qMRI-BIDS: An extension to the brain imaging data structure for quantitative magnetic resonance imaging data

Agah Karakuzu; Stefan Appelhoff; Tibor Auer; Mathieu Boudreau; Franklin Feingold; Ali R. Khan; Alberto Lazari; Chris Markiewicz; M Mulder; Christophe Phillips; Taylor Salo; Nikola Stikov; Kirstie Whitaker; Gilles de Hollander

doi:10.1038/s41597-022-01571-4

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qMRI-BIDS: An extension to the brain imaging data structure for quantitative magnetic resonance imaging data

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qMRI-BIDS: An extension to the brain imaging data structure for quantitative magnetic resonance imaging data

Agah Karakuzu, Stefan Appelhoff, Tibor Auer, Mathieu Boudreau, Franklin Feingold, Ali R. Khan, Alberto Lazari, Chris Markiewicz, M Mulder, Christophe Phillips, …

Scientific Data, Vol.9, 517

24/08/2022

DOI: https://doi.org/10.1038/s41597-022-01571-4

Abstract

Biomedical Engineering

Neuroscience

The Brain Imaging Data Structure (BIDS) established community consensus on the organization of data and metadata for several neuroimaging modalities. Traditionally, BIDS had a strong focus on functional magnetic resonance imaging (MRI) datasets and lacked guidance on how to store multimodal structural MRI datasets. Here, we present and describe the BIDS Extension Proposal 001 (BEP001), which adds a range of quantitative MRI (qMRI) applications to the BIDS. In general, the aim of qMRI is to characterize brain microstructure by quantifying the physical MR parameters of the tissue via computational, biophysical models. By proposing this new standard, we envision standardization of qMRI through multicenter dissemination of interoperable datasets. This way, BIDS can act as a catalyst of convergence between qMRI methods development and application-driven neuroimaging studies that can help develop quantitative biomarkers for neural tissue characterization. In conclusion, this BIDS extension offers a common ground for developers to exchange novel imaging data and tools, reducing the entrance barrier for qMRI in the field of neuroimaging.

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Title: qMRI-BIDS: An extension to the brain imaging data structure for quantitative magnetic resonance imaging data
Creators: Agah Karakuzu - NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal
Stefan Appelhoff - Center for Adaptive Rationality, Max Planck Institute for Human Development
Tibor Auer
Mathieu Boudreau - NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal
Franklin Feingold - Stanford University
Ali R. Khan - Department of Medical Biophysics, Robarts Research Institute, University of Western Ontario
Alberto Lazari - Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford
Chris Markiewicz - Stanford University
M Mulder - Department of Experimental Psychology, Utrecht University
Christophe Phillips - GIGA Cyclotron Research Centre in vivo imaging, GIGA Institute, University of Liège
Taylor Salo - Florida International University
Nikola Stikov - NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal
Kirstie Whitaker - The Alan Turing Institute
Gilles de Hollander - Zurich Center for Neuroeconomics (ZNE), Department of Economics, University of Zurich
Publication Details: Scientific Data, Vol.9, 517
Publisher: Nature Research
Date published: 24/08/2022
Date accepted: 19/07/2022
Grants: Dynamic modulation of brain states using brain stimulation and neuroadaptive Bayesian optimization, BB/S008314/1, Biotechnology and Biological Sciences Research Council (United Kingdom, Swindon) - BBSRC
Oxford, Neuroscience., 109062/Z/15/Z, Wellcome Trust (United Kingdom, London) - WT
Dutch Research Council (Netherlands, The Hague) - NWO
International Society for Magnetic Resonance in Medicine (United States, Berkeley) - ISMRM
Grant note: The authors would like to acknowledge the work by other contributors to BIDS, and those who contributed to BEP-001 via the Github repository, intermediate meetings, as well as a first draft on Google Drive. For BEP-001, recorded contributions include those from Suyash Bhogawar, Julien Cohen-Adad, Elizabeth Dupre, Chris Gorgolewski, Daniel Handwerker, Michael Harms, Ilana Leppert, Tobias Leutritz, Dylan Nielson, Julien Sein, Isla Staden, Wietske van der Zwaag, and Tobias Wood. This research was funded in part by the Wellcome Trust [Grant 109062/Z/15/Z to AL]. For Open Access, the author has applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission. TA’s work has been funded by the Biotechnology and Biological Sciences Research Council, London (BB/S008314/1). C.P. is supported by the F.R.S.-FNRS, Belgium. G.H. was funded by a Rubicon grant from the Dutch Research Council (NWO). A.K. is supported by \Canada First Research Excellence Fund through the TransMedTech Institute, Canadian Open Neuroscience Platform (CONP) and International Society for Magnetic Resonance in Medicine (ISMRM).
Identifiers: 99683565202346
Copyright: © The Author(s) 2022. Open Access. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
Academic Unit: School of Psychology
Language: English
Resource Type: Journal article

qMRI-BIDS: An extension to the brain imaging data structure for quantitative magnetic resonance imaging data

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