Reproducing FSL's fMRI data analysis via Nipype: Relevance, challenges, and solutions

Yibei Chen, Frederic R. Hopp, Musa Malik, Paula T. Wang, Kylie Woodman, Sungbin Youk, Rene Weber

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

The “replication crisis” in neuroscientific research has led to calls for improving reproducibility. In traditional neuroscience analyses, irreproducibility may occur as a result of issues across various stages of the methodological process. For example, different operating systems, different software packages, and even different versions of the same package can lead to variable results. Nipype, an open-source Python project, integrates different neuroimaging software packages uniformly to improve the reproducibility of neuroimaging analyses. Nipype has the advantage over traditional software packages (e.g., FSL, ANFI, SPM, etc.) by (1) providing comprehensive software development frameworks and usage information, (2) improving computational efficiency, (3) facilitating reproducibility through sufficient details, and (4) easing the steep learning curve. Despite the rich tutorials it has provided, the Nipype community lacks a standard three-level GLM tutorial for FSL. Using the classical Flanker task dataset, we first precisely reproduce a three-level GLM analysis with FSL via Nipype. Next, we point out some undocumented discrepancies between Nipype and FSL functions that led to substantial differences in results. Finally, we provide revised Nipype code in re-executable notebooks that assure result invariability between FSL and Nipype. Our analyses, notebooks, and operating software specifications (e.g., docker build files) are available on the Open Science Framework platform.

Original languageEnglish
Article number953215
JournalFrontiers in Neuroimaging
Volume1
DOIs
StatePublished - 2022

Bibliographical note

Publisher Copyright:
Copyright © 2022 Chen, Hopp, Malik, Wang, Woodman, Youk and Weber.

Keywords

  • fMRI analysis
  • FSL
  • methods
  • Nipype
  • reproducibility

Fingerprint

Dive into the research topics of 'Reproducing FSL's fMRI data analysis via Nipype: Relevance, challenges, and solutions'. Together they form a unique fingerprint.

Cite this