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...

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Autores principales:	Gregory Kiar, Yohan Chatelain, Pablo de Oliveira Castro, Eric Petit, Ariel Rokem, Gaël Varoquaux, Bratislav Misic, Alan C Evans, Tristan Glatard
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/a057d7bfb45249f0a3577e4a634d7a36
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spelling	oai:doaj.org-article:a057d7bfb45249f0a3577e4a634d7a362021-12-02T20:04:34ZNumerical uncertainty in analytical pipelines lead to impactful variability in brain networks.1932-620310.1371/journal.pone.0250755https://doaj.org/article/a057d7bfb45249f0a3577e4a634d7a362021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0250755https://doaj.org/toc/1932-6203The 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.Gregory KiarYohan ChatelainPablo de Oliveira CastroEric PetitAriel RokemGaël VaroquauxBratislav MisicAlan C EvansTristan GlatardPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 11, p e0250755 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Gregory Kiar Yohan Chatelain Pablo de Oliveira Castro Eric Petit Ariel Rokem Gaël Varoquaux Bratislav Misic Alan C Evans Tristan Glatard Numerical uncertainty in analytical pipelines lead to impactful variability in brain networks.
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	article
author	Gregory Kiar Yohan Chatelain Pablo de Oliveira Castro Eric Petit Ariel Rokem Gaël Varoquaux Bratislav Misic Alan C Evans Tristan Glatard
author_facet	Gregory Kiar Yohan Chatelain Pablo de Oliveira Castro Eric Petit Ariel Rokem Gaël Varoquaux Bratislav Misic Alan C Evans Tristan Glatard
author_sort	Gregory Kiar
title	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_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_sort	numerical uncertainty in analytical pipelines lead to impactful variability in brain networks.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/a057d7bfb45249f0a3577e4a634d7a36
work_keys_str_mv	AT gregorykiar numericaluncertaintyinanalyticalpipelinesleadtoimpactfulvariabilityinbrainnetworks AT yohanchatelain numericaluncertaintyinanalyticalpipelinesleadtoimpactfulvariabilityinbrainnetworks AT pablodeoliveiracastro numericaluncertaintyinanalyticalpipelinesleadtoimpactfulvariabilityinbrainnetworks AT ericpetit numericaluncertaintyinanalyticalpipelinesleadtoimpactfulvariabilityinbrainnetworks AT arielrokem numericaluncertaintyinanalyticalpipelinesleadtoimpactfulvariabilityinbrainnetworks AT gaelvaroquaux numericaluncertaintyinanalyticalpipelinesleadtoimpactfulvariabilityinbrainnetworks AT bratislavmisic numericaluncertaintyinanalyticalpipelinesleadtoimpactfulvariabilityinbrainnetworks AT alancevans numericaluncertaintyinanalyticalpipelinesleadtoimpactfulvariabilityinbrainnetworks AT tristanglatard numericaluncertaintyinanalyticalpipelinesleadtoimpactfulvariabilityinbrainnetworks
_version_	1718375559503806464

Numerical uncertainty in analytical pipelines lead to impactful variability in brain networks.

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