Reproducibility of neuroimaging analyses across operating systems Article Swipe
Tristan Glatard
,
Lindsay B. Lewis
,
Rafael Ferreira da Silva
,
Reza Adalat
,
Natacha Beck
,
Claude Lepage
,
Pierre Rioux
,
Marc-Étienne Rousseau
,
Tarek Sherif
,
Ewa Deelman
,
Najmeh Khalili‐Mahani
,
Alan C. Evans
·
YOU?
·
· 2015
· Open Access
·
· DOI: https://doi.org/10.3389/fninf.2015.00012
YOU?
·
· 2015
· Open Access
·
· DOI: https://doi.org/10.3389/fninf.2015.00012
Neuroimaging pipelines are known to generate different results depending on the computing platform where they are compiled and executed. We quantify these differences for brain tissue classification, fMRI analysis, and cortical thickness (CT) extraction, using three of the main neuroimaging packages (FSL, Freesurfer and CIVET) and different versions of GNU/Linux. We also identify some causes of these differences using library and system call interception. We find that these packages use mathematical functions based on single-precision floating-point arithmetic whose implementations in operating systems continue to evolve. While these differences have little or no impact on simple analysis pipelines such as brain extraction and cortical tissue classification, their accumulation creates important differences in longer pipelines such as subcortical tissue classification, fMRI analysis, and cortical thickness extraction. With FSL, most Dice coefficients between subcortical classifications obtained on different operating systems remain above 0.9, but values as low as 0.59 are observed. Independent component analyses (ICA) of fMRI data differ between operating systems in one third of the tested subjects, due to differences in motion correction. With Freesurfer and CIVET, in some brain regions we find an effect of build or operating system on cortical thickness. A first step to correct these reproducibility issues would be to use more precise representations of floating-point numbers in the critical sections of the pipelines. The numerical stability of pipelines should also be reviewed.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3389/fninf.2015.00012
- https://www.frontiersin.org/articles/10.3389/fninf.2015.00012/pdf
- OA Status
- gold
- Cited By
- 146
- References
- 19
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W1979051296
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- OpenAlex ID
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https://openalex.org/W1979051296Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3389/fninf.2015.00012Digital Object Identifier
- Title
-
Reproducibility of neuroimaging analyses across operating systemsWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2015Year of publication
- Publication date
-
2015-04-24Full publication date if available
- Authors
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Tristan Glatard, Lindsay B. Lewis, Rafael Ferreira da Silva, Reza Adalat, Natacha Beck, Claude Lepage, Pierre Rioux, Marc-Étienne Rousseau, Tarek Sherif, Ewa Deelman, Najmeh Khalili‐Mahani, Alan C. EvansList of authors in order
- Landing page
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https://doi.org/10.3389/fninf.2015.00012Publisher landing page
- PDF URL
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https://www.frontiersin.org/articles/10.3389/fninf.2015.00012/pdfDirect link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://www.frontiersin.org/articles/10.3389/fninf.2015.00012/pdfDirect OA link when available
- Concepts
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Computer science, Reproducibility, Neuroimaging, Pipeline transport, Artificial intelligence, Pattern recognition (psychology), Neuroscience, Mathematics, Psychology, Statistics, Environmental engineering, EngineeringTop concepts (fields/topics) attached by OpenAlex
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146Total citation count in OpenAlex
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2025: 14, 2024: 15, 2023: 14, 2022: 17, 2021: 15Per-year citation counts (last 5 years)
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19Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.between | 129, 156 |
| abstract_inverted_index.correct | 196 |
| abstract_inverted_index.creates | 107 |
| abstract_inverted_index.evolve. | 84 |
| abstract_inverted_index.library | 59 |
| abstract_inverted_index.numbers | 209 |
| abstract_inverted_index.precise | 205 |
| abstract_inverted_index.regions | 179 |
| abstract_inverted_index.results | 7 |
| abstract_inverted_index.systems | 81, 136, 158 |
| abstract_inverted_index.analyses | 150 |
| abstract_inverted_index.analysis | 95 |
| abstract_inverted_index.compiled | 16 |
| abstract_inverted_index.continue | 82 |
| abstract_inverted_index.cortical | 30, 102, 121, 190 |
| abstract_inverted_index.critical | 212 |
| abstract_inverted_index.generate | 5 |
| abstract_inverted_index.identify | 52 |
| abstract_inverted_index.obtained | 132 |
| abstract_inverted_index.packages | 40, 68 |
| abstract_inverted_index.platform | 12 |
| abstract_inverted_index.quantify | 20 |
| abstract_inverted_index.sections | 213 |
| abstract_inverted_index.versions | 47 |
| abstract_inverted_index.analysis, | 28, 119 |
| abstract_inverted_index.component | 149 |
| abstract_inverted_index.computing | 11 |
| abstract_inverted_index.depending | 8 |
| abstract_inverted_index.different | 6, 46, 134 |
| abstract_inverted_index.executed. | 18 |
| abstract_inverted_index.functions | 71 |
| abstract_inverted_index.important | 108 |
| abstract_inverted_index.numerical | 218 |
| abstract_inverted_index.observed. | 147 |
| abstract_inverted_index.operating | 80, 135, 157, 187 |
| abstract_inverted_index.pipelines | 1, 96, 112, 221 |
| abstract_inverted_index.reviewed. | 225 |
| abstract_inverted_index.stability | 219 |
| abstract_inverted_index.subjects, | 165 |
| abstract_inverted_index.thickness | 31, 122 |
| abstract_inverted_index.Freesurfer | 42, 173 |
| abstract_inverted_index.GNU/Linux. | 49 |
| abstract_inverted_index.arithmetic | 76 |
| abstract_inverted_index.extraction | 100 |
| abstract_inverted_index.pipelines. | 216 |
| abstract_inverted_index.thickness. | 191 |
| abstract_inverted_index.Independent | 148 |
| abstract_inverted_index.correction. | 171 |
| abstract_inverted_index.differences | 22, 57, 87, 109, 168 |
| abstract_inverted_index.extraction, | 33 |
| abstract_inverted_index.extraction. | 123 |
| abstract_inverted_index.subcortical | 115, 130 |
| abstract_inverted_index.Neuroimaging | 0 |
| abstract_inverted_index.accumulation | 106 |
| abstract_inverted_index.coefficients | 128 |
| abstract_inverted_index.mathematical | 70 |
| abstract_inverted_index.neuroimaging | 39 |
| abstract_inverted_index.interception. | 63 |
| abstract_inverted_index.floating-point | 75, 208 |
| abstract_inverted_index.classification, | 26, 104, 117 |
| abstract_inverted_index.classifications | 131 |
| abstract_inverted_index.implementations | 78 |
| abstract_inverted_index.representations | 206 |
| abstract_inverted_index.reproducibility | 198 |
| abstract_inverted_index.single-precision | 74 |
| cited_by_percentile_year.max | 100 |
| cited_by_percentile_year.min | 96 |
| countries_distinct_count | 3 |
| institutions_distinct_count | 12 |
| citation_normalized_percentile.value | 0.98319479 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |