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

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

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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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Bibliographic Details
Main Authors: Gregory Kiar, Yohan Chatelain, Pablo de Oliveira Castro, Eric Petit, Ariel Rokem, Gaël Varoquaux, Bratislav Misic, Alan C. Evans, Tristan Glatard
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2021-01-01
Series:PLoS ONE
Online Access:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8559953/?tool=EBI

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https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8559953/?tool=EBI

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