRI preprocessing pipelines. The inflating sample size and number of scanning sites for fMRI studies further add to the difficulty and working load of the QC procedure. Therefore, as a constituent part of the Demonstrating Quality Control Procedures in fMRI research topic in Frontiers, we preprocessed a well-organized open-available dataset using DPABI pipelines to illustrate the QC procedure in DPABI. Six categories of DPABI-derived reports were used to eliminate images without adequate quality. After the QC procedure, twelve participants (8.6%) were categorized as excluded and eight participants (5.8%) were categorized as uncertain. More automatic QC tools were needed in the big-data era while visually inspecting images was still indispensable now.
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spelling pubmed-99892082023-03-08 Demonstrating quality control procedures for fMRI in DPABI Lu, Bin Yan, Chao-Gan Front Neurosci Neuroscience Quality control (QC) is an important stage for functional magnetic resonance imaging (fMRI) studies. The methods for fMRI QC vary in different fMRI preprocessing pipelines. The inflating sample size and number of scanning sites for fMRI studies further add to the difficulty and working load of the QC procedure. Therefore, as a constituent part of the Demonstrating Quality Control Procedures in fMRI research topic in Frontiers, we preprocessed a well-organized open-available dataset using DPABI pipelines to illustrate the QC procedure in DPABI. Six categories of DPABI-derived reports were used to eliminate images without adequate quality. After the QC procedure, twelve participants (8.6%) were categorized as excluded and eight participants (5.8%) were categorized as uncertain. More automatic QC tools were needed in the big-data era while visually inspecting images was still indispensable now. Frontiers Media S.A. 2023-02-21 /pmc/articles/PMC9989208/ /pubmed/36895416 http://dx.doi.org/10.3389/fnins.2023.1069639 Text en Copyright © 2023 Lu and Yan. 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 Neuroscience
Lu, Bin
Yan, Chao-Gan
Demonstrating quality control procedures for fMRI in DPABI
title Demonstrating quality control procedures for fMRI in DPABI
title_full Demonstrating quality control procedures for fMRI in DPABI
title_fullStr Demonstrating quality control procedures for fMRI in DPABI
title_full_unstemmed Demonstrating quality control procedures for fMRI in DPABI
title_short Demonstrating quality control procedures for fMRI in DPABI
title_sort demonstrating quality control procedures for fmri in dpabi
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9989208/
https://www.ncbi.nlm.nih.gov/pubmed/36895416
http://dx.doi.org/10.3389/fnins.2023.1069639
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