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Hayashi S, Caron BA, Heinsfeld AS, Vinci-Booher S, McPherson B, Bullock DN, Bertò G, Niso G, Hanekamp S, Levitas D, Ray K, MacKenzie A, Avesani P, Kitchell L, Leong JK, Nascimento-Silva F, Koudoro S, Willis H, Jolly JK, Pisner D, Zuidema TR, Kurzawski JW, Mikellidou K, Bussalb A, Chaumon M, George N, Rorden C, Victory C, Bhatia D, Aydogan DB, Yeh FCF, Delogu F, Guaje J, Veraart J, Fischer J, Faskowitz J, Fabrega R, Hunt D, McKee S, Brown ST, Heyman S, Iacovella V, Mejia AF, Marinazzo D, Craddock RC, Olivetti E, Hanson JL, Garyfallidis E, Stanzione D, Carson J, Henschel R, Hancock DY, Stewart CA, Schnyer D, Eke DO, Poldrack RA, Bollmann S, Stewart A, Bridge H, Sani I, Freiwald WA, Puce A, Port NL, Pestilli F. brainlife.io: a decentralized and open-source cloud platform to support neuroscience research. Nat Methods 2024:10.1038/s41592-024-02237-2. [PMID: 38605111 DOI: 10.1038/s41592-024-02237-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Accepted: 03/05/2024] [Indexed: 04/13/2024]
Abstract
Neuroscience is advancing standardization and tool development to support rigor and transparency. Consequently, data pipeline complexity has increased, hindering FAIR (findable, accessible, interoperable and reusable) access. brainlife.io was developed to democratize neuroimaging research. The platform provides data standardization, management, visualization and processing and automatically tracks the provenance history of thousands of data objects. Here, brainlife.io is described and evaluated for validity, reliability, reproducibility, replicability and scientific utility using four data modalities and 3,200 participants.
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Affiliation(s)
| | - Bradley A Caron
- Indiana University, Bloomington, IN, USA
- The University of Texas, Austin, TX, USA
| | | | - Sophia Vinci-Booher
- Indiana University, Bloomington, IN, USA
- Vanderbilt University, Nashville, TN, USA
| | - Brent McPherson
- Indiana University, Bloomington, IN, USA
- McGill University, Montréal, Quebec, Canada
| | | | | | - Guiomar Niso
- Indiana University, Bloomington, IN, USA
- Cajal Institute, CSIC, Madrid, Spain
| | | | - Daniel Levitas
- Indiana University, Bloomington, IN, USA
- The University of Texas, Austin, TX, USA
| | | | | | | | - Lindsey Kitchell
- Indiana University, Bloomington, IN, USA
- Applied Physics Laboratory, Johns Hopkins University, Laurel, MD, USA
| | - Josiah K Leong
- Indiana University, Bloomington, IN, USA
- University of Arkansas, Fayetteville, AR, USA
| | | | | | | | | | | | | | | | - Kyriaki Mikellidou
- University of Limassol, Nicosia, Cyprus
- University of Cyprus, Nicosia, Cyprus
| | - Aurore Bussalb
- Institut du Cerveau, CNRS, Sorbonne Université, Paris, France
| | | | - Nathalie George
- Institut du Cerveau, CNRS, Sorbonne Université, Paris, France
| | | | | | | | - Dogu Baran Aydogan
- University of Eastern Finland, Kuopio, Finland
- Aalto University School of Science, Espoo, Finland
| | | | - Franco Delogu
- Lawrence Technological University, Southfield, MI, USA
| | | | | | | | | | | | - David Hunt
- Indiana University, Bloomington, IN, USA
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Ashley Stewart
- University of Queensland, St Lucia, Queensland, Australia
| | | | - Ilaria Sani
- The Rockefeller University, New York, NY, USA
- University of Geneva, Geneva, Switzerland
| | | | - Aina Puce
- Indiana University, Bloomington, IN, USA
| | | | - Franco Pestilli
- Indiana University, Bloomington, IN, USA.
- The University of Texas, Austin, TX, USA.
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2
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Levitas D, Hayashi S, Vinci-Booher S, Heinsfeld A, Bhatia D, Lee N, Galassi A, Niso G, Pestilli F. ezBIDS: Guided standardization of neuroimaging data interoperable with major data archives and platforms. Sci Data 2024; 11:179. [PMID: 38332144 PMCID: PMC10853279 DOI: 10.1038/s41597-024-02959-0] [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] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Accepted: 01/12/2024] [Indexed: 02/10/2024] Open
Abstract
Data standardization promotes a common framework through which researchers can utilize others' data and is one of the leading methods neuroimaging researchers use to share and replicate findings. As of today, standardizing datasets requires technical expertise such as coding and knowledge of file formats. We present ezBIDS, a tool for converting neuroimaging data and associated metadata to the Brain Imaging Data Structure (BIDS) standard. ezBIDS contains four major features: (1) No installation or programming requirements. (2) Handling of both imaging and task events data and metadata. (3) Semi-automated inference and guidance for adherence to BIDS. (4) Multiple data management options: download BIDS data to local system, or transfer to OpenNeuro.org or to brainlife.io. In sum, ezBIDS requires neither coding proficiency nor knowledge of BIDS, and is the first BIDS tool to offer guided standardization, support for task events conversion, and interoperability with OpenNeuro.org and brainlife.io.
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Affiliation(s)
- Daniel Levitas
- Department of Psychology, Department of Neuroscience, Center for Perceptual Systems, Center for Learning and Memory, Center for Aging Population Sciences, University of Texas, Austin, TX, 78712, USA
| | - Soichi Hayashi
- Department of Psychology, Department of Neuroscience, Center for Perceptual Systems, Center for Learning and Memory, Center for Aging Population Sciences, University of Texas, Austin, TX, 78712, USA
| | - Sophia Vinci-Booher
- Department of Psychology and Human Development, Peabody College, Vanderbilt University, Nashville, TN, 37203, USA
| | - Anibal Heinsfeld
- Department of Psychology, Department of Neuroscience, Center for Perceptual Systems, Center for Learning and Memory, Center for Aging Population Sciences, University of Texas, Austin, TX, 78712, USA
| | - Dheeraj Bhatia
- Department of Psychology, Department of Neuroscience, Center for Perceptual Systems, Center for Learning and Memory, Center for Aging Population Sciences, University of Texas, Austin, TX, 78712, USA
| | - Nicholas Lee
- Department of Psychology, Department of Neuroscience, Center for Perceptual Systems, Center for Learning and Memory, Center for Aging Population Sciences, University of Texas, Austin, TX, 78712, USA
| | - Anthony Galassi
- Center for Multimodal Neuroimaging, National Institute of Mental Health, Bethesda, MD, USA
| | | | - Franco Pestilli
- Department of Psychology, Department of Neuroscience, Center for Perceptual Systems, Center for Learning and Memory, Center for Aging Population Sciences, University of Texas, Austin, TX, 78712, USA.
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3
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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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4
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Hayashi S, Caron BA, Heinsfeld AS, Vinci-Booher S, McPherson B, Bullock DN, Bertò G, Niso G, Hanekamp S, Levitas D, Ray K, MacKenzie A, Kitchell L, Leong JK, Nascimento-Silva F, Koudoro S, Willis H, Jolly JK, Pisner D, Zuidema TR, Kurzawski JW, Mikellidou K, Bussalb A, Rorden C, Victory C, Bhatia D, Baran Aydogan D, Yeh FCF, Delogu F, Guaje J, Veraart J, Bollman S, Stewart A, Fischer J, Faskowitz J, Chaumon M, Fabrega R, Hunt D, McKee S, Brown ST, Heyman S, Iacovella V, Mejia AF, Marinazzo D, Craddock RC, Olivetti E, Hanson JL, Avesani P, Garyfallidis E, Stanzione D, Carson J, Henschel R, Hancock DY, Stewart CA, Schnyer D, Eke DO, Poldrack RA, George N, Bridge H, Sani I, Freiwald WA, Puce A, Port NL, Pestilli F. brainlife.io: A decentralized and open source cloud platform to support neuroscience research. ArXiv 2023:arXiv:2306.02183v3. [PMID: 37332566 PMCID: PMC10274934] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/20/2023]
Abstract
Neuroscience research has expanded dramatically over the past 30 years by advancing standardization and tool development to support rigor and transparency. Consequently, the complexity of the data pipeline has also increased, hindering access to FAIR data analysis to portions of the worldwide research community. brainlife.io was developed to reduce these burdens and democratize modern neuroscience research across institutions and career levels. Using community software and hardware infrastructure, the platform provides open-source data standardization, management, visualization, and processing and simplifies the data pipeline. brainlife.io automatically tracks the provenance history of thousands of data objects, supporting simplicity, efficiency, and transparency in neuroscience research. Here brainlife.io's technology and data services are described and evaluated for validity, reliability, reproducibility, replicability, and scientific utility. Using data from 4 modalities and 3,200 participants, we demonstrate that brainlife.io's services produce outputs that adhere to best practices in modern neuroscience research.
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5
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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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6
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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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7
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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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8
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Niso G, Tjepkema-Cloostermans MC, Lenders MWPM, de Vos CC. Modulation of the Somatosensory Evoked Potential by Attention and Spinal Cord Stimulation. Front Neurol 2021; 12:694310. [PMID: 34413825 PMCID: PMC8369157 DOI: 10.3389/fneur.2021.694310] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [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: 04/12/2021] [Accepted: 07/07/2021] [Indexed: 12/17/2022] Open
Abstract
Introduction: Spinal Cord Stimulation (SCS) is a last-resort treatment for patients with intractable chronic pain in whom pharmacological and other treatments have failed. Conventional tonic SCS is accompanied by tingling sensations. More recent stimulation protocols like burst SCS are not sensed by the patient while providing similar levels of pain relief. It has been previously reported that conventional tonic SCS can attenuate sensory-discriminative processing in several brain areas, but that burst SCS might have additional effects on the medial, motivational-affective pain system. In this explorative study we assessed the influence of attention on the somatosensory evoked brain responses under conventional tonic SCS as well as burst SCS regime. Methods: Twelve chronic pain patients with an implanted SCS device had 2-weeks evaluation periods with three different SCS settings (conventional tonic SCS, burst SCS, and sham SCS). At the end of each period, an electro-encephalography (EEG) measurement was done, at which patients received transcutaneous electrical pulses at the tibial nerve to induce somatosensory evoked potentials (SEP). SEP data was acquired while patients were attending the applied pulses and while they were mind wandering. The effects of attention as well as SCS regimes on the SEP were analyzed by comparing amplitudes of early and late latencies at the vertex as well as brain activity at full cortical maps. Results: Pain relief obtained by the various SCS settings varied largely among patients. Early SEP responses were not significantly affected by attention nor SCS settings (i.e., burst, tonic, and sham). However, late SEP responses (P300) were reduced with tonic and burst SCS: conventional tonic SCS reduced P300 brain activity in the unattended condition, while burst SCS reduced P300 brain activity in both attended and unattended conditions. Conclusion: Burst spinal cord stimulation for the treatment of chronic pain seems to reduce cortical attention that is or can be directed to somatosensory stimuli to a larger extent than conventional spinal cord stimulation treatment. This is a first step in understanding why in selected chronic pain patients burst SCS is more effective than tonic SCS and how neuroimaging could assist in personalizing SCS treatment.
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Affiliation(s)
- Guiomar Niso
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC, Canada.,Psychological & Brain Sciences, Indiana University, Bloomington, IN, United States.,ETSI Telecomunicación, Universidad Politécnica de Madrid and Center for Biomedical Research Network CIBER-BBN, Madrid, Spain
| | - Marleen C Tjepkema-Cloostermans
- Department of Neurology and Neurosurgery, Medisch Spectrum Twente, Enschede, Netherlands.,Department of Clinical Neurophysiology, Institute for Technical Medicine, University of Twente, Enschede, Netherlands
| | - Mathieu W P M Lenders
- Department of Neurology and Neurosurgery, Medisch Spectrum Twente, Enschede, Netherlands
| | - Cecile C de Vos
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC, Canada.,Department of Neurology and Neurosurgery, Medisch Spectrum Twente, Enschede, Netherlands.,Center for Pain Medicine, Erasmus University Medical Center, Rotterdam, Netherlands
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9
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Levitis E, van Praag CDG, Gau R, Heunis S, DuPre E, Kiar G, Bottenhorn KL, Glatard T, Nikolaidis A, Whitaker KJ, Mancini M, Niso G, Afyouni S, Alonso-Ortiz E, Appelhoff S, Arnatkeviciute A, Atay SM, Auer T, Baracchini G, Bayer JMM, Beauvais MJS, Bijsterbosch JD, Bilgin IP, Bollmann S, Bollmann S, Botvinik-Nezer R, Bright MG, Calhoun VD, Chen X, Chopra S, Chuan-Peng H, Close TG, Cookson SL, Craddock RC, De La Vega A, De Leener B, Demeter DV, Di Maio P, Dickie EW, Eickhoff SB, Esteban O, Finc K, Frigo M, Ganesan S, Ganz M, Garner KG, Garza-Villarreal EA, Gonzalez-Escamilla G, Goswami R, Griffiths JD, Grootswagers T, Guay S, Guest O, Handwerker DA, Herholz P, Heuer K, Huijser DC, Iacovella V, Joseph MJE, Karakuzu A, Keator DB, Kobeleva X, Kumar M, Laird AR, Larson-Prior LJ, Lautarescu A, Lazari A, Legarreta JH, Li XY, Lv J, Mansour L S, Meunier D, Moraczewski D, Nandi T, Nastase SA, Nau M, Noble S, Norgaard M, Obungoloch J, Oostenveld R, Orchard ER, Pinho AL, Poldrack RA, Qiu A, Raamana PR, Rokem A, Rutherford S, Sharan M, Shaw TB, Syeda WT, Testerman MM, Toro R, Valk SL, Van Den Bossche S, Varoquaux G, Váša F, Veldsman M, Vohryzek J, Wagner AS, Walsh RJ, White T, Wong FT, Xie X, Yan CG, Yang YF, Yee Y, Zanitti GE, Van Gulick AE, Duff E, Maumet C. Centering inclusivity in the design of online conferences-An OHBM-Open Science perspective. Gigascience 2021; 10:6355274. [PMID: 34414422 DOI: 10.1093/gigascience/giab051] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023] Open
Abstract
As the global health crisis unfolded, many academic conferences moved online in 2020. This move has been hailed as a positive step towards inclusivity in its attenuation of economic, physical, and legal barriers and effectively enabled many individuals from groups that have traditionally been underrepresented to join and participate. A number of studies have outlined how moving online made it possible to gather a more global community and has increased opportunities for individuals with various constraints, e.g., caregiving responsibilities. Yet, the mere existence of online conferences is no guarantee that everyone can attend and participate meaningfully. In fact, many elements of an online conference are still significant barriers to truly diverse participation: the tools used can be inaccessible for some individuals; the scheduling choices can favour some geographical locations; the set-up of the conference can provide more visibility to well-established researchers and reduce opportunities for early-career researchers. While acknowledging the benefits of an online setting, especially for individuals who have traditionally been underrepresented or excluded, we recognize that fostering social justice requires inclusivity to actively be centered in every aspect of online conference design. Here, we draw from the literature and from our own experiences to identify practices that purposefully encourage a diverse community to attend, participate in, and lead online conferences. Reflecting on how to design more inclusive online events is especially important as multiple scientific organizations have announced that they will continue offering an online version of their event when in-person conferences can resume.
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Affiliation(s)
- Elizabeth Levitis
- Section on Developmental Neurogenomics, National Institute of Mental Health, Bethesda, MD 20892, USA.,Centre for Medical Image Computing, Department of Computer Science, University College London, London, WC1E 6BT, UK
| | - Cassandra D Gould van Praag
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, OX3 9DU, UK.,Department of Psychiatry, University of Oxford, Oxford, OX3 7JX, UK
| | - Rémi Gau
- Institute of Psychology, Université Catholique de Louvain, Louvain la Neuve 1348, Belgium
| | - Stephan Heunis
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, 5612 AP, The Netherlands
| | - Elizabeth DuPre
- NeuroDataScience - ORIGAMI laboratory, McGill University, Montreal, QC, H3A 2B4, Canada
| | - Gregory Kiar
- Department of Biomedical Engineering, McGill University, Montreal, QC, H3A 2B4, Canada.,Center for the Developing Brain, The Child Mind Institute, New York City, NY 10022, USA
| | | | - Tristan Glatard
- Department of Computer Science and Software Engineering, Concordia University, Montreal, QC, H3G 1M8, Canada
| | - Aki Nikolaidis
- Center for the Developing Brain, The Child Mind Institute, New York City, NY 10022, USA
| | | | - Matteo Mancini
- Department of Neuroscience, Brighton and Sussex Medical School, University of Sussex, Brighton, BN1 9RR, UK.,Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, CF24 4HQ, UK.,NeuroPoly Lab, Polytechnique Montreal, Montreal, QC, H3T 1J4, Canada
| | - Guiomar Niso
- Departement of Psychological & Brain Sciences, Indiana University, Bloomington, IN 47405, USA.,ETSI Telecomunicación, Universidad Politécnica de Madrid and CIBER-BBN, 28040 Madrid, Spain
| | - Soroosh Afyouni
- Big Data Institute, University of Oxford, Oxford, OX3 7LF, UK.,Department of Psychology, University of Cambridge, CB2 3EB, Cambridge, UK
| | - Eva Alonso-Ortiz
- Department of Electrical Engineering, Polytechnique Montréal, Montréal, QC, H3T 1J4, Canada
| | - Stefan Appelhoff
- Center for Adaptive Rationality, Max Planck Institute for Human Development, Berlin 14195, Germany
| | - Aurina Arnatkeviciute
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Victoria, VIC, Clayton 3168, Australia
| | - Selim Melvin Atay
- Neuroscience and Neurotechnology, Middle East Technical University, Ankara 06800, Turkey
| | - Tibor Auer
- School of Psychology, University of Surrey, Guildford GU2 7XH, UK
| | - Giulia Baracchini
- Department of Neurology and Neurosurgery, McGill University, Montréal, QC, H3A 2B4, Canada.,Montréal Neurological Institute, Montréal, QC, H3A 2B4, Canada
| | - Johanna M M Bayer
- Centre for Youth Mental Health, University of Melbourne, Melbourne, VIC, 3010, Parkville, Melbourne, Australia.,Orygen Youth Health, Melbourne, VIC, 3052, Royal Park, Melbourne, Australia
| | | | - Janine D Bijsterbosch
- Department of Radiology, Washington University School of Medicine, Saint Louis, MO, 63110, USA
| | - Isil P Bilgin
- Department of Biomedical Engineering, Cybernetics, The School of Biological Sciences, The University of Reading, Reading, RG6 6AH, UK
| | - Saskia Bollmann
- Centre for Advanced Imaging, The University of Queensland, Brisbane, QLD 4072, Australia
| | - Steffen Bollmann
- School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, QLD 4072, Australia.,ARC Training Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, Brisbane, QLD 4072, Australia
| | - Rotem Botvinik-Nezer
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH 03755, USA
| | - Molly G Bright
- Physical Therapy and Human Movement Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA.,Biomedical Engineering, McCormick School of Engineering and Applied Sciences, Northwestern University, Evanston, IL 60208, USA
| | - Vince D Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State, Georgia Tech, Emory, Atlanta, GA 30303, USA
| | - Xiao Chen
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, 100101, Beijing, China.,Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences, 100101, Beijing, China.,International Big-Data Center for Depression Research, Chinese Academy of Sciences, Beijing 100101, Beijing, China
| | - Sidhant Chopra
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Victoria, VIC, Clayton 3168, Australia
| | - Hu Chuan-Peng
- School of Psychology, Nanjing Normal University, Nanjing 210024, China
| | - Thomas G Close
- Department of Biomedical Engineering, The University of Sydney, Sydney, NSW 2006, Australia.,National Imaging Facility, The University of Sydney, Sydney, NSW 2006, Australia
| | - Savannah L Cookson
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA 94720, USA
| | - R Cameron Craddock
- Department of Diagnostic Medicine, The University of Texas at Austin Dell Medical School, Austin, TX 78712, USA
| | - Alejandro De La Vega
- Department of Psychology, The University of Texas at Austin, Austin, TX 78712, USA
| | - Benjamin De Leener
- Department of Computer and Software Engineering, Polytechnique Montreal, Montreal, QC H3T 1J4, Canada.,Research Centre, Sainte-Justine University Hospital Center, Montreal, QC, H3T 1C5, Canada
| | - Damion V Demeter
- Department of Psychology, The University of Texas at Austin, Austin, TX 78712, USA
| | - Paola Di Maio
- Center for Systems, Knowledge Representation and Neuroscience, Edinburgh and Taipei, UK and Taiwan.,Institute for Globally Distributed Open Research and Education (IGDORE)
| | - Erin W Dickie
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON, M5T 1R8, Canada.,Department of Psychiatry, University of Toronto, Toronto, ON, M5T 1R8, Canada
| | - Simon B Eickhoff
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf 40225, Germany.,Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich 52425, Germany
| | - Oscar Esteban
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne 1003, Switzerland
| | - Karolina Finc
- Centre for Modern Interdisciplinary Technologies, Nicolaus Copernicus University, Toruń 87-100, Poland
| | - Matteo Frigo
- Athena Project Team, Université Côte D'Azur, Inria, 06103 Nice, France
| | - Saampras Ganesan
- Melbourne Neuropsychiatry Centre, The University of Melbourne, Melbourne, VIC 3010, Australia.,Department of Biomedical Engineering, The University of Melbourne, Melbourne, VIC 3010, Australia
| | - Melanie Ganz
- Neurobiology Research Unit, Rigshospitalet, Copenhagen DK-2100, Denmark.,Department of Computer Science, University of Copenhagen, Copenhagen DK-2100, Denmark
| | - Kelly G Garner
- Queensland Brain Institute, University of Queensland, St. Lucia, QLD 4072, Australia.,School of Psychology, University of Birmingham, Birmingham, B15 2TT, UK.,School of Psychology, University of Queensland, St. Lucia, QLD 4072, Australia
| | - Eduardo A Garza-Villarreal
- Laboratorio Nacional de Imagenología por Resonancia Magnética, Instituto de Neurobiología, Universidad Nacional Autónoma de México campus Juriquilla, Querétaro, Qro 76230, Mexico
| | - Gabriel Gonzalez-Escamilla
- Department of Neurology, Focus Program Translational Neuroscience (FTN), Rhine Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg University Mainz, Mainz 55131, Germany
| | - Rohit Goswami
- Faculty of Physical Sciences, University of Iceland, 102 Reykjavík, Iceland.,Department of Chemistry, Indian Institute of Technology Kanpur, Kanpur 208016, India
| | - John D Griffiths
- Department of Psychiatry, University of Toronto, Toronto, ON M5T 1R8, Canada.,Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON M6J 1H4, Canada
| | - Tijl Grootswagers
- The MARCS Institute for Brain, Behaviour & Development, Western Sydney University, Sydney 2751, NSW, Australia
| | - Samuel Guay
- Department of Psychology, Université de Montréal, Montreal, QC H3C 3J7, Canada
| | - Olivia Guest
- Donders Centre for Cognitive Neuroimaging, Radboud University, Nijmegen, 6525 EN, Netherlands
| | - Daniel A Handwerker
- Section on Functional Imaging Methods, Laboratory of Brain and Cognition, National Institute of Mental Health, Bethesda, MD 20892-9663, USA
| | - Peer Herholz
- NeuroDataScience - ORIGAMI laboratory, McGill University, Montreal, QC, H3A 2B4, Canada
| | - Katja Heuer
- Center for Research and Interdisciplinarity (CRI), INSERM U1284, Université de Paris, 75004 Paris, France.,Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, 04103 Leipzig, Germany
| | - Dorien C Huijser
- Erasmus School of Social and Behavioral Sciences, Erasmus University Rotterdam, Rotterdam 3062, the Netherlands.,Developmental and Educational Psychology, Leiden University, Leiden 2333, the Netherlands
| | - Vittorio Iacovella
- Center for Mind/Brain Sciences - CIMeC, University of Trento, Rovereto 38068, Italy
| | - Michael J E Joseph
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON M5T 1R8, Canada
| | - Agah Karakuzu
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montréal, Montréal, QC, H3T 1N8, Canada.,Montréal Heart Institute, University of Montréal, Montréal, QC, H1T 1C8, Canada
| | - David B Keator
- Psychiatry and Human Behavior, University of California Irvine, Irvine, CA 92697, USA
| | - Xenia Kobeleva
- Department of Neurology, University Hospital Bonn, 53127 Bonn, Germany.,Clinical Research, German Center for Neurodegenerative Diseases, 53127 Bonn, Germany
| | - Manoj Kumar
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA
| | - Angela R Laird
- Department of Physics, Florida International University, Miami, FL 33199, USA
| | - Linda J Larson-Prior
- Psychiatric Research Institute, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA.,Arkansas Children's Nutrition Center, Little Rock, AR, USA.,Department of Neurology, Pediatrics, Neuroscience & Developmental Sciences, Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA
| | - Alexandra Lautarescu
- Department of Perinatal Imaging and Health, Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London SE5 8AF, UK.,Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London SE1 7EH, UK
| | - Alberto Lazari
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, OX3 9DU, UK
| | - Jon Haitz Legarreta
- Department of Computer Science, Université de Sherbrooke, Sherbrooke, QC J1K 2R1, Canada
| | - Xue-Ying Li
- Sino-Danish College, University of Chinese Academy of Sciences, Beijing101408, China.,CAS Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China.,Sino-Danish Center for Education and Research, Graduate University of Chinese Academy of Sciences, Beijing 101408, China.,CFIN and PET Center, Aarhus University, 8000 Aarhus, Denmark
| | - Jinglei Lv
- School of Biomedical Engineering & Brain and Mind Center, University of Sydney, Sydney, NSW 2006, Australia
| | - Sina Mansour L
- Melbourne Neuropsychiatry Centre, The University of Melbourne, Melbourne, VIC 3010, Australia.,Department of Biomedical Engineering, The University of Melbourne, Melbourne, VIC 3010, Australia
| | - David Meunier
- Aix Marseille Univ, CNRS, INT, Institut de Neurosciences de la Timone, 13005 Marseille, France
| | | | - Tulika Nandi
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX3 7LF, UK
| | - Samuel A Nastase
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA
| | - Matthias Nau
- Section on Learning and Plasticity, Laboratory of Brain and Cognition, National Institute of Mental Health, Bethesda, MD 20892-9663, USA.,Max-Planck-Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Stephanie Noble
- Radiology & Biomedical Imaging, Yale University, New Haven, CT 06519, USA
| | - Martin Norgaard
- Center for Reproducible Neuroscience, Department of Psychology, Stanford University, Stanford, CA 94305Ci, USA.,Neurobiology Research Unit, Department of Neurology, Rigshospitalet, Copenhagen University Hospital, Copenhagen 2100, Denmark
| | - Johnes Obungoloch
- Department of Biomedical Sciences and Engineering, Mbarara University of Science and Technology, Mbarara City, Uganda
| | - Robert Oostenveld
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, 6500 GL, The Netherlands.,NatMEG, Karolinska Institutet, Stockholm 171 77, Sweden
| | - Edwina R Orchard
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Victoria, VIC, Clayton 3168, Australia
| | - Ana Luísa Pinho
- Université Paris-Saclay, Inria, CEA, 91120 Palaiseau, France
| | | | - Anqi Qiu
- Department of Biomedical Engineering, The N.1 Institute for Health, Smart Systems Institute, National University of Singapore, Singapore 117583, Singapore.,Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | | | - Ariel Rokem
- Department of Psychology & eScience Institute, University of Washington, Seattle, WA 98195, USA
| | - Saige Rutherford
- Department of Cognitive Neuroscience, Radboud University Medical Center, Nijmegen 6525 EN, The Netherlands.,Department of Psychiatry, University of Michigan, Ann Arbor, MI 48109, USA
| | | | - Thomas B Shaw
- Centre for Advanced Imaging, The University of Queensland, Brisbane, QLD 4072, Australia
| | - Warda T Syeda
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne, VIC 3053, Australia
| | | | - Roberto Toro
- Center for Research and Interdisciplinarity (CRI), INSERM U1284, Université de Paris, 75004 Paris, France.,Neuroscience Department, Institut Pasteur, 75015 Paris, France
| | - Sofie L Valk
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf 40225, Germany.,Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich 52425, Germany.,Otto Hahn Group Cognitive Neurogenetics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig 04303, Germany
| | - Sofie Van Den Bossche
- Department of Data Analysis, Faculty of Psychology and Educational Sciences, Ghent University, Ghent 9000, Belgium
| | - Gaël Varoquaux
- Université Paris-Saclay, Inria, CEA, 91120 Palaiseau, France.,Montreal Neurological Institute, McGill, Montreal, QC, H3A 2B4, Canada
| | - František Váša
- Department of Neuroimaging, Institute of Psychiatry Psychology & Neuroscience, King's College London SE5 8AF, London, UK
| | - Michele Veldsman
- Department of Experimental Psychology, University of Oxford, Oxfordshire, OX2 6GG, Oxford, UK
| | - Jakub Vohryzek
- Department of Psychiatry, University of Oxford, Oxford, OX3 7JX, UK.,Center for Music in the Brain, Department of Clinical Medicine, Aarhus University, Aarhus 8000, Denmark
| | - Adina S Wagner
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich 52425, Germany
| | - Reubs J Walsh
- Department of Clinical, Neuro-, and Developmental Psychology, Vrije Universiteit Amsterdam, Amsterdam,1081BT, The Netherlands.,Center for Applied Transgender Studies , Chicago, USA
| | - Tonya White
- Department of Child and Adolescent Psychiatry, Erasmus University Medical Centre, Rotterdam, 3000CB, The Netherlands.,Department of Radiology and Nuclear Medicine, Erasmus University Medical Centre, Rotterdam 3000CB, The Netherlands
| | - Fu-Te Wong
- Institute of Linguistics, Academia Sinica, Taipei, Taiwan.,Institute of Medical Science, University of Toronto, Toronto, ON, M5S 1A8, Canada
| | - Xihe Xie
- Department of Neuroscience, Weill Cornell Graduate School, New York City, NY 10065, USA
| | - Chao-Gan Yan
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, 100101, Beijing, China.,Department of Psychology, University of Chinese Academy of Sciences, 100101 Beijing, China.,International Big-Data Center for Depression Research, Chinese Academy of Sciences, 100101, Beijing, China
| | - Yu-Fang Yang
- Department of Psychology, University of Würzburg, Würzburg 97074, Germany
| | - Yohan Yee
- Department of Medical Biophysics, University of Toronto, Toronto, ON, M5G 1L7, Canada.,Mouse Imaging Centre, The Hospital for Sick Children, Toronto, ON, M5T 3H7, Canada
| | | | - Ana E Van Gulick
- Figshare, Cambridge, MA 02139, USA.,University Libraries, Carnegie Mellon University, Pittsburgh, PA, 15213, USA
| | - Eugene Duff
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, OX3 9DU, UK.,Department of Paediatrics, University of Oxford, Oxford, OX3 9DU, UK
| | - Camille Maumet
- Inria, Univ Rennes, CNRS, Inserm, IRISA UMR 6074, Empenn ERL U 1228, 35042 Rennes, France
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10
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Pavlov YG, Adamian N, Appelhoff S, Arvaneh M, Benwell CSY, Beste C, Bland AR, Bradford DE, Bublatzky F, Busch NA, Clayson PE, Cruse D, Czeszumski A, Dreber A, Dumas G, Ehinger B, Ganis G, He X, Hinojosa JA, Huber-Huber C, Inzlicht M, Jack BN, Johannesson M, Jones R, Kalenkovich E, Kaltwasser L, Karimi-Rouzbahani H, Keil A, König P, Kouara L, Kulke L, Ladouceur CD, Langer N, Liesefeld HR, Luque D, MacNamara A, Mudrik L, Muthuraman M, Neal LB, Nilsonne G, Niso G, Ocklenburg S, Oostenveld R, Pernet CR, Pourtois G, Ruzzoli M, Sass SM, Schaefer A, Senderecka M, Snyder JS, Tamnes CK, Tognoli E, van Vugt MK, Verona E, Vloeberghs R, Welke D, Wessel JR, Zakharov I, Mushtaq F. #EEGManyLabs: Investigating the replicability of influential EEG experiments. Cortex 2021; 144:213-229. [PMID: 33965167 DOI: 10.1016/j.cortex.2021.03.013] [Citation(s) in RCA: 39] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2021] [Revised: 03/02/2021] [Accepted: 03/09/2021] [Indexed: 12/29/2022]
Abstract
There is growing awareness across the neuroscience community that the replicability of findings about the relationship between brain activity and cognitive phenomena can be improved by conducting studies with high statistical power that adhere to well-defined and standardised analysis pipelines. Inspired by recent efforts from the psychological sciences, and with the desire to examine some of the foundational findings using electroencephalography (EEG), we have launched #EEGManyLabs, a large-scale international collaborative replication effort. Since its discovery in the early 20th century, EEG has had a profound influence on our understanding of human cognition, but there is limited evidence on the replicability of some of the most highly cited discoveries. After a systematic search and selection process, we have identified 27 of the most influential and continually cited studies in the field. We plan to directly test the replicability of key findings from 20 of these studies in teams of at least three independent laboratories. The design and protocol of each replication effort will be submitted as a Registered Report and peer-reviewed prior to data collection. Prediction markets, open to all EEG researchers, will be used as a forecasting tool to examine which findings the community expects to replicate. This project will update our confidence in some of the most influential EEG findings and generate a large open access database that can be used to inform future research practices. Finally, through this international effort, we hope to create a cultural shift towards inclusive, high-powered multi-laboratory collaborations.
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Affiliation(s)
- Yuri G Pavlov
- University of Tuebingen, Germany; Ural Federal University, Russia.
| | | | | | | | | | | | | | | | | | | | | | | | | | - Anna Dreber
- Stockholm School of Economics, Sweden; University of Innsbruck, Austria
| | - Guillaume Dumas
- Université de Montréal, Montréal, Quebec, Canada; CHU Sainte-Justine Research Center, Montréal, Quebec, Canada
| | | | | | - Xun He
- Bournemouth University, UK
| | - José A Hinojosa
- Universidad Complutense de Madrid, Spain; Universidad Nebrija, Spain
| | | | | | | | | | | | | | - Laura Kaltwasser
- Berlin School of Mind and Brain, Humboldt-Universität zu Berlin, Germany
| | | | | | - Peter König
- University Osnabrück, Germany; University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | | | - Louisa Kulke
- Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany
| | | | - Nicolas Langer
- University of Zurich, Switzerland; Neuroscience Center Zurich, Switzerland
| | | | - David Luque
- Universidad Autónoma de Madrid, Spain; Universidad de Málaga, Spain
| | | | - Liad Mudrik
- School of Psychological Sciences & Sagol School of Neuroscience, Tel Aviv University, Israel
| | | | | | | | - Guiomar Niso
- Indiana University, Bloomington, USA; Universidad Politecnica de Madrid and CIBER-BBN, Spain
| | | | | | | | | | | | | | | | | | - Joel S Snyder
- Department of Psychology, University of Nevada, Las Vegas, USA
| | | | | | | | | | | | - Dominik Welke
- Max-Planck-Institute for Empirical Aesthetics, Germany
| | - Jan R Wessel
- University of Iowa Hospitals and Clinics, Iowa City, USA; University of Iowa, Iowa City, USA
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11
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Müller F, Niso G, Samiee S, Ptito M, Baillet S, Kupers R. A thalamocortical pathway for fast rerouting of tactile information to occipital cortex in congenital blindness. Nat Commun 2019; 10:5154. [PMID: 31727882 PMCID: PMC6856176 DOI: 10.1038/s41467-019-13173-7] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2018] [Accepted: 10/21/2019] [Indexed: 11/09/2022] Open
Abstract
In congenitally blind individuals, the occipital cortex responds to various nonvisual inputs. Some animal studies raise the possibility that a subcortical pathway allows fast re-routing of tactile information to the occipital cortex, but this has not been shown in humans. Here we show using magnetoencephalography (MEG) that tactile stimulation produces occipital cortex activations, starting as early as 35 ms in congenitally blind individuals, but not in blindfolded sighted controls. Given our measured thalamic response latencies of 20 ms and a mean estimated lateral geniculate nucleus to primary visual cortex transfer time of 15 ms, we claim that this early occipital response is mediated by a direct thalamo-cortical pathway. We also observed stronger directed connectivity in the alpha band range from posterior thalamus to occipital cortex in congenitally blind participants. Our results strongly suggest the contribution of a fast thalamo-cortical pathway in the cross-modal activation of the occipital cortex in congenitally blind humans. In congenitally blind people, tactile stimuli can activate the occipital (visual) cortex. Here, the authors show using magnetoencephalography (MEG) that occipital activation can occur within 35 ms following tactile stimulation, suggesting the existence of a fast thalamocortical pathway for touch in congenitally blind humans.
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Affiliation(s)
- Franziska Müller
- BRAINlab, Department of Neuroscience, Panum Institute, University of Copenhagen, Copenhagen, Denmark
| | - Guiomar Niso
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC, Canada.,Centre for Biomedical Technology, Universidad Politécnica de Madrid, Madrid, Spain.,Biomedical Image Technologies, ETSI Telecomunicación, Universidad Politécnica de Madrid and CIBER-BBN, Madrid, Spain
| | - Soheila Samiee
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Maurice Ptito
- BRAINlab, Department of Neuroscience, Panum Institute, University of Copenhagen, Copenhagen, Denmark.,École d'Optométrie, Université de Montréal, Montréal, QC, Canada
| | - Sylvain Baillet
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC, Canada.
| | - Ron Kupers
- BRAINlab, Department of Neuroscience, Panum Institute, University of Copenhagen, Copenhagen, Denmark. .,École d'Optométrie, Université de Montréal, Montréal, QC, Canada. .,Department of Radiology & Biomedical Imaging, Yale University, New Haven, CT, USA. .,Institute of Neuroscience, Université catholique de Louvain, Brussels, Belgium.
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12
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Xifra-Porxas A, Niso G, Larivière S, Kassinopoulos M, Baillet S, Mitsis GD, Boudrias MH. Older adults exhibit a more pronounced modulation of beta oscillations when performing sustained and dynamic handgrips. Neuroimage 2019; 201:116037. [PMID: 31330245 DOI: 10.1016/j.neuroimage.2019.116037] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2019] [Revised: 07/12/2019] [Accepted: 07/19/2019] [Indexed: 11/20/2022] Open
Abstract
Muscle contractions are associated with a decrease in beta oscillatory activity, known as movement-related beta desynchronization (MRBD). Older adults exhibit a MRBD of greater amplitude compared to their younger counterparts, even though their beta power remains higher both at rest and during muscle contractions. Further, a modulation in MRBD has been observed during sustained and dynamic pinch contractions, whereby beta activity during periods of steady contraction following a dynamic contraction is elevated. However, how the modulation of MRBD is affected by aging has remained an open question. In the present work, we investigated the effect of aging on the modulation of beta oscillations and their putative link with motor performance. We collected magnetoencephalography (MEG) data from younger and older adults during a resting-state period and motor handgrip paradigms, which included sustained and dynamic contractions, to quantify spontaneous and motor-related beta oscillatory activity. Beta power at rest was found to be significantly increased in the motor cortex of older adults. During dynamic hand contractions, MRBD was more pronounced in older participants in frontal, premotor and motor brain regions. These brain areas also exhibited age-related decreases in cortical thickness; however, the magnitude of MRBD and cortical thickness were not found to be associated after controlling for age. During sustained hand contractions, MRBD exhibited a decrease in magnitude compared to dynamic contraction periods in both groups and did not show age-related differences. This suggests that the amplitude change in MRBD between dynamic and sustained contractions is larger in older compared to younger adults. We further probed for a relationship between beta oscillations and motor behaviour and found that greater MRBD in primary motor cortices was related to degraded motor performance beyond age, but our results suggested that age-related differences in beta oscillations were not predictive of motor performance.
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Affiliation(s)
- Alba Xifra-Porxas
- Graduate Program in Biological and Biomedical Engineering, McGill University, Montréal, Canada; Center for Interdisciplinary Research in Rehabilitation of Greater Montreal (CRIR), Montréal, Canada
| | - Guiomar Niso
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montréal, Canada; Center for Biomedical Technology, Universidad Politécnica de Madrid, Madrid, Spain; Biomedical Image Technologies, Universidad Politécnica de Madrid and CIBER-BBN, Madrid, Spain
| | - Sara Larivière
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montréal, Canada; Integrated Program in Neuroscience, McGill University, Montréal, Canada
| | - Michalis Kassinopoulos
- Graduate Program in Biological and Biomedical Engineering, McGill University, Montréal, Canada
| | - Sylvain Baillet
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montréal, Canada
| | | | - Marie-Hélène Boudrias
- Center for Interdisciplinary Research in Rehabilitation of Greater Montreal (CRIR), Montréal, Canada; School of Physical and Occupational Therapy, McGill University, Montréal, Canada.
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13
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Niso G, Tadel F, Bock E, Cousineau M, Santos A, Baillet S. Brainstorm Pipeline Analysis of Resting-State Data From the Open MEG Archive. Front Neurosci 2019; 13:284. [PMID: 31024228 PMCID: PMC6461064 DOI: 10.3389/fnins.2019.00284] [Citation(s) in RCA: 42] [Impact Index Per Article: 8.4] [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: 11/15/2018] [Accepted: 03/11/2019] [Indexed: 11/20/2022] Open
Abstract
We present a simple, reproducible analysis pipeline applied to resting-state magnetoencephalography (MEG) data from the Open MEG Archive (OMEGA). The data workflow was implemented with Brainstorm, which like OMEGA is free and openly accessible. The proposed pipeline produces group maps of ongoing brain activity decomposed in the typical frequency bands of electrophysiology. The procedure is presented as a technical proof of concept for streamlining a broader range and more sophisticated studies of resting-state electrophysiological data. It also features the recently introduced extension of the brain imaging data structure (BIDS) to MEG data, highlighting the scalability and generalizability of Brainstorm analytical pipelines to other, and potentially larger data volumes.
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Affiliation(s)
- Guiomar Niso
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC, Canada.,Center for Biomedical Technology, Universidad Politécnica de Madrid, Madrid, Spain.,Biomedical Image Technologies, ETSI Telecomunicación, Universidad Politécnica de Madrid, Madrid, Spain.,Biomedical Research Networking Centre on Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Madrid, Spain
| | - Francois Tadel
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC, Canada.,Inserm U1216, Grenoble, France.,Grenoble Institut des Neurosciences, Université Grenoble Alpes, Grenoble, France
| | - Elizabeth Bock
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Martin Cousineau
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Andrés Santos
- Biomedical Image Technologies, ETSI Telecomunicación, Universidad Politécnica de Madrid, Madrid, Spain.,Biomedical Research Networking Centre on Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Madrid, Spain
| | - Sylvain Baillet
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
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14
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Larivière S, Xifra-Porxas A, Kassinopoulos M, Niso G, Baillet S, Mitsis GD, Boudrias MH. Functional and effective reorganization of the aging brain during unimanual and bimanual hand movements. Hum Brain Mapp 2019; 40:3027-3040. [PMID: 30866155 DOI: 10.1002/hbm.24578] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [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: 09/23/2018] [Revised: 02/20/2019] [Accepted: 03/04/2019] [Indexed: 02/03/2023] Open
Abstract
Motor performance decline observed during aging is linked to changes in brain structure and function, however, the precise neural reorganization associated with these changes remains largely unknown. We investigated the neurophysiological correlates of this reorganization by quantifying functional and effective brain network connectivity in elderly individuals (n = 11; mean age = 67.5 years), compared to young adults (n = 12; mean age = 23.7 years), while they performed visually-guided unimanual and bimanual handgrips inside the magnetoencephalography (MEG) scanner. Through a combination of principal component analysis and Granger causality, we observed age-related increases in functional and effective connectivity in whole-brain, task-related motor networks. Specifically, elderly individuals demonstrated (i) greater information flow from contralateral parietal and ipsilateral secondary motor regions to the left primary motor cortex during the unimanual task and (ii) decreased interhemispheric temporo-frontal communication during the bimanual task. Maintenance of motor performance and task accuracy in elderly was achieved by hyperactivation of the task-specific motor networks, reflecting a possible mechanism by which the aging brain recruits additional resources to counteract known myelo- and cytoarchitectural changes. Furthermore, resting-state sessions acquired before and after each motor task revealed that both older and younger adults maintain the capacity to adapt to task demands via network-wide increases in functional connectivity. Collectively, our study consolidates functional connectivity and directionality of information flow in systems-level cortical networks during aging and furthers our understanding of neuronal flexibility in motor processes.
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Affiliation(s)
- Sara Larivière
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montréal, Québec, Canada.,Department of Neurology and Neurosurgery, McGill University, Montréal, Québec, Canada
| | - Alba Xifra-Porxas
- Department of Biological and Biomedical Engineering, McGill University, Montréal, Québec, Canada
| | - Michalis Kassinopoulos
- Department of Biological and Biomedical Engineering, McGill University, Montréal, Québec, Canada
| | - Guiomar Niso
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montréal, Québec, Canada.,Center for Biomedical Technology, Technical University of Madrid, Madrid, Spain.,Biomedical Image Technologies, Technical University of Madrid and CIBER-BBN, Madrid, Spain
| | - Sylvain Baillet
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montréal, Québec, Canada.,Department of Neurology and Neurosurgery, McGill University, Montréal, Québec, Canada
| | - Georgios D Mitsis
- Department of Bioengineering, McGill University, Montréal, Québec, Canada
| | - Marie-Hélène Boudrias
- School of Physical and Occupational Therapy, McGill University, Montréal, Québec, Canada.,Center for Interdisciplinary Research in Rehabilitation of Greater Montreal (CRIR), Montréal, Québec, Canada
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15
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Tadel F, Bock E, Niso G, Mosher JC, Cousineau M, Pantazis D, Leahy RM, Baillet S. MEG/EEG Group Analysis With Brainstorm. Front Neurosci 2019; 13:76. [PMID: 30804744 PMCID: PMC6378958 DOI: 10.3389/fnins.2019.00076] [Citation(s) in RCA: 84] [Impact Index Per Article: 16.8] [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: 11/09/2018] [Accepted: 01/23/2019] [Indexed: 11/24/2022] Open
Abstract
Brainstorm is a free, open-source Matlab and Java application for multimodal electrophysiology data analytics and source imaging [primarily MEG, EEG and depth recordings, and integration with MRI and functional near infrared spectroscopy (fNIRS)]. We also provide a free, platform-independent executable version to users without a commercial Matlab license. Brainstorm has a rich and intuitive graphical user interface, which facilitates learning and augments productivity for a wider range of neuroscience users with little or no knowledge of scientific coding and scripting. Yet, it can also be used as a powerful scripting tool for reproducible and shareable batch processing of (large) data volumes. This article describes these Brainstorm interactive and scripted features via illustration through the complete analysis of group data from 16 participants in a MEG vision study.
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Affiliation(s)
- François Tadel
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC, Canada.,INSERM U1216 Grenoble Institut des Neurosciences (GIN), Grenoble, France.,Grenoble Institut des Neurosciences, Université Grenoble Alpes, Grenoble, France
| | - Elizabeth Bock
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Guiomar Niso
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC, Canada.,Center for Biomedical Technology, Universidad Politécnica de Madrid, Madrid, Spain.,Biomedical Image Technologies, Universidad Politécnica de Madrid and CIBER-BBN, Madrid, Spain
| | - John C Mosher
- Epilepsy Center, Neurological Institute, Cleveland Clinic, Cleveland, OH, United States
| | - Martin Cousineau
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Dimitrios Pantazis
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Richard M Leahy
- Signal and Image Processing Institute, University of Southern California, Los Angeles, CA, United States
| | - Sylvain Baillet
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
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16
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Xifra-Porxas A, Kostoglou K, Lariviere S, Niso G, Kassinopoulos M, Boudrias MH, Mitsis GD. Identification of Time-Varying Cortico-cortical and Cortico-Muscular Coherence during Motor Tasks with Multivariate Autoregressive Models. Annu Int Conf IEEE Eng Med Biol Soc 2018; 2018:1024-1021. [PMID: 30440565 DOI: 10.1109/embc.2018.8512475] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Neural populations coordinate at fast subsecond time-scales during rest and task execution. As a result, functional brain connectivity assessed with different neuroimaging modalities (EEG, MEG, fMRI) may also change over different time scales. In addition to the more commonly used sliding window techniques, the General Linear Kalman Filter (GLFK) approach has been proposed to estimate time-varying brain connectivity. In the present work, we propose a modification of the GLFK approach to model timevarying connectivity. We also propose a systematic method to select the hyper-parameters of the model. We evaluate the performance of the method using MEG and EMG data collected from 12 young subjects performing two motor tasks (unimanual and bimanual hand grips), by quantifying time-varying cortico-cortical and corticomuscular coherence (CCC and CMC). The CMC results revealed patterns in accordance with earlier findings, as well as an improvement in both time and frequency resolution compared to sliding window approaches. These results suggest that the proposed methodology is able to unveil accurate time-varying connectivity patterns with an excellent time resolution.
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17
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Soriano MC, Niso G, Clements J, Ortín S, Carrasco S, Gudín M, Mirasso CR, Pereda E. Automated Detection of Epileptic Biomarkers in Resting-State Interictal MEG Data. Front Neuroinform 2017; 11:43. [PMID: 28713260 PMCID: PMC5491593 DOI: 10.3389/fninf.2017.00043] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [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: 03/24/2017] [Accepted: 06/13/2017] [Indexed: 11/13/2022] Open
Abstract
Certain differences between brain networks of healthy and epilectic subjects have been reported even during the interictal activity, in which no epileptic seizures occur. Here, magnetoencephalography (MEG) data recorded in the resting state is used to discriminate between healthy subjects and patients with either idiopathic generalized epilepsy or frontal focal epilepsy. Signal features extracted from interictal periods without any epileptiform activity are used to train a machine learning algorithm to draw a diagnosis. This is potentially relevant to patients without frequent or easily detectable spikes. To analyze the data, we use an up-to-date machine learning algorithm and explore the benefits of including different features obtained from the MEG data as inputs to the algorithm. We find that the relative power spectral density of the MEG time-series is sufficient to distinguish between healthy and epileptic subjects with a high prediction accuracy. We also find that a combination of features such as the phase-locked value and the relative power spectral density allow to discriminate generalized and focal epilepsy, when these features are calculated over a filtered version of the signals in certain frequency bands. Machine learning algorithms are currently being applied to the analysis and classification of brain signals. It is, however, less evident to identify the proper features of these signals that are prone to be used in such machine learning algorithms. Here, we evaluate the influence of the input feature selection on a clinical scenario to distinguish between healthy and epileptic subjects. Our results indicate that such distinction is possible with a high accuracy (86%), allowing the discrimination between idiopathic generalized and frontal focal epilepsy types.
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Affiliation(s)
- Miguel C Soriano
- Instituto de Física Interdisciplinar y Sistemas Complejos, Consejo Superior de Investigaciones Científicas (CSIC), Campus Universitat Illes BalearsPalma de Mallorca, Spain
| | - Guiomar Niso
- McConnell Brain Imaging Center, Montreal Neurological Institute, McGill UniversityMontreal, QC, Canada.,Laboratory of Cognitive and Computational Neuroscience, Center of Biomedical Technology, Politechnical University of MadridMadrid, Spain
| | - Jillian Clements
- Department of Electrical and Computer Engineering, Duke UniversityDurham, NC, United States
| | - Silvia Ortín
- Instituto de Física Interdisciplinar y Sistemas Complejos, Consejo Superior de Investigaciones Científicas (CSIC), Campus Universitat Illes BalearsPalma de Mallorca, Spain
| | - Sira Carrasco
- Teaching General Hospital of Ciudad RealCiudad Real, Spain
| | - María Gudín
- Teaching General Hospital of Ciudad RealCiudad Real, Spain
| | - Claudio R Mirasso
- Instituto de Física Interdisciplinar y Sistemas Complejos, Consejo Superior de Investigaciones Científicas (CSIC), Campus Universitat Illes BalearsPalma de Mallorca, Spain
| | - Ernesto Pereda
- Laboratory of Cognitive and Computational Neuroscience, Center of Biomedical Technology, Politechnical University of MadridMadrid, Spain.,Electrical Engineering and Bioengineering Group, Department of Industrial Engineering, Instituto Universitario de Neurociencia, Universidad de La LagunaTenerife, Spain
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18
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de León SCG, Niso G, Canuet L, Burriel-Lobo L, Maestú F, Rodríguez-Magariños MG. Praxis-induced seizures in a patient with juvenile myoclonic epilepsy: MEG-EEG coregistration study. Epilepsy Behav Case Rep 2016; 5:1-5. [PMID: 26744699 PMCID: PMC4681872 DOI: 10.1016/j.ebcr.2015.10.002] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/22/2015] [Revised: 10/10/2015] [Accepted: 10/22/2015] [Indexed: 11/20/2022]
Abstract
Purpose Juvenile myoclonic epilepsy (JME) is one of the most common generalized idiopathic epilepsies of childhood and adolescence. In some patients with JME, mathematical calculus and praxis may induce myoclonic seizures. Methods A reflex myoclonic seizure was recorded by simultaneous magnetoencephalography (MEG) and electroencephalography (EEG) when a generalized spike–wave synchronous pattern at 3 Hz was observed. Results Source reconstruction localized the epileptogenic area to the right premotor frontal cortex. Conclusions The present study demonstrates that the origin of epileptiform activity in JME can be localized in brain areas associated with the premotor frontal cortex.
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Affiliation(s)
- Sira Carrasco-García de León
- Department of Neurology, Teaching General Hospital, Ciudad Real, Spain
- Corresponding author at: Department of Neurology, Teaching General Hospital, Calle Obispo Rafael Torija S/N, Ciudad Real CP 13005, Spain. Tel.: + 34 926 278000.Department of NeurologyTeaching General HospitalCalle Obispo Rafael Torija S/NCiudad RealCP 13005Spain
| | - Guiomar Niso
- Center for Biomedical Technology, Technical University of Madrid, Madrid, Spain
- McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University, Montreal, Canada
| | - Leonidas Canuet
- Center for Biomedical Technology, Technical University of Madrid, Madrid, Spain
| | - Laura Burriel-Lobo
- Department of Neuropsychology, Teaching General Hospital, Ciudad Real, Spain
| | - Fernando Maestú
- Center for Biomedical Technology, Technical University of Madrid, Madrid, Spain
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19
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Das S, Glatard T, MacIntyre LC, Madjar C, Rogers C, Rousseau ME, Rioux P, MacFarlane D, Mohades Z, Gnanasekaran R, Makowski C, Kostopoulos P, Adalat R, Khalili-Mahani N, Niso G, Moreau JT, Evans AC. The MNI data-sharing and processing ecosystem. Neuroimage 2015; 124:1188-1195. [PMID: 26364860 DOI: 10.1016/j.neuroimage.2015.08.076] [Citation(s) in RCA: 39] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2015] [Revised: 08/22/2015] [Accepted: 08/24/2015] [Indexed: 11/29/2022] Open
Abstract
Neuroimaging has been facing a data deluge characterized by the exponential growth of both raw and processed data. As a result, mining the massive quantities of digital data collected in these studies offers unprecedented opportunities and has become paramount for today's research. As the neuroimaging community enters the world of "Big Data", there has been a concerted push for enhanced sharing initiatives, whether within a multisite study, across studies, or federated and shared publicly. This article will focus on the database and processing ecosystem developed at the Montreal Neurological Institute (MNI) to support multicenter data acquisition both nationally and internationally, create database repositories, facilitate data-sharing initiatives, and leverage existing software toolkits for large-scale data processing.
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Affiliation(s)
- Samir Das
- McGill Centre for Integrative Neuroscience (MCIN), Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute (MNI), McGill University, Montreal, Canada.
| | - Tristan Glatard
- McGill Centre for Integrative Neuroscience (MCIN), Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute (MNI), McGill University, Montreal, Canada; Université de Lyon, CREATIS ; CNRS UMR5220 ; Inserm U1044 ; INSA-Lyon ; Université Claude Bernard Lyon 1, France
| | - Leigh C MacIntyre
- McGill Centre for Integrative Neuroscience (MCIN), Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute (MNI), McGill University, Montreal, Canada
| | - Cecile Madjar
- Douglas Mental Health University Institute, Montreal, Canada
| | - Christine Rogers
- McGill Centre for Integrative Neuroscience (MCIN), Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute (MNI), McGill University, Montreal, Canada
| | - Marc-Etienne Rousseau
- McGill Centre for Integrative Neuroscience (MCIN), Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute (MNI), McGill University, Montreal, Canada
| | - Pierre Rioux
- McGill Centre for Integrative Neuroscience (MCIN), Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute (MNI), McGill University, Montreal, Canada
| | - Dave MacFarlane
- McGill Centre for Integrative Neuroscience (MCIN), Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute (MNI), McGill University, Montreal, Canada
| | - Zia Mohades
- McGill Centre for Integrative Neuroscience (MCIN), Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute (MNI), McGill University, Montreal, Canada
| | - Rathi Gnanasekaran
- McGill Centre for Integrative Neuroscience (MCIN), Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute (MNI), McGill University, Montreal, Canada
| | - Carolina Makowski
- McGill Centre for Integrative Neuroscience (MCIN), Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute (MNI), McGill University, Montreal, Canada
| | - Penelope Kostopoulos
- McGill Centre for Integrative Neuroscience (MCIN), Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute (MNI), McGill University, Montreal, Canada
| | - Reza Adalat
- McGill Centre for Integrative Neuroscience (MCIN), Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute (MNI), McGill University, Montreal, Canada
| | - Najmeh Khalili-Mahani
- McGill Centre for Integrative Neuroscience (MCIN), Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute (MNI), McGill University, Montreal, Canada
| | - Guiomar Niso
- McConnell Brain Imaging Centre (BIC), Montreal Neurological Institute (MNI), McGill University, Montreal, Canada
| | - Jeremy T Moreau
- McConnell Brain Imaging Centre (BIC), Montreal Neurological Institute (MNI), McGill University, Montreal, Canada
| | - Alan C Evans
- McGill Centre for Integrative Neuroscience (MCIN), Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute (MNI), McGill University, Montreal, Canada
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20
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Niso G, Carrasco S, Gudín M, Maestú F, Del-Pozo F, Pereda E. What graph theory actually tells us about resting state interictal MEG epileptic activity. Neuroimage Clin 2015; 8:503-15. [PMID: 26106575 PMCID: PMC4475779 DOI: 10.1016/j.nicl.2015.05.008] [Citation(s) in RCA: 54] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/03/2014] [Revised: 05/07/2015] [Accepted: 05/19/2015] [Indexed: 01/21/2023]
Abstract
Graph theory provides a useful framework to study functional brain networks from neuroimaging data. In epilepsy research, recent findings suggest that it offers unique insight into the fingerprints of this pathology on brain dynamics. Most studies hitherto have focused on seizure activity during focal epilepsy, but less is known about functional epileptic brain networks during interictal activity in frontal focal and generalized epilepsy. Besides, it is not clear yet which measures are most suitable to characterize these networks. To address these issues, we recorded magnetoencephalographic (MEG) data using two orthogonal planar gradiometers from 45 subjects from three groups (15 healthy controls (7 males, 24 ± 6 years), 15 frontal focal (8 male, 32 ± 16 years) and 15 generalized epileptic (6 male, 27 ± 7 years) patients) during interictal resting state with closed eyes. Then, we estimated the total and relative spectral power of the largest principal component of the gradiometers, and the degree of phase synchronization between each sensor site in the frequency range [0.5–40 Hz]. We further calculated a comprehensive battery of 15 graph-theoretic measures and used the affinity propagation clustering algorithm to elucidate the minimum set of them that fully describe these functional brain networks. The results show that differences in spectral power between the control and the other two groups have a distinctive pattern: generalized epilepsy presents higher total power for all frequencies except the alpha band over a widespread set of sensors; frontal focal epilepsy shows higher relative power in the beta band bilaterally in the fronto-central sensors. Moreover, all network indices can be clustered into three groups, whose exemplars are the global network efficiency, the eccentricity and the synchronizability. Again, the patterns of differences were clear: the brain network of the generalized epilepsy patients presented greater efficiency and lower eccentricity than the control subjects for the high frequency bands, without a clear topography. Besides, the frontal focal epileptic patients showed only reduced eccentricity for the theta band over fronto-temporal and central sensors. These outcomes indicate that functional epileptic brain networks are different to those of healthy subjects during interictal stage at rest, with a unique pattern of dissimilarities for each type of epilepsy. Further, when properly selected, three network indices suffice to provide a comprehensive description of these differences. Yet, since such uniqueness in the pattern of differences is also evident in the power spectrum, we conclude that the added value of the graph theory approach in this context should not be overestimated. We study MEG activity during interictal resting state with closed eyes. Generalized epilepsy presents higher total power over a widespread set of sensors. Frontal epilepsy shows higher relative power in beta band on fronto-central sensors. We also found altered functional brain networks in epilepsy using graph theory. The pattern of differences from control subjects is unique for each type of epilepsy.
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Affiliation(s)
- Guiomar Niso
- Center for Biomedical Technology, Technical University of Madrid, Madrid, Spain ; McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University, Montreal, Canada
| | - Sira Carrasco
- Teaching General Hospital of Ciudad Real, Ciudad Real, Spain
| | - María Gudín
- Teaching General Hospital of Ciudad Real, Ciudad Real, Spain
| | - Fernando Maestú
- Center for Biomedical Technology, Technical University of Madrid, Madrid, Spain ; Biomedical Research Networking Center in Bioengineering Biomaterials and Nanomedicine (CIBER-BBN), Madrid, Spain
| | - Francisco Del-Pozo
- Center for Biomedical Technology, Technical University of Madrid, Madrid, Spain ; Biomedical Research Networking Center in Bioengineering Biomaterials and Nanomedicine (CIBER-BBN), Madrid, Spain
| | - Ernesto Pereda
- Dept. of Industrial Engineering, Electrical Engineering and Bioengineering Group, Institute of Biomedical Technology (ITB-CIBICAN), University of La Laguna, Tenerife, Spain
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21
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Ariza P, Solesio-Jofre E, Martínez JH, Pineda-Pardo JA, Niso G, Maestú F, Buldú JM. Evaluating the effect of aging on interference resolution with time-varying complex networks analysis. Front Hum Neurosci 2015; 9:255. [PMID: 26029079 PMCID: PMC4428067 DOI: 10.3389/fnhum.2015.00255] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [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: 12/23/2014] [Accepted: 04/20/2015] [Indexed: 12/31/2022] Open
Abstract
In this study we used graph theory analysis to investigate age-related reorganization of functional networks during the active maintenance of information that is interrupted by external interference. Additionally, we sought to investigate network differences before and after averaging network parameters between both maintenance and interference windows. We compared young and older adults by measuring their magnetoencephalographic recordings during an interference-based working memory task restricted to successful recognitions. Data analysis focused on the topology/temporal evolution of functional networks during both the maintenance and interference windows. We observed that: (a) Older adults require higher synchronization between cortical brain sites in order to achieve a successful recognition, (b) The main differences between age groups arise during the interference window,
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Affiliation(s)
- Pedro Ariza
- Laboratory of Biological Networks, Centre for Biomedical Technology, Technical University of Madrid Madrid, Spain
| | - Elena Solesio-Jofre
- Department of Basic Psychology, Universidad Autónoma de Madrid Madrid, Spain
| | - Johann H Martínez
- Complex Systems Group, Technical University of Madrid Madrid, Spain ; Universidad del Rosario de Colombia Bogotá, Colombia
| | - José A Pineda-Pardo
- CINAC, HM Puerta del Sur, Hospitales de Madrid, Móstoles, and CEU-San Pablo University Madrid, Spain ; Laboratory of Neuroimaging, Centre for Biomedical Technology Madrid, Spain
| | - Guiomar Niso
- Laboratory of Cognitive and Computational Neuroscience, Centre for Biomedical Technology, Madrid Spain ; Montreal Neurological Institute, McConnell Brain Imaging Centre, McGill University Montreal, Canada ; Biomedical Research Networking Center in Bioengineering Biomaterials and Nanomedicine (CIBER-BBN) Madrid, Spain
| | - Fernando Maestú
- Laboratory of Cognitive and Computational Neuroscience, Centre for Biomedical Technology, Madrid Spain
| | - Javier M Buldú
- Laboratory of Biological Networks, Centre for Biomedical Technology, Technical University of Madrid Madrid, Spain ; Complex Systems Group & GISC, Universidad Rey Juan Carlos Madrid, Spain
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22
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Niso G, Rogers C, Moreau JT, Chen LY, Madjar C, Das S, Bock E, Tadel F, Evans AC, Jolicoeur P, Baillet S. OMEGA: The Open MEG Archive. Neuroimage 2015; 124:1182-1187. [PMID: 25896932 DOI: 10.1016/j.neuroimage.2015.04.028] [Citation(s) in RCA: 65] [Impact Index Per Article: 7.2] [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: 02/04/2015] [Revised: 04/10/2015] [Accepted: 04/10/2015] [Indexed: 11/30/2022] Open
Abstract
In contrast with other imaging modalities, there is presently a scarcity of fully open resources in magnetoencephalography (MEG) available to the neuroimaging community. Here we present a collaborative effort led by the McConnell Brain Imaging Centre of the Montreal Neurological Institute, and the Université de Montréal to build and share a centralised repository to curate MEG data in raw and processed form for open dissemination. The Open MEG Archive (OMEGA, omega.bic.mni.mcgill.ca) is bound to become a continuously expanding repository of multimodal data with a primary focus on MEG, in addition to storing anatomical MRI volumes, demographic participant data and questionnaires, and other forms of electrophysiological data such as EEG. The OMEGA initiative offers both the technological framework for multi-site MEG data aggregation, and serves as one of the largest freely available resting-state and eventually task-related MEG datasets presently available.
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Affiliation(s)
- Guiomar Niso
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, 3801 University St. Montreal, QC H3A 2B4, Canada.
| | - Christine Rogers
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, 3801 University St. Montreal, QC H3A 2B4, Canada.
| | - Jeremy T Moreau
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, 3801 University St. Montreal, QC H3A 2B4, Canada.
| | - Li-Yuan Chen
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, 3801 University St. Montreal, QC H3A 2B4, Canada.
| | - Cecile Madjar
- Douglas Mental Health University Institute, 6875 Boulevard Lasalle, Verdun, QC H4H 1R3 Canada.
| | - Samir Das
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, 3801 University St. Montreal, QC H3A 2B4, Canada.
| | - Elizabeth Bock
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, 3801 University St. Montreal, QC H3A 2B4, Canada.
| | - François Tadel
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, 3801 University St. Montreal, QC H3A 2B4, Canada.
| | - Alan C Evans
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, 3801 University St. Montreal, QC H3A 2B4, Canada.
| | - Pierre Jolicoeur
- Institut Universitaire de Gériatrie de Montréal, Université de Montréal, Département de psychologie Université de Montréal, C.P. 6128, Succ. Centre-Ville, Montréal, QC H3C 3J7, Canada.
| | - Sylvain Baillet
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, 3801 University St. Montreal, QC H3A 2B4, Canada.
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23
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López ME, Aurtenetxe S, Pereda E, Cuesta P, Castellanos NP, Bruña R, Niso G, Maestú F, Bajo R. Cognitive reserve is associated with the functional organization of the brain in healthy aging: a MEG study. Front Aging Neurosci 2014; 6:125. [PMID: 24982632 PMCID: PMC4056015 DOI: 10.3389/fnagi.2014.00125] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.1] [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: 01/17/2014] [Accepted: 05/28/2014] [Indexed: 01/01/2023] Open
Abstract
The proportion of elderly people in the population has increased rapidly in the last century and consequently “healthy aging” is expected to become a critical area of research in neuroscience. Evidence reveals how healthy aging depends on three main behavioral factors: social lifestyle, cognitive activity, and physical activity. In this study, we focused on the role of cognitive activity, concentrating specifically on educational and occupational attainment factors, which were considered two of the main pillars of cognitive reserve (CR). Twenty-one subjects with similar rates of social lifestyle, physical and cognitive activity were selected from a sample of 55 healthy adults. These subjects were divided into two groups according to their level of CR; one group comprised subjects with high CR (9 members) and the other one contained those with low CR (12 members). To evaluate the cortical brain connectivity network, all participants were recorded by Magnetoencephalography (MEG) while they performed a memory task (modified version of the Sternberg's Task). We then applied two algorithms [Phase Locking Value (PLV) and Phase Lag Index (PLI)] to study the dynamics of functional connectivity. In response to the same task, the subjects with lower CR presented higher functional connectivity than those with higher CR. These results may indicate that participants with low CR needed a greater “effort” than those with high CR to achieve the same level of cognitive performance. Therefore, we conclude that CR contributes to the modulation of the functional connectivity patterns of the aging brain.
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Affiliation(s)
- María E López
- Laboratory of Cognitive and Computational Neuroscience (UCM-UPM), Centre for Biomedical Technology, Universidad Politécnica de Madrid Madrid, Spain ; Department of Basic Psychology II, Complutense University of Madrid Spain
| | - Sara Aurtenetxe
- Laboratory of Cognitive and Computational Neuroscience (UCM-UPM), Centre for Biomedical Technology, Universidad Politécnica de Madrid Madrid, Spain ; Department of Basic Psychology II, Complutense University of Madrid Spain
| | - Ernesto Pereda
- Grupo de Ingeniería Eléctrica y Bioingeniería, Department of Industrial Engineering and Institute of Biomedical Technology, Universidad de La Laguna La Laguna, Tenerife
| | - Pablo Cuesta
- Laboratory of Cognitive and Computational Neuroscience (UCM-UPM), Centre for Biomedical Technology, Universidad Politécnica de Madrid Madrid, Spain ; Department of Basic Psychology II, Complutense University of Madrid Spain
| | - Nazareth P Castellanos
- Laboratory of Cognitive and Computational Neuroscience (UCM-UPM), Centre for Biomedical Technology, Universidad Politécnica de Madrid Madrid, Spain
| | - Ricardo Bruña
- Laboratory of Cognitive and Computational Neuroscience (UCM-UPM), Centre for Biomedical Technology, Universidad Politécnica de Madrid Madrid, Spain
| | - Guiomar Niso
- Laboratory of Cognitive and Computational Neuroscience (UCM-UPM), Centre for Biomedical Technology, Universidad Politécnica de Madrid Madrid, Spain
| | - Fernando Maestú
- Laboratory of Cognitive and Computational Neuroscience (UCM-UPM), Centre for Biomedical Technology, Universidad Politécnica de Madrid Madrid, Spain ; Department of Basic Psychology II, Complutense University of Madrid Spain
| | - Ricardo Bajo
- Laboratory of Cognitive and Computational Neuroscience (UCM-UPM), Centre for Biomedical Technology, Universidad Politécnica de Madrid Madrid, Spain ; Departamento de Matemáticas, Universidad Internacional de La Rioja (UNIR) Logroño, Spain
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