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

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). (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. Author summaryMagnetic Resonance Imaging (MRI) is a non-invasive way to measure human brain structure and activity that has been used for over 25 years. There are thousands 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 interface with data 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.

Modulation of amygdala reactivity following rapidly acting interventions for major depression

“…Imaging data were preprocessed using the HCP minimal pipelines (Glasser et al, ) implemented within the BIDS‐App (Gorgolewski et al, ). After preprocessing, the functional images were further denoised using FSL's FIX (https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FIX).…”

Generalizing age effects on brain structure and cognition: A two‐study comparison approach

“…Anatomical images from both samples were preprocessed using the same automated surface‐based processing stream of the FreeSurfer Software package (version 6.0.0). For the LHAB sample, this was done via the FreeSurfer BIDS App (v6.0.0‐2; Gorgolewski et al (). A detailed description of this pipeline is provided by Dale, Fischl, and Sereno () as well as on http://surfer.nmr.mgh.harvard.edu.…”

Progress toward openness, transparency, and reproducibility in cognitive neuroscience

“… A long‐term goal of the CRN is not only to facilitate sharing, but also to provide containerized, modular, and fully reproducible cloud‐based tools that can be easily executed via a graphical web interface. This will bring reproducible state‐of‐the‐art neuroimaging data analysis within reach of researchers who lack the resources to deploy their own pipelines locally …”