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1. 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, and Melanie Ganz

Subjects
Science
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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2. 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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3. Fine-grain atlases of functional modes for fMRI analysis

Author

Kamalaker Dadi, Gaël Varoquaux, Antonia Machlouzarides-Shalit, Krzysztof J. Gorgolewski, Demian Wassermann, Bertrand Thirion, and Arthur Mensch

Subjects
Brain imaging atlases, Functional networks, Functional parcellations, Multi-resolution, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Abstract

Population imaging markedly increased the size of functional-imaging datasets, shedding new light on the neural basis of inter-individual differences. Analyzing these large data entails new scalability challenges, computational and statistical. For this reason, brain images are typically summarized in a few signals, for instance reducing voxel-level measures with brain atlases or functional modes. A good choice of the corresponding brain networks is important, as most data analyses start from these reduced signals. We contribute finely-resolved atlases of functional modes, comprising from 64 to 1024 networks. These dictionaries of functional modes (DiFuMo) are trained on millions of fMRI functional brain volumes of total size 2.4 ​TB, spanned over 27 studies and many research groups. We demonstrate the benefits of extracting reduced signals on our fine-grain atlases for many classic functional data analysis pipelines: stimuli decoding from 12,334 brain responses, standard GLM analysis of fMRI across sessions and individuals, extraction of resting-state functional-connectomes biomarkers for 2500 individuals, data compression and meta-analysis over more than 15,000 statistical maps. In each of these analysis scenarii, we compare the performance of our functional atlases with that of other popular references, and to a simple voxel-level analysis. Results highlight the importance of using high-dimensional “soft” functional atlases, to represent and analyze brain activity while capturing its functional gradients. Analyses on high-dimensional modes achieve similar statistical performance as at the voxel level, but with much reduced computational cost and higher interpretability. In addition to making them available, we provide meaningful names for these modes, based on their anatomical location. It will facilitate reporting of results.

Published
2020
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4. 2015 Brainhack Proceedings

Author

R. Cameron Craddock, Pierre Bellec, Daniel S. Margules, B. Nolan Nichols, Jörg P. Pfannmöller, AmanPreet Badhwar, David Kennedy, Jean-Baptiste Poline, Roberto Toro, Ben Cipollini, Ariel Rokem, Daniel Clark, Krzysztof J. Gorgolewski, Daniel J. Clark, Samir Das, Cécile Madjar, Ayan Sengupta, Zia Mohades, Sebastien Dery, Weiran Deng, Eric Earl, Damion V. Demeter, Kate Mills, Glad Mihai, Luka Ruzic, Nick Ketz, Andrew Reineberg, Marianne C. Reddan, Anne-Lise Goddings, Javier Gonzalez-Castillo, Caroline Froehlich, Gil Dekel, Daniel S. Margulies, Ben D. Fulcher, Tristan Glatard, Reza Adalat, Natacha Beck, Rémi Bernard, Najmeh Khalili-Mahani, Pierre Rioux, Marc-Étienne Rousseau, Alan C. Evans, Yaroslav O. Halchenko, Matteo Visconti di Oleggio Castello, Raúl Hernández-Pérez, Edgar A. Morales, Laura V. Cuaya, Kaori L. Ito, Sook-Lei Liew, Hans J. Johnson, Erik Kan, Julia Anglin, Michael Borich, Neda Jahanshad, Paul Thompson, Marcel Falkiewicz, Julia M. Huntenburg, David O’Connor, Michael P. Milham, Ramon Fraga Pereira, Anibal Sólon Heinsfeld, Alexandre Rosa Franco, Augusto Buchweitz, Felipe Meneguzzi, Rickson Mesquita, Luis C. T. Herrera, Daniela Dentico, Vanessa Sochat, Julio E. Villalon-Reina, and Eleftherios Garyfallidis

Subjects
Computer applications to medicine. Medical informatics, R858-859.7
Abstract

