1
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Wang HT, Meisler SL, Sharmarke H, Clarke N, Gensollen N, Markiewicz CJ, Paugam F, Thirion B, Bellec P. Continuous evaluation of denoising strategies in resting-state fMRI connectivity using fMRIPrep and Nilearn. PLoS Comput Biol 2024; 20:e1011942. [PMID: 38498530 PMCID: PMC10977879 DOI: 10.1371/journal.pcbi.1011942] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Revised: 03/28/2024] [Accepted: 02/23/2024] [Indexed: 03/20/2024] Open
Abstract
Reducing contributions from non-neuronal sources is a crucial step in functional magnetic resonance imaging (fMRI) connectivity analyses. Many viable strategies for denoising fMRI are used in the literature, and practitioners rely on denoising benchmarks for guidance in the selection of an appropriate choice for their study. However, fMRI denoising software is an ever-evolving field, and the benchmarks can quickly become obsolete as the techniques or implementations change. In this work, we present a denoising benchmark featuring a range of denoising strategies, datasets and evaluation metrics for connectivity analyses, based on the popular fMRIprep software. The benchmark prototypes an implementation of a reproducible framework, where the provided Jupyter Book enables readers to reproduce or modify the figures on the Neurolibre reproducible preprint server (https://neurolibre.org/). We demonstrate how such a reproducible benchmark can be used for continuous evaluation of research software, by comparing two versions of the fMRIprep. Most of the benchmark results were consistent with prior literature. Scrubbing, a technique which excludes time points with excessive motion, combined with global signal regression, is generally effective at noise removal. Scrubbing was generally effective, but is incompatible with statistical analyses requiring the continuous sampling of brain signal, for which a simpler strategy, using motion parameters, average activity in select brain compartments, and global signal regression, is preferred. Importantly, we found that certain denoising strategies behave inconsistently across datasets and/or versions of fMRIPrep, or had a different behavior than in previously published benchmarks. This work will hopefully provide useful guidelines for the fMRIprep users community, and highlight the importance of continuous evaluation of research methods.
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Affiliation(s)
- Hao-Ting Wang
- Centre de recherche de l’institut Universitaire de gériatrie de Montréal (CRIUGM), Montréal, Québec, Canada
| | - Steven L. Meisler
- Program in Speech and Hearing Bioscience and Technology, Harvard University, Massachusetts, United States of America
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Massachusetts, United States of America
| | - Hanad Sharmarke
- Centre de recherche de l’institut Universitaire de gériatrie de Montréal (CRIUGM), Montréal, Québec, Canada
| | - Natasha Clarke
- Centre de recherche de l’institut Universitaire de gériatrie de Montréal (CRIUGM), Montréal, Québec, Canada
| | | | | | - François Paugam
- Centre de recherche de l’institut Universitaire de gériatrie de Montréal (CRIUGM), Montréal, Québec, Canada
- Computer Science and Operations Research Department, Université de Montréal, Montréal, Québec, Canada
- Mila—Institut Québécois d’Intelligence Artificielle, Montréal, Canada
| | | | - Pierre Bellec
- Centre de recherche de l’institut Universitaire de gériatrie de Montréal (CRIUGM), Montréal, Québec, Canada
- Psychology Department, Université de Montréal, Montréal, Québec, Canada
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2
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Poldrack RA, Markiewicz CJ, Appelhoff S, Ashar YK, Auer T, Baillet S, Bansal S, Beltrachini L, Benar CG, Bertazzoli G, Bhogawar S, Blair RW, Bortoletto M, Boudreau M, Brooks TL, Calhoun VD, Castelli FM, Clement P, Cohen AL, Cohen-Adad J, D'Ambrosio S, de Hollander G, de la Iglesia-Vayá M, de la Vega A, Delorme A, Devinsky O, Draschkow D, Duff EP, DuPre E, Earl E, Esteban O, Feingold FW, Flandin G, Galassi A, Gallitto G, Ganz M, Gau R, Gholam J, Ghosh SS, Giacomel A, Gillman AG, Gleeson P, Gramfort A, Guay S, Guidali G, Halchenko YO, Handwerker DA, Hardcastle N, Herholz P, Hermes D, Honey CJ, Innis RB, Ioanas HI, Jahn A, Karakuzu A, Keator DB, Kiar G, Kincses B, Laird AR, Lau JC, Lazari A, Legarreta JH, Li A, Li X, Love BC, Lu H, Marcantoni E, Maumet C, Mazzamuto G, Meisler SL, Mikkelsen M, Mutsaerts H, Nichols TE, Nikolaidis A, Nilsonne G, Niso G, Norgaard M, Okell TW, Oostenveld R, Ort E, Park PJ, Pawlik M, Pernet CR, Pestilli F, Petr J, Phillips C, Poline JB, Pollonini L, Raamana PR, Ritter P, Rizzo G, Robbins KA, Rockhill AP, Rogers C, Rokem A, Rorden C, Routier A, Saborit-Torres JM, Salo T, Schirner M, Smith RE, Spisak T, Sprenger J, Swann NC, Szinte M, Takerkart S, Thirion B, Thomas AG, Torabian S, Varoquaux G, Voytek B, Welzel J, Wilson M, Yarkoni T, Gorgolewski KJ. The Past, Present, and Future of the Brain Imaging Data Structure (BIDS). ArXiv 2024:arXiv:2309.05768v2. [PMID: 37744469 PMCID: PMC10516110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 09/26/2023]
Abstract
The Brain Imaging Data Structure (BIDS) is a community-driven standard for the organization of data and metadata from a growing range of neuroscience modalities. This paper is meant as a history of how the standard has developed and grown over time. We outline the principles behind the project, the mechanisms by which it has been extended, and some of the challenges being addressed as it evolves. We also discuss the lessons learned through the project, with the aim of enabling researchers in other domains to learn from the success of BIDS.
