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NeuroCI: Continuous Integration of Neuroimaging Results Across Software Pipelines and Datasets

View ORCID ProfileJacob Sanz-Robinson, Arman Jahanpour, View ORCID ProfileNatalie Phillips, View ORCID ProfileTristan Glatard, View ORCID ProfileJean-Baptiste Poline
doi: https://doi.org/10.1101/2022.05.30.494062
Jacob Sanz-Robinson
1Integrated Program in Neuroscience, McGill University, Montreal, Canada
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  • For correspondence: jacob.sanz-robinson@mail.mcgill.ca
Arman Jahanpour
2Department of Engineering and Computer Science, Concordia University, Montreal, Canada
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Natalie Phillips
3Department of Psychology, Concordia University, Montreal, Canada
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Tristan Glatard
4Department of Engineering and Computer Science, Concordia University, Montreal, Canada
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Jean-Baptiste Poline
5Department of Neurology and Neurosurgery, McGill University, Montreal, Canada
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Abstract

—Neuroimaging study results can vary significantly depending on the datasets and processing pipelines utilized by researchers to run their analyses, contributing to reproducibility issues. These issues are compounded by the fact that there are a large variety of seemingly equivalent tools and methodologies available to researchers for processing neuroimaging data. Here we present NeuroCI, a novel software framework that allows users to evaluate the variability of their results across multiple pipelines and datasets. NeuroCI makes use of Continuous Integration (CI), a software engineering technique, to facilitate the reproducibility of computational experiments by launching a series of automated tests when code or data is added to a repository. However, unlike regular CI services, our CI-based framework uses distributed computation and storage to meet the large memory and storage requirements of neuroimaging pipelines and datasets. Moreover, the framework’s modular design enables it to continuously ingest pipelines and datasets provided by the user, and to compute and visualize results across the multiple different pipelines and datasets. This allows researchers and practitioners to quantify the variability and reliability of results in their domain across a large range of computational methods.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • jacob.sanz-robinson{at}mail.mcgill.ca

  • a_jahanp{at}live.concordia.ca

  • natalie.phillips{at}concordia.ca

  • tristan.glatard{at}concordia.ca

  • jean-baptiste.poline{at}mcgill.ca

  • https://github.com/neurodatascience/NeuroCI/

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 4.0 International license.
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Posted May 31, 2022.
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NeuroCI: Continuous Integration of Neuroimaging Results Across Software Pipelines and Datasets
Jacob Sanz-Robinson, Arman Jahanpour, Natalie Phillips, Tristan Glatard, Jean-Baptiste Poline
bioRxiv 2022.05.30.494062; doi: https://doi.org/10.1101/2022.05.30.494062
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NeuroCI: Continuous Integration of Neuroimaging Results Across Software Pipelines and Datasets
Jacob Sanz-Robinson, Arman Jahanpour, Natalie Phillips, Tristan Glatard, Jean-Baptiste Poline
bioRxiv 2022.05.30.494062; doi: https://doi.org/10.1101/2022.05.30.494062

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