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Reproducibility of neuroimaging analyses across operating systems

Neuroimaging pipelines are known to generate different results depending on the computing platform where they are compiled and executed. We quantify these differences for brain tissue classification, fMRI analysis, and cortical thickness (CT) extraction, using three of the main neuroimaging packages...

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Autores principales: Glatard, Tristan, Lewis, Lindsay B., Ferreira da Silva, Rafael, Adalat, Reza, Beck, Natacha, Lepage, Claude, Rioux, Pierre, Rousseau, Marc-Etienne, Sherif, Tarek, Deelman, Ewa, Khalili-Mahani, Najmeh, Evans, Alan C.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4408913/
https://www.ncbi.nlm.nih.gov/pubmed/25964757
http://dx.doi.org/10.3389/fninf.2015.00012
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author Glatard, Tristan
Lewis, Lindsay B.
Ferreira da Silva, Rafael
Adalat, Reza
Beck, Natacha
Lepage, Claude
Rioux, Pierre
Rousseau, Marc-Etienne
Sherif, Tarek
Deelman, Ewa
Khalili-Mahani, Najmeh
Evans, Alan C.
author_facet Glatard, Tristan
Lewis, Lindsay B.
Ferreira da Silva, Rafael
Adalat, Reza
Beck, Natacha
Lepage, Claude
Rioux, Pierre
Rousseau, Marc-Etienne
Sherif, Tarek
Deelman, Ewa
Khalili-Mahani, Najmeh
Evans, Alan C.
author_sort Glatard, Tristan
collection PubMed
description Neuroimaging pipelines are known to generate different results depending on the computing platform where they are compiled and executed. We quantify these differences for brain tissue classification, fMRI analysis, and cortical thickness (CT) extraction, using three of the main neuroimaging packages (FSL, Freesurfer and CIVET) and different versions of GNU/Linux. We also identify some causes of these differences using library and system call interception. We find that these packages use mathematical functions based on single-precision floating-point arithmetic whose implementations in operating systems continue to evolve. While these differences have little or no impact on simple analysis pipelines such as brain extraction and cortical tissue classification, their accumulation creates important differences in longer pipelines such as subcortical tissue classification, fMRI analysis, and cortical thickness extraction. With FSL, most Dice coefficients between subcortical classifications obtained on different operating systems remain above 0.9, but values as low as 0.59 are observed. Independent component analyses (ICA) of fMRI data differ between operating systems in one third of the tested subjects, due to differences in motion correction. With Freesurfer and CIVET, in some brain regions we find an effect of build or operating system on cortical thickness. A first step to correct these reproducibility issues would be to use more precise representations of floating-point numbers in the critical sections of the pipelines. The numerical stability of pipelines should also be reviewed.
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spelling pubmed-44089132015-05-11 Reproducibility of neuroimaging analyses across operating systems Glatard, Tristan Lewis, Lindsay B. Ferreira da Silva, Rafael Adalat, Reza Beck, Natacha Lepage, Claude Rioux, Pierre Rousseau, Marc-Etienne Sherif, Tarek Deelman, Ewa Khalili-Mahani, Najmeh Evans, Alan C. Front Neuroinform Neuroscience Neuroimaging pipelines are known to generate different results depending on the computing platform where they are compiled and executed. We quantify these differences for brain tissue classification, fMRI analysis, and cortical thickness (CT) extraction, using three of the main neuroimaging packages (FSL, Freesurfer and CIVET) and different versions of GNU/Linux. We also identify some causes of these differences using library and system call interception. We find that these packages use mathematical functions based on single-precision floating-point arithmetic whose implementations in operating systems continue to evolve. While these differences have little or no impact on simple analysis pipelines such as brain extraction and cortical tissue classification, their accumulation creates important differences in longer pipelines such as subcortical tissue classification, fMRI analysis, and cortical thickness extraction. With FSL, most Dice coefficients between subcortical classifications obtained on different operating systems remain above 0.9, but values as low as 0.59 are observed. Independent component analyses (ICA) of fMRI data differ between operating systems in one third of the tested subjects, due to differences in motion correction. With Freesurfer and CIVET, in some brain regions we find an effect of build or operating system on cortical thickness. A first step to correct these reproducibility issues would be to use more precise representations of floating-point numbers in the critical sections of the pipelines. The numerical stability of pipelines should also be reviewed. Frontiers Media S.A. 2015-04-24 /pmc/articles/PMC4408913/ /pubmed/25964757 http://dx.doi.org/10.3389/fninf.2015.00012 Text en Copyright © 2015 Glatard, Lewis, Ferreira da Silva, Adalat, Beck, Lepage, Rioux, Rousseau, Sherif, Deelman, Khalili-Mahani and Evans. http://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) or licensor 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
Glatard, Tristan
Lewis, Lindsay B.
Ferreira da Silva, Rafael
Adalat, Reza
Beck, Natacha
Lepage, Claude
Rioux, Pierre
Rousseau, Marc-Etienne
Sherif, Tarek
Deelman, Ewa
Khalili-Mahani, Najmeh
Evans, Alan C.
Reproducibility of neuroimaging analyses across operating systems
title Reproducibility of neuroimaging analyses across operating systems
title_full Reproducibility of neuroimaging analyses across operating systems
title_fullStr Reproducibility of neuroimaging analyses across operating systems
title_full_unstemmed Reproducibility of neuroimaging analyses across operating systems
title_short Reproducibility of neuroimaging analyses across operating systems
title_sort reproducibility of neuroimaging analyses across operating systems
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4408913/
https://www.ncbi.nlm.nih.gov/pubmed/25964757
http://dx.doi.org/10.3389/fninf.2015.00012
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