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Affiliation(s)
| | | | | | - Yoni K Ashar
- University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Tibor Auer
- School of Psychology, University of Surrey, Guildford, UK
- Artificial Intelligence and Informatics group, Rosalind Franklin Institute, Harwell Campus, Didcot, UK
| | - Sylvain Baillet
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada
| | - Shashank Bansal
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
| | - Leandro Beltrachini
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Physics and Astronomy, Cardiff University, Wales, UK
| | - Christian G Benar
- Aix Marseille Université, INSERM, INS, Inst Neurosci Syst, Marseille, France
| | - Giacomo Bertazzoli
- Neurophysiology Lab, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
- Center for Mind/Brain Sciences - CIMeC, University of Trento, Rovereto, TN, Italy
- Brigham and Women's Hospital, Boston, MA, USA
- Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | | | - Ross W Blair
- Department of Psychology, Stanford University, Stanford, CA, USA
| | - Marta Bortoletto
- Neurophysiology Lab, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | | | - Teon L Brooks
- Department of Psychology, Stanford University, Stanford, CA, USA
| | - Vince D Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State, Georgia Tech, Emory, Atlanta, GA, USA
| | - Filippo Maria Castelli
- European Laboratory for Non-Linear Spectroscopy (LENS), University of Florence, Sesto Fiorentino, Italy
- Bioretics srl, Cesena, Italy
| | - Patricia Clement
- Department of Medical Imaging, Ghent University Hospital, Ghent, Belgium
- Department of Diagnostic Sciences, Ghent University, Ghent, Belgium
| | - Alexander L Cohen
- Department of Neurology, Boston Children's Hospital, Boston, MA, USA
| | | | - Sasha D'Ambrosio
- Dipartimento di Scienze della Salute dell'Università degli Studi di Milano, Milan, Italy
- Department of Clinical and Experimental Epilepsy, University College London, UK
| | - Gilles de Hollander
- Zurich Center for Neuroeconomics, Department of Economics, University of Zurich, Zurich, Switzerland
| | | | | | - Arnaud Delorme
- SCCN, University of California, San Diego, La Jolla CA USA
| | - Orrin Devinsky
- Department of Neurology, NYU Langone Medical Center, New York, NY, USA
| | - Dejan Draschkow
- Department of Experimental Psychology, University of Oxford, Oxford, UK
| | - Eugene Paul Duff
- UK Dementia Research Institute, Department of Brain Sciences, Imperial College London, London, UK
| | - Elizabeth DuPre
- Department of Psychology, Stanford University, Stanford, CA, USA
| | - Eric Earl
- Intramural Research Program, National Institute of Mental Health, Bethesda, MD, USA
| | - Oscar Esteban
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | | | - Guillaume Flandin
- Wellcome Centre for Human Neuroimaging, University College London, London, England, UK
| | - Anthony Galassi
- Intramural Research Program, National Institute of Mental Health, Bethesda, MD, USA
| | - Giuseppe Gallitto
- Center for Translational Neuro- and Behavioral Sciences, University Medicine Essen, Essen, Germany
- Department of Neurology, University Medicine Essen, Essen, Germany
| | - Melanie Ganz
- Department of Computer Science, University of Copenhagen, Copenhagen, Denmark
- Neurobiology Research Unit, Copenhagen University Hospital, Copenhagen, Denmark
| | - Rémi Gau
- Origamin Lab, The Neuro, McGill University, Montreal, Quebec, Canada
| | - James Gholam
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Wales, UK
| | | | - Alessio Giacomel
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, England, UK
| | - Ashley G Gillman
- The Australian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation, Townsville, Queensland, Australia
| | - Padraig Gleeson
- Department of Neuroscience, Physiology and Pharmacology, University College London, London, England, UK
| | | | - Samuel Guay
- Université de Montréal, Montréal, QC, Canada
| | - Giacomo Guidali
- Department of Psychology & NeuroMI - Milan Centre for Neuroscience, University of Milano-Bicocca, Milan, Italy
| | - Yaroslav O Halchenko
- Center for Open Neuroscience, Department of Psychological and Brain Sciences, Dartmouth College, NH, USA
| | - Daniel A Handwerker
- Intramural Research Program, National Institute of Mental Health, Bethesda, MD, USA
| | - Nell Hardcastle
- Department of Psychology, Stanford University, Stanford, CA, USA
| | - Peer Herholz
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Quebec, Canada
| | - Dora Hermes
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN, USA
| | - Christopher J Honey
- Department of Psychological & Brain Sciences, Johns Hopkins University, Baltimore, MD, USA
| | - Robert B Innis
- Intramural Research Program, National Institute of Mental Health, Bethesda, MD, USA
| | - Horea-Ioan Ioanas
- Center for Open Neuroscience, Department of Psychological and Brain Sciences, Dartmouth College, NH, USA
| | - Andrew Jahn
- Functional MRI Laboratory, University of Michigan, Ann Arbor, MI, USA
| | - Agah Karakuzu
- NeuroPoly Lab, Polytechnique Montréal, Montréal, Quebec, Canada
| | - David B Keator
- Change Your Brain Change Your Life Foundation, Costa Mesa, CA, USA
- Amen Clinics, Costa Mesa, CA, USA
- Department of Psychiatry and Human Behavior, School of Medicine, University of California, Irvine, CA, USA
| | - Gregory Kiar
- Center for Data Analytics, Innovation, and Rigor, Child Mind Institute, New York, NY USA
| | - Balint Kincses
- Center for Translational Neuro- and Behavioral Sciences, University Medicine Essen, Essen, Germany
- Department of Neurology, University Medicine Essen, Essen, Germany
| | - Angela R Laird
- Department of Physics, Florida International University, Miami, FL, USA
| | - Jonathan C Lau
- Department of Clinical Neurological Sciences, Western University, London, Ontario, Canada
| | - Alberto Lazari
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Jon Haitz Legarreta
- Department of Radiology, Brigham and Women's Hospital, Mass General Brigham/Harvard Medical School, Boston, MA, USA
| | - Adam Li
- Columbia University, New York, NY, USA
| | - Xiangrui Li
