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Reproducing FSL's fMRI data analysis via Nipype: Relevance, challenges, and solutions

The “replication crisis” in neuroscientific research has led to calls for improving reproducibility. In traditional neuroscience analyses, irreproducibility may occur as a result of issues across various stages of the methodological process. For example, different operating systems, different softwa...

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Autores principales: Chen, Yibei, Hopp, Frederic R., Malik, Musa, Wang, Paula T., Woodman, Kylie, Youk, Sungbin, Weber, René
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10406235/
https://www.ncbi.nlm.nih.gov/pubmed/37555184
http://dx.doi.org/10.3389/fnimg.2022.953215
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author Chen, Yibei
Hopp, Frederic R.
Malik, Musa
Wang, Paula T.
Woodman, Kylie
Youk, Sungbin
Weber, René
author_facet Chen, Yibei
Hopp, Frederic R.
Malik, Musa
Wang, Paula T.
Woodman, Kylie
Youk, Sungbin
Weber, René
author_sort Chen, Yibei
collection PubMed
description The “replication crisis” in neuroscientific research has led to calls for improving reproducibility. In traditional neuroscience analyses, irreproducibility may occur as a result of issues across various stages of the methodological process. For example, different operating systems, different software packages, and even different versions of the same package can lead to variable results. Nipype, an open-source Python project, integrates different neuroimaging software packages uniformly to improve the reproducibility of neuroimaging analyses. Nipype has the advantage over traditional software packages (e.g., FSL, ANFI, SPM, etc.) by (1) providing comprehensive software development frameworks and usage information, (2) improving computational efficiency, (3) facilitating reproducibility through sufficient details, and (4) easing the steep learning curve. Despite the rich tutorials it has provided, the Nipype community lacks a standard three-level GLM tutorial for FSL. Using the classical Flanker task dataset, we first precisely reproduce a three-level GLM analysis with FSL via Nipype. Next, we point out some undocumented discrepancies between Nipype and FSL functions that led to substantial differences in results. Finally, we provide revised Nipype code in re-executable notebooks that assure result invariability between FSL and Nipype. Our analyses, notebooks, and operating software specifications (e.g., docker build files) are available on the Open Science Framework platform.
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spelling pubmed-104062352023-08-08 Reproducing FSL's fMRI data analysis via Nipype: Relevance, challenges, and solutions Chen, Yibei Hopp, Frederic R. Malik, Musa Wang, Paula T. Woodman, Kylie Youk, Sungbin Weber, René Front Neuroimaging Neuroimaging The “replication crisis” in neuroscientific research has led to calls for improving reproducibility. In traditional neuroscience analyses, irreproducibility may occur as a result of issues across various stages of the methodological process. For example, different operating systems, different software packages, and even different versions of the same package can lead to variable results. Nipype, an open-source Python project, integrates different neuroimaging software packages uniformly to improve the reproducibility of neuroimaging analyses. Nipype has the advantage over traditional software packages (e.g., FSL, ANFI, SPM, etc.) by (1) providing comprehensive software development frameworks and usage information, (2) improving computational efficiency, (3) facilitating reproducibility through sufficient details, and (4) easing the steep learning curve. Despite the rich tutorials it has provided, the Nipype community lacks a standard three-level GLM tutorial for FSL. Using the classical Flanker task dataset, we first precisely reproduce a three-level GLM analysis with FSL via Nipype. Next, we point out some undocumented discrepancies between Nipype and FSL functions that led to substantial differences in results. Finally, we provide revised Nipype code in re-executable notebooks that assure result invariability between FSL and Nipype. Our analyses, notebooks, and operating software specifications (e.g., docker build files) are available on the Open Science Framework platform. Frontiers Media S.A. 2022-07-26 /pmc/articles/PMC10406235/ /pubmed/37555184 http://dx.doi.org/10.3389/fnimg.2022.953215 Text en Copyright © 2022 Chen, Hopp, Malik, Wang, Woodman, Youk and Weber. 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
Chen, Yibei
Hopp, Frederic R.
Malik, Musa
Wang, Paula T.
Woodman, Kylie
Youk, Sungbin
Weber, René
Reproducing FSL's fMRI data analysis via Nipype: Relevance, challenges, and solutions
title Reproducing FSL's fMRI data analysis via Nipype: Relevance, challenges, and solutions
title_full Reproducing FSL's fMRI data analysis via Nipype: Relevance, challenges, and solutions
title_fullStr Reproducing FSL's fMRI data analysis via Nipype: Relevance, challenges, and solutions
title_full_unstemmed Reproducing FSL's fMRI data analysis via Nipype: Relevance, challenges, and solutions
title_short Reproducing FSL's fMRI data analysis via Nipype: Relevance, challenges, and solutions
title_sort reproducing fsl's fmri data analysis via nipype: relevance, challenges, and solutions
topic Neuroimaging
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10406235/
https://www.ncbi.nlm.nih.gov/pubmed/37555184
http://dx.doi.org/10.3389/fnimg.2022.953215
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