BIDS apps: Improving ease of use, accessibility, and reproducibility of neuroimaging data analysis methods.

Krzysztof J Gorgolewski,J Swaroop Guntupalli,Stephen C Strother,Anders Eklund,David Raffelt,Christopher J Steele,Pierre-Olivier Quirion,Franziskus Liem,Gabriel A Devenyi,Anisha Keshavan,Mihai Capotă,Nathan W Churchill,Robert E Smith,Russell A Poldrack,R Cameron Craddock,Pierre Bellec,Fidel Alfaro-Almagro,Mark Jenkinson,Gaël Varoquaux,Tibor Auer,Pradeep Reddy Raamana,Gregory Kiar,Guillaume Flandin,Tal Yarkoni,M Mallar Chakravarty,Yida Wang,Alexander L Cohen ,Samik Ghosh ,Oscar Estéban

doi:10.1371/journal.pcbi.1005209

Abstract

The rate of progress in human neurosciences is limited by the inability to easily apply a wide range of analysis methods to the plethora of different datasets acquired in labs around the world. 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 comprehensiveness 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 22 ready to use BIDS Apps, packaging a diverse set of commonly used neuroimaging algorithms.

Highlights

The last 25 years have witnessed a proliferation of methods for imaging the human brain
There are thousands Magnetic Resonance Imaging (MRI) studies performed every year generating a substantial amount of data
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

Summary

Introduction

The last 25 years have witnessed a proliferation of methods for imaging the human brain (including structural, diffusion and functional Magnetic Resonance Imaging, Positron Emission Tomography, Electroencephalography and Magnetoencephalography). These methods have been accompanied by literally thousands of different algorithms for signal denoising, normalization, feature extraction, and statistical analysis. The increasing complexity of neuroimaging data analysis has led to many discoveries, and the flexibility provided by the plethora of feature extraction methods has allowed cognitive and clinical neuroscientists to develop new theories about the relationships between brain and behavior in healthy and diseased populations. Many scientific packages depend on external libraries (often requiring a particular, sometimes outdated version), and require complex configurations of environment variables and/or data files

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