- Center for Cognitive and Behavioral Brain Imaging, The Ohio State University, Columbus, OH, USA
| | | | - Hanzhang Lu
- Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Eleonora Marcantoni
- School for Psychology and Neuroscience and Centre for Cognitive Neuroimaging, University of Glasgow, Glasgow
| | - Camille Maumet
- Inria, Univ Rennes, CNRS, Inserm, IRISA UMR 6074, Empenn ERL U 1228, Rennes, France
| | - Giacomo Mazzamuto
- National Research Council - National Institute of Optics (CNR-INO), Florence, Italy
| | - Steven L Meisler
- Program in Speech and Hearing Bioscience and Technology, Harvard University, Cambridge, MA, USA
| | - Mark Mikkelsen
- Department of Radiology, Weill Cornell Medicine, New York, NY, USA
| | - Henk Mutsaerts
- Radiology and Nuclear Medicine, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Brain Imaging, Amsterdam, The Netherlands
| | - Thomas E Nichols
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Aki Nikolaidis
- Center for the Developing Brain, Child Mind Institute, New York, NY, USA
| | - Gustav Nilsonne
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
- Swedish National Data Service, Gothenburg University, Gothenburg, Sweden
| | | | - Martin Norgaard
- Intramural Research Program, National Institute of Mental Health, Bethesda, MD, USA
- Department of Computer Science, University of Copenhagen, Copenhagen, Denmark
| | - Thomas W Okell
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Robert Oostenveld
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, The Netherlands
- NatMEG, Karolinska Institutet, Stockholm, Sweden
| | - Eduard Ort
- Heinrich Heine University, Department of Biological Psychology of Decision Making, Düsseldorf, Germany
| | | | - Mateusz Pawlik
- Paris-Lodron-University of Salzburg, Department of Psychology, Centre for Cognitive Neuroscience, Salzburg, Austria
| | - Cyril R Pernet
- Neurobiology Research Unit, Copenhagen University Hospital, Copenhagen, Denmark
| | | | - Jan Petr
- Helmholtz-Zentrum Dresden-Rossendorf, Institute of Radiopharmaceutical Cancer Research, Dresden, Germany
| | | | - Jean-Baptiste Poline
- Neuro Data Science ORIGAMI Laboratory, McConnell Brain Imaging Centre, Faculty of Medicine, McGill University, Montréal, Canada
| | - Luca Pollonini
- Department of Engineering Technology, University of Houston, Houston, TX
- Basque Center on Cognition, Brain and Language, Donostia-San Sebastián, Spain
| | | | - Petra Ritter
- Berlin Institute of Health at Charité, Universitätsmedizin Berlin, Charitéplatz 1, Berlin 10117, Germany
- Department of Neurology with Experimental Neurology, Charité, Universitätsmedizin Berlin, Corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Charitéplatz 1, Berlin 10117, Germany
- Bernstein Focus State Dependencies of Learning and Bernstein Center for Computational Neuroscience, Berlin, Germany
- Einstein Center for Neuroscience Berlin, Charitéplatz 1, Berlin 10117, Germany
- Einstein Center Digital Future, Wilhelmstraße 67, Berlin 10117, Germany
| | - Gaia Rizzo
- Invicro, London, UK
- Division of Brain Sciences, Imperial College London, London, UK
| | - Kay A Robbins
- Department of Computer Science, University of Texas at San Antonio, San Antonio, TX, USA
| | - Alexander P Rockhill
- Department of Neurosurgery, Oregon Health & Science University, Portland, OR, USA
| | - Christine Rogers
- McGill Centre for Integrative Neuroscience (MCIN), Montréal Neurological Institute, McGill University, Montréal, QC, Canada
| | - Ariel Rokem
- University of Washington, Department of Psychology and eScience Institute, Seattle, WA, USA
| | - Chris Rorden
- University of South Carolina, Department of Psychology, Columbia, SC, USA
| | | | | | - Taylor Salo
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Michael Schirner
- Berlin Institute of Health at Charité, Universitätsmedizin Berlin, Charitéplatz 1, Berlin 10117, Germany
- Department of Neurology with Experimental Neurology, Charité, Universitätsmedizin Berlin, Corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Charitéplatz 1, Berlin 10117, Germany
- Bernstein Focus State Dependencies of Learning and Bernstein Center for Computational Neuroscience, Berlin, Germany
- Einstein Center for Neuroscience Berlin, Charitéplatz 1, Berlin 10117, Germany
- Einstein Center Digital Future, Wilhelmstraße 67, Berlin 10117, Germany
| | - Robert E Smith
- The Florey Institute of Neuroscience and Mental Health, Heidelberg, Victoria, Australia
- The Florey Department of Neuroscience and Mental Heath, The University of Melbourne, Parkville, Victoria, Australia
| | - Tamas Spisak
- Center for Translational Neuro- and Behavioral Sciences, University Medicine Essen, Essen, Germany
- Institute for Diagnostic and Interventional Radiology and Neuroradiology, University Medicine Essen, Essen, Germany
| | - Julia Sprenger
- Institut de Neurosciences de la Timone (INT), UMR7289, CNRS, Aix-Marseille Université, France
| | - Nicole C Swann
- University of Oregon, Department of Human Physiology, Eugene, OR, USA
| | - Martin Szinte
- Institut de Neurosciences de la Timone (INT), UMR7289, CNRS, Aix-Marseille Université, France
| | - Sylvain Takerkart
- Institut de Neurosciences de la Timone (INT), UMR7289, CNRS, Aix-Marseille Université, France
| | | | - Adam G Thomas
- Intramural Research Program, National Institute of Mental Health, Bethesda, MD, USA
| | | | | | - Bradley Voytek
- Department of Cognitive Science, Halıcıoğlu Data Science Institute, and Neurosciences Graduate Program, University of California, San Diego, La Jolla, CA, USA
| | | | - Martin Wilson
- University of Birmingham, Centre for Human Brain Health and School of Psychology, Birmingham, UK
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3