Table of contents I1 Introduction to the 2015 Brainhack Proceedings R. Cameron Craddock, Pierre Bellec, Daniel S. Margules, B. Nolan Nichols, Jörg P. Pfannmöller A1 Distributed collaboration: the case for the enhancement of Brainspell’s interface AmanPreet Badhwar, David Kennedy, Jean-Baptiste Poline, Roberto Toro A2 Advancing open science through NiData Ben Cipollini, Ariel Rokem A3 Integrating the Brain Imaging Data Structure (BIDS) standard into C-PAC Daniel Clark, Krzysztof J. Gorgolewski, R. Cameron Craddock A4 Optimized implementations of voxel-wise degree centrality and local functional connectivity density mapping in AFNI R. Cameron Craddock, Daniel J. Clark A5 LORIS: DICOM anonymizer Samir Das, Cécile Madjar, Ayan Sengupta, Zia Mohades A6 Automatic extraction of academic collaborations in neuroimaging Sebastien Dery A7 NiftyView: a zero-footprint web application for viewing DICOM and NIfTI files Weiran Deng A8 Human Connectome Project Minimal Preprocessing Pipelines to Nipype Eric Earl, Damion V. Demeter, Kate Mills, Glad Mihai, Luka Ruzic, Nick Ketz, Andrew Reineberg, Marianne C. Reddan, Anne-Lise Goddings, Javier Gonzalez-Castillo, Krzysztof J. Gorgolewski A9 Generating music with resting-state fMRI data Caroline Froehlich, Gil Dekel, Daniel S. Margulies, R. Cameron Craddock A10 Highly comparable time-series analysis in Nitime Ben D. Fulcher A11 Nipype interfaces in CBRAIN Tristan Glatard, Samir Das, Reza Adalat, Natacha Beck, Rémi Bernard, Najmeh Khalili-Mahani, Pierre Rioux, Marc-Étienne Rousseau, Alan C. Evans A12 DueCredit: automated collection of citations for software, methods, and data Yaroslav O. Halchenko, Matteo Visconti di Oleggio Castello A13 Open source low-cost device to register dog’s heart rate and tail movement Raúl Hernández-Pérez, Edgar A. Morales, Laura V. Cuaya A14 Calculating the Laterality Index Using FSL for Stroke Neuroimaging Data Kaori L. Ito, Sook-Lei Liew A15 Wrapping FreeSurfer 6 for use in high-performance computing environments Hans J. Johnson A16 Facilitating big data meta-analyses for clinical neuroimaging through ENIGMA wrapper scripts Erik Kan, Julia Anglin, Michael Borich, Neda Jahanshad, Paul Thompson, Sook-Lei Liew A17 A cortical surface-based geodesic distance package for Python Daniel S Margulies, Marcel Falkiewicz, Julia M Huntenburg A18 Sharing data in the cloud David O’Connor, Daniel J. Clark, Michael P. Milham, R. Cameron Craddock A19 Detecting task-based fMRI compliance using plan abandonment techniques Ramon Fraga Pereira, Anibal Sólon Heinsfeld, Alexandre Rosa Franco, Augusto Buchweitz, Felipe Meneguzzi A20 Self-organization and brain function Jörg P. Pfannmöller, Rickson Mesquita, Luis C.T. Herrera, Daniela Dentico A21 The Neuroimaging Data Model (NIDM) API Vanessa Sochat, B Nolan Nichols A22 NeuroView: a customizable browser-base utility Anibal Sólon Heinsfeld, Alexandre Rosa Franco, Augusto Buchweitz, Felipe Meneguzzi A23 DIPY: Brain tissue classification Julio E. Villalon-Reina, Eleftherios Garyfallidis

Published
2016
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5. 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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6. HeuDiConv - flexible DICOM conversion into structured directory layouts.

Author

Yaroslav O. Halchenko, Mathias Goncalves, Satrajit S. Ghosh, Pablo Velasco, Matteo Visconti di Oleggio Castello, Taylor Salo, John T. Wodder, Michael Hanke, Patrick S. Sadil, Krzysztof J. Gorgolewski, Horea-Ioan Ioanas, Chris Rorden, Timothy J. Hendrickson, Michael Dayan, Sean Dae Houlihan, James D. Kent, Ted J. Strauss, John A. Lee, Isaac To, Christopher J. Markiewicz, Darren Lukas, Ellyn R. Butler, Todd Thompson, Maite Termenon, David V. Smith, Austin Macdonald, and David N. Kennedy

Published
2024
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8. 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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17. 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

18. 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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19. Boutiques: a flexible framework for automated application integration in computing platforms.

