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Numerical Uncertainty in Analytical Pipelines Lead to Impactful Variability in Brain Networks

View ORCID ProfileGregory Kiar, View ORCID ProfileYohan Chatelain, View ORCID ProfileOliveira Castro Pablo de, Eric Petit, View ORCID ProfileAriel Rokem, View ORCID ProfileGaël Varoquaux, View ORCID ProfileBratislav Misic, Alan C. Evans, View ORCID ProfileTristan Glatard
doi: https://doi.org/10.1101/2020.10.15.341495
Gregory Kiar
1Montréal Neurological Institute, McGill University, Montréal, QC, Canada
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Yohan Chatelain
2Department of Computer Science and Software Engineering, Concordia University, Montréal, QC, Canada
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Oliveira Castro Pablo de
3Department of Computer Science, Université of Versailles, Versailles, France
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Eric Petit
4Exascale Computing Lab, Intel, Paris, France
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Ariel Rokem
5Department of Psychology and eScience Institute, University of Washington, Seattle, WA, USA
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Gaël Varoquaux
6Parietal project-team, INRIA Saclay-ile de France, France
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Bratislav Misic
1Montréal Neurological Institute, McGill University, Montréal, QC, Canada
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Alan C. Evans
1Montréal Neurological Institute, McGill University, Montréal, QC, Canada
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Tristan Glatard
2Department of Computer Science and Software Engineering, Concordia University, Montréal, QC, Canada
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  • ORCID record for Tristan Glatard
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Abstract

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 conclusions1-7. We explored the instability of results by instrumenting a connectome estimation pipeline with Monte Carlo Arithmetic8,9 to introduce random noise throughout. We evaluated the reliability of the connectomes, their features10,11, and the impact on analysis12,13. The stability of results was found to range from perfectly stable to highly unstable. This paper highlights the potential of leveraging induced variance in estimates of brain connectivity to reduce the bias in networks alongside increasing the robustness 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 science. Overall, while the extreme variability in results due to analytical instabilities could severely hamper our understanding of brain organization, it also leads to an increase in the reliability of datasets.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • updated statistical analysis

  • https://doi.org/10.5281/zenodo.4041549

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-ND 4.0 International license.
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Posted March 22, 2021.
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Numerical Uncertainty in Analytical Pipelines Lead to Impactful Variability in Brain Networks
Gregory Kiar, Yohan Chatelain, Oliveira Castro Pablo de, Eric Petit, Ariel Rokem, Gaël Varoquaux, Bratislav Misic, Alan C. Evans, Tristan Glatard
bioRxiv 2020.10.15.341495; doi: https://doi.org/10.1101/2020.10.15.341495
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Numerical Uncertainty in Analytical Pipelines Lead to Impactful Variability in Brain Networks
Gregory Kiar, Yohan Chatelain, Oliveira Castro Pablo de, Eric Petit, Ariel Rokem, Gaël Varoquaux, Bratislav Misic, Alan C. Evans, Tristan Glatard
bioRxiv 2020.10.15.341495; doi: https://doi.org/10.1101/2020.10.15.341495

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