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Wang HT, Meisler SL, Sharmarke H, Clarke N, Gensollen N, Markiewicz CJ, Paugam F, Thirion B, Bellec P. Continuous Evaluation of Denoising Strategies in Resting-State fMRI Connectivity Using fMRIPrep and Nilearn. bioRxiv 2023:2023.04.18.537240. [PMID: 37131781 PMCID: PMC10153168 DOI: 10.1101/2023.04.18.537240] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Reducing contributions from non-neuronal sources is a crucial step in functional magnetic resonance imaging (fMRI) connectivity analyses. Many viable strategies for denoising fMRI are used in the literature, and practitioners rely on denoising benchmarks for guidance in the selection of an appropriate choice for their study. However, fMRI denoising software is an ever-evolving field, and the benchmarks can quickly become obsolete as the techniques or implementations change. In this work, we present a denoising benchmark featuring a range of denoising strategies, datasets and evaluation metrics for connectivity analyses, based on the popular fMRIprep software. The benchmark is implemented in a fully reproducible framework, where the provided research objects enable readers to reproduce or modify core computations, as well as the figures of the article using the Jupyter Book project and the Neurolibre reproducible preprint server (https://neurolibre.org/). We demonstrate how such a reproducible benchmark can be used for continuous evaluation of research software, by comparing two versions of the fMRIprep software package. The majority of benchmark results were consistent with prior literature. Scrubbing, a technique which excludes time points with excessive motion, combined with global signal regression, is generally effective at noise removal. Scrubbing however disrupts the continuous sampling of brain images and is incompatible with some statistical analyses, e.g. auto-regressive modeling. In this case, a simple strategy using motion parameters, average activity in select brain compartments, and global signal regression should be preferred. Importantly, we found that certain denoising strategies behave inconsistently across datasets and/or versions of fMRIPrep, or had a different behavior than in previously published benchmarks. This work will hopefully provide useful guidelines for the fMRIprep users community, and highlight the importance of continuous evaluation of research methods. Our reproducible benchmark infrastructure will facilitate such continuous evaluation in the future, and may also be applied broadly to different tools or even research fields.
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Affiliation(s)
- Hao-Ting Wang
- Centre de recherche de l'institut Universitaire de gériatrie de Montréal (CRIUGM), Montréal, Québec, Canada
| | - Steven L Meisler
- Program in Speech and Hearing Bioscience and Technology, Harvard University, MA, USA
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, MA, USA
| | - Hanad Sharmarke
- Centre de recherche de l'institut Universitaire de gériatrie de Montréal (CRIUGM), Montréal, Québec, Canada
| | - Natasha Clarke
- Centre de recherche de l'institut Universitaire de gériatrie de Montréal (CRIUGM), Montréal, Québec, Canada
| | | | | | - Fraçois Paugam
- Centre de recherche de l'institut Universitaire de gériatrie de Montréal (CRIUGM), Montréal, Québec, Canada
- Computer Science and Operations Research Department, Université de Montréal, Montréal, Québec, Canada
- Mila - Institut Québécois d'Intelligence Artificielle, Montréal, Canada
| | | | - Pierre Bellec
- Centre de recherche de l'institut Universitaire de gériatrie de Montréal (CRIUGM), Montréal, Québec, Canada
- Psychology Department, Université de Montréal, Montréal, Québec, Canada
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4
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Kiar G, Clucas J, Feczko E, Goncalves M, Jarecka D, Markiewicz CJ, Halchenko YO, Hermosillo R, Li X, Miranda-Dominguez O, Ghosh S, Poldrack RA, Satterthwaite TD, Milham MP, Fair D. Align with the NMIND consortium for better neuroimaging. Nat Hum Behav 2023; 7:1027-1028. [PMID: 37386112 PMCID: PMC11024722 DOI: 10.1038/s41562-023-01647-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/01/2023]
Affiliation(s)
- Gregory Kiar
- Center for Data Analytics, Innovation, and Rigor, Child Mind Institute, New York, NY, USA.
| | - Jon Clucas
- Center for Data Analytics, Innovation, and Rigor, Child Mind Institute, New York, NY, USA
| | - Eric Feczko
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
| | | | - Dorota Jarecka
- McGovern Institute for Brain Research, MIT, Cambridge, MA, USA
| | | | - Yaroslav O Halchenko
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, USA
| | - Robert Hermosillo
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
| | - Xinhui Li
- Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | | | - Satrajit Ghosh
- McGovern Institute for Brain Research, MIT, Cambridge, MA, USA
| | | | - Theodore D Satterthwaite
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Michael P Milham
- Center for Data Analytics, Innovation, and Rigor, Child Mind Institute, New York, NY, USA
| | - Damien Fair
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
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5
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Ciric R, Thompson WH, Lorenz R, Goncalves M, MacNicol EE, Markiewicz CJ, Halchenko YO, Ghosh SS, Gorgolewski KJ, Poldrack RA, Esteban O. TemplateFlow: FAIR-sharing of multi-scale, multi-species brain models. Nat Methods 2022; 19:1568-1571. [PMID: 36456786 PMCID: PMC9718663 DOI: 10.1038/s41592-022-01681-2] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Accepted: 10/14/2022] [Indexed: 12/03/2022]
Abstract
Reference anatomies of the brain ('templates') and corresponding atlases are the foundation for reporting standardized neuroimaging results. Currently, there is no registry of templates and atlases; therefore, the redistribution of these resources occurs either bundled within existing software or in ad hoc ways such as downloads from institutional sites and general-purpose data repositories. We introduce TemplateFlow as a publicly available framework for human and non-human brain models. The framework combines an open database with software for access, management, and vetting, allowing scientists to share their resources under FAIR-findable, accessible, interoperable, and reusable-principles. TemplateFlow enables multifaceted insights into brains across species, and supports multiverse analyses testing whether results generalize across standard references, scales, and in the long term, species.
