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Guidelines for the content and format of PET brain data in publications and archives: A consensus paper.

Gitte M Knudsen | Melanie Ganz | Stefan Appelhoff | Ronald Boellaard | Guy Bormans | Richard E Carson | Ciprian Catana | Doris Doudet | Antony D Gee | Douglas N Greve | Roger N Gunn | Christer Halldin | Peter Herscovitch | Henry Huang | Sune H Keller | Adriaan A Lammertsma | Rupert Lanzenberger | Jeih-San Liow | Talakad G Lohith | Mark Lubberink | Chul H Lyoo | J John Mann | Granville J Matheson | Thomas E Nichols | Martin Nørgaard | Todd Ogden | Ramin Parsey | Victor W Pike | Julie Price | Gaia Rizzo | Pedro Rosa-Neto | Martin Schain | Peter Jh Scott | Graham Searle | Mark Slifstein | Tetsuya Suhara | Peter S Talbot | Adam Thomas | Mattia Veronese | Dean F Wong | Maqsood Yaqub | Francesca Zanderigo | Sami Zoghbi | Robert B Innis
Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism | 2020

It is a growing concern that outcomes of neuroimaging studies often cannot be replicated. To counteract this, the magnetic resonance (MR) neuroimaging community has promoted acquisition standards and created data sharing platforms, based on a consensus on how to organize and share MR neuroimaging data. Here, we take a similar approach to positron emission tomography (PET) data. To facilitate comparison of findings across studies, we first recommend publication standards for tracer characteristics, image acquisition, image preprocessing, and outcome estimation for PET neuroimaging data. The co-authors of this paper, representing more than 25 PET centers worldwide, voted to classify information as mandatory, recommended, or optional. Second, we describe a framework to facilitate data archiving and data sharing within and across centers. Because of the high cost of PET neuroimaging studies, sample sizes tend to be small and relatively few sites worldwide have the required multidisciplinary expertise to properly conduct and analyze PET studies. Data sharing will make it easier to combine datasets from different centers to achieve larger sample sizes and stronger statistical power to test hypotheses. The combining of datasets from different centers may be enhanced by