Author

Tristan Glatard, Gregory Kiar, Tristan Aumentado-Armstrong, Natacha Beck, Pierre Bellec, Rémi Bernard, Axel Bonnet, Sorina Camarasu-Pop, Frédéric Cervenansky, Samir Das, Rafael Ferreira da Silva, Guillaume Flandin, Pascal Girard, Krzysztof J. Gorgolewski, Charles R. G. Guttmann, Valérie Hayot-Sasson, Pierre-Olivier Quirion, Pierre Rioux, Marc-Etienne Rousseau, and Alan C. Evans

Published
2017

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

Author

Krzysztof J. Gorgolewski, Fidel Alfaro-Almagro, Tibor Auer, Pierre Bellec, Mihai Capota, M. Mallar Chakravarty, Nathan William Churchill, Alexander Li Cohen, R. Cameron Craddock, Gabriel A. Devenyi, Anders Eklund 0002, Oscar Esteban, Guillaume Flandin, Satrajit S. Ghosh, J. Swaroop Guntupalli, Mark Jenkinson, Anisha Keshavan, Gregory Kiar, Franziskus Liem, Pradeep Reddy Raamana, David Raffelt, Christopher John Steele, Pierre-Olivier Quirion, Robert E. Smith 0002, Stephen C. Strother, Gaël Varoquaux, Yida Wang 0003, Tal Yarkoni, and Russell A. Poldrack

Published
2017
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23. PyBIDS: Python tools for BIDS datasets.

Author

Tal Yarkoni, Christopher J. Markiewicz, Alejandro de la Vega, Krzysztof J. Gorgolewski, Taylor Salo, Yaroslav O. Halchenko, Quinten McNamara, Krista Destasio, Jean-Baptiste Poline, Dmitry Petrov, Valérie Hayot-Sasson, Dylan Nielson, Johan D. Carlin, Gregory Kiar, Kirstie J. Whitaker, Elizabeth Dupre, Adina S. Wagner, Lee S. Tirrell, Mainak Jas, Michael Hanke, Russell A. Poldrack, Oscar Esteban, Stefan Appelhoff, Chris Holdgraf, Isla Staden, Bertrand Thirion, Dave F. Kleinschmidt, John A. Lee, Matteo di Castello, Michael Notter, and Ross Blair

Published
2019
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24. MRIQC: Advancing the automatic prediction of image quality in MRI from unseen sites.

Author

Oscar Esteban, Daniel Birman, Marie Schaer, Oluwasanmi O Koyejo, Russell A Poldrack, and Krzysztof J Gorgolewski

Subjects
Medicine, Science
Abstract

Quality control of MRI is essential for excluding problematic acquisitions and avoiding bias in subsequent image processing and analysis. Visual inspection is subjective and impractical for large scale datasets. Although automated quality assessments have been demonstrated on single-site datasets, it is unclear that solutions can generalize to unseen data acquired at new sites. Here, we introduce the MRI Quality Control tool (MRIQC), a tool for extracting quality measures and fitting a binary (accept/exclude) classifier. Our tool can be run both locally and as a free online service via the OpenNeuro.org portal. The classifier is trained on a publicly available, multi-site dataset (17 sites, N = 1102). We perform model selection evaluating different normalization and feature exclusion approaches aimed at maximizing across-site generalization and estimate an accuracy of 76%±13% on new sites, using leave-one-site-out cross-validation. We confirm that result on a held-out dataset (2 sites, N = 265) also obtaining a 76% accuracy. Even though the performance of the trained classifier is statistically above chance, we show that it is susceptible to site effects and unable to account for artifacts specific to new sites. MRIQC performs with high accuracy in intra-site prediction, but performance on unseen sites leaves space for improvement which might require more labeled data and new approaches to the between-site variability. Overcoming these limitations is crucial for a more objective quality assessment of neuroimaging data, and to enable the analysis of extremely large and multi-site samples.

Published
2017
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27. 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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28. Author response: The OpenNeuro resource for sharing of neuroscience data

Author

Yaroslav O. Halchenko, Joe Wexler, Krzysztof J. Gorgolewski, Oscar Esteban, Russell A. Poldrack, Ross Blair, Eric N. Miller, Anita Jwa, Nell Hardcastle, Christopher J. Markiewicz, Franklin Feingold, and Mathias Goncavles

Subjects
Resource (biology), Knowledge management, business.industry, Computer science, business
Published
2021
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29. 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

30. 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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31. 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

32. 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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33. 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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34. 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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35. 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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36. 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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37. 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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39. 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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40. PyBIDS: Python tools for BIDS datasets