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Affiliation(s)
- Rastko Ciric
- Department of Psychology, Stanford University, Stanford, CA, USA.
- Department of Bioengineering, Stanford University, Stanford, CA, USA.
| | - William H Thompson
- Department of Psychology, Stanford University, Stanford, CA, USA
- Department of Applied Information Technology, University of Gothenburg, Gothenburg, Sweden
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Romy Lorenz
- Department of Psychology, Stanford University, Stanford, CA, USA
- MRC CBU, University of Cambridge, Cambridge, UK
- Department of Neurophysics, MPI, Leipzig, Germany
| | | | - Eilidh E MacNicol
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | | | - Yaroslav O Halchenko
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, USA
| | - Satrajit S Ghosh
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Otolaryngology, Harvard Medical School, Boston, MA, USA
| | | | | | - Oscar Esteban
- Department of Psychology, Stanford University, Stanford, CA, USA.
- Department of Radiology, University Hospital of Lausanne and University of Lausanne, Lausanne, Switzerland.
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6
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Niso G, Botvinik-Nezer R, Appelhoff S, De La Vega A, Esteban O, Etzel JA, Finc K, Ganz M, Gau R, Halchenko YO, Herholz P, Karakuzu A, Keator DB, Markiewicz CJ, Maumet C, Pernet CR, Pestilli F, Queder N, Schmitt T, Sójka W, Wagner AS, Whitaker KJ, Rieger JW. Open and reproducible neuroimaging: From study inception to publication. Neuroimage 2022; 263:119623. [PMID: 36100172 PMCID: PMC10008521 DOI: 10.1016/j.neuroimage.2022.119623] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Revised: 08/17/2022] [Accepted: 09/09/2022] [Indexed: 10/31/2022] Open
Abstract
Empirical observations of how labs conduct research indicate that the adoption rate of open practices for transparent, reproducible, and collaborative science remains in its infancy. This is at odds with the overwhelming evidence for the necessity of these practices and their benefits for individual researchers, scientific progress, and society in general. To date, information required for implementing open science practices throughout the different steps of a research project is scattered among many different sources. Even experienced researchers in the topic find it hard to navigate the ecosystem of tools and to make sustainable choices. Here, we provide an integrated overview of community-developed resources that can support collaborative, open, reproducible, replicable, robust and generalizable neuroimaging throughout the entire research cycle from inception to publication and across different neuroimaging modalities. We review tools and practices supporting study inception and planning, data acquisition, research data management, data processing and analysis, and research dissemination. An online version of this resource can be found at https://oreoni.github.io. We believe it will prove helpful for researchers and institutions to make a successful and sustainable move towards open and reproducible science and to eventually take an active role in its future development.
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Affiliation(s)
- Guiomar Niso
- Psychological & Brain Sciences, Indiana University, Bloomington, IN, USA; Universidad Politecnica de Madrid, Madrid and CIBER-BBN, Spain; Instituto Cajal, CSIC, Madrid, Spain.
| | - Rotem Botvinik-Nezer
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, USA.
| | - Stefan Appelhoff
- Center for Adaptive Rationality, Max Planck Institute for Human Development, Berlin, Germany
| | | | - Oscar Esteban
- Dept. of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland; Department of Psychology, Stanford University, Stanford, CA, USA
| | - Joset A Etzel
- Department of Psychological and Brain Sciences, Washington University in St. Louis, St. Louis, MO, USA
| | - Karolina Finc
- Centre for Modern Interdisciplinary Technologies, Nicolaus Copernicus University, Toruń, Poland
| | - Melanie Ganz
- Neurobiology Research Unit, Rigshospitalet, Copenhagen, Denmark; Department of Computer Science, University of Copenhagen, Copenhagen, Denmark
| | - Rémi Gau
- Institute of Psychology, Université catholique de Louvain, Louvain la Neuve, Belgium
| | - Yaroslav O Halchenko
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, USA
| | - Peer Herholz
- Montreal Neurological Institute-Hospital, McGill University, Montréal, Quebec, Canada
| | - Agah Karakuzu
- Biomedical Engineering Institute, Polytechnique Montréal, Montréal, Quebec, Canada; Montréal Heart Institute, Montréal, Quebec, Canada
| | - David B Keator
- Department of Psychiatry and Human Behavior, University of California, Irvine, CA, USA
| | | | - Camille Maumet
- Inria, Univ Rennes, CNRS, Inserm - IRISA UMR 6074, Empenn ERL U 1228, Rennes, France
| | - Cyril R Pernet
- Neurobiology Research Unit, Rigshospitalet, Copenhagen, Denmark
| | - Franco Pestilli
- Psychological & Brain Sciences, Indiana University, Bloomington, IN, USA; Department of Psychology, The University of Texas at Austin, Austin, TX, USA
| | - Nazek Queder
- Montreal Neurological Institute-Hospital, McGill University, Montréal, Quebec, Canada; Department of Neurobiology and Behavior, University of California, Irvine, CA, USA
| | - Tina Schmitt
- Neuroimaging Unit, Carl-von-Ossietzky Universität, Oldenburg, Germany
| | - Weronika Sójka
- Faculty of Philosophy and Social Sciences, Nicolaus Copernicus University, Toruń, Poland
| | - Adina S Wagner
- Institute for Neuroscience and Medicine, Research Centre Juelich, Germany
| | | | - Jochem W Rieger
- Neuroimaging Unit, Carl-von-Ossietzky Universität, Oldenburg, Germany; Department of Psychology, Carl-von-Ossietzky Universität, Oldenburg, Germany.
