TY - JOUR
T1 - Reproducing FSL's fMRI data analysis via Nipype
T2 - Relevance, challenges, and solutions
AU - Chen, Yibei
AU - Hopp, Frederic R.
AU - Malik, Musa
AU - Wang, Paula T.
AU - Woodman, Kylie
AU - Youk, Sungbin
AU - Weber, Rene
N1 - Publisher Copyright:
Copyright © 2022 Chen, Hopp, Malik, Wang, Woodman, Youk and Weber.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - fMRI analysis
KW - FSL
KW - methods
KW - Nipype
KW - reproducibility
UR - http://www.scopus.com/inward/record.url?scp=105005555813&partnerID=8YFLogxK
U2 - 10.3389/fnimg.2022.953215
DO - 10.3389/fnimg.2022.953215
M3 - Article
AN - SCOPUS:105005555813
SN - 2813-1193
VL - 1
JO - Frontiers in Neuroimaging
JF - Frontiers in Neuroimaging
M1 - 953215
ER -