Author

Elizabeth DuPre, John A. Lee, Jean-Baptiste Poline, Dave F. Kleinschmidt, Taylor Salo, Yaroslav O. Halchenko, Michael Hanke, Dmitry Petrov, Adina Wagner, Valerie Hayot-Sasson, Christopher J. Markiewicz, Matteo Visconti di Oleggio Castello, Alejandro de la Vega, Johan D. Carlin, Krista DeStasio, Stefan Appelhoff, Tal Yarkoni, Chris Holdgraf, Oscar Esteban, Kirstie Whitaker, Dylan M. Nielson, Ross Blair, Lee S. Tirrell, Bertrand Thirion, Michael Notter, Gregory Kiar, Isla Staden, Mainak Jas, Krzysztof J. Gorgolewski, Russell A. Poldrack, and Quinten McNamara

Subjects
Computer science, Programming language, Neuroinformatics, Python (programming language), ddc:004, computer.software_genre, computer, Article, computer.programming_language
Abstract

Brain imaging researchers regularly work with large, heterogeneous, high-dimensional datasets. Historically, researchers have dealt with this complexity idiosyncratically, with every lab or individual implementing their own preprocessing and analysis procedures. The resulting lack of field-wide standards has severely limited reproducibility and data sharing and reuse.To address this problem, we and others recently introduced the Brain Imaging Data Standard (BIDS; (Gorgolewski et al., 2016)), a specification meant to standardize the process of representing brain imaging data. BIDS is deliberately designed with adoption in mind; it adheres to a user-focused philosophy that prioritizes common use cases and discourages complexity. By successfully encouraging a large and ever-growing subset of the community to adopt a common standard for naming and organizing files, BIDS has made it much easier for researchers to share, reuse, and process their data (Gorgolewski et al., 2017).The ability to efficiently develop high-quality spec-compliant applications itself depends to a large extent on the availability of good tooling. Because many operations recur widely across diverse contexts—for example, almost every tool designed to work with BIDS datasets involves regular file-filtering operations—there is a strong incentive to develop utility libraries that provide common functionality via a standardized, simple API.PyBIDS is a Python package that makes it easier to work with BIDS datasets. In principle, its scope includes virtually any functionality that is likely to be of general use when working with BIDS datasets (i.e., that is not specific to one narrow context). At present, its core and most widely used module supports simple and flexible querying and manipulation of BIDS datasets. PyBIDS makes it easy for researchers and developers working in Python to search for BIDS files by keywords and/or metadata; to consolidate and retrieve file-associated metadata spread out across multiple levels of a BIDS hierarchy; to construct BIDS-valid path names for new files; and to validate projects against the BIDS specification, among other applications.In addition to this core functionality, PyBIDS also contains an ever-growing set of modules that support additional capabilities meant to keep up with the evolution and expansion of the BIDS specification itself. Currently, PyBIDS includes tools for (1) reading and manipulating data contained in various BIDS-defined files (e.g., physiological recordings, event files, or participant-level variables); (2) constructing design matrices and contrasts that support the new BIDS-StatsModel specification (for machine-readable representation of fMRI statistical models); and (3) automated generation of partial Methods sections for inclusion in publications.PyBIDS can be easily installed on all platforms via pip (pip install pybids), though currently it is not officially supported on Windows. The package has few dependencies outside of standard Python numerical and image analysis libraries (i.e., numpy, scipy, pandas, and NiBabel). The core API is deliberately kept minimalistic: nearly all interactions with PyBIDS functionality occur through a core BIDSLayout object initialized by passing in a path to a BIDS dataset. For most applications, no custom configuration should be required.Although technically still in alpha release, PyBIDS is already being used both as a dependency in dozens of other open-source brain imaging packages –e.g., fMRIPrep (Esteban et al.,2019), MRIQC (Esteban et al., 2017), datalad-neuroimaging (https://github.com/datalad/datalad-neuroimaging), and fitlins (https://github.com/poldracklab/fitlins) – and directly in many researchers’ custom Python workflows. Development is extremely active, with bug fixes and new features continually being added (https://github.com/bids-standard/pybids), and major releases occurring approximately every 6 months. As of this writing, 29 people have contributed code to PyBIDS, and many more have provided feedback and testing. The API is relatively stable, and documentation and testing standards follow established norms for open-source scientific software. We encourage members of the brain imaging community currently working in Python to try using PyBIDS, and welcome new contributions.