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7
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de la Vega A, Rocca R, Blair RW, Markiewicz CJ, Mentch J, Kent JD, Herholz P, Ghosh SS, Poldrack RA, Yarkoni T. Neuroscout, a unified platform for generalizable and reproducible fMRI research. eLife 2022; 11:79277. [PMID: 36040302 PMCID: PMC9489206 DOI: 10.7554/elife.79277] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Accepted: 08/27/2022] [Indexed: 11/28/2022] Open
Abstract
Functional magnetic resonance imaging (fMRI) has revolutionized cognitive neuroscience, but methodological barriers limit the generalizability of findings from the lab to the real world. Here, we present Neuroscout, an end-to-end platform for analysis of naturalistic fMRI data designed to facilitate the adoption of robust and generalizable research practices. Neuroscout leverages state-of-the-art machine learning models to automatically annotate stimuli from dozens of fMRI studies using naturalistic stimuli—such as movies and narratives—allowing researchers to easily test neuroscientific hypotheses across multiple ecologically-valid datasets. In addition, Neuroscout builds on a robust ecosystem of open tools and standards to provide an easy-to-use analysis builder and a fully automated execution engine that reduce the burden of reproducible research. Through a series of meta-analytic case studies, we validate the automatic feature extraction approach and demonstrate its potential to support more robust fMRI research. Owing to its ease of use and a high degree of automation, Neuroscout makes it possible to overcome modeling challenges commonly arising in naturalistic analysis and to easily scale analyses within and across datasets, democratizing generalizable fMRI research.
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Affiliation(s)
- Alejandro de la Vega
- Department of Psychology, The University of Texas at Austin, Austin, United States
| | - Roberta Rocca
- Interacting Minds Centre, Aarhus University, Aarhus, Denmark
| | - Ross W Blair
- Department of Psychology, Stanford University, Stanford, United States
| | | | - Jeff Mentch
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, United States
| | - James D Kent
- Department of Psychology, The University of Texas at Austin, Austin, United States
| | - Peer Herholz
- Montreal Neurological Institute, McGill University, Montreal, Canada
| | - Satrajit S Ghosh
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, United States
| | | | - Tal Yarkoni
- Department of Psychology, The University of Texas at Austin, Austin, United States
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8
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Bourget MH, Kamentsky L, Ghosh SS, Mazzamuto G, Lazari A, Markiewicz CJ, Oostenveld R, Niso G, Halchenko YO, Lipp I, Takerkart S, Toussaint PJ, Khan AR, Nilsonne G, Castelli FM, Cohen-Adad J. Microscopy-BIDS: An Extension to the Brain Imaging Data Structure for Microscopy Data. Front Neurosci 2022; 16:871228. [PMID: 35516811 PMCID: PMC9063519 DOI: 10.3389/fnins.2022.871228] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Accepted: 03/01/2022] [Indexed: 11/13/2022] Open
Abstract
The Brain Imaging Data Structure (BIDS) is a specification for organizing, sharing, and archiving neuroimaging data and metadata in a reusable way. First developed for magnetic resonance imaging (MRI) datasets, the community-led specification evolved rapidly to include other modalities such as magnetoencephalography, positron emission tomography, and quantitative MRI (qMRI). In this work, we present an extension to BIDS for microscopy imaging data, along with example datasets. Microscopy-BIDS supports common imaging methods, including 2D/3D, ex/in vivo, micro-CT, and optical and electron microscopy. Microscopy-BIDS also includes comprehensible metadata definitions for hardware, image acquisition, and sample properties. This extension will facilitate future harmonization efforts in the context of multi-modal, multi-scale imaging such as the characterization of tissue microstructure with qMRI.
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Affiliation(s)
- Marie-Hélène Bourget
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada
| | - Lee Kamentsky
- Kwanghun Chung Lab, Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Satrajit S. Ghosh
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, United States
- Department of Otolaryngology–Head and Neck Surgery, Harvard Medical School, Boston, MA, United States
| | - Giacomo Mazzamuto
- National Research Council, National Institute of Optics, Sesto Fiorentino, Italy
- European Laboratory for Non-Linear Spectroscopy (LENS), Sesto Fiorentino, Italy
| | - Alberto Lazari
- Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging, FMRIB, University of Oxford, Oxford, United Kingdom
| | | | - Robert Oostenveld
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Netherlands
- NatMEG, Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Guiomar Niso
- Psychological and Brain Sciences, Indiana University, Bloomington, IN, United States
| | - Yaroslav O. Halchenko
- Department of Psychological and Brain Sciences, Center for Open Neuroscience, Dartmouth College, Hanover, NH, United States
| | - Ilona Lipp
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Sylvain Takerkart
- Institut de Neurosciences de la Timone, CNRS–Aix Marseille Université, Marseille, France
| | - Paule-Joanne Toussaint
- Department of Neurology and Neurosurgery, Faculty of Medicine and Health Sciences, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Ali R. Khan
- Robarts Research Institute, University of Western Ontario, London, ON, Canada
| | - Gustav Nilsonne
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
- Swedish National Data Service, Gothenburg University, Gothenburg, Sweden
| | | | - Julien Cohen-Adad
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada
- Mila – Quebec AI Institute, Montreal, QC, Canada
- Functional Neuroimaging Unit, Centre de Recherche de l’Institut Universitaire de Montréal (CRIUGM), Université de Montréal, Montreal, QC, Canada
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9
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Norgaard M, Matheson GJ, Hansen HD, Thomas A, Searle G, Rizzo G, Veronese M, Giacomel A, Yaqub M, Tonietto M, Funck T, Gillman A, Boniface H, Routier A, Dalenberg JR, Betthauser T, Feingold F, Markiewicz CJ, Gorgolewski KJ, Blair RW, Appelhoff S, Gau R, Salo T, Niso G, Pernet C, Phillips C, Oostenveld R, Gallezot JD, Carson RE, Knudsen GM, Innis RB, Ganz M. PET-BIDS, an extension to the brain imaging data structure for positron emission tomography. Sci Data 2022; 9:65. [PMID: 35236846 PMCID: PMC8891322 DOI: 10.1038/s41597-022-01164-1] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Accepted: 02/11/2022] [Indexed: 11/15/2022] Open
Affiliation(s)
- Martin Norgaard
- Neurobiology Research Unit, Rigshospitalet, and Institute of Clinical Medicine, Univ. Copenhagen, København, Denmark.,Department of Psychology, Stanford University, California, USA
| | - Granville J Matheson
- Department of Psychiatry, Columbia University, New York, NY, 10032, USA.,Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet and Stockholm Health Care Services, Stockholm, Sweden
| | - Hanne D Hansen
- Neurobiology Research Unit, Rigshospitalet, and Institute of Clinical Medicine, Univ. Copenhagen, København, Denmark.,Athinoula A. Martinos Center for Biomedical Imaging, MGH/HST, Charlestown, MA, USA
| | - Adam Thomas
- Intramural Research Program, NIMH, Bethesda, USA
| | - Graham Searle
- Invicro and Division of Brain Sciences, Imperial College London, London, UK
| | - Gaia Rizzo
- Invicro and Division of Brain Sciences, Imperial College London, London, UK
| | - Mattia Veronese
- Centre for Neuroimaging Sciences, King's College London, London, UK.,Department of Information Engineering, University of Padua, Padua, Italy
| | - Alessio Giacomel
- Centre for Neuroimaging Sciences, King's College London, London, UK
| | - Maqsood Yaqub
- Amsterdam UMC, location VUmc, department of radiology and nuclear medicine, Amsterdam, Netherlands
| | - Matteo Tonietto
- Université Paris-Saclay, CEA, CNRS, Inserm, BioMaps, Service Hospitalier Frédéric Joliot, Orsay, France
| | - Thomas Funck
- INM-1, Jülich Forschungszentrum, Jülich, Germany
| | - Ashley Gillman
- Aust. e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation, Townsville, Australia
| | - Hugo Boniface
- Centre d'Acquisition et de Traitement des Images, CEA, Paris, France
| | - Alexandre Routier
- Inria, Aramis project-team, Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, Inserm, CNRS, AP-HP, Hôpital de la Pitié Salpêtriére, Paris, France
| | - Jelle R Dalenberg
- Department of Neurology, University of Groningen, University Medical Center Groningen, Groningen, Netherlands
| | - Tobey Betthauser
- Wisconsin Alzheimer's Disease Research Center, Division of Geriatrics, Department of Medicine, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI, USA
| | | | | | | | - Ross W Blair
- Department of Psychology, Stanford University, California, USA
| | - Stefan Appelhoff
- Center for Adaptive Rationality, Max Planck Institute for Human Development, Berlin, Germany
| | - Remi Gau
- Institute of psychology, Université catholique de Louvain, Louvain la Neuve, Belgium
| | - Taylor Salo
- Department of Psychology, Florida International University, Miami, FL, USA
| | - Guiomar Niso
- Psychological Brain Sciences, Indiana University, Bloomington, IN, USA
| | - Cyril Pernet
- Neurobiology Research Unit, Rigshospitalet, and Institute of Clinical Medicine, Univ. Copenhagen, København, Denmark
| | - Christophe Phillips
- GIGA Cyclotron Research Centre in vivo imaging, University of Liege, Liege, Belgium
| | - Robert Oostenveld
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Netherlands.,NatMEG, Karolinska Institutet, Stockholm, Sweden
| | | | - Richard E Carson
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, USA
| | - Gitte M Knudsen
- Neurobiology Research Unit, Rigshospitalet, and Institute of Clinical Medicine, Univ. Copenhagen, København, Denmark
| | - Robert B Innis
- Molecular Imaging Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, USA
| | - Melanie Ganz
- Neurobiology Research Unit, Rigshospitalet, and Institute of Clinical Medicine, Univ. Copenhagen, København, Denmark. .,Department of Computer Science, University of Copenhagen, Copenhagen, Denmark.
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10
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Markiewicz CJ, Gorgolewski KJ, Feingold F, Blair R, Halchenko YO, Miller E, Hardcastle N, Wexler J, Esteban O, Goncavles M, Jwa A, Poldrack R. The OpenNeuro resource for sharing of neuroscience data. eLife 2021; 10:e71774. [PMID: 34658334 PMCID: PMC8550750 DOI: 10.7554/elife.71774] [Citation(s) in RCA: 84] [Impact Index Per Article: 28.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Accepted: 10/15/2021] [Indexed: 11/25/2022] Open
Abstract
The sharing of research data is essential to ensure reproducibility and maximize the impact of public investments in scientific research. Here, we describe OpenNeuro, a BRAIN Initiative data archive that provides the ability to openly share data from a broad range of brain imaging data types following the FAIR principles for data sharing. We highlight the importance of the Brain Imaging Data Structure standard for enabling effective curation, sharing, and reuse of data. The archive presently shares more than 600 datasets including data from more than 20,000 participants, comprising multiple species and measurement modalities and a broad range of phenotypes. The impact of the shared data is evident in a growing number of published reuses, currently totalling more than 150 publications. We conclude by describing plans for future development and integration with other ongoing open science efforts.
