↓ Skip to main content

Reproducibility of neuroimaging analyses across operating systems

Overview of attention for article published in Frontiers in Neuroinformatics, April 2015
Altmetric Badge

About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#16 of 818)
  • High Attention Score compared to outputs of the same age (97th percentile)
  • High Attention Score compared to outputs of the same age and source (90th percentile)

Mentioned by

blogs
1 blog
twitter
89 X users
facebook
1 Facebook page
googleplus
4 Google+ users

Citations

dimensions_citation
110 Dimensions

Readers on

mendeley
150 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Reproducibility of neuroimaging analyses across operating systems
Published in
Frontiers in Neuroinformatics, April 2015
DOI 10.3389/fninf.2015.00012
Pubmed ID
Authors

Tristan Glatard, Lindsay B. Lewis, Rafael Ferreira da Silva, Reza Adalat, Natacha Beck, Claude Lepage, Pierre Rioux, Marc-Etienne Rousseau, Tarek Sherif, Ewa Deelman, Najmeh Khalili-Mahani, Alan C. Evans

Abstract

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.

X Demographics

X Demographics

The data shown below were collected from the profiles of 89 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 150 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 2 1%
Japan 1 <1%
United States 1 <1%
Unknown 146 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 41 27%
Researcher 35 23%
Professor > Associate Professor 10 7%
Student > Master 10 7%
Other 6 4%
Other 18 12%
Unknown 30 20%
Readers by discipline Count As %
Neuroscience 31 21%
Psychology 26 17%
Computer Science 15 10%
Agricultural and Biological Sciences 12 8%
Engineering 9 6%
Other 21 14%
Unknown 36 24%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 64. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 04 December 2020.
All research outputs
#652,838
of 25,116,143 outputs
Outputs from Frontiers in Neuroinformatics
#16
of 818 outputs
Outputs of similar age
#7,724
of 271,013 outputs
Outputs of similar age from Frontiers in Neuroinformatics
#2
of 11 outputs
Altmetric has tracked 25,116,143 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 97th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 818 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 7.8. This one has done particularly well, scoring higher than 98% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 271,013 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 97% of its contemporaries.
We're also able to compare this research output to 11 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 90% of its contemporaries.