Positron-Emission Tomography; Neuroimaging; Information Systems; Computer Science Applications; Library and Information Sciences
Abstract :
[en] 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.
Disciplines :
Radiology, nuclear medicine & imaging Engineering, computing & technology: Multidisciplinary, general & others
Author, co-author :
Norgaard, Martin ; Neurobiology Research Unit, Rigshospitalet, and Institute of Clinical Medicine, Univ. Copenhagen, København, Denmark ; Department of Psychology, Stanford University, California, USA
Matheson, Granville J; Department of Psychiatry, Columbia University, New York, NY, 10032, USA ; Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet and Stockholm Health Care Services, Stockholm, Sweden
Hansen, Hanne D; Neurobiology Research Unit, Rigshospitalet, and Institute of Clinical Medicine, Univ. Copenhagen, København, Denmark ; Athinoula A. Martinos Center for Biomedical Imaging, MGH/HST, Charlestown, MA, USA
Thomas, Adam ; Intramural Research Program, NIMH, Bethesda, USA
Searle, Graham; Invicro and Division of Brain Sciences, Imperial College London, London, UK
Rizzo, Gaia ; Invicro and Division of Brain Sciences, Imperial College London, London, UK
Veronese, Mattia ; Centre for Neuroimaging Sciences, King's College London, London, UK ; Department of Information Engineering, University of Padua, Padua, Italy
Giacomel, Alessio; Centre for Neuroimaging Sciences, King's College London, London, UK
Yaqub, Maqsood; Amsterdam UMC, location VUmc, department of radiology and nuclear medicine, Amsterdam, Netherlands
Tonietto, Matteo; Université Paris-Saclay, CEA, CNRS, Inserm, BioMaps, Service Hospitalier Frédéric Joliot, Orsay, France
Gillman, Ashley ; Aust. e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation, Townsville, Australia
Boniface, Hugo; Centre d'Acquisition et de Traitement des Images, CEA, Paris, France
Routier, Alexandre ; Inria, Aramis project-team, Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, Inserm, CNRS, AP-HP, Hôpital de la Pitié Salpêtriére, Paris, France
Dalenberg, Jelle R; Department of Neurology, University of Groningen, University Medical Center Groningen, Groningen, Netherlands
Betthauser, Tobey; Wisconsin Alzheimer's Disease Research Center, Division of Geriatrics, Department of Medicine, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI, USA
Feingold, Franklin ; Department of Psychology, Stanford University, California, USA
Markiewicz, Christopher J ; Department of Psychology, Stanford University, California, USA
Gorgolewski, Krzysztof J ; Department of Psychology, Stanford University, California, USA
Blair, Ross W; Department of Psychology, Stanford University, California, USA
Appelhoff, Stefan; Center for Adaptive Rationality, Max Planck Institute for Human Development, Berlin, Germany
Gau, Remi; Institute of psychology, Université catholique de Louvain, Louvain la Neuve, Belgium
Salo, Taylor ; Department of Psychology, Florida International University, Miami, FL, USA
Niso, Guiomar; Psychological Brain Sciences, Indiana University, Bloomington, IN, USA
Pernet, Cyril; Neurobiology Research Unit, Rigshospitalet, and Institute of Clinical Medicine, Univ. Copenhagen, København, Denmark
Phillips, Christophe ; Université de Liège - ULiège > GIGA > GIGA CRC In vivo Imaging - Neuroimaging, data acquisition and processing
Oostenveld, Robert ; Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Netherlands ; NatMEG, Karolinska Institutet, Stockholm, Sweden
Gallezot, Jean-Dominique ; Department of Radiology and Biomedical Imaging, Yale University, New Haven, USA
Carson, Richard E ; Department of Radiology and Biomedical Imaging, Yale University, New Haven, USA
Knudsen, Gitte M; Neurobiology Research Unit, Rigshospitalet, and Institute of Clinical Medicine, Univ. Copenhagen, København, Denmark
Innis, Robert B; Molecular Imaging Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, USA