Published
2019
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41. 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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42. 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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43. Data sharing in neuroimaging research.

Author

Jean-Baptiste Poline, Janis L. Breeze, Satrajit S. Ghosh, Krzysztof J. Gorgolewski, Yaroslav O. Halchenko, Michael Hanke, Christian Haselgrove, Karl G. Helmer, David B. Keator, Daniel S. Marcus, Russell A. Poldrack, Yannick Schwartz, John Ashburner, and David N. Kennedy

Published
2012
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45. OpenfMRI: Open sharing of task fMRI data

Author

Krzysztof J. Gorgolewski and Russell A. Poldrack

Subjects
Adult, Male, Adolescent, Databases, Factual, Computer science, Cognitive Neuroscience, Article, 050105 experimental psychology, Task (project management), Young Adult, 03 medical and health sciences, 0302 clinical medicine, Text mining, Humans, 0501 psychology and cognitive sciences, Child, Task fmri, Information retrieval, Information Dissemination, business.industry, Functional Neuroimaging, 05 social sciences, Brain, Middle Aged, Magnetic Resonance Imaging, Neurology, Female, business, 030217 neurology & neurosurgery
Abstract

OpenfMRI is a repository for the open sharing of task-based fMRI data. Here we outline its goals, architecture, and current status of the repository, as well as outlining future plans for the project.

Published
2017
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46. 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

47. 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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48. Switching Software in Science: Motivations, Challenges, and Solutions

Author

Krzysztof J. Gorgolewski, Pierre Bellec, and Jan R. Wessel

Subjects
Biomedical Research, business.industry, Scientific programming, Cognitive Neuroscience, Science, 05 social sciences, Experimental and Cognitive Psychology, 050105 experimental psychology, 03 medical and health sciences, 0302 clinical medicine, Neuropsychology and Physiological Psychology, Software, Stack (abstract data type), Online debate, Key (cryptography), Humans, 0501 psychology and cognitive sciences, Programming Languages, Software engineering, business, Psychology, 030217 neurology & neurosurgery
Abstract

A laboratory's programming language has wide-ranging implications. As demands towards scientific programming change and languages evolve, investigators may look to change their existing software stack. Following up on a recent online debate, we discuss key considerations and challenges in choosing and changing languages and suggest solutions for investigators looking to switch.

Published
2018

49. 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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50. fMRIPrep: a robust preprocessing pipeline for functional MRI

Author

Joke Durnez, Russell A. Poldrack, James D. Kent, Jessey Wright, Krzysztof J. Gorgolewski, Ayse Ilkay Isik, Craig A. Moodie, Mathias Goncalves, Elizabeth DuPre, Ross Blair, Hiroyuki Oya, Christopher J. Markiewicz, Satrajit S. Ghosh, Oscar Esteban, Asier Erramuzpe, and Snyder M

Subjects
Computer science, Inference, Image processing, computer.software_genre, Biochemistry, Article, Workflow, 03 medical and health sciences, Image Processing, Computer-Assisted, Preprocessor, Humans, Molecular Biology, 030304 developmental biology, Interpretability, 0303 health sciences, Thesaurus (information retrieval), Brain Mapping, Data collection, Reproducibility of Results, Cell Biology, Pipeline (software), Magnetic Resonance Imaging, Data mining, computer, Biotechnology
Abstract

Preprocessing of functional magnetic resonance imaging (fMRI) involves numerous steps to clean and standardize the data before statistical analysis. Generally, researchers create ad hoc preprocessing workflows for each dataset, building upon a large inventory of available tools. The complexity of these workflows has snowballed with rapid advances in acquisition and processing. We introduce fMRIPrep, an analysis-agnostic tool that addresses the challenge of robust and reproducible preprocessing for fMRI data. fMRIPrep automatically adapts a best-in-breed workflow to the idiosyncrasies of virtually any dataset, ensuring high-quality preprocessing without manual intervention. By introducing visual assessment checkpoints into an iterative integration framework for software testing, we show that fMRIPrep robustly produces high-quality results on a diverse fMRI data collection. Additionally, fMRIPrep introduces less uncontrolled spatial smoothness than observed with commonly used preprocessing tools. fMRIPrep equips neuroscientists with an easy-to-use and transparent preprocessing workflow, which can help ensure the validity of inference and the interpretability of results.

Published
2018

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