Author: "Krzysztof J. Gorgolewski" / Topic: neuroimaging - Searchworks@Jio Institute Digital Library Search Results

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1. Sharing voxelwise neuroimaging results from rhesus monkeys and other species with Neurovault

Author: Andrew S. Fox, Daniel Holley, Peter Christiaan Klink, Spencer A. Arbuckle, Carol A. Barnes, Jörn Diedrichsen, Sze Chai Kwok, Colin Kyle, J. Andrew Pruszynski, Jakob Seidlitz, XuFeng Zhou, Russell A. Poldrack, and Krzysztof J. Gorgolewski
Subjects: Nonhuman primate, Neuroimaging, Data sharing, PET, fMRI, Animal, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Abstract: Animal neuroimaging studies can provide unique insights into brain structure and function, and can be leveraged to bridge the gap between animal and human neuroscience. In part, this power comes from the ability to combine mechanistic interventions with brain-wide neuroimaging. Due to their phylogenetic proximity to humans, nonhuman primate neuroimaging holds particular promise. Because nonhuman primate neuroimaging studies are often underpowered, there is a great need to share data amongst translational researchers. Data sharing efforts have been limited, however, by the lack of standardized tools and repositories through which nonhuman neuroimaging data can easily be archived and accessed. Here, we provide an extension of the Neurovault framework to enable sharing of statistical maps and related voxelwise neuroimaging data from other species and template-spaces. Neurovault, which was previously limited to human neuroimaging data, now allows researchers to easily upload and share nonhuman primate neuroimaging results. This promises to facilitate open, integrative, cross-species science while affording researchers the increased statistical power provided by data aggregation. In addition, the Neurovault code-base now enables the addition of other species and template-spaces. Together, these advances promise to bring neuroimaging data sharing to research in other species, for supplemental data, location-based atlases, and data that would otherwise be relegated to a ''file-drawer''. As increasing numbers of researchers share their nonhuman neuroimaging data on Neurovault, this resource will enable novel, large-scale, cross-species comparisons that were previously impossible.
Published: 2021
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2. Preprocessed Consortium for Neuropsychiatric Phenomics dataset [version 2; referees: 2 approved]

Author: Krzysztof J. Gorgolewski, Joke Durnez, and Russell A. Poldrack
Subjects: Bioinformatics, Neuroimaging, Medicine, Science
Abstract: Here we present preprocessed MRI data of 265 participants from the Consortium for Neuropsychiatric Phenomics (CNP) dataset. The preprocessed dataset includes minimally preprocessed data in the native, MNI and surface spaces accompanied with potential confound regressors, tissue probability masks, brain masks and transformations. In addition the preprocessed dataset includes unthresholded group level and single subject statistical maps from all tasks included in the original dataset. We hope that availability of this dataset will greatly accelerate research.
Published: 2017
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3. TemplateFlow: FAIR-sharing of multi-scale, multi-species brain models

Author: Rastko Ciric, William H. Thompson, Romy Lorenz, Mathias Goncalves, Eilidh MacNicol, Christopher J. Markiewicz, Yaroslav O. Halchenko, Satrajit S. Ghosh, Krzysztof J. Gorgolewski, Russell A. Poldrack, Oscar Esteban, Ciric, Rastko [0000-0001-6347-7939], MacNicol, Eilidh E [0000-0003-3715-7012], Markiewicz, Christopher J [0000-0002-6533-164X], Ghosh, Satrajit S [0000-0002-5312-6729], Gorgolewski, Krzysztof J [0000-0003-3321-7583], Poldrack, Russell A [0000-0001-6755-0259], Esteban, Oscar [0000-0001-8435-6191], and Apollo - University of Cambridge Repository
Subjects: Databases, Factual, brief-communication, Brain, Neuroimaging, Cell Biology, Brief Communication, Biochemistry, 706/648/697/129, 631/114/1564, 631/114/794, 692/308/575, Nervous System Physiological Phenomena, 631/114/116, Molecular Biology, Problem Solving, Biotechnology
Abstract: Funder: Laura and John Arnold Foundation (Arnold Foundation); doi: https://doi.org/10.13039/100009827, Funder: RCUK | Medical Research Council (MRC); doi: https://doi.org/10.13039/100004440, Funder: Kings College London; doi: https://doi.org/10.13039/501100000764, Reference anatomies of the brain ('templates') and corresponding atlases are the foundation for reporting standardized neuroimaging results. Currently, there is no registry of templates and atlases; therefore, the redistribution of these resources occurs either bundled within existing software or in ad hoc ways such as downloads from institutional sites and general-purpose data repositories. We introduce TemplateFlow as a publicly available framework for human and non-human brain models. The framework combines an open database with software for access, management, and vetting, allowing scientists to share their resources under FAIR-findable, accessible, interoperable, and reusable-principles. TemplateFlow enables multifaceted insights into brains across species, and supports multiverse analyses testing whether results generalize across standard references, scales, and in the long term, species.
Published: 2022

4. Preprocessed Consortium for Neuropsychiatric Phenomics dataset [version 1; referees: 2 approved with reservations]

Author: Krzysztof J. Gorgolewski, Joke Durnez, and Russell A. Poldrack
Subjects: Data Note, Articles, Bioinformatics, Neuroimaging, fMRI, human, cognition, preprocessed
Abstract: Here we present preprocessed MRI data of 265 participants from the Consortium for Neuropsychiatric Phenomics (CNP) dataset. The preprocessed dataset includes minimally preprocessed data in the native, MNI and surface spaces accompanied with potential confound regressors, tissue probability masks, brain masks and transformations. In addition the preprocessed dataset includes unthresholded group level and single subject statistical maps from all tasks included in the original dataset. We hope that availability of this dataset will greatly accelerate research.
Published: 2017
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5. Analysis of task-based functional MRI data preprocessed with fMRIPrep

Author: Mathias Goncalves, Jessey Wright, Yaroslav O. Halchenko, Craig A. Moodie, James D. Kent, Oscar Esteban, Tal Yarkoni, Karolina Finc, Matthew Cieslak, Daniel E. P. Gomez, Nir Jacoby, Satrajit S. Ghosh, Hrvoje Stojic, Elizabeth DuPre, Romain Valabregue, Franziskus Liem, Ross Blair, Christopher J. Markiewicz, Sebastian Urchs, Krzysztof J. Gorgolewski, Rastko Ciric, Inge K Amlien, Alejandro de la Vega, Russell A. Poldrack, Zhifang Ye, William Hedley Thompson, and Taylor Salo
Subjects: Computer science, Rest, Image processing, Article, General Biochemistry, Genetics and Molecular Biology, 030218 nuclear medicine & medical imaging, Workflow, 03 medical and health sciences, 0302 clinical medicine, Software, Neuroimaging, Image Processing, Computer-Assisted, medicine, Animals, Humans, Preprocessor, 030304 developmental biology, Protocol (science), 0303 health sciences, Neural correlates of consciousness, medicine.diagnostic_test, business.industry, Brain, Pattern recognition, Reference Standards, Data structure, Magnetic Resonance Imaging, Artificial intelligence, Functional magnetic resonance imaging, business, 030217 neurology & neurosurgery
Abstract: Functional magnetic resonance imaging (fMRI) is a standard tool to investigate the neural correlates of cognition. fMRI noninvasively measures brain activity, allowing identification of patterns evoked by tasks performed during scanning. Despite the long history of this technique, the idiosyncrasies of each dataset have led to the use of ad-hoc preprocessing protocols customized for nearly every different study. This approach is time-consuming, error-prone, and unsuitable for combining datasets from many sources. Here we showcasefMRIPrep(http://fmriprep.org), a robust tool to prepare human fMRI data for statistical analysis. This software instrument addresses the reproducibility concerns of the established protocols for fMRI preprocessing. By leveraging the Brain Imaging Data Structure (BIDS) to standardize both the input datasets —MRI data as stored by the scanner— and the outputs —data ready for modeling and analysis—,fMRIPrepis capable of preprocessing a diversity of datasets without manual intervention. In support of the growing popularity offMRIPrep, this protocol describes how to integrate the tool in a task-based fMRI investigation workflow.
Published: 2020
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6. PET-BIDS, an extension to the brain imaging data structure for positron emission tomography

Author: Martin Norgaard, Granville J. Matheson, Hanne D. Hansen, Adam Thomas, Graham Searle, Gaia Rizzo, Mattia Veronese, Alessio Giacomel, Maqsood Yaqub, Matteo Tonietto, Thomas Funck, Ashley Gillman, Hugo Boniface, Alexandre Routier, Jelle R. Dalenberg, Tobey Betthauser, Franklin Feingold, Christopher J. Markiewicz, Krzysztof J. Gorgolewski, Ross W. Blair, Stefan Appelhoff, Remi Gau, Taylor Salo, Guiomar Niso, Cyril Pernet, Christophe Phillips, Robert Oostenveld, Jean-Dominique Gallezot, Richard E. Carson, Gitte M. Knudsen, Robert B. Innis, Melanie Ganz, Radiology and nuclear medicine, and Amsterdam Neuroscience - Brain Imaging
Subjects: Statistics and Probability, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Neuroimaging, Library and Information Sciences, urologic and male genital diseases, Magnetic Resonance Imaging, Multimodal Imaging, GeneralLiterature_MISCELLANEOUS, digestive system diseases, Computer Science Applications, Education, Positron-Emission Tomography, ComputingMilieux_COMPUTERSANDSOCIETY, heterocyclic compounds, ddc:500, Statistics, Probability and Uncertainty, biological phenomena, cell phenomena, and immunity, neoplasms, Information Systems
Abstract: The Brain Imaging Data Structure (BIDS) is a standard for organizing and describing neuroimaging datasets, serving not only to facilitate the process of data sharing and aggregation, but also to simplify the application and development of new methods and software for working with neuroimaging data. Here, we present an extension of BIDS to include positron emission tomography (PET) data, also known as PET-BIDS, and share several open-access datasets curated following PET-BIDS along with tools for conversion, validation and analysis of PET-BIDS datasets.
Published: 2022
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7. The OpenNeuro resource for sharing of neuroscience data

Author: Ross Blair, Yaroslav O. Halchenko, Oscar Esteban, Eric N. Miller, Franklin Feingold, Anita Jwa, Joe Wexler, Christopher J. Markiewicz, Mathias Goncavles, Nell Hardcastle, Krzysztof J. Gorgolewski, and Russell A. Poldrack
Subjects: Open science, Databases, Factual, Computer science, QH301-705.5, data sharing, Science, Reuse, Data type, General Biochemistry, Genetics and Molecular Biology, Resource (project management), open science, Humans, EEG, Biology (General), Modalities, neuroimaging, MEG, General Immunology and Microbiology, Information Dissemination, General Neuroscience, Neurosciences, Brain, General Medicine, Data structure, Multiple species, Data science, Data sharing, Medicine, MRI
Abstract: The sharing of research data is essential to ensure reproducibility and maximize the impact of public investments in scientific research. Here, we describe OpenNeuro, a BRAIN Initiative data archive that provides the ability to openly share data from a broad range of brain imaging data types following the FAIR principles for data sharing. We highlight the importance of the Brain Imaging Data Structure standard for enabling effective curation, sharing, and reuse of data. The archive presently shares more than 600 datasets including data from more than 20,000 participants, comprising multiple species and measurement modalities and a broad range of phenotypes. The impact of the shared data is evident in a growing number of published reuses, currently totalling more than 150 publications. We conclude by describing plans for future development and integration with other ongoing open science efforts.
Published: 2021

8. TemplateFlow: FAIR-sharing of multi-scale, multi-species brain models

Author: Rastko Ciric, Romy Lorenz, Yaroslav O. Halchenko, William Hedley Thompson, Eilidh MacNicol, Christopher J. Markiewicz, Krzysztof J. Gorgolewski, Russell A. Poldrack, Satrajit S. Ghosh, Mathias Goncalves, and Oscar Esteban
Subjects: Workflow, Software, Neuroimaging, Computer science, Vetting, business.industry, Scale (chemistry), Reliability (computer networking), Interoperability, business, Data science, Term (time)
Abstract: Reference anatomies of the brain and corresponding atlases play a central role in experimental neuroimaging workflows and are the foundation for reporting standardized results. The choice of such references —i.e., templates— and atlases is one relevant source of methodological variability across studies, which has recently been brought to attention as an important challenge to reproducibility in neuroscience. TemplateFlow is a publicly available framework for human and nonhuman brain models. The framework combines an open database with software for access, management, and vetting, allowing scientists to distribute their resources under FAIR —findable, accessible, interoperable, reusable— principles. TemplateFlow supports a multifaceted insight into brains across species, and enables multiverse analyses testing whether results generalize across standard references, scales, and in the long term, species, thereby contributing to increasing the reliability of neuroimaging results.
Published: 2021
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9. TemplateFlow: FAIR-sharing of multi-scale, multi-species brain models