Ganz, Melanie ; Neurobiology Research Unit, Rigshospitalet, and Institute of Clinical Medicine, Univ. Copenhagen, København, Denmark. mganz@nru.dk ; Department of Computer Science, University of Copenhagen, Copenhagen, Denmark. mganz@nru.dk
Sundhed og Sygdom, Det Frie Forskningsråd DH | National Institute for Health Research SLaM - South London and Maudsley NHS Foundation Trust GSK - GlaxoSmithKline Elsass Fonden NNF - Novo Nordisk Fonden
Funding text :
We are grateful to the Neurobiology Research Unit, Copenhagen, Denmark, and the Centre for Neuroimaging Sciences, IoPPN, Kings College London, London, UK, for providing the data presented in this manuscript, including uploading it to OpenNeuro. This work was supported by the Novo Nordisk Foundation (NNF20OC0063277) and the NIH (1ZIAMH002977-01). MN was supported by the Independent Research Fund Denmark (DFF-0129-00004B), and MG was supported by the Elsass foundation (18-3-0147). AG is supported by the KCL funded CDT in Data-Driven Health and this represents independent research part funded by the National Institute for Health Research (NIHR) Maudsley Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London and part funded by GlaxoSmithKline (GSK). FF, CM, and RB were supported by the National Institute Of Mental Health of the National Institutes of Health (R24MH117179).We are grateful to the Neurobiology Research Unit, Copenhagen, Denmark, and the Centre for Neuroimaging Sciences, IoPPN, Kings College London, London, UK, for providing the data presented in this manuscript, including uploading it to OpenNeuro. This work was supported by the Novo Nordisk Foundation (NNF20OC0063277) and the NIH (1ZIAMH002977-01). MN was supported by the Independent Research Fund Denmark (DFF-0129-00004B), and MG was supported by the Elsass foundation (18-3-0147). AG is supported by the KCL funded CDT in Data-Driven Health and this represents independent research part funded by the National Institute for Health Research (NIHR) Maudsley Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London and part funded by GlaxoSmithKline (GSK). FF, CM, and RB were supported by the National Institute Of Mental Health of the National Institutes of Health (R24MH117179).
Bonte, F. J. Nuclear medicine pioneer citation, 1976: David e. kuhl, m.d. Journal of Nuclear Medicine 17, 518–519, https://jnm.snmjournals.org/content/17/6/518 (1976).
Phelps, M. E., Hoffman, E. J., Mullani, N. A. & Ter-Pogossian, M. M. Application of annihilation coincidence detection to transaxial reconstruction tomography. Journal of Nuclear Medicine 16, 210–224 (1975).
Ter-Pogossian, M. M., Phelps, M. E., Hoffman, E. J. & Mullani, N. A. A positron-emission transaxial tomograph for nuclear imaging (pett). Radiology 114, 89–98 (1975). DOI: 10.1148/114.1.89
Boellaard, R. et al. FDG PET/CT: EANM procedure guidelines for tumour imaging: version 2.0. European Journal of Nuclear Medicine and Molecular Imaging 42, 328–354, 10.1007/s00259-014-2961-x (2015). DOI: 10.1007/s00259-014-2961-x
Cohen, A. D. & Klunk, W. E. Early detection of Alzheimer’s disease using PiB and FDG PET. Neurobiology of Disease 72, 117–122, http://www.sciencedirect.com/science/article/pii/S0969996114001107 (2014). DOI: 10.1016/j.nbd.2014.05.001
Gunn, R. N., Slifstein, M., Searle, G. E. & Price, J. C. Quantitative imaging of protein targets in the human brain with PET. Physics in Medicine and Biology 60, R363–R411, 10.1088/0031-9155/60/22/R363 (2015). DOI: 10.1088/0031-9155/60/22/R363
Innis, R. B. et al. Consensus nomenclature for in vivo imaging of reversibly binding radioligands. Journal of Cerebral Blood Flow & Metabolism 27, 1533–1539 (2007). DOI: 10.1038/sj.jcbfm.9600493
Knudsen, G. M. et al. Guidelines for the content and format of pet brain data in publications and archives: A consensus paper. Journal of Cerebral Blood Flow & Metabolism 0271678X20905433 (2020).
Gorgolewski, K. J. et al. The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments. Scientific Data 3, 10.1038/sdata.2016.44 (2016).
Niso, G. et al. MEG-BIDS, the brain imaging data structure extended to magnetoencephalography. Scientific data 5, 1–5 (2018). DOI: 10.1038/sdata.2018.110
Pernet, C. R. et al. EEG-BIDS, an extension to the brain imaging data structure for electroencephalography. Scientific Data 6, 1–5 (2019). DOI: 10.1038/s41597-019-0104-8
Holdgraf, C. et al. iEEG-BIDS, extending the brain imaging data structure specification to human intracranial electrophysiology. Scientific data 6, 1–6 (2019). DOI: 10.1038/s41597-019-0105-7
Moreau, C. A. et al. The genetics-bids extension: Easing the search for genetic data associated with human brain imaging. GigaScience 9, giaa104 (2020). DOI: 10.1093/gigascience/giaa104
Wilkinson, M. D. et al. The fair guiding principles for scientific data management and stewardship. Scientific Data 3, 160018, 10.1038/sdata.2016.18 (2016). DOI: 10.1038/sdata.2016.18
Heeman, F. et al. Optimized dual-time-window protocols for quantitative [18F]flutemetamol and [18F]florbetaben PET studies. EJNMMI Research 9, 1–14, 10.1186/s13550-019-0499-4 (2019). DOI: 10.1186/s13550-019-0499-4
Frokjaer, V. G. et al. Role of serotonin transporter changes in depressive responses to sex-steroid hormone manipulation: A positron emission tomography study. Biological Psychiatry 78, 534–543, 10.1016/j.biopsych.2015.04.015 (2015). DOI: 10.1016/j.biopsych.2015.04.015
Knudsen, G. M. et al. The Center for Integrated Molecular Brain Imaging (Cimbi) database. NeuroImage 124, 1213–1219, 10.1016/j.neuroimage.2015.04.025 (2016). DOI: 10.1016/j.neuroimage.2015.04.025
Ganz-Benjaminsen, M. & Noergaard, M. [11C]DASB PET Cimbi database example, OpenNeuro, 10.18112/openneuro.ds001420.v1.0.1 (2021).
Veronese, M. et al. Reproduciblity of findings in modern PET neuroimaging: insight from the NRM2018 Grand Challenge. Journal of Cerebral Blood Flow & Metabolism 10.1177/0271678X211015101 (2021).
Veronese, M. NRM2018 PET Grand Challenge Dataset, OpenNeuro, 10.18112/openneuro.ds001705.v1.0.1 (2021).
Hansen, H. D. et al. Visual stimuli induce serotonin release in occipital cortex: A simultaneous positron emission tomography/magnetic resonance imaging study. Human Brain Mapping 41, 4753–4763, 10.1002/hbm.25156 (2020). DOI: 10.1002/hbm.25156
Blair, R. et al. bids-validator. zenodo 10.5281/zenodo.4711003 (2021).
Greve, D. N. et al. Cortical surface-based analysis reduces bias and variance in kinetic modeling of brain pet data. Neuroimage 92, 225–236 (2014). DOI: 10.1016/j.neuroimage.2013.12.021
Li, X., Morgan, P. S., Ashburner, J., Smith, J. & Rorden, C. The first step for neuroimaging data analysis: Dicom to nifti conversion. Journal of Neuroscience Methods 264, 47–56, https://www.sciencedirect.com/science/article/pii/S0165027016300073, https://doi.org/10.1016/j.jneumeth.2016.03.001 (2016)
Gorgolewski, K., Esteban, O., Schaefer, G., Wandell, B. & Poldrack, R. Openneuro—a free online platform for sharing and analysis of neuroimaging data. Organization for human brain mapping. Vancouver, Canada 1677 (2017).
Gorgolewski, K. J. et al. BIDS apps: Improving ease of use, accessibility, and reproducibility of neuroimaging data analysis methods. PLOS Computational Biology 13, e1005209, 10.1371/journal.pcbi.1005209 (2017). DOI: 10.1371/journal.pcbi.1005209