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Numerical uncertainty in analytical pipelines lead to impactful variability in brain networks
The analysis of brain-imaging data requires complex processing pipelines to support findings on brain function or pathologies. Recent work has shown that variability in analytical decisions, small amounts of noise, or computational environments can lead to substantial differences in the results, end...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8559953/ https://www.ncbi.nlm.nih.gov/pubmed/34724000 http://dx.doi.org/10.1371/journal.pone.0250755 |
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author | Kiar, Gregory Chatelain, Yohan de Oliveira Castro, Pablo Petit, Eric Rokem, Ariel Varoquaux, Gaël Misic, Bratislav Evans, Alan C. Glatard, Tristan |
author_facet | Kiar, Gregory Chatelain, Yohan de Oliveira Castro, Pablo Petit, Eric Rokem, Ariel Varoquaux, Gaël Misic, Bratislav Evans, Alan C. Glatard, Tristan |
author_sort | Kiar, Gregory |
collection | PubMed |
description | The analysis of brain-imaging data requires complex processing pipelines to support findings on brain function or pathologies. Recent work has shown that variability in analytical decisions, small amounts of noise, or computational environments can lead to substantial differences in the results, endangering the trust in conclusions. We explored the instability of results by instrumenting a structural connectome estimation pipeline with Monte Carlo Arithmetic to introduce random noise throughout. We evaluated the reliability of the connectomes, the robustness of their features, and the eventual impact on analysis. The stability of results was found to range from perfectly stable (i.e. all digits of data significant) to highly unstable (i.e. 0 − 1 significant digits). This paper highlights the potential of leveraging induced variance in estimates of brain connectivity to reduce the bias in networks without compromising reliability, alongside increasing the robustness and potential upper-bound of their applications in the classification of individual differences. We demonstrate that stability evaluations are necessary for understanding error inherent to brain imaging experiments, and how numerical analysis can be applied to typical analytical workflows both in brain imaging and other domains of computational sciences, as the techniques used were data and context agnostic and globally relevant. Overall, while the extreme variability in results due to analytical instabilities could severely hamper our understanding of brain organization, it also affords us the opportunity to increase the robustness of findings. |
format | Online Article Text |
id | pubmed-8559953 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-85599532021-11-02 Numerical uncertainty in analytical pipelines lead to impactful variability in brain networks Kiar, Gregory Chatelain, Yohan de Oliveira Castro, Pablo Petit, Eric Rokem, Ariel Varoquaux, Gaël Misic, Bratislav Evans, Alan C. Glatard, Tristan PLoS One Research Article The analysis of brain-imaging data requires complex processing pipelines to support findings on brain function or pathologies. Recent work has shown that variability in analytical decisions, small amounts of noise, or computational environments can lead to substantial differences in the results, endangering the trust in conclusions. We explored the instability of results by instrumenting a structural connectome estimation pipeline with Monte Carlo Arithmetic to introduce random noise throughout. We evaluated the reliability of the connectomes, the robustness of their features, and the eventual impact on analysis. The stability of results was found to range from perfectly stable (i.e. all digits of data significant) to highly unstable (i.e. 0 − 1 significant digits). This paper highlights the potential of leveraging induced variance in estimates of brain connectivity to reduce the bias in networks without compromising reliability, alongside increasing the robustness and potential upper-bound of their applications in the classification of individual differences. We demonstrate that stability evaluations are necessary for understanding error inherent to brain imaging experiments, and how numerical analysis can be applied to typical analytical workflows both in brain imaging and other domains of computational sciences, as the techniques used were data and context agnostic and globally relevant. Overall, while the extreme variability in results due to analytical instabilities could severely hamper our understanding of brain organization, it also affords us the opportunity to increase the robustness of findings. Public Library of Science 2021-11-01 /pmc/articles/PMC8559953/ /pubmed/34724000 http://dx.doi.org/10.1371/journal.pone.0250755 Text en © 2021 Kiar et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Kiar, Gregory Chatelain, Yohan de Oliveira Castro, Pablo Petit, Eric Rokem, Ariel Varoquaux, Gaël Misic, Bratislav Evans, Alan C. Glatard, Tristan Numerical uncertainty in analytical pipelines lead to impactful variability in brain networks |
title | Numerical uncertainty in analytical pipelines lead to impactful variability in brain networks |
title_full | Numerical uncertainty in analytical pipelines lead to impactful variability in brain networks |
title_fullStr | Numerical uncertainty in analytical pipelines lead to impactful variability in brain networks |
title_full_unstemmed | Numerical uncertainty in analytical pipelines lead to impactful variability in brain networks |
title_short | Numerical uncertainty in analytical pipelines lead to impactful variability in brain networks |
title_sort | numerical uncertainty in analytical pipelines lead to impactful variability in brain networks |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8559953/ https://www.ncbi.nlm.nih.gov/pubmed/34724000 http://dx.doi.org/10.1371/journal.pone.0250755 |
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