Author: Rastko, Ciric, William H, Thompson, Romy, Lorenz, Mathias, Goncalves, Eilidh E, MacNicol, Christopher J, Markiewicz, Yaroslav O, Halchenko, Satrajit S, Ghosh, Krzysztof J, Gorgolewski, Russell A, Poldrack, and Oscar, Esteban
Subjects: Databases, Factual, Brain, Nervous System Physiological Phenomena, Neuroimaging, Problem Solving
Abstract: Reference anatomies of the brain ('templates') and corresponding atlases are the foundation for reporting standardized neuroimaging results. Currently, there is no registry of templates and atlases; therefore, the redistribution of these resources occurs either bundled within existing software or in ad hoc ways such as downloads from institutional sites and general-purpose data repositories. We introduce TemplateFlow as a publicly available framework for human and non-human brain models. The framework combines an open database with software for access, management, and vetting, allowing scientists to share their resources under FAIR-findable, accessible, interoperable, and reusable-principles. TemplateFlow enables multifaceted insights into brains across species, and supports multiverse analyses testing whether results generalize across standard references, scales, and in the long term, species.
Published: 2021

10. Computational and Informatic Advances for Reproducible Data Analysis in Neuroimaging

Author: Russell A. Poldrack, Krzysztof J. Gorgolewski, and Gaël Varoquaux
Subjects: 0301 basic medicine, 03 medical and health sciences, Open science, 030104 developmental biology, 0302 clinical medicine, Neuroimaging, Computer science, General Medicine, Data science, Transparency (behavior), 030217 neurology & neurosurgery
Abstract: The reproducibility of scientific research has become a point of critical concern. We argue that openness and transparency are critical for reproducibility, and we outline an ecosystem for open and transparent science that has emerged within the human neuroimaging community. We discuss the range of open data-sharing resources that have been developed for neuroimaging data, as well as the role of data standards (particularly the brain imaging data structure) in enabling the automated sharing, processing, and reuse of large neuroimaging data sets. We outline how the open source Python language has provided the basis for a data science platform that enables reproducible data analysis and visualization. We also discuss how new advances in software engineering, such as containerization, provide the basis for greater reproducibility in data analysis. The emergence of this new ecosystem provides an example for many areas of science that are currently struggling with reproducibility.
Published: 2019
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11. OpenNeuro: An open resource for sharing of neuroimaging data

Author: Mathias Goncalves, Russel A. Poldrack, Joseph Wexler, Franklin Feingold, Miller E, Krzysztof J. Gorgolewski, Hardcastle N, Anita Jwa, Chris Markiewicz, Yaroslav O. Halchenko, Ross Blair, and Oscar Esteban
Subjects: Data sharing, Open science, Resource (project management), Modalities, Neuroimaging, Computer science, Reuse, Data structure, Data science, Data type
Abstract: The sharing of research data is essential to ensure reproducibility and maximize the impact of public investments in scientific research. Here we describe OpenNeuro, a BRAIN Initiative data archive that provides the ability to openly share data from a broad range of brain imaging data types following the FAIR principles for data sharing. We highlight the importance of the Brain Imaging Data Structure (BIDS) standard for enabling effective curation, sharing, and reuse of data. The archive presently shares more than 600 datasets including data from more than 20,000 participants, comprising multiple species and measurement modalities and a broad range of phenotypes. The impact of the shared data is evident in a growing number of published reuses, currently totalling more than 150 publications. We conclude by describing plans for future development and integration with other ongoing open science efforts.
Published: 2021
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12. PET-BIDS, an extension to the brain imaging data structure for positron emission tomography

Author: Ross Blair, Stefan Appelhoff, Mattia Veronese, Tobey J. Betthauser, Jean-Dominique Gallezot, Remi Gau, Matteo Tonietto, Granville J. Matheson, Cyril Pernet, Alexandre Routier, Robert B. Innis, Richard E. Carson, Guiomar Niso, Gaia Rizzo, Graham E. Searle, Gitte M. Knudsen, Franklin Feingold, Thomas Funck, Adam G. Thomas, Martin Nørgaard, Christophe Phillips, Hugo Boniface, Christopher J. Markiewicz, Hanne D. Hansen, Maqsood Yaqub, Krzysztof J. Gorgolewski, Alessio Giacomel, Robert Oostenveld, Melanie Ganz, Ashley Gillman, Jelle R. Dalenberg, and Taylor Salo
Subjects: medicine.diagnostic_test, Computer science, business.industry, Extension (predicate logic), urologic and male genital diseases, computer.software_genre, Data structure, GeneralLiterature_MISCELLANEOUS, digestive system diseases, Data sharing, Software, Neuroimaging, Positron emission tomography, medicine, ComputingMilieux_COMPUTERSANDSOCIETY, heterocyclic compounds, Data mining, biological phenomena, cell phenomena, and immunity, business, neoplasms, computer
Abstract: The Brain Imaging Data Structure (BIDS) is a standard for organizing and describing neuroimaging datasets. It serves not only to facilitate the process of data sharing and aggregation, but also to simplify the application and development of new methods and software for working with neuroimaging data. Here, we present an extension of BIDS to include positron emission tomography (PET) data (PET-BIDS). We describe the PET-BIDS standard in detail and share several open-access datasets curated following PET-BIDS. Additionally, we highlight several tools which are already available for converting, validating and analyzing PET-BIDS datasets.
Published: 2021
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13. TemplateFlow: a community archive of imaging templates and atlases for improved consistency in neuroimaging

Author: Christopher J. Markiewicz, Oscar Esteban, William Hedley Thompson, Satrajit S. Ghosh, Romy Lorenz, Yaroslav O. Halchenko, Rastko Ciric, Eilidh MacNicol, Krzysztof J. Gorgolewski, Mathias Goncalves, and Russell A. Poldrack
Subjects: Identification (information), Consistency (database systems), Resource (project management), Neuroimaging, Computer science, Data science
Abstract: Neuroimaging templates and corresponding atlases play a central role in experimental workflows and are the foundation for reporting standardised results. The proliferation of templates and atlases is one relevant source of methodological variability across studies, which has been recently brought to attention as an important challenge to reproducibility in neuroscience. Unclear nomenclature, an overabundance of template variants and options, inadequate provenance tracking and maintenance, and poor concordance between atlases introduce further unreliability into reported results. We introduce TemplateFlow, a cloud-based repository of human and nonhuman imaging templates paired with a client application for programmatically accessing resources. TemplateFlow is designed to be extensible, providing a transparent pathway for researchers to contribute and vet templates and their associated atlases. Following software engineering best practices, TemplateFlow leverages technologies for unambiguous resource identification, data management, versioning and synchronisation, programmatic extensibility, and continuous integration. By equipping researchers with a robust resource for using and evaluating templates, TemplateFlow will contribute to increasing the reliability of neuroimaging results.
Published: 2021
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14. The Open Brain Consent: Informing research participants and obtaining consent to share brain imaging data

Author: Nicolas Pascual Leone Espinosa, Matteo Visconti di Oleggio Castello, Stephan Heunis, Camille Maumet, Peer Herholz, Radim Jančálek, Cheng Wang, Vasileios K. Katsaros, Cyril Pernet, Henk-Jan Mutsaerts, Yaroslav O. Halchenko, Elise Bannier, Robert Oostenveld, Clara Moreau, Marko Havu, Nils Broeckx, Fengjuan Wang, Chuan-Peng Hu, Patricia Clement, Yue Hu, Dorien C. Huijser, Esin Ozturk-Isik, Paule-Joanne Toussaint, Satrajit S. Ghosh, Marie-Luise Kieseler, Enrico Glerean, Francesca B. Pizzini, Hua Zhu, Valentina Borghesani, John Pellman, María de la Iglesia Vayá, Krzysztof J. Gorgolewski, Gareth J. Barker, Kyrre E. Emblem, Anne Hespel, Amira Serifovic Trbalic, Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Département de Radiologie [Rennes], Université de Rennes (UR), King‘s College London, University of California [San Francisco] (UC San Francisco), University of California (UC), University of Antwerp (UA), Universiteit Gent = Ghent University (UGENT), Oslo University Hospital [Oslo], Massachusetts Institute of Technology (MIT), Harvard Medical School [Boston] (HMS), Aalto University, Vysšaja škola èkonomiki = National Research University Higher School of Economics [Moscow] (HSE), Stanford University, Dartmouth College [Hanover], McConnell Brain Imaging Centre (MNI), Montreal Neurological Institute and Hospital, McGill University = Université McGill [Montréal, Canada]-McGill University = Université McGill [Montréal, Canada], Centre Hospitalier Universitaire [Rennes], Eindhoven University of Technology [Eindhoven] (TU/e), Heinrich Heine Universität Düsseldorf = Heinrich Heine University [Düsseldorf], Nanjing Normal University (NNU), Erasmus University Rotterdam, Fundación para el Fomento de la Investigación Sanitaria y Biomédica de la Comunitat Valenciana [Espagne] (FISABIO), St. Anne’s University Hospital [Brno], National and Kapodistrian University of Athens (NKUA), Neuroimagerie: méthodes et applications (Empenn), Institut National de la Santé et de la Recherche Médicale (INSERM)-Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE (IRISA-D5), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Pasteur [Paris] (IP), VU University Medical Center [Amsterdam], Radboud University [Nijmegen], Boǧaziçi üniversitesi = Boğaziçi University [Istanbul], Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University [New York], University of Edinburgh, Università degli studi di Verona = University of Verona (UNIVR), Univerzitet u Tuzli [Tuzla, Bosnie-Herzégovine], McGill University = Université McGill [Montréal, Canada], The Helen Wills Neuroscience Institute (HWNI), University of California [Berkeley] (UC Berkeley), University of California (UC)-University of California (UC), Nanyang Technological University [Singapour], Fujian Normal University [Fujian], Beihang University (BUAA), European Cooperation in Science and Technology, Grant/Award Number: CA‐18206, National Institute of Health, Grant/Award Number: NIH‐NIBIB P41 EB019936, National Science Foundation, Grant/Award Number: NSF 1429999, EU INTER‐EXCELLENCE program, Grant/Award Number: LTC20027, NIMH, Grant/Award Number: R01MH096906, International Laboratory of Social Neurobiology ICN HSE RF, Grant/Award Number: 075–15–2019–1930, Maumet, Camille, Radiology and nuclear medicine, Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-CentraleSupélec-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES), University of California [San Francisco] (UCSF), University of California, Universiteit Gent = Ghent University [Belgium] (UGENT), National Research University Higher School of Economics [Moscow] (HSE), Empenn, Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Institut Pasteur [Paris], Radboud university [Nijmegen], Boğaziçi University [Istanbul], University of Verona (UNIVR), University of California [Berkeley], University of California-University of California, Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes 1 (UR1), and Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique)
Subjects: Research Subjects, Internet privacy, Context (language use), Neuroimaging, brain imaging, ComputingMilieux_LEGALASPECTSOFCOMPUTING, 050105 experimental psychology, general data protection regulation, 03 medical and health sciences, 0302 clinical medicine, Informed consent, Obtaining consent, Humans, 0501 psychology and cognitive sciences, Radiology, Nuclear Medicine and imaging, 030304 developmental biology, Computer. Automation, 0303 health sciences, Radiological and Ultrasound Technology, business.industry, Information Dissemination, [SCCO.NEUR]Cognitive science/Neuroscience, 05 social sciences, informed consent, [SCCO.NEUR] Cognitive science/Neuroscience, Brain, Data sharing, Editorial, Neurology, General Data Protection Regulation, Key (cryptography), Neurology (clinical), Human research, Human medicine, Anatomy, Psychology, business, Consent Forms, 030217 neurology & neurosurgery
Abstract: Online ahead of print; International audience; Having the means to share research data openly is essential to modern science. For human research, a key aspect in this endeavor is obtaining consent from participants, not just to take part in a study, which is a basic ethical principle, but also to share their data with the scientific community. To ensure that the participants' privacy is respected, national and/or supranational regulations and laws are in place. It is, however, not always clear to researchers what the implications of those are, nor how to comply with them. The Open Brain Consent (https://open-brain-consent.readthedocs.io) is an international initiative that aims to provide researchers in the brain imaging community with information about data sharing options and tools. We present here a short history of this project and its latest developments, and share pointers to consent forms, including a template consent form that is compliant with the EU general data protection regulation. We also share pointers to an associated data user agreement that is not only useful in the EU context, but also for any researchers dealing with personal (clinical) data elsewhere.
Published: 2020
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15. Variability in the analysis of a single neuroimaging dataset by many teams

Author: Richard C. Reynolds, Jeremy Hogeveen, Joseph T. McGuire, Vittorio Iacovella, Gregory R. Samanez-Larkin, Cemal Koba, Paolo Avesani, Mikella A. Green, Margaret L. Schlichting, Claudio Toro-Serey, Tristan Glatard, Phui Cheng Lim, Emily A. Yearling, Simon B. Eickhoff, Katherine L. Bottenhorn, Susanne Weis, Andrew Erhart, Doris Pischedda, Mauricio R. Delgado, William A. Cunningham, Robert Langner, Matthew B. Wall, Amr Eed, Jorge Moll, Jeffrey B. Dennison, Wouter D. Weeda, Annabel B. Losecaat Vermeer, Anthony C. Juliano, Felix Hoffstaedter, Julia A. Camilleri, Nadège Bault, Cristian Buc Calderon, Xu Zhang, Gustav Tinghög, Vuong Truong, Leah Bakst, Stephan Heunis, Bertrand Thirion, Colin F. Camerer, Bradley C. Love, Tom Verguts, Luca Cecchetti, Patricia A. Reuter-Lorenz, Camille Maumet, Gabrielle Herman, Giacomo Handjaras, Michalis Kassinopoulos, Anthony Romyn, Xiangzhen Kong, Michael Notter, Nina Lauharatanahirun, Claire Donnat, Norberto Malpica, Jelle J. Goeman, Kamalaker Dadi, Michael Mack, Joke Durnez, Alberto De Luca, Margaret A. Sheridan, Shruti Ray, Colin Hawco, Enrico Glerean, J. Paul Hamilton, Qiang Shen, Sebastian Bobadilla-Suarez, Andrea Leo, David Wisniewski, Ayse Ilkay Isik, Lei Zhang, Bharat B. Biswal, Rotem Botvinik-Nezer, Flora Li, Bronson Harry, Andrew Jahn, Elise Lesage, Jean M. Vettel, Marco Barilari, Adrian I. Onicas, Susan Holmes, Khoi Vo, Aahana Bajracharya, Matthew Hughes, Rui Yuan, Nuri Erkut Kucukboyaci, Remi Gau, Sangsuk Yoon, Hongmi Lee, Alexandru D. Iordan, John Thorp, Russell A. Poldrack, Anna Dreber, Joshua E. Zosky, Dylan M. Nielson, Adriana S. Méndez Leal, Erin W. Dickie, Roeland Hancock, Tiago Bortolini, Kaustubh R. Patil, Monica Y.C. Li, Kristin N. Meyer, Fu Shiguang, Kenny Skagerlund, Juan Carlos de la Torre, Jennifer A. Silvers, R. Alison Adcock, Gustav Nilsonne, Zachary J. Cole, Emilio Sanz-Morales, Charles P. Davis, Michael Joseph, Sangil Lee, Tom Johnstone, Peder M. Isager, Jonathan E. Peelle, Emiliano Ricciardi, Scott A. Huettel, G. Matthew Fricke, Leonardo Tozzi, Brice A. Kuhl, Sagana Vijayarajah, Alexander Bowring, Jeanette A. Mumford, Blazej M. Baczkowski, Shabnam Hakimi, Sebastian Kupek, Michael Kirchler, Yanina Prystauka, Niall W. Duncan, Anna van 't Veer, Emily G. Brudner, Jamil P. Bhanji, Sarah M. Tashjian, Senne Braem, Paolo Papale, Simon R. Steinkamp, Georgios D. Mitsis, Kenneth S. L. Yuen, Magnus Johannesson, David J. White, Alec Smith, Krzysztof J. Gorgolewski, Alexander Leemans, Robert W. Cox, Emanuele Olivetti, Luca Turella, Juergen Huber, Tom Schonberg, David V. Smith, Lysia Demetriou, Peter Sokol-Hessner, Juergen Dukart, Glad Mihai, Taylor Salo, Jean-Baptiste Poline, Angela R. Laird, Sergej A.E. Golowin, Sarah Genon, Adriana Galván, Claus Lamm, Joseph W. Kable, Roni Iwanir, Theo Marins, Matthew R. Johnson, Anais Rodriguez-Thompson, Benjamin Meyer, Stefan Czoschke, Loreen Tisdall, Douglas H. Schultz, Evan N. Lintz, Derek Beaton, Peer Herholz, Olivia Guest, Jenny R. Rieck, Laura Fontanesi, Ekaterina Dobryakova, Alexandre Pérez, Olivier Collignon, Xin Di, Ruud Berkers, Sheryl B. Ball, Carlos González-García, Schuyler W. Liphardt, Edna C. Cieslik, Roland G. Benoit, João F. Guassi Moreira, Helena Melero, Jaime J. Castrellon, Hayley R. Brooks, Chuan-Peng Hu, Felix Holzmeister, Cheryl L. Grady, Thomas E. Nichols, Alba Xifra-Porxas, Steven H. Tompson, Kelsey McDonald, Julia Beitner, Timothy R. Koscik, Tel Aviv University (TAU), Dartmouth College [Hanover], Leopold Franzens Universität Innsbruck - University of Innsbruck, California Institute of Technology (CALTECH), Stockholm School of Economics (SSE), University of Wisconsin-Madison, Duke University [Durham], Fondazione Bruno Kessler [Trento, Italy] (FBK), University of Trento [Trento], Max Planck Institute for Human Cognitive and Brain Sciences [Leipzig] (IMPNSC), Max-Planck-Gesellschaft, Washington University in Saint Louis (WUSTL), Boston University [Boston] (BU), Virginia Tech [Blacksburg], Université Catholique de Louvain = Catholic University of Louvain (UCL), Plymouth University, Rotman Research Institute at the Baycrest Centre (RRI), University of Amsterdam [Amsterdam] (UvA), Goethe-University Frankfurt am Main, Rutgers University [Newark], Rutgers University System (Rutgers), New Jersey Institute of Technology [Newark] (NJIT), University of Electronic Science and Technology of China [Chengdu] (UESTC), University College of London [London] (UCL), D'Or Institute for Research and Education [Rio de Janeiro], Florida International University [Miami] (FIU), University of Oxford, Vrije Universiteit Brussel (VUB), Universiteit Gent = Ghent University (UGENT), University of Denver, Heinrich Heine Universität Düsseldorf = Heinrich Heine University [Düsseldorf], Jülich Research Centre, School for Advanced Studies Lucca (IMT), University of Nebraska–Lincoln, University of Nebraska System, National Institutes of Health [Bethesda] (NIH), University of Toronto, Modelling brain structure, function and variability based on high-field MRI data (PARIETAL), Service NEUROSPIN (NEUROSPIN), Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Lancashire Teaching Hospital, University of Connecticut (UCONN), University Medical Center [Utrecht], Imperial College London, Temple University [Philadelphia], Pennsylvania Commonwealth System of Higher Education (PCSHE), Kessler Foundation [East Hanover], Stanford University, Taipei Medical University, TMU-ShuangHo Hospital [New Taipei City], Instituto de Neurociencias de Alicante, University of Basel (Unibas), The University of New Mexico [Albuquerque], Zhejiang University of Technology, University of California [Los Angeles] (UCLA), University of California (UC), Université catholique de Lille (UCL), Concordia University [Montreal], Aalto University, Leiden University Medical Center (LUMC), Universiteit Leiden, Linköping University (LIU), Western Sydney University, Centre for Addiction and Mental Health [Toronto] (CAMH), McGill University = Université McGill [Montréal, Canada], Eindhoven University of Technology [Eindhoven] (TU/e), Epilepsy Centre Kempenhaeghe [Heeze], Leibniz-Institut für Resilienzforschung [Mainz] (LIR), Swinburne University of Technology (Hawthorn campus), University of Michigan [Ann Arbor], University of Michigan System, Max Planck Institute for Empirical Aesthetics [Frankfurt am Main], University of Pennsylvania, Max Planck Institute for Psycholinguistics, Carver College of Medicine, University of Iowa, Rutgers New Jersey Medical School (NJMS), University of Oregon [Eugene], University of Vienna [Vienna], Johns Hopkins University (JHU), Nanjing University (NJU), Haskins Laboratories, The Alan Turing Institute, Universidad Rey Juan Carlos [Madrid] (URJC), Neuroimagerie: méthodes et applications (Empenn), Institut National de la Santé et de la Recherche Médicale (INSERM)-Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE (IRISA-D5), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), University of North Carolina [Chapel Hill] (UNC), University of North Carolina System (UNC), Technische Universität Dresden = Dresden University of Technology (TU Dresden), Karolinska Institutet [Stockholm], Stockholm University, Université de Lausanne = University of Lausanne (UNIL), Netherlands Institute for Neuroscience (NIN), Royal Netherlands Academy of Arts and Sciences (KNAW), Charité - UniversitätsMedizin = Charité - University Hospital [Berlin], Humboldt University Of Berlin, Technical University of Berlin / Technische Universität Berlin (TU), University of California [Berkeley] (UC Berkeley), Columbia University [New York], Human Research and Engineering Directorate [Aberdeen Proving Ground], Stanford School of Medicine [Stanford], Stanford Medicine, Stanford University-Stanford University, University of California [Santa Barbara] (UC Santa Barbara), University of Dayton, Maumet, Camille, Technische Universität Berlin (TU), Leiden University, European Commission, Fundación 'la Caixa', Tel Aviv University [Tel Aviv], University of Innsbruck, University of Electronic Science and Technology of China (UESTC), University of Oxford [Oxford], Universiteit Gent = Ghent University [Belgium] (UGENT), University of Nebraska [Lincoln], Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Service NEUROSPIN (NEUROSPIN), Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay, University of California, University of Pennsylvania [Philadelphia], Empenn, Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-CentraleSupélec-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Université de Lausanne (UNIL), Humboldt-Universität zu Berlin, University of California [Berkeley], University of California [Santa Barbara] (UCSB), Signal Processing Systems, and Human Technology Interaction
Subjects: Data Analysis, Male, Scientific community, Computer science, Decision, Datasets as Topic, Variation (game tree), Field (computer science), 0302 clinical medicine, Multidisciplinary approach, Data Science/methods, Models, Meta-Analysis as Topic, 0303 health sciences, Multidisciplinary, medicine.diagnostic_test, Brain, Magnetic Resonance Imaging, Research Personnel, Datasets as Topic/statistics & numerical data, Neurological, Female, ddc:500, Research Personnel/organization & administration, Models, Neurological, BF, Article, 03 medical and health sciences, Neuroimaging, Functional neuroimaging, Human behaviour, medicine, Humans, 030304 developmental biology, Statistical hypothesis testing, Flexibility (engineering), [SCCO.NEUR]Cognitive science/Neuroscience, Functional Neuroimaging, [SCCO.NEUR] Cognitive science/Neuroscience, Data Science, Reproducibility of Results, Brain/diagnostic imaging, Pipeline (software), Data science, Workflow, Logistic Models, Functional magnetic resonance imaging, Decision making, 030217 neurology & neurosurgery, Software
Abstract: Botvinik-Nezer, Rotem et al., Data analysis workflows in many scientific domains have become increasingly complex and flexible. Here we assess the effect of this flexibility on the results of functional magnetic resonance imaging by asking 70 independent teams to analyse the same dataset, testing the same 9 ex-ante hypotheses. The flexibility of analytical approaches is exemplified by the fact that no two teams chose identical workflows to analyse the data. This flexibility resulted in sizeable variation in the results of hypothesis tests, even for teams whose statistical maps were highly correlated at intermediate stages of the analysis pipeline. Variation in reported results was related to several aspects of analysis methodology. Notably, a meta-analytical approach that aggregated information across teams yielded a significant consensus in activated regions. Furthermore, prediction markets of researchers in the field revealed an overestimation of the likelihood of significant findings, even by researchers with direct knowledge of the dataset. Our findings show that analytical flexibility can have substantial effects on scientific conclusions, and identify factors that may be related to variability in the analysis of functional magnetic resonance imaging. The results emphasize the importance of validating and sharing complex analysis workflows, and demonstrate the need for performing and reporting multiple analyses of the same data. Potential approaches that could be used to mitigate issues related to analytical variability are discussed., European Union’s Horizon 2020 research and innovation programme (https://ec.europa.eu/programmes/horizon2020/en) under the Marie Skłodowska-Curie grant agreement no. 665501. A. Eed was supported by a predoctoral fellowship La Caixa-Severo Ochoa from Obra Social La Caixa. S.B.E. was supported by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement no. 785907 (HBP SGA2).
Published: 2020
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16. iEEG-BIDS, extending the Brain Imaging Data Structure specification to human intracranial electrophysiology

Author: Olivier David, Rob Knight, Christopher J. Honey, Chris Holdgraf, Jean-Philippe Lachaux, Andrea Pigorini, Sasha D'Ambrosio, Nick F. Ramsey, Brian A. Wandell, Liberty S. Hamilton, Christopher Lee-Messer, Nader Pouratian, Jonathan Winawer, Kristofer E. Bouchard, Lyuba Zehl, Benjamin Dichter, Jeffrey G. Ojemann, Dora Hermes, Orrin Devinsky, Bradley Voytek, Arjen Stolk, David M. Groppe, Jonathan C. Lau, Natalia Petridou, Krzysztof J. Gorgolewski, Nicole C. Swann, Robert Oostenveld, François Tadel, Stefan Appelhoff, Gio Piantoni, Brian Nils Lundstrom, Adeen Flinker, Iris I. A. Groen, Mainak Jas, Kai J. Miller, Kirstie Whitaker, Stephan Bickel, Aysegul Gunduz, Brett L. Foster, Appelhoff, Stefan [0000-0001-8002-0877], D'Ambrosio, Sasha [0000-0002-6600-6419], Devinsky, Orrin [0000-0003-0044-4632], Flinker, Adeen [0000-0003-1247-1283], Gorgolewski, Krzysztof J [0000-0003-3321-7583], Groen, Iris [0000-0002-5536-6128], Hamilton, Liberty [0000-0003-0182-2500], Lundstrom, Brian N [0000-0002-5310-5549], Oostenveld, Robert [0000-0002-1974-1293], Piantoni, Gio [0000-0002-5308-926X], Ramsey, Nick F [0000-0002-7136-259X], Stolk, Arjen [0000-0003-3798-4923], Winawer, Jonathan [0000-0001-7475-5586], Zehl, Lyuba [0000-0002-5947-9939], and Apollo - University of Cambridge Repository
Subjects: Statistics and Probability, 010504 meteorology & atmospheric sciences, Computer science, 1.1 Normal biological development and functioning, Datasets as Topic, Information Storage and Retrieval, Neuroimaging, Library and Information Sciences, computer.software_genre, 01 natural sciences, Intracranial Electroencephalography, Data publication and archiving, Education, 03 medical and health sciences, Underpinning research, medicine, Humans, lcsh:Science, 030304 developmental biology, 0105 earth and related environmental sciences, 0303 health sciences, Comment, Neurosciences, Brain, Cognitive neuroscience, Electroencephalography, Human brain, Data structure, Brain Disorders, Computer Science Applications, Metadata, Electrophysiology, medicine.anatomical_structure, Temporal resolution, Neurological, lcsh:Q, Data mining, ddc:500, Statistics, Probability and Uncertainty, computer, Software, Information Systems
Abstract: The Brain Imaging Data Structure (BIDS) is a community-driven specification for organizing neuroscience data and metadata with the aim to make datasets more transparent, reusable, and reproducible. Intracranial electroencephalography (iEEG) data offer a unique combination of high spatial and temporal resolution measurements of the living human brain. To improve internal (re)use and external sharing of these unique data, we present a specification for storing and sharing iEEG data: iEEG-BIDS.
Published: 2019
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17. Crowdsourced MRI quality metrics and expert quality annotations for training of humans and machines

Author: Krzysztof J. Gorgolewski, Dylan M. Nielson, Jan C. Varada, Sean Marrett, Oscar Esteban, Russell A. Poldrack, Ross Blair, and Adam G. Thomas
Subjects: Statistics and Probability, Data Descriptor, 010504 meteorology & atmospheric sciences, Databases, Factual, Computer science, Image quality, media_common.quotation_subject, Neuroimaging, Library and Information Sciences, Machine learning, computer.software_genre, Crowdsourcing, Web API, 01 natural sciences, 030218 nuclear medicine & medical imaging, Education, Machine Learning, 03 medical and health sciences, Databases, 0302 clinical medicine, Resource (project management), Magnetic resonance imaging, Image processing, Education, Professional, Visual assessment, Humans, Quality (business), lcsh:Science, 030304 developmental biology, 0105 earth and related environmental sciences, media_common, 0303 health sciences, Information retrieval, business.industry, Brain, Large sample, Computer Science Applications, Computational neuroscience, lcsh:Q, Artificial intelligence, Mr images, Statistics, Probability and Uncertainty, business, computer, 030217 neurology & neurosurgery, Information Systems
Abstract: The neuroimaging community is steering towards increasingly large sample sizes, which are highly heterogeneous because they can only be acquired by multi-site consortia. The visual assessment of every imaging scan is a necessary quality control step, yet arduous and time-consuming. A sizeable body of evidence shows that images of low quality are a source of variability that may be comparable to the effect size under study. We present the MRIQC Web-API, an open crowdsourced database that collects image quality metrics extracted from MR images and corresponding manual assessments by experts. The database is rapidly growing, and currently contains over 100,000 records of image quality metrics of functional and anatomical MRIs of the human brain, and over 200 expert ratings. The resource is designed for researchers to share image quality metrics and annotations that can readily be reused in training human experts and machine learning algorithms. The ultimate goal of the database is to allow the development of fully automated quality control tools that outperform expert ratings in identifying subpar images., Design Type(s)data validation objective • source-based data transformation objective • anatomical image analysis objectiveMeasurement Type(s)Image QualityTechnology Type(s)digital curationFactor Type(s)Sample Characteristic(s)Homo sapiens • brain Machine-accessible metadata file describing the reported data (ISA-Tab format)
Published: 2019
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18. Sharing voxelwise neuroimaging results from rhesus monkeys and other species with Neurovault

Author: Colin T. Kyle, J. Andrew Pruszynski, Russell A. Poldrack, Jakob Seidlitz, Krzysztof J. Gorgolewski, Peter Christiaan Klink, Xu Feng Zhou, Daniel Holley, Sze Chai Kwok, Jörn Diedrichsen, Carol A. Barnes, Spencer A. Arbuckle, Andrew S. Fox, and Netherlands Institute for Neuroscience (NIN)
Subjects: Aging, Databases, Factual, Computer science, Cognitive Neuroscience, Brain Structure and Function, Neuroimaging, Medical and Health Sciences, 050105 experimental psychology, Article, lcsh:RC321-571, 03 medical and health sciences, Databases, 0302 clinical medicine, Resource (project management), Nonhuman primate, Psychology, Animals, 0501 psychology and cognitive sciences, lcsh:Neurosciences. Biological psychiatry. Neuropsychiatry, Factual, Neurology & Neurosurgery, Animal, Information Dissemination, Functional Neuroimaging, 05 social sciences, Psychology and Cognitive Sciences, fMRI, Neurosciences, Brain, Data science, Macaca mulatta, Magnetic Resonance Imaging, PET, Neurology, Positron-Emission Tomography, Neurological, Data sharing, 030217 neurology & neurosurgery
Abstract: © 2020 The Authors Animal neuroimaging studies can provide unique insights into brain structure and function, and can be leveraged to bridge the gap between animal and human neuroscience. In part, this power comes from the ability to combine mechanistic interventions with brain-wide neuroimaging. Due to their phylogenetic proximity to humans, nonhuman primate neuroimaging holds particular promise. Because nonhuman primate neuroimaging studies are often underpowered, there is a great need to share data amongst translational researchers. Data sharing efforts have been limited, however, by the lack of standardized tools and repositories through which nonhuman neuroimaging data can easily be archived and accessed. Here, we provide an extension of the Neurovault framework to enable sharing of statistical maps and related voxelwise neuroimaging data from other species and template-spaces. Neurovault, which was previously limited to human neuroimaging data, now allows researchers to easily upload and share nonhuman primate neuroimaging results. This promises to facilitate open, integrative, cross-species science while affording researchers the increased statistical power provided by data aggregation. In addition, the Neurovault code-base now enables the addition of other species and template-spaces. Together, these advances promise to bring neuroimaging data sharing to research in other species, for supplemental data, location-based atlases, and data that would otherwise be relegated to a "file-drawer". As increasing numbers of researchers share their nonhuman neuroimaging data on Neurovault, this resource will enable novel, large-scale, cross-species comparisons that were previously impossible.
Published: 2020

19. EEG-BIDS, an extension to the brain imaging data structure for electroencephalography

Author: Krzysztof J. Gorgolewski, Cyril Pernet, Robert Oostenveld, Guillaume Flandin, Arnaud Delorme, Christophe Phillips, and Stefan Appelhoff
Subjects: Statistics and Probability, 010504 meteorology & atmospheric sciences, Computer science, Datasets as Topic, Information Storage and Retrieval, Neuroimaging, Library and Information Sciences, Electroencephalography, Machine learning, computer.software_genre, Data publication and archiving, 01 natural sciences, 150 000 MR Techniques in Brain Function, Education, 03 medical and health sciences, Research community, medicine, Humans, lcsh:Science, 030304 developmental biology, 0105 earth and related environmental sciences, 0303 health sciences, medicine.diagnostic_test, business.industry, Comment, Brain, Extension (predicate logic), Data structure, Magnetic Resonance Imaging, Computer Science Applications, ComputingMethodologies_PATTERNRECOGNITION, Research management, lcsh:Q, Human brain imaging, Artificial intelligence, Statistics, Probability and Uncertainty, business, computer, Information Systems
Abstract: Contains fulltext : 204554.pdf (Publisher’s version ) (Open Access) The Brain Imaging Data Structure (BIDS) project is a rapidly evolving effort in the human brain imaging research community to create standards allowing researchers to readily organize and share study data within and between laboratories. Here we present an extension to BIDS for electroencephalography (EEG) data, EEG-BIDS, along with tools and references to a series of public EEG datasets organized using this new standard. 5 p.
Published: 2019
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20. The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments

Author: Eugene P. Duff, Gunnar Schaefer, R. Cameron Craddock, Xiangrui Li, Vince D. Calhoun, Tibor Auer, Michael Hanke, David Keator, Camille Maumet, Ariel Rokem, Thomas E. Nichols, Jean-Baptiste Poline, Daniel A. Handwerker, Guillaume Flandin, Yaroslav O. Halchenko, Vanessa Sochat, Samir Das, Satrajit S. Ghosh, Gaël Varoquaux, Zachary Michael, Russell A. Poldrack, Tristan Glatard, B. Nolan Nichols, John Pellman, William Triplett, Jessica A. Turner, Krzysztof J. Gorgolewski, Stanford University, University of Cambridge [UK] (CAM), Department of Electrical and Computer Engineering [Albuquerque] (ECE Department), The University of New Mexico [Albuquerque], The Mind Research Network, Nathan S. Kline Institute for Psychiatric Research (NKI), New York State Office of Mental Health, McGill University = Université McGill [Montréal, Canada], Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), University of Oxford, Wellcome Trust Centre for Neuroimaging, University College of London [London] (UCL), Institut de Recherches sur les lois Fondamentales de l'Univers (IRFU), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay, Images et Modèles, Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Department of Psychological and Brain Sciences, Dartmouth College [Hanover], National Institute of Mental Health (NIMH), Otto-von-Guericke-Universität Magdeburg = Otto-von-Guericke University [Magdeburg] (OVGU), University of California [Irvine] (UC Irvine), University of California (UC), Shandong University, Squishymedia [Portland], Warwick Manufacturing Group [Coventry] (WMG), University of Warwick [Coventry], Silsoe Research Institute (SRI), Biotechnology and Biological Sciences Research Council (BBSRC), Department of Statistics [Warwick], Child Mind Institute, The Helen Wills Neuroscience Institute (HWNI), University of California [Berkeley] (UC Berkeley), University of California (UC)-University of California (UC), Lawrence Berkeley National Laboratory [Berkeley] (LBNL), University of Washington [Seattle], Georgia State University, University System of Georgia (USG), Service NEUROSPIN (NEUROSPIN), Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Modelling brain structure, function and variability based on high-field MRI data (PARIETAL), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), California Institute of Technology (CALTECH), Maumet, Camille, University of Oxford [Oxford], Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM), Otto-von-Guericke University [Magdeburg] (OVGU), University of California [Irvine] (UCI), University of California, Biotechnology and Biological Sciences Research Council, University of California [Berkeley], University of California-University of California, Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay, Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Service NEUROSPIN (NEUROSPIN), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), and Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)
Subjects: 0301 basic medicine, Statistics and Probability, Computer science, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Datasets as Topic, Neuroimaging, Library and Information Sciences, computer.software_genre, Data publication and archiving, Field (computer science), Article, Education, 03 medical and health sciences, 0302 clinical medicine, Software, Humans, [SDV.NEU] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Data processing, business.industry, Data Collection, Neurosciences, Data structure, File format, Magnetic Resonance Imaging, R1, Brain Disorders, Research data, Computer Science Applications, Metadata, 030104 developmental biology, Neurological, Biomedical Imaging, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Data mining, Statistics, Probability and Uncertainty, business, computer, 030217 neurology & neurosurgery, Information Systems
Abstract: The development of magnetic resonance imaging (MRI) techniques has defined modern neuroimaging. Since its inception, tens of thousands of studies using techniques such as functional MRI and diffusion weighted imaging have allowed for the non-invasive study of the brain. Despite the fact that MRI is routinely used to obtain data for neuroscience research, there has been no widely adopted standard for organizing and describing the data collected in an imaging experiment. This renders sharing and reusing data (within or between labs) difficult if not impossible and unnecessarily complicates the application of automatic pipelines and quality assurance protocols. To solve this problem, we have developed the Brain Imaging Data Structure (BIDS), a standard for organizing and describing MRI datasets. The BIDS standard uses file formats compatible with existing software, unifies the majority of practices already common in the field, and captures the metadata necessary for most common data processing operations.
Published: 2018
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21. MEG-BIDS, the brain imaging data structure extended to magnetoencephalography

Author: François Tadel, Sylvain Baillet, Robert Oostenveld, Elizabeth Bock, Vladimir Litvak, Julia Guiomar Niso Galan, Jeremy T. Moreau, Guillaume Flandin, Mainak Jas, Richard N. Henson, Jan-Mathijs Schoffelen, Joseph Wexler, Krzysztof J. Gorgolewski, Alexandre Gramfort, Teon L. Brooks, Apollo-University Of Cambridge Repository, Gorgolewski, Krzysztof J [0000-0003-3321-7583], Brooks, Teon L [0000-0001-7344-3230], Henson, Richard N [0000-0002-0712-2639], Oostenveld, Robert [0000-0002-1974-1293], Baillet, Sylvain [0000-0002-6762-5713], and Apollo - University of Cambridge Repository
Subjects: 0301 basic medicine, Statistics and Probability, 110 000 Neurocognition of Language, Computer science, Brain activity and meditation, Medicina, Neuroimaging, Library and Information Sciences, 150 000 MR Techniques in Brain Function, Education, 03 medical and health sciences, 0302 clinical medicine, Software, medicine, Humans, Telecomunicaciones, Modality (human–computer interaction), Information retrieval, medicine.diagnostic_test, business.industry, Comment, Brain, Magnetoencephalography, Data structure, Computer Science Applications, Research data, Metadata, Electrophysiology, 030104 developmental biology, Feature (computer vision), Statistics, Probability and Uncertainty, business, 030217 neurology & neurosurgery, Information Systems, Neuroscience
Abstract: Contains fulltext : 202961.pdf (Publisher’s version ) (Open Access) We present a significant extension of the Brain Imaging Data Structure (BIDS) to support the specific aspects of magnetoencephalography (MEG) data. MEG measures brain activity with millisecond temporal resolution and unique source imaging capabilities. So far, BIDS was a solution to organise magnetic resonance imaging (MRI) data. The nature and acquisition parameters of MRI and MEG data are strongly dissimilar. Although there is no standard data format for MEG, we propose MEG-BIDS as a principled solution to store, organise, process and share the multidimensional data volumes produced by the modality. The standard also includes well-defined metadata, to facilitate future data harmonisation and sharing efforts. This responds to unmet needs from the multimodal neuroimaging community and paves the way to further integration of other techniques in electrophysiology. MEG-BIDS builds on MRI-BIDS, extending BIDS to a multimodal data structure. We feature several data-analytics software that have adopted MEG-BIDS, and a diverse sample of open MEG-BIDS data resources available to everyone. 12 p.
Published: 2018
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22. Boutiques: a flexible framework to integrate command-line applications in computing platforms

Author: Natacha Beck, M. Rousseau, Rafael Ferreira da Silva, Valerie Hayot-Sasson, Guillaume Flandin, Alan C. Evans, Shawn T. Brown, Axel Bonnet, Tristan Aumentado-Armstrong, Charles R.G. Guttmann, Krzysztof J. Gorgolewski, Pierre Rioux, Pierre-Olivier Quirion, Samir Das, Tristan Glatard, Sorina Camarasu-Pop, Gregory Kiar, Frederic Cervenansky, Pierre Bellec, Pascal Girard, Rémi Bernard, Department of Computer Science and Software Engineering [Montreal] (CSE), Concordia University [Montreal], McConnell Brain Imaging Centre (MNI), Montreal Neurological Institute and Hospital, McGill University = Université McGill [Montréal, Canada]-McGill University = Université McGill [Montréal, Canada], Département d'Informatique et de Recherche Opérationnelle [Montreal] (DIRO), Université de Montréal (UdeM), Aix-Marseille Université - Département Sciences, Arts et Techniques des Images et du Son (AMU SATIS), Aix Marseille Université (AMU), Images et Modèles, Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM), Service Informatique et développements, Department of Developmental, Molecular and Chemical Biology, Tufts University School of Medicine [Boston], Wellcome Trust Centre for Neuroimaging, University College of London [London] (UCL), Laboratoire de l'intégration, du matériau au système (IMS), Université Sciences et Technologies - Bordeaux 1-Institut Polytechnique de Bordeaux-Centre National de la Recherche Scientifique (CNRS), Stanford University, Brigham and Women's Hospital [Boston], Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Université Sciences et Technologies - Bordeaux 1 (UB)-Institut Polytechnique de Bordeaux-Centre National de la Recherche Scientifique (CNRS), and Centre National de la Recherche Scientifique (CNRS)-Institut Polytechnique de Bordeaux-Université Sciences et Technologies - Bordeaux 1
Subjects: 0301 basic medicine, Computer science, Neuroimaging, Health Informatics, 03 medical and health sciences, 0302 clinical medicine, Software, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, Technical Note, Redundancy (engineering), Humans, Publication, ComputingMilieux_MISCELLANEOUS, computer.programming_language, business.industry, application integration, Brain, Computational Biology, Reproducibility of Results, Neuroinformatics, neuroinformatics, JSON, Computer Science Applications, 030104 developmental biology, containers, [INFO.INFO-ET]Computer Science [cs]/Emerging Technologies [cs.ET], Software engineering, business, computer, 030217 neurology & neurosurgery
Abstract: We present Boutiques, a system to automatically publish, integrate, and execute command-line applications across computational platforms. Boutiques applications are installed through software containers described in a rich and flexible JSON language. A set of core tools facilitates the construction, validation, import, execution, and publishing of applications. Boutiques is currently supported by several distinct virtual research platforms, and it has been used to describe dozens of applications in the neuroinformatics domain. We expect Boutiques to improve the quality of application integration in computational platforms, to reduce redundancy of effort, to contribute to computational reproducibility, and to foster Open Science.
Published: 2018
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23. Reward learning over weeks versus minutes increases the neural representation of value in the human brain

Author: Krzysztof J. Gorgolewski, Jamie K. Li, G. Elliott Wimmer, and Russell A. Poldrack
Subjects: Adult, Male, 0301 basic medicine, Time Factors, Adolescent, media_common.quotation_subject, education, Prefrontal Cortex, Hippocampus, Neuroimaging, Session (web analytics), Temporal lobe, Young Adult, 03 medical and health sciences, 0302 clinical medicine, Reward, medicine, Humans, Reinforcement learning, Prefrontal cortex, Reinforcement, Research Articles, 030304 developmental biology, media_common, 0303 health sciences, Brain Mapping, Working memory, General Neuroscience, Addiction, medicine.disease, 16. Peace & justice, Magnetic Resonance Imaging, Temporal Lobe, 030104 developmental biology, Mood, Mood disorders, Conditioning, Conditioning, Operant, Female, Psychology, Value (mathematics), Reinforcement, Psychology, Photic Stimulation, 030217 neurology & neurosurgery, Cognitive psychology
Abstract: Over the past few decades, neuroscience research has illuminated the neural mechanisms supporting learning from reward feedback. Learning paradigms are increasingly being extended to study mood and psychiatric disorders as well as addiction. However, one potentially critical characteristic that this research ignores is the effect of time on learning: human feedback learning paradigms are usually conducted in a single rapidly paced session, while learning experiences in ecologically relevant circumstances and in animal research are almost always separated by longer periods of time. In our experiments, we examined reward learning in short condensed sessions distributed across weeks vs. learning completed in a single “massed” session in male and female participants. As expected, we found that after equal amounts of training, accuracy was matched between the spaced and massed conditions. However, in a 3-week follow-up, we found that participants exhibited significantly greater memory for the value of spaced-trained stimuli. Supporting a role for short-term memory in massed learning, we found a significant positive correlation between initial learning and working memory capacity. Neurally, we found that patterns of activity in the medial temporal lobe and prefrontal cortex showed stronger discrimination of spaced-vs. massed-trained reward values. Further, patterns in the striatum discriminated between spaced-and massed-trained stimuli overall. Our results indicate that single-session learning tasks engage partially distinct learning mechanisms from spaced sessions of training. Our studies begin to address a large gap in our knowledge of human learning from reinforcement, with potential implications for our understanding of mood disorders and addiction.Significance statementHumans and animals learn to associate predictive value with stimuli and actions, and these values then guide future behavior. Such reinforcement-based learning often happens over long time periods, in contrast to most studies of reward-based learning in humans. In experiments that tested the effect of spacing on learning, we found that associations learned in a single massed session were correlated with short-term memory and significantly decayed over time, while associations learned in short massed sessions over weeks were well-maintained. Additionally, patterns of activity in the medial temporal lobe and prefrontal cortex discriminated the values of stimuli learned over weeks but not minutes. These results highlight the importance of studying learning over time, with potential applications to drug addiction and psychiatry.
Published: 2018

24. Decoding brain activity using a large-scale probabilistic functional-anatomical atlas of human cognition

Author: Oluwasanmi Koyejo, Tal Yarkoni, Timothy N. Rubin, Michael N. Jones, Krzysztof J. Gorgolewski, and Russell A. Poldrack
Subjects: 0301 basic medicine, Computer science, Brain activity and meditation, Emotions, Social Sciences, computer.software_genre, Diagnostic Radiology, 0302 clinical medicine, Cognition, Learning and Memory, Functional Magnetic Resonance Imaging, Medicine and Health Sciences, Image Processing, Computer-Assisted, Psychology, Biology (General), Language, 0303 health sciences, Brain Mapping, Ecology, Cognitive Neurology, Radiology and Imaging, Brain, Human brain, Magnetic Resonance Imaging, Semantics, medicine.anatomical_structure, Computational Theory and Mathematics, Neurology, Modeling and Simulation, Physical Sciences, symbols, Decoding methods, Research Article, Imaging Techniques, QH301-705.5, Cognitive Neuroscience, Bayesian probability, Models, Neurological, Neuroimaging, Cognitive neuroscience, Machine learning, Research and Analysis Methods, Dirichlet distribution, symbols.namesake, 03 medical and health sciences, Cellular and Molecular Neuroscience, Diagnostic Medicine, Prior probability, medicine, Genetics, Learning, Humans, Molecular Biology, Ecology, Evolution, Behavior and Systematics, 030304 developmental biology, business.industry, Probabilistic logic, Cognitive Psychology, Biology and Life Sciences, Linguistics, Probability Theory, Probability Distribution, 030104 developmental biology, Cognitive Science, Artificial intelligence, business, computer, 030217 neurology & neurosurgery, Mathematics, Neuroscience
Abstract: A central goal of cognitive neuroscience is to decode human brain activity—that is, to infer mental processes from observed patterns of whole-brain activation. Previous decoding efforts have focused on classifying brain activity into a small set of discrete cognitive states. To attain maximal utility, a decoding framework must be open-ended, systematic, and context-sensitive—that is, capable of interpreting numerous brain states, presented in arbitrary combinations, in light of prior information. Here we take steps towards this objective by introducing a probabilistic decoding framework based on a novel topic model—Generalized Correspondence Latent Dirichlet Allocation—that learns latent topics from a database of over 11,000 published fMRI studies. The model produces highly interpretable, spatially-circumscribed topics that enable flexible decoding of whole-brain images. Importantly, the Bayesian nature of the model allows one to “seed” decoder priors with arbitrary images and text—enabling researchers, for the first time, to generate quantitative, context-sensitive interpretations of whole-brain patterns of brain activity., Author summary A central goal of cognitive neuroscience is to decode human brain activity—i.e., to be able to infer mental processes from observed patterns of whole-brain activity. However, existing approaches to brain decoding suffer from a number of important limitations—for example, they often work only in one narrow domain of cognition, and cannot be easily generalized to novel contexts. Here we address such limitations by introducing a simple probabilistic framework based on a novel topic modeling approach. We use our approach to extract a set of highly interpretable latent “topics” from a large meta-analytic database of over 11,000 published fMRI studies. Each topic is associated with a single brain region and a set of semantically coherent cognitive functions. We demonstrate how these topics can be used to automatically “decode” brain activity in an open-ended way, enabling researchers to draw tentative conclusions about mental function on the basis of virtually any pattern of whole-brain activity. We highlight several important features of our framework, notably including the ability to take into account knowledge of the experimental context and/or prior experimenter belief.
Published: 2017

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

Author: Gabriel A. Devenyi, Nathan W. Churchill, Pierre-Olivier Quirion, Tal Yarkoni, Anisha Keshavan, Gregory Kiar, Christopher J. Steele, Stephen C. Strother, Gaël Varoquaux, R. Cameron Craddock, Alexander L. Cohen, J. Swaroop Guntupalli, Mihai Capota, Guillaume Flandin, Robert E. Smith, Oscar Esteban, Pradeep Reddy Raamana, Krzysztof J. Gorgolewski, Tibor Auer, Franziskus Liem, Russell A. Poldrack, Mark Jenkinson, Fidel Alfaro-Almagro, M. Mallar Chakravarty, David Raffelt, Pierre Bellec, Satrajit S. Ghosh, Anders Eklund, Yida Wang, McGovern Institute for Brain Research at MIT, Ghosh, Satrajit S, University of Zurich, and Gorgolewski, Krzysztof J
Subjects: 0301 basic medicine, Computer science, Functional magnetic resonance imaging, 2804 Cellular and Molecular Neuroscience, Diagnostic Radiology, User-Computer Interface, 0302 clinical medicine, Human–computer interaction, Functional Magnetic Resonance Imaging, Medicine and Health Sciences, Software tools, Biology (General), Operating systems, 0303 health sciences, Brain Mapping, Data Processing, Ecology, 10093 Institute of Psychology, Radiology and Imaging, Software Development, Software Engineering, Brain, Magnetic Resonance Imaging, Data processing, Radiology Information Systems, Computational Theory and Mathematics, Proof of concept, Modeling and Simulation, Data analysis, Engineering and Technology, Information Technology, Software versioning, Algorithms, Research Article, Computer and Information Sciences, QH301-705.5, Imaging Techniques, Operating Systems, Brain Morphometry, Medical Engineering, UFSP13-4 Dynamics of Healthy Aging, Neuroimaging, Research and Analysis Methods, 03 medical and health sciences, Cellular and Molecular Neuroscience, Software portability, Magnetic resonance imaging, 1311 Genetics, Diagnostic Medicine, Image Interpretation, Computer-Assisted, 1312 Molecular Biology, Genetics, Humans, Molecular Biology, Simulation, Ecology, Evolution, Behavior and Systematics, Medicinteknik, 030304 developmental biology, business.industry, Software Tools, Diffusion Weighted Imaging, Software development, Information technology, Biology and Life Sciences, Usability, Diffusion weighted imaging, Data structure, 1105 Ecology, Evolution, Behavior and Systematics, 030104 developmental biology, Container (abstract data type), 150 Psychology, business, 2303 Ecology, 030217 neurology & neurosurgery, Software, 2611 Modeling and Simulation, 1703 Computational Theory and Mathematics, Neuroscience
Abstract: The rate of progress in human neurosciences is limited by the inability to easily apply a wide range of analysis methods to the plethora of different datasets acquired in labs around the world. In this work, we introduce a framework for creating, testing, versioning and archiving portable applications for analyzing neuroimaging data organized and described in compliance with the Brain Imaging Data Structure (BIDS). The portability of these applications (BIDS Apps) is achieved by using container technologies that encapsulate all binary and other dependencies in one convenient package. BIDS Apps run on all three major operating systems with no need for complex setup and configuration and thanks to the comprehensiveness of the BIDS standard they require little manual user input. Previous containerized data processing solutions were limited to single user environments and not compatible with most multi-tenant High Performance Computing systems. BIDS Apps overcome this limitation by taking advantage of the Singularity container technology. As a proof of concept, this work is accompanied by 22 ready to use BIDS Apps, packaging a diverse set of commonly used neuroimaging algorithms., United States. National Institutes of Health (NIH-NIBIB R01 EB020740)
Published: 2017

26. Scanning the horizon: towards transparent and reproducible neuroimaging research

Author: Thomas E. Nichols, Krzysztof J. Gorgolewski, Chris I. Baker, Jean-Baptiste Poline, Tal Yarkoni, Marcus R. Munafò, Edward Vul, Russell A. Poldrack, Paul M. Matthews, Joke Durnez, Stanford University, National Institutes of Health [Bethesda] (NIH), Service NEUROSPIN (NEUROSPIN), Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Hammersmith Hospital & Imperial College, University of Bristol [Bristol], Department of Statistics [Warwick], University of Warwick [Coventry], The Helen Wills Neuroscience Institute (HWNI), University of California [Berkeley], University of California-University of California, University of California [San Diego] (UC San Diego), University of California, University of Texas at Austin [Austin], European Project: 706561,H2020 Pilier Excellent Science,H2020-MSCA-IF-2015,Neuroimaging power(2016), University of California [Berkeley] (UC Berkeley), University of California (UC)-University of California (UC), and University of California (UC)
Subjects: HUMAN BRAIN-FUNCTION, Computer science, FLEXIBILITY, Statistics as Topic, Brain and Behaviour, Field (computer science), 0302 clinical medicine, WIDE ASSOCIATION, Cognitive science, medicine.diagnostic_test, General Neuroscience, Tobacco and Alcohol, 05 social sciences, Flexibility (personality), Magnetic Resonance Imaging, Identification (information), NEUROSCIENCE, FMRI, Practice Guidelines as Topic, RELIABILITY, Psychology, Life Sciences & Biomedicine, BEHAVIOR, CORTEX, Best practice, 050105 experimental psychology, 03 medical and health sciences, Neuroimaging, Functional neuroimaging, medicine, Humans, 0501 psychology and cognitive sciences, Spurious relationship, Science & Technology, Neurology & Neurosurgery, IDENTIFICATION, Functional Neuroimaging, [SCCO.NEUR]Cognitive science/Neuroscience, Neurosciences, Reproducibility of Results, 1702 Cognitive Science, Replication (computing), REPRESENTATIONS, Neurosciences & Neurology, 1109 Neurosciences, Functional magnetic resonance imaging, Software, 030217 neurology & neurosurgery
Abstract: International audience; Functional neuroimaging techniques have transformed our ability to probe the neurobiological basis of behaviour and are increasingly being applied by the wider neuroscience community. However, concerns have recently been raised that the conclusions that are drawn from some human neuroimaging studies are either spurious or not generalizable. Problems such as low statistical power, flexibility in data analysis, software errors and a lack of direct replication apply to many fields, but perhaps particularly to functional MRI. Here, we discuss these problems, outline current and suggested best practices, and describe how we think the field should evolve to produce the most meaningful and reliable answers to neuroscientific questions.
Published: 2017
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27. Making big data open: data sharing in neuroimaging

Author: Krzysztof J. Gorgolewski and Russell A. Poldrack
Subjects: Data collection, Databases, Factual, Information Dissemination, business.industry, Data Collection, General Neuroscience, Big data, Brain, Neuroimaging, Neuroinformatics, Magnetic Resonance Imaging, Data sharing, Open data, Functional neuroimaging, Humans, Psychology, business, Neuroscience, Circular analysis
Abstract: In the last decade, major advances have been made in the availability of shared neuroimaging data, such that there are more than 8,000 shared MRI (magnetic resonance imaging) data sets available online. Here we outline the state of data sharing for task-based functional MRI (fMRI) data, with a focus on various forms of data and their relative utility for subsequent analyses. We also discuss challenges to the future success of data sharing and highlight the ethical argument that data sharing may be necessary to maximize the contribution of human subjects.
Published: 2014
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28. Preprocessed Consortium for Neuropsychiatric Phenomics dataset

Author: Joke Durnez, Russell A. Poldrack, and Krzysztof J. Gorgolewski
Subjects: cognition, 0301 basic medicine, Bioinformatics, Neuroimaging, Data Note, computer.software_genre, General Biochemistry, Genetics and Molecular Biology, preprocessed, 03 medical and health sciences, 0302 clinical medicine, Phenomics, Medicine, human, General Pharmacology, Toxicology and Pharmaceutics, Group level, General Immunology and Microbiology, business.industry, fMRI, Subject (documents), Articles, General Medicine, ComputingMethodologies_PATTERNRECOGNITION, 030104 developmental biology, Artificial intelligence, business, Neuroscience, computer, 030217 neurology & neurosurgery, Natural language processing
Abstract: Here we present preprocessed MRI data of 265 participants from the Consortium for Neuropsychiatric Phenomics (CNP) dataset. The preprocessed dataset includes minimally preprocessed data in the native, MNI and surface spaces accompanied with potential confound regressors, tissue probability masks, brain masks and transformations. In addition the preprocessed dataset includes unthresholded group level and single subject statistical maps from all tasks included in the original dataset. We hope that availability of this dataset will greatly accelerate research.
Published: 2017
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29. Sharing brain mapping statistical results with the neuroimaging data model

Author: B. Nolan Nichols, Guillaume Flandin, Satrajit S. Ghosh, Richard C. Reynolds, Karl G. Helmer, Thomas E. Nichols, Tristan Glatard, Samir Das, Alexander Bowring, Vanessa Sochat, Camille Maumet, Krzysztof J. Gorgolewski, Tibor Auer, Mark Jenkinson, Jessica A. Turner, David Keator, Jean-Baptiste Poline, Gang Chen, Warwick Manufacturing Group [Coventry] (WMG), University of Warwick [Coventry], University of Cambridge [UK] (CAM), McConnell Brain Imaging Centre (MNI), Montreal Neurological Institute and Hospital, McGill University = Université McGill [Montréal, Canada]-McGill University = Université McGill [Montréal, Canada], Wellcome Trust Centre for Neuroimaging, University College of London [London] (UCL), Institut de Recherches sur les lois Fondamentales de l'Univers (IRFU), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay, Images et Modèles, Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM), Stanford University, University of Paderborn, University of Oxford [Oxford], University of California [Irvine] (UCI), University of California, Silsoe Research Institute (SRI), Biotechnology and Biological Sciences Research Council, Lawrence Berkeley National Laboratory [Berkeley] (LBNL), The Helen Wills Neuroscience Institute (HWNI), University of California [Berkeley], University of California-University of California, Scientific and Statistical Computing Core [Bethesda, US], National Institutes of Health [Bethesda] (NIH), University of York [York, UK], Department of Statistics [Warwick], Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), University of Oxford, University of California [Irvine] (UC Irvine), University of California (UC), Biotechnology and Biological Sciences Research Council (BBSRC), University of California [Berkeley] (UC Berkeley), University of California (UC)-University of California (UC), McGovern Institute for Brain Research at MIT, Ghosh, Satrajit S, Maumet, Camille, Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), and Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)
Subjects: 0301 basic medicine, Data Interpretation, Computer science, Information Storage and Retrieval, Ontology (information science), computer.software_genre, Brain mapping, 0302 clinical medicine, Software, 0303 health sciences, Brain Mapping, Level of detail (writing), Linear model, Brain, Transparency (human–computer interaction), Statistical, Magnetic Resonance Imaging, Computer Science Applications, Data Interpretation, Statistical, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Data mining, Statistics, Probability and Uncertainty, Information Systems, Statistics and Probability, Bioengineering, Library and Information Sciences, Article, Education, QA76, 03 medical and health sciences, Medical research, Neuroimaging, Meta-Analysis as Topic, Humans, [SDV.NEU] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Representation (mathematics), 030304 developmental biology, Information retrieval, business.industry, Information Dissemination, Neurosciences, Reproducibility of Results, Research data, Metadata, Data model (ArcGIS), 030104 developmental biology, Key (cryptography), Linear Models, business, computer, 030217 neurology & neurosurgery
Abstract: Only a tiny fraction of the data and metadata produced by an fMRI study is finally conveyed to the community. This lack of transparency not only hinders the reproducibility of neuroimaging results but also impairs future meta-analyses. In this work we introduce NIDM-Results, a format specification providing a machine-readable description of neuroimaging statistical results along with key image data summarising the experiment. NIDM-Results provides a unified representation of mass univariate analyses including a level of detail consistent with available best practices. This standardized representation allows authors to relay methods and results in a platform-independent regularized format that is not tied to a particular neuroimaging software package. Tools are available to export NIDM-Result graphs and associated files from the widely used SPM and FSL software packages, and the NeuroVault repository can import NIDM-Results archives. The specification is publically available at: http://nidm.nidash.org/specs/nidm-results.html., National Institutes of Health (U.S.) (Grant 1R01EB020740-01A1), National Institutes of Health (U.S.) (Grant 1P41EB019936-01A)
Published: 2016
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30. Integrating the Brain Imaging Data Structure (BIDS) standard into C-PAC

Author: Krzysztof J. Gorgolewski, Daniel Clark, and R. Cameron Craddock
Subjects: Neuroimaging, Computer science, business.industry, Health Informatics, Pattern recognition, Artificial intelligence, Data structure, business, Computer Science Applications
Published: 2016
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31. A phenome-wide examination of neural and cognitive function

Author: Russell A. Poldrack, Edythe D. London, Tyrone D. Cannon, Katherine H. Karlsgodt, Eliza Congdon, Robert M. Bilder, Fred W. Sabb, Krzysztof J. Gorgolewski, William Triplett, Jeanette A. Mumford, and Nelson B. Freimer
Subjects: 0301 basic medicine, Male, Data Descriptor, Bipolar Disorder, Functional magnetic resonance imaging, Audiology, Long-term memory, 030218 nuclear medicine & medical imaging, Drug Abuse, Phenomics, 0302 clinical medicine, Cognition, Task Performance and Analysis, 2.1 Biological and endogenous factors, Aetiology, Inhibition, medicine.diagnostic_test, Substance Abuse, Middle Aged, Serious Mental Illness, Magnetic Resonance Imaging, Healthy Volunteers, Computer Science Applications, Inhibition, Psychological, Mental Health, Schizophrenia, Neurological, Cognitive control, Biomedical Imaging, Female, Statistics, Probability and Uncertainty, Psychology, Information Systems, Statistics and Probability, Adult, medicine.medical_specialty, Drug Abuse (NIDA Only), Brain imaging, Phenome, Library and Information Sciences, Education, 03 medical and health sciences, Young Adult, Neuroimaging, Functional neuroimaging, Clinical Research, Memory, Behavioral and Social Science, medicine, Humans, Bipolar disorder, Set (psychology), Information Dissemination, Functional Neuroimaging, Neurosciences, Working memory, Magnetic resonance imaging, medicine.disease, Brain Disorders, 030104 developmental biology, Attention Deficit Disorder with Hyperactivity, Psychological, 030217 neurology & neurosurgery, Diffusion MRI
Abstract: This data descriptor outlines a shared neuroimaging dataset from the UCLA Consortium for Neuropsychiatric Phenomics, which focused on understanding the dimensional structure of memory and cognitive control (response inhibition) functions in both healthy individuals (138 subjects) and individuals with neuropsychiatric disorders including schizophrenia (58 subjects), bipolar disorder (49 subjects), and attention deficit/hyperactivity disorder (45 subjects). The dataset includes an extensive set of task-based fMRI assessments, resting fMRI, structural MRI, and high angular resolution diffusion MRI. The dataset is shared through the OpenfMRI project, and is formatted according to the Brain Imaging Data Structure (BIDS) standard.
Published: 2016
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32. Standardizing Metadata in Brain Imaging

