TY  - JOUR
AU  - Zhao, Chenying
AU  - Jarecka, Dorota
AU  - Covitz, Sydney
AU  - Chen, Yibei
AU  - Eickhoff, Simon B.
AU  - Fair, Damien A.
AU  - Franco, Alexandre R.
AU  - Halchenko, Yaroslav O.
AU  - Hendrickson, Timothy J.
AU  - Hoffstaedter, Felix
AU  - Houghton, Audrey
AU  - Kiar, Gregory
AU  - Macdonald, Austin
AU  - Mehta, Kahini
AU  - Milham, Michael P.
AU  - Salo, Taylor
AU  - Hanke, Michael
AU  - Ghosh, Satrajit S.
AU  - Cieslak, Matthew
AU  - Satterthwaite, Theodore D.
TI  - A reproducible and generalizable software workflow for analysis of large-scale neuroimaging data collections using BIDS Apps
JO  - Imaging neuroscience
VL  - 2
SN  - 2837-6056
CY  - Cambridge, MA
PB  - MIT Press
M1  - FZJ-2024-02338
SP  - 1 - 19
PY  - 2024
AB  - Neuroimaging research faces a crisis of reproducibility. With massive sample sizes and greater data complexity, this problem becomes more acute. Software that operates on imaging data defined using the Brain Imaging Data Structure (BIDS)—the BIDS App—has provided a substantial advance. However, even using BIDS Apps, a full audit trail of data processing is a necessary prerequisite for fully reproducible research. Obtaining a faithful record of the audit trail is challenging—especially for large datasets. Recently, the FAIRly big framework was introduced as a way to facilitate reproducible processing of large-scale data by leveraging DataLad—a version control system for data management. However, the current implementation of this framework was more of a proof of concept, and could not be immediately reused by other investigators for different use cases. Here, we introduce the BIDS App Bootstrap (BABS), a user-friendly and generalizable Python package for reproducible image processing at scale. BABS facilitates the reproducible application of BIDS Apps to large-scale datasets. Leveraging DataLad and the FAIRly big framework, BABS tracks the full audit trail of data processing in a scalable way by automatically preparing all scripts necessary for data processing and version tracking on high performance computing (HPC) systems. Currently, BABS supports jobs submissions and audits on Sun Grid Engine (SGE) and Slurm HPCs with a parsimonious set of programs. To demonstrate its scalability, we applied BABS to data from the Healthy Brain Network (HBN; n = 2,565). Taken together, BABS allows reproducible and scalable image processing and is broadly extensible via an open-source development model.Reproducibility, BIDS Apps, software, MRI, big data, image processing
LB  - PUB:(DE-HGF)16
C6  - 37645999
UR  - <Go to ISI:>//WOS:001531527700014
DO  - DOI:10.1162/imag_a_00074
UR  - https://juser.fz-juelich.de/record/1024665
ER  -