| Literature DB >> 36064140 |
Sydney Covitz1, Tinashe M Tapera1, Azeez Adebimpe1, Aaron F Alexander-Bloch2, Maxwell A Bertolero1, Eric Feczko3, Alexandre R Franco4, Raquel E Gur5, Ruben C Gur5, Timothy Hendrickson6, Audrey Houghton3, Kahini Mehta1, Kristin Murtha1, Anders J Perrone3, Tim Robert-Fitzgerald7, Jenna M Schabdach2, Russell T Shinohara7, Jacob W Vogel1, Chenying Zhao8, Damien A Fair3, Michael P Milham9, Matthew Cieslak1, Theodore D Satterthwaite10.
Abstract
The Brain Imaging Data Structure (BIDS) is a specification accompanied by a software ecosystem that was designed to create reproducible and automated workflows for processing neuroimaging data. BIDS Apps flexibly build workflows based on the metadata detected in a dataset. However, even BIDS valid metadata can include incorrect values or omissions that result in inconsistent processing across sessions. Additionally, in large-scale, heterogeneous neuroimaging datasets, hidden variability in metadata is difficult to detect and classify. To address these challenges, we created a Python-based software package titled "Curation of BIDS" (CuBIDS), which provides an intuitive workflow that helps users validate and manage the curation of their neuroimaging datasets. CuBIDS includes a robust implementation of BIDS validation that scales to large samples and incorporates DataLad--a version control software package for data--as an optional dependency to ensure reproducibility and provenance tracking throughout the entire curation process. CuBIDS provides tools to help users perform quality control on their images' metadata and identify unique combinations of imaging parameters. Users can then execute BIDS Apps on a subset of participants that represent the full range of acquisition parameters that are present, accelerating pipeline testing on large datasets.Entities:
Keywords: BIDS; Brain; Curation; Heterogeneity; MRI; Metadata; Neuroimaging; Software; Validation; Version control
Year: 2022 PMID: 36064140 DOI: 10.1016/j.neuroimage.2022.119609
Source DB: PubMed Journal: Neuroimage ISSN: 1053-8119 Impact factor: 7.400