RT Journal Article
SR Electronic
T1 BIDS Apps: Improving ease of use, accessibility and reproducibility of neuroimaging data analysis methods
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 079145
DO 10.1101/079145
A1 Krzysztof J. Gorgolewski
A1 Fidel Alfaro-Almagro
A1 Tibor Auer
A1 Pierre Bellec
A1 Mihai Capotă
A1 M. Mallar Chakravarty
A1 Nathan W. Churchill
A1 R. Cameron Craddock
A1 Gabriel A. Devenyi
A1 Anders Eklund
A1 Oscar Esteban
A1 Guillaume Flandin
A1 Satrajit S. Ghosh
A1 J. Swaroop Guntupalli
A1 Mark Jenkinson
A1 Anisha Keshavan
A1 Gregory Kiar
A1 Pradeep Reddy Raamana
A1 David Raffelt
A1 Christopher J. Steele
A1 Pierre-Olivier Quirion
A1 Robert E. Smith
A1 Stephen C. Strother
A1 Gaël Varoquaux
A1 Tal Yarkoni
A1 Yida Wang
A1 Russell A. Poldrack
YR 2016
UL http://biorxiv.org/content/early/2016/10/20/079145.abstract
AB In this work, we introduce a framework for creating, testing, versioning and archiving portable applications for analyzing neuroimaging data organized and described in compliance with the Brain Imaging Data Structure (BIDS). The portability of these applications (BIDS Apps) is achieved by using container technologies that encapsulate all binary and other dependencies in one convenient package. BIDS Apps run on all three major operating systems with no need for complex setup and configuration and thanks to the richness of the BIDS standard they require little manual user input. Previous containerized data processing solutions were limited to single user environments and not compatible with most multi-tenant High Performance Computing systems. BIDS Apps overcome this limitation by taking advantage of the Singularity container technology. As a proof of concept, this work is accompanied by 20 ready to use BIDS Apps, packaging a diverse set of commonly used neuroimaging algorithms.Author Summary Magnetic Resonance Imaging (MRI) is a noninvasive way to measure human brain structure and activity that has been used for over 25 years. There are hundreds MRI studies performed every year generating a substantial amount of data. At the same time, many new data analysis methods are being developed every year. The potential of using new analysis methods on the variety of existing and newly acquired data is hindered by difficulties in software deployment and lack of support for standardized input data. Here we propose to use container technology to make deployment of a wide range of data analysis techniques easy. In addition, we adapt the existing data analysis tools to understand and work with data in organized in a standardized way. We hope that this approach will enable researchers to access a wider range of methods when analyzing their data which will lead to accelerated progress in human neuroscience.