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A reproducible and generalizable software workflow for analysis of large-scale neuroimaging data collections using
Chenying Zhao1,2,3,4, Dorota Jarecka5, Sydney Covitz1,2,4
1Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Biorxiv : the Preprint Server for Biology
|August 30, 2023
Summary
The BIDS App Bootstrap (BABS) package enhances neuroimaging reproducibility for large datasets. It automates data processing and audit trails on high-performance computing systems, ensuring scalable and reliable research.
Area of Science:
- Neuroimaging
- Computational Neuroscience
- Data Science
Background:
- Neuroimaging research faces a reproducibility crisis due to large datasets and complex processing.
- Existing Brain Imaging Data Structure (BIDS) Apps improve data organization but lack comprehensive audit trails for full reproducibility.
- Scalable, reproducible processing of large-scale neuroimaging data remains a significant challenge.
Approach:
- Introduced the BIDS App Bootstrap (BABS), a Python package for scalable and reproducible neuroimaging data processing.
- Leveraged DataLad for data management and the FAIRly big framework to track full audit trails.
- Automated script preparation for data processing and version tracking on High-Performance Computing (HPC) systems (SGE, Slurm).
Key Points:
- BABS facilitates the reproducible application of BIDS Apps to large-scale datasets.
- It automatically generates necessary scripts for data processing and version tracking on HPC environments.
- The package supports job submission and auditing on Sun Grid Engine (SGE) and Slurm HPC systems.
Conclusions:
- BABS provides a user-friendly and generalizable solution for reproducible, large-scale neuroimaging data processing.
- Demonstrated scalability by applying BABS to the Healthy Brain Network (HBN) dataset (n=2,565).
- The open-source model allows for broad extensibility and adoption by the research community.