Author: Karl G. Helmer, Guillaume Flandin, Jean-Baptiste Poline, Michael Hanke, Yaroslav O. Halchenko, Richard C. Reynolds, Frank Michel, Arno Klein, Samir Das, Krzysztof J. Gorgolewski, John D. Van Horn, Daniel S. Margulies, Camille Maumet, Tanya Schmah, Gang Chen, Russell A. Poldrack, Tibor Auer, E Thomas Nichols, Ziad S. Saad, Mark Jenkinson, David N. Kennedy, Satrajit S. Ghosh, Jessica A. Turner, B. Nolan Nichols, R. Cameron Craddock, Christian Haselgrove, David Keator, Jason Steffener, Daniel S. Marcus, Linda Lanyon, Department of Psychiatry and Human Behavior [Irvine], University of California [Irvine] (UCI), University of California-University of California, Lawrence Berkeley National Laboratory [Berkeley] (LBNL), SRI International [Menlo Park] (SRI), Massachusetts Institute of Technology (MIT), Institut Charles Gerhardt Montpellier - Institut de Chimie Moléculaire et des Matériaux de Montpellier (ICGM ICMMM), Ecole Nationale Supérieure de Chimie de Montpellier (ENSCM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Université Montpellier 1 (UM1)-Université Montpellier 2 - Sciences et Techniques (UM2)-Institut de Chimie du CNRS (INC), Stanford University, University of Cambridge [UK] (CAM), Child Mind Institute, Scientific and Statistics Computing Core [Bethesda, MA, USA], National Institutes of Health [Bethesda] (NIH), Wellcome Trust Centre for Neuroimaging, University College of London [London] (UCL), Psychology and Brain Sciences [Hanover, USA], Dartmouth College [Hanover], Ruhr-Universität Bochum [Bochum], Department of Psychiatry [Massachusetts, USA], University of Massachusetts System (UMASS), University of Paderborn, Sage Bionetworks [Seattle, WA, USA], International Neuroinformatics Coordinating Facility Secretariat - INCF [Stockholm, Sweden] (Karolinska Institutet), Centre de Recherche en Éthique [UdeM - Montréal] (CREUM), Université de Montréal (UdeM), Rheinisch-Westfälische Technische Hochschule Aachen (RWTH), Physikalisches Institut [Bern], Universität Bern [Bern], California Institute of Technology (CALTECH), Chimie Agro-Industrielle (CAI), Institut National de la Recherche Agronomique (INRA)-Ecole nationale supérieure des ingénieurs en arts chimiques et technologiques-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, University of Toronto, Department of Neurology [New York, NY, USA], Columbia University [New York], Psychology and Neuroscience [Atlanta, Georgia, États-Unis], Georgia State University, University System of Georgia (USG)-University System of Georgia (USG), United Kingdom Met Office [Exeter], Montreal Neurological Institute and Hospital, McGill University = Université McGill [Montréal, Canada], INCF, Université Montpellier 1 (UM1)-Université Montpellier 2 - Sciences et Techniques (UM2)-Ecole Nationale Supérieure de Chimie de Montpellier (ENSCM)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Stanford University [Stanford], Centre de Recherche en Éthique de l'Université de Montréal (CREUM), Université de Montréal [Montréal], Laboratoire de Chimie Agro-Industrielle (LCA), Institut National de la Recherche Agronomique (INRA)-Ecole Nationale Supérieure de Chimie de Toulouse, McGill University, University of California [Irvine] (UC Irvine), University of California (UC)-University of California (UC), Institut Charles Gerhardt Montpellier - Institut de Chimie Moléculaire et des Matériaux de Montpellier (ICGM), Ecole Nationale Supérieure de Chimie de Montpellier (ENSCM)-Institut de Chimie du CNRS (INC)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Rheinisch-Westfälische Technische Hochschule Aachen University (RWTH), Universität Bern [Bern] (UNIBE), and Maumet, Camille
Subjects: Information retrieval, Computer science, General Neuroscience, 05 social sciences, 050105 experimental psychology, World Wide Web, Metadata, 03 medical and health sciences, 0302 clinical medicine, Neuroimaging, 0501 psychology and cognitive sciences, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], [SDV.NEU] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], 030217 neurology & neurosurgery, ComputingMilieux_MISCELLANEOUS
Abstract: International audience
Published: 2015
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33. NeuroVault.org: A repository for sharing unthresholded statistical maps, parcellations, and atlases of the human brain

Author: Vanessa Sochat, Satrajit S. Ghosh, Gabriel Rivera, Thomas E. Nichols, Daniel S. Margulies, Jean-Baptiste Poline, Russell A. Poldrack, Tal Yarkoni, Yannick Schwartz, Camille Maumet, Gaël Varoquaux, Krzysztof J. Gorgolewski, Department of Psychology [Stanford], Stanford University, Max Planck Institute for Human Cognitive and Brain Sciences [Leipzig] (IMPNSC), Max-Planck-Gesellschaft, Modelling brain structure, function and variability based on high-field MRI data (PARIETAL), Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Service NEUROSPIN (NEUROSPIN), Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), InfoCortex UG [Frankfurt], Department of Biomedical Data Science [Stanford] (DBDS), Stanford Medicine, Stanford University-Stanford University, McGovern Institute for Brain Research [Cambridge], Massachusetts Institute of Technology (MIT), Warwick Manufacturing Group [Coventry] (WMG), University of Warwick [Coventry], Department of Statistics [Coventry], The Helen Wills Neuroscience Institute (HWNI), University of California [Berkeley], University of California-University of California, Department of Psychology [Austin] (University of Texas), University of Texas at Austin [Austin], This work was partially funded by the National Institutes of Health (NIH), R01MH096906 [TY], NSF OCI-1131441 [RP], International Neuroinformatics Coordinating Facility (INCF) and the Max Planck Society [KJG, DSM]., Service NEUROSPIN (NEUROSPIN), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), University of California [Berkeley] (UC Berkeley), University of California (UC)-University of California (UC), and Maumet, Camille
Subjects: Databases, Factual, Computer science, Cognitive Neuroscience, Neuroimaging, Image processing, computer.software_genre, Article, 050105 experimental psychology, Access to Information, 03 medical and health sciences, 0302 clinical medicine, Image Processing, Computer-Assisted, medicine, Humans, 0501 psychology and cognitive sciences, [SDV.NEU] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Brain Mapping, Information retrieval, medicine.diagnostic_test, Information Dissemination, 05 social sciences, Magnetic resonance imaging, Magnetic Resonance Imaging, Visualization, Metadata, Neurology, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Data mining, computer, 030217 neurology & neurosurgery
Abstract: [Epub ahead of print]; International audience; NeuroVault.org is dedicated to storing outputs of analyses in the form of statistical maps, parcellations and atlases, a unique strategy that contrasts with most neuroimaging repositories that store raw acquisition data or stereotaxic coordinates. Such maps are indispensable for performing meta-analyses, validating novel methodology, and deciding on precise outlines for regions of interest (ROIs). NeuroVault is open to maps derived from both healthy and clinical populations, as well as from various imaging modalities (sMRI, fMRI, EEG, MEG, PET, etc.). The repository uses modern web technologies such as interactive web-based visualization, cognitive decoding, and comparison with other maps to provide researchers with efficient, intuitive tools to improve the understanding of their results. Each dataset and map is assigned a permanent Universal Resource Locator (URL), and all of the data is accessible through a REST Application Programming Interface (API). Additionally, the repository supports the NIDM-Results standard and has the ability to parse outputs from popular FSL and SPM software packages to automatically extract relevant metadata. This ease of use, modern web-integration, and pioneering functionality holds promise to improve the workflow for making inferences about and sharing whole-brain statistical maps.
Published: 2015
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34. NeuroVault.org: a web-based repository for collecting and sharing unthresholded statistical maps of the human brain

Author: Jean-Baptiste Poline, Thomas E. Nichols, Russell A. Poldrack, Tal Yarkoni, Camille Maumet, Krzysztof J. Gorgolewski, Gaël Varoquaux, Yannick Schwartz, Satrajit S. Ghosh, Gabriel Rivera, Daniel S. Margulies, Max Planck Research Group Neuroanatomy and Connectivity, Max Planck Institute for Human Cognitive and Brain Sciences [Leipzig] (IMPNSC), Max-Planck-Gesellschaft-Max-Planck-Gesellschaft, Department of Psychology [Stanford], Stanford University, Modelling brain structure, function and variability based on high-field MRI data (PARIETAL), Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Service NEUROSPIN (NEUROSPIN), Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay, InfoCortex UG [Frankfurt], McGovern Institute for Brain Research [Cambridge], Massachusetts Institute of Technology (MIT), Warwick Manufacturing Group [Coventry] (WMG), University of Warwick [Coventry], Department of Statistics, The Helen Wills Neuroscience Institute (HWNI), University of California [Berkeley], University of California-University of California, Department of Psychology, University of Texas at Austin [Austin], This work was partially funded by the National Institutes of Health (NIH), R01MH096906 [TY],NSF OCI1131441[RP], International Neuroinformatics Coordinating Facility (INCF) and theMax Planck Society [KJG, DSM]., McGovern Institute for Brain Research at MIT, Ghosh, Satrajit S., Service NEUROSPIN (NEUROSPIN), Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), University of California [Berkeley] (UC Berkeley), University of California (UC)-University of California (UC), and Maumet, Camille
Subjects: 0303 health sciences, Computer science, business.industry, Neuroimaging, statistical parameter mapping (SPM), Visualization, World Wide Web, 03 medical and health sciences, repository, Meta-analysis, 0302 clinical medicine, Resource (project management), Software, Methods, RC0321, Web application, Data sharing, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], [SDV.NEU] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], business, 030217 neurology & neurosurgery, database, 030304 developmental biology, Neuroscience
Abstract: International audience; Here we present NeuroVault — a web based repository that allows researchers to store, share, visualize, and decode statistical maps of the human brain. NeuroVault is easy to use and employs modern web technologies to provide informative visualization of data without the need to install additional software. In addition, it leverages the power of the Neurosynth database to provide cognitive decoding of deposited maps. The data are exposed through a public REST API enabling other services and tools to take advantage of it. NeuroVault is a new resource for researchers interested in conducting meta- and coactivation analyses.
Published: 2015
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35. Brain Imaging Data Structure - a new standard for describing and organizing human neuroimaging data

Author: Karl Helmer, Jessica A. Turner, Jean-Baptiste Poline, David Keator, Guillaume Flandin, Camille Maumet, Yaroslav O. Halchenko, Russell A. Poldrack, Krzysztof J. Gorgolewski, Michael Hanke, Satrajit S. Ghosh, Tibor Auer, David N. Kennedy, Samir Das, R. Cameron Craddock, Christian Haselgrove, Ariel Rokem, Thomas E. Nichols, Vanessa Sochat, B. Nolan Nichols, Stanford University, Lawrence Berkeley National Laboratory [Berkeley] (LBNL), SRI International [Menlo Park] (SRI), University of Cambridge [UK] (CAM), Child Mind Institute, Wellcome Trust Centre for Neuroimaging, University College of London [London] (UCL), Massachusetts Institute of Technology (MIT), University of Washington [Seattle], Psychology and Brain Sciences, Dartmouth College [Hanover], Ruhr-Universität Bochum [Bochum], Earth System Science Center [Huntsville], University of Alabama in Huntsville (UAH), University of Paderborn, Institut Charles Gerhardt Montpellier - Institut de Chimie Moléculaire et des Matériaux de Montpellier (ICGM), Ecole Nationale Supérieure de Chimie de Montpellier (ENSCM)-Institut de Chimie du CNRS (INC)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Physikalisches Institut [Bern], Universität Bern [Bern] (UNIBE), Psychology and Neuroscience, Georgia State University, University System of Georgia (USG)-University System of Georgia (USG), Montreal Neurological Institute and Hospital, McGill University = Université McGill [Montréal, Canada], California Institute of Technology (CALTECH), INCF, Maumet, Camille, Institut Charles Gerhardt Montpellier - Institut de Chimie Moléculaire et des Matériaux de Montpellier (ICGM ICMMM), Ecole Nationale Supérieure de Chimie de Montpellier (ENSCM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Université Montpellier 1 (UM1)-Université Montpellier 2 - Sciences et Techniques (UM2)-Institut de Chimie du CNRS (INC), and Universität Bern [Bern]
Subjects: Neuroimaging, Computer science, General Neuroscience, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], [SDV.NEU] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Data structure, Data science, ComputingMilieux_MISCELLANEOUS
Abstract: International audience
Published: 2015
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36. 2.1 Making Neuroimaging More Reproducible and Transparent

