Zhao Chenying, Jarecka Dorota, Covitz Sydney, Chen Yibei, Eickhoff Simon B, Fair Damien A, Franco Alexandre R, Halchenko Yaroslav O, Hendrickson Timothy J, Hoffstaedter Felix, Houghton Audrey, Kiar Gregory, Macdonald Austin, Mehta Kahini, Milham Michael P, Salo Taylor, Hanke Michael, Ghosh Satrajit S, Cieslak Matthew, Satterthwaite Theodore D
Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States.
Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA, United States.
Imaging Neurosci (Camb). 2024 Jan 25;2. doi: 10.1162/imag_a_00074. eCollection 2024.
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.
神经影像学研究面临可重复性危机。随着样本量的大幅增加和数据复杂性的提高，这个问题变得更加尖锐。基于脑成像数据结构（BIDS）定义的成像数据运行的软件——BIDS应用程序——已经取得了重大进展。然而，即使使用BIDS应用程序，数据处理的完整审计跟踪也是完全可重复研究的必要前提。获得审计跟踪的忠实记录具有挑战性，尤其是对于大型数据集。最近，引入了FAIRly big框架，作为一种通过利用DataLad（一种用于数据管理的版本控制系统）来促进大规模数据可重复处理的方法。然而，该框架的当前实现更多的是一个概念验证，其他研究人员无法立即将其用于不同的用例。在这里，我们介绍了BIDS应用程序引导程序（BABS），这是一个用户友好且可推广的Python包，用于大规模的可重复图像处理。BABS有助于将BIDS应用程序可重复地应用于大规模数据集。通过利用DataLad和FAIRly big框架，BABS通过在高性能计算（HPC）系统上自动准备数据处理和版本跟踪所需的所有脚本，以可扩展的方式跟踪数据处理的完整审计跟踪。目前，BABS通过一组简约的程序支持在Sun Grid Engine（SGE）和Slurm HPC上提交作业和进行审计。为了证明其可扩展性，我们将BABS应用于健康脑网络（HBN；n = 2565）的数据。综上所述，BABS允许进行可重复和可扩展的图像处理，并且通过开源开发模型具有广泛的可扩展性。