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Affiliation(s)
| | | | | | - Ross Blair
- Department of Psychology, Stanford UniversityStanfordUnited States
| | - Yaroslav O Halchenko
- Department of Psychological & Brain Sciences, Dartmouth CollegeHanoverUnited States
| | | | | | - Joe Wexler
- Department of Psychology, Stanford UniversityStanfordUnited States
| | - Oscar Esteban
- Department of Psychology, Stanford UniversityStanfordUnited States
- Lausanne University Hospital and University of LausanneLausanneSwitzerland
| | | | - Anita Jwa
- Department of Psychology, Stanford UniversityStanfordUnited States
| | - Russell Poldrack
- Department of Psychology, Stanford UniversityStanfordUnited States
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11
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Abstract
Decentralized research data management (dRDM) systems handle digital research objects across participating nodes without critically relying on central services. We present four perspectives in defense of dRDM, illustrating that, in contrast to centralized or federated research data management solutions, a dRDM system based on heterogeneous but interoperable components can offer a sustainable, resilient, inclusive, and adaptive infrastructure for scientific stakeholders: An individual scientist or laboratory, a research institute, a domain data archive or cloud computing platform, and a collaborative multisite consortium. All perspectives share the use of a common, self-contained, portable data structure as an abstraction from current technology and service choices. In conjunction, the four perspectives review how varying requirements of independent scientific stakeholders can be addressed by a scalable, uniform dRDM solution and present a working system as an exemplary implementation.
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Affiliation(s)
- Michael Hanke
- Corresponding author: Michael Hanke, Institute of Neuroscience and Medicine Brain & Behavior (INM-7), Research Center Jülich, Wilhelm-Johnen-Straße, 52425 Jülich, Germany; and Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University, 40225 Düsseldorf, Germany,
| | - Franco Pestilli
- Department of Psychology, The University of Texas at Austin, 108 E Dean Keeton St, Austin, TX 78712, TX, USA
| | - Adina S. Wagner
- Institute of Neuroscience and Medicine Brain & Behavior (INM-7), Research Center Jülich, Wilhelm-Johnen-Straße, 52425 Jülich, Germany
| | - Christopher J. Markiewicz
- Department of Psychology, Stanford University, 450 Jane Stanford Way, Building 420, Stanford, CA 94305, CA, USA
| | - Jean-Baptiste Poline
- McConnell Brain Imaging Centre, Faculty of Medicine, McGill University, 3801 University Street, Montreal, Quebec, H3A 2B4, Canada
| | - Yaroslav O. Halchenko
- Department of Psychological and Brain Sciences, Dartmouth College, 419 Moore Hall, Hinman Box 6207, Hanover, NH 03755, NH, USA
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12
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Moreau CA, Jean-Louis M, Blair R, Markiewicz CJ, Turner JA, Calhoun VD, Nichols TE, Pernet CR. The genetics-BIDS extension: Easing the search for genetic data associated with human brain imaging. Gigascience 2020; 9:5928221. [PMID: 33068112 PMCID: PMC7568436 DOI: 10.1093/gigascience/giaa104] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2020] [Revised: 09/10/2020] [Accepted: 09/14/2020] [Indexed: 11/25/2022] Open
Abstract
Metadata are what makes databases searchable. Without them, researchers would have difficulty finding data with features they are interested in. Brain imaging genetics is at the intersection of two disciplines, each with dedicated dictionaries and ontologies facilitating data search and analysis. Here, we present the genetics Brain Imaging Data Structure extension, consisting of metadata files for human brain imaging data to which they are linked, and describe succinctly the genomic and transcriptomic data associated with them, which may be in different databases. This extension will facilitate identifying micro-scale molecular features that are linked to macro-scale imaging repositories, facilitating data aggregation across studies.
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Affiliation(s)
- Clara A Moreau
- Sainte Justine Research Center, University of Montreal, 3175 Chemin de la Côte-Sainte-Catherine, Montréal, QC H3T 1C5, Canada, Montreal, QC, Canada
| | - Martineau Jean-Louis
- Sainte Justine Research Center, University of Montreal, 3175 Chemin de la Côte-Sainte-Catherine, Montréal, QC H3T 1C5, Canada, Montreal, QC, Canada
| | - Ross Blair
- Centre for Reproducible Neuroscience, Stanford University, 450 Jane Stanford Way, Stanford, CA 94305, CA, USA
| | - Christopher J Markiewicz
- Centre for Reproducible Neuroscience, Stanford University, 450 Jane Stanford Way, Stanford, CA 94305, CA, USA
| | - Jessica A Turner
- Imaging Genetics and Informatics Lab, Georgia State University, Atlanta, GA 30302, GA, USA.,Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Atlanta, GA 30302, GA, USA
| | - Vince D Calhoun
- Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Atlanta, GA 30302, GA, USA
| | - Thomas E Nichols
- Oxford Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Population Health, University of Oxford, Old Road Campus OX3 7LF, UK
| | - Cyril R Pernet
- Centre for Clinical Brain Sciences & Edinburgh Imaging, University of Edinburgh, 49 Little France Crescent, Edinburgh BioQuarter EH16 4SB, UK
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13
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Yarkoni T, Markiewicz CJ, de la Vega A, Gorgolewski KJ, Salo T, Halchenko YO, McNamara Q, DeStasio K, Poline JB, Petrov D, Hayot-Sasson V, Nielson DM, Carlin J, Kiar G, Whitaker K, DuPre E, Wagner A, Tirrell LS, Jas M, Hanke M, Poldrack RA, Esteban O, Appelhoff S, Holdgraf C, Staden I, Thirion B, Kleinschmidt DF, Lee JA, Visconti di Oleggio Castello M, Notter MP, Blair R. PyBIDS: Python tools for BIDS datasets. J Open Source Softw 2019; 4:1294. [PMID: 32775955 PMCID: PMC7409983 DOI: 10.21105/joss.01294] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
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14
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Markiewicz CJ, Bohland JW. Mapping the cortical representation of speech sounds in a syllable repetition task. Neuroimage 2016; 141:174-190. [DOI: 10.1016/j.neuroimage.2016.07.023] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2016] [Revised: 07/08/2016] [Accepted: 07/10/2016] [Indexed: 11/17/2022] Open
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