A numerical variability approach to results stability tests and its application to neuroimaging

Yohan Chatelain; Loïc Tetrel; Christopher J. Markiewicz; Mathias Goncalves; Gregory Kiar; Oscar Esteban; Pierre Bellec; Tristan Glatard

arXiv · 2307.01373

A numerical variability approach to results stability tests and its application to neuroimaging

Yohan Chatelain·Loïc Tetrel·Christopher J. Markiewicz·Mathias Goncalves·Gregory Kiar·Oscar Esteban·Pierre Bellec·Tristan Glatard

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Abstract

Ensuring the long-term reproducibility of data analyses requires results stability tests to verify that analysis results remain within acceptable variation bounds despite inevitable software updates and hardware evolutions. This paper introduces a numerical variability approach for results stability tests, which determines acceptable variation bounds using random rounding of floating-point calculations. By applying the resulting stability test to \fmriprep, a widely-used neuroimaging tool, we show that the test is sensitive enough to detect subtle updates in image processing methods while remaining specific enough to accept numerical variations within a reference version of the application. This result contributes to enhancing the reliability and reproducibility of data analyses by providing a robust and flexible method for stability testing.

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Yohan Chatelain, Loïc Tetrel, Christopher J. Markiewicz, Mathias Goncalves, Gregory Kiar, Oscar Esteban, Pierre Bellec, Tristan Glatard. 2023-07-03. A numerical variability approach to results stability tests and its application to neuroimaging. https://arxiv.org/abs/2307.01373

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