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Quality control practices in FMRI analysis: Philosophy, methods and examples using AFNI

Quality control (QC) is a necessary, but often an under-appreciated, part of FMRI processing. Here we describe procedures for performing QC on acquired or publicly available FMRI datasets using the widely used AFNI software package. This work is part of the Research Topic, “Demonstrating Quality Con...

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Detalles Bibliográficos
Autores principales: Reynolds, Richard C., Taylor, Paul A., Glen, Daniel R.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9922690/
https://www.ncbi.nlm.nih.gov/pubmed/36793774
http://dx.doi.org/10.3389/fnins.2022.1073800
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author Reynolds, Richard C.
Taylor, Paul A.
Glen, Daniel R.
author_facet Reynolds, Richard C.
Taylor, Paul A.
Glen, Daniel R.
author_sort Reynolds, Richard C.
collection PubMed
description Quality control (QC) is a necessary, but often an under-appreciated, part of FMRI processing. Here we describe procedures for performing QC on acquired or publicly available FMRI datasets using the widely used AFNI software package. This work is part of the Research Topic, “Demonstrating Quality Control (QC) Procedures in fMRI.” We used a sequential, hierarchical approach that contained the following major stages: (1) GTKYD (getting to know your data, esp. its basic acquisition properties), (2) APQUANT (examining quantifiable measures, with thresholds), (3) APQUAL (viewing qualitative images, graphs, and other information in systematic HTML reports) and (4) GUI (checking features interactively with a graphical user interface); and for task data, and (5) STIM (checking stimulus event timing statistics). We describe how these are complementary and reinforce each other to help researchers stay close to their data. We processed and evaluated the provided, publicly available resting state data collections (7 groups, 139 total subjects) and task-based data collection (1 group, 30 subjects). As specified within the Topic guidelines, each subject’s dataset was placed into one of three categories: Include, exclude or uncertain. The main focus of this paper, however, is the detailed description of QC procedures: How to understand the contents of an FMRI dataset, to check its contents for appropriateness, to verify processing steps, and to examine potential quality issues. Scripts for the processing and analysis are freely available.
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spelling pubmed-99226902023-02-14 Quality control practices in FMRI analysis: Philosophy, methods and examples using AFNI Reynolds, Richard C. Taylor, Paul A. Glen, Daniel R. Front Neurosci Neuroscience Quality control (QC) is a necessary, but often an under-appreciated, part of FMRI processing. Here we describe procedures for performing QC on acquired or publicly available FMRI datasets using the widely used AFNI software package. This work is part of the Research Topic, “Demonstrating Quality Control (QC) Procedures in fMRI.” We used a sequential, hierarchical approach that contained the following major stages: (1) GTKYD (getting to know your data, esp. its basic acquisition properties), (2) APQUANT (examining quantifiable measures, with thresholds), (3) APQUAL (viewing qualitative images, graphs, and other information in systematic HTML reports) and (4) GUI (checking features interactively with a graphical user interface); and for task data, and (5) STIM (checking stimulus event timing statistics). We describe how these are complementary and reinforce each other to help researchers stay close to their data. We processed and evaluated the provided, publicly available resting state data collections (7 groups, 139 total subjects) and task-based data collection (1 group, 30 subjects). As specified within the Topic guidelines, each subject’s dataset was placed into one of three categories: Include, exclude or uncertain. The main focus of this paper, however, is the detailed description of QC procedures: How to understand the contents of an FMRI dataset, to check its contents for appropriateness, to verify processing steps, and to examine potential quality issues. Scripts for the processing and analysis are freely available. Frontiers Media S.A. 2023-01-30 /pmc/articles/PMC9922690/ /pubmed/36793774 http://dx.doi.org/10.3389/fnins.2022.1073800 Text en Copyright © 2023 Reynolds, Taylor and Glen. 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
Reynolds, Richard C.
Taylor, Paul A.
Glen, Daniel R.
Quality control practices in FMRI analysis: Philosophy, methods and examples using AFNI
title Quality control practices in FMRI analysis: Philosophy, methods and examples using AFNI
title_full Quality control practices in FMRI analysis: Philosophy, methods and examples using AFNI
title_fullStr Quality control practices in FMRI analysis: Philosophy, methods and examples using AFNI
title_full_unstemmed Quality control practices in FMRI analysis: Philosophy, methods and examples using AFNI
title_short Quality control practices in FMRI analysis: Philosophy, methods and examples using AFNI
title_sort quality control practices in fmri analysis: philosophy, methods and examples using afni
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9922690/
https://www.ncbi.nlm.nih.gov/pubmed/36793774
http://dx.doi.org/10.3389/fnins.2022.1073800
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