Author: Krzysztof J. Gorgolewski and Russell A. Poldrack
Subjects: Psychiatry and Mental health, Neuroimaging, Computer science, Developmental and Educational Psychology, Neuroscience
Published: 2017
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37. An open science resource for establishing reliability and reproducibility in functional connectomics

Author: Hao Ming Dong, Stephen M. LaConte, Zhe Zhang, Gao-Xia Wei, Krzysztof J. Gorgolewski, William Courtney, Xiao Hui Hou, Jiangtao Chen, William M. Kelley, Vivek Prabhakaran, Avram J. Holmes, Weijun Tang, John C.S. Breitner, Michael P. Milham, Janet E. Lainhart, Lei Zhang, Zonglei Zhen, Rasmus M. Birn, Zhigang Qi, Tianzi Jiang, Janusch Blautzik, Xing Ting Zhu, Zhiqiang Zhang, Qixiang Lin, Vince D. Calhoun, Ning Yang, Adriana Di Martino, Bharat B. Biswal, F. Xavier Castellanos, Xu Chu Weng, Clare Kelly, Beatriz Luna, Yijun Liu, Yu-Feng Zang, R. Cameron Craddock, Xiaolan Fu, Dong-Qiang Liu, Jeremy F. Huckins, Ye He, Xi-Nian Zuo, Bing Chen, Qiyong Gong, Jia Liu, Chunhong Shao, Zarrar Shehzad, Daniel S. Margulies, Weizhi Nan, Dongtao Wei, Ying Mao, Jeffrey S. Anderson, Yi Jiang, Arno Villringer, Kaiming Li, Xuehai Wu, Yong He, Yuan Zhou, Ke Zhao, Jie Lu, David J. Paulsen, Guangming Lu, Kuncheng Li, Xu Chen, Jing Luo, Andrew R. Mayer, Huijie Li, Xu Lei, Antao Chen, Daniel J. Lurie, David H. O’Connor, Ying Han, Qinglin Zhang, Jared A. Nielsen, Ting Xu, Kai Wang, Mary E. Meyerand, Jiang Qiu, Pierre Bellec, Huiling Wang, Zhi Yang, Randy L. Buckner, Stanley J. Colcombe, Erica J. Ho, Xun Liu, Margaret D. King, and Thomas Meindl
Subjects: Statistics and Probability, Data Descriptor, Connectomics, Open science, Computer science, Datasets as Topic, Library and Information Sciences, Machine learning, computer.software_genre, Bioinformatics, Education, Neuroimaging, Connectome, Humans, Reliability (statistics), Resting state fMRI, business.industry, Brain, Reproducibility of Results, Magnetic Resonance Imaging, Computer Science Applications, Functional imaging, Artificial intelligence, Statistics, Probability and Uncertainty, business, computer, Information Systems
Abstract: Efforts to identify meaningful functional imaging-based biomarkers are limited by the ability to reliably characterize inter-individual differences in human brain function. Although a growing number of connectomics-based measures are reported to have moderate to high test-retest reliability, the variability in data acquisition, experimental designs, and analytic methods precludes the ability to generalize results. The Consortium for Reliability and Reproducibility (CoRR) is working to address this challenge and establish test-retest reliability as a minimum standard for methods development in functional connectomics. Specifically, CoRR has aggregated 1,629 typical individuals’ resting state fMRI (rfMRI) data (5,093 rfMRI scans) from 18 international sites, and is openly sharing them via the International Data-sharing Neuroimaging Initiative (INDI). To allow researchers to generate various estimates of reliability and reproducibility, a variety of data acquisition procedures and experimental designs are included. Similarly, to enable users to assess the impact of commonly encountered artifacts (for example, motion) on characterizations of inter-individual variation, datasets of varying quality are included.
Published: 2014
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38. Bridging psychology and genetics using large-scale spatial analysis of neuroimaging and neurogenetic data

Author: Luke J. Chang, Tal Yarkoni, Andrew S. Fox, and Krzysztof J. Gorgolewski
Subjects: Cognitive science, 0303 health sciences, business.industry, Cognition, Genome-wide association study, Bridging (programming), 03 medical and health sciences, Spatial similarity, 0302 clinical medicine, Neuroimaging, Functional neuroimaging, Artificial intelligence, Psychology, business, Biological sciences, 030217 neurology & neurosurgery, 030304 developmental biology
Abstract: Understanding how microscopic molecules give rise to complex cognitive processes is a major goal of the biological sciences. The countless hypothetical molecule-cognition relationships necessitate discovery-based techniques to guide scientists toward the most productive lines of investigation. To this end, we present a novel discovery tool that uses spatial patterns of neural gene expression from the Allen Brain Institute (ABI) and large-scale functional neuroimaging meta-analyses from the Neurosynth framework to bridge neurogenetic and neuroimaging data. We quantified the spatial similarity between over 20,000 genes from the ABI and 48 psychological topics derived from lexical analysis of neuroimaging articles, producing a comprehensive set of gene/cognition mappings that we term the Neurosynth-gene atlas. We demonstrate the ability to independently replicate known gene/cognition associations (e.g., between dopamine and reward), and subsequently use it to identify a range of novel associations between individual molecules or genes and complex psychological phenomena such as reward, memory and emotion. Our results complement existing discovery-based methods such as GWAS, and provide a novel means of generating hypotheses about the neurogenetic substrates of complex cognitive functions.
Published: 2014
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39. Developing and using the data models for neuroimaging: the NIDASH Working Group

Author: Camille Maumet, Jean-Baptiste Poline, Yaroslav O. Halchenko, Daniel S. Marcus, Russell A. Poldrack, Thomas E. Nichols, Michael P. Milham, Arno Klein, David Keator, Michael Hanke, B Federick, C Craddock, Jason Steffener, Rich Stoner, Yannick Schwartz, Krzysztof J. Gorgolewski, Nolan Nichols, Christian Haselgrove, R Bruehl, F Michel, Gully A. P. C. Burns, Jessica A. Turner, Satrajit S. Ghosh, Karl G. Helmer, David N. Kennedy, and Guillaume Flandin
Subjects: Neuroimaging, Group (mathematics), Computer science, Biomedical Engineering, Neuroscience (miscellaneous), Data science, Computer Science Applications, Data modeling
Published: 2014
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40. Data sharing in neuroimaging research

Author: Yannick Schwartz, Janis L. Breeze, David Keator, Russell A. Poldrack, Karl G. Helmer, Krzysztof J. Gorgolewski, Daniel S. Marcus, Christian Haselgrove, John Ashburner, Michael Hanke, Jean-Baptiste Poline, Yaroslav O. Halchenko, David N. Kennedy, and Satrajit S. Ghosh
Subjects: Electronic data capture, Computer science, data sharing, Biomedical Engineering, Neuroscience (miscellaneous), brain imaging, Review Article, Cognitive neuroscience, Field (computer science), GeneralLiterature_MISCELLANEOUS, Task (project management), lcsh:RC321-571, 03 medical and health sciences, 0302 clinical medicine, Neuroimaging, Medicine and Health Sciences, magnetic resonance imaging, lcsh:Neurosciences. Biological psychiatry. Neuropsychiatry, 030304 developmental biology, 0303 health sciences, Data collection, fMRI, Data science, Computer Science Applications, Metadata, Data sharing, standards, EEG-MEG, 030217 neurology & neurosurgery, Neuroscience
Abstract: Significant resources around the world have been invested in neuroimaging studies of brain function and disease. Easier access to this large body of work should have profound impact on research in cognitive neuroscience and psychiatry, leading to advances in the diagnosis and treatment of psychiatric and neurological disease. A trend toward increased sharing of neuroimaging data has emerged in recent years. Nevertheless, a number of barriers continue to impede momentum. Many researchers and institutions remain uncertain about how to share data or lack the tools and expertise to participate in data sharing. The use of electronic data capture (EDC) methods for neuroimaging greatly simplifies the task of data collection and has the potential to help standardize many aspects of data sharing. We review here the motivations for sharing neuroimaging data, the current data sharing landscape, and the sociological or technical barriers that still need to be addressed. The INCF Task Force on Neuroimaging Datasharing, in conjunction with several collaborative groups around the world, has started work on several tools to ease and eventually automate the practice of data sharing. It is hoped that such tools will allow researchers to easily share raw, processed, and derived neuroimaging data, with appropriate metadata and provenance records, and will improve the reproducibility of neuroimaging studies. By providing seamless integration of data sharing and analysis tools within a commodity research environment, the Task Force seeks to identify and minimize barriers to data sharing in the field of neuroimaging.
Published: 2012

41. Nipype: A Flexible, Lightweight and Extensible Neuroimaging Data Processing Framework in Python

Author: Christopher Burns, Michael Waskom, Dav Clark, Krzysztof J. Gorgolewski, Cindee Madison, Satrajit S. Ghosh, Yaroslav O. Halchenko, Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology. Research Laboratory of Electronics, McGovern Institute for Brain Research at MIT, Waskom, Michael L., and Ghosh, Satrajit S
Subjects: Computer science, workflow, Biomedical Engineering, Neuroscience (miscellaneous), computer.software_genre, reproducible research, Extensibility, 03 medical and health sciences, 0302 clinical medicine, Software, 030304 developmental biology, computer.programming_language, Original Research, 0303 health sciences, Data processing, neuroimaging, business.industry, pipeline, Python (programming language), Data science, Computer Science Applications, Workflow, Scripting language, Learning curve, Package development process, business, computer, 030217 neurology & neurosurgery, data processing, Neuroscience, Python
Abstract: Current neuroimaging software offer users an incredible opportunity to analyze their data in different ways, with different underlying assumptions. Several sophisticated software packages (e.g., AFNI, BrainVoyager, FSL, FreeSurfer, Nipy, R, SPM) are used to process and analyze large and often diverse (highly multi-dimensional) data. However, this heterogeneous collection of specialized applications creates several issues that hinder replicable, efficient, and optimal use of neuroimaging analysis approaches: (1) No uniform access to neuroimaging analysis software and usage information; (2) No framework for comparative algorithm development and dissemination; (3) Personnel turnover in laboratories often limits methodological continuity and training new personnel takes time; (4) Neuroimaging software packages do not address computational efficiency; and (5) Methods sections in journal articles are inadequate for reproducing results. To address these issues, we present Nipype (Neuroimaging in Python: Pipelines and Interfaces; http://nipy.org/nipype), an open-source, community-developed, software package, and scriptable library. Nipype solves the issues by providing Interfaces to existing neuroimaging software with uniform usage semantics and by facilitating interaction between these packages using Workflows. Nipype provides an environment that encourages interactive exploration of algorithms, eases the design of Workflows within and between packages, allows rapid comparative development of algorithms and reduces the learning curve necessary to use different packages. Nipype supports both local and remote execution on multi-core machines and clusters, without additional scripting. Nipype is Berkeley Software Distribution licensed, allowing anyone unrestricted usage. An open, community-driven development philosophy allows the software to quickly adapt and address the varied needs of the evolving neuroimaging community, especially in the context of increasing demand for reproducible research., National Institute for Biomedical Imaging and Bioengineering (U.S.) (Grant R03 EB008673), Ellison Medical Foundation
Published: 2011
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42. The OpenNeuro resource for sharing of neuroscience data

Author: Christopher J Markiewicz, Krzysztof J Gorgolewski, Franklin Feingold, Ross Blair, Yaroslav O Halchenko, Eric Miller, Nell Hardcastle, Joe Wexler, Oscar Esteban, Mathias Goncavles, Anita Jwa, and Russell Poldrack
Subjects: neuroimaging, data sharing, open science, MRI, MEG, EEG, Medicine, Science, Biology (General), QH301-705.5
Abstract: The sharing of research data is essential to ensure reproducibility and maximize the impact of public investments in scientific research. Here, we describe OpenNeuro, a BRAIN Initiative data archive that provides the ability to openly share data from a broad range of brain imaging data types following the FAIR principles for data sharing. We highlight the importance of the Brain Imaging Data Structure standard for enabling effective curation, sharing, and reuse of data. The archive presently shares more than 600 datasets including data from more than 20,000 participants, comprising multiple species and measurement modalities and a broad range of phenotypes. The impact of the shared data is evident in a growing number of published reuses, currently totalling more than 150 publications. We conclude by describing plans for future development and integration with other ongoing open science efforts.
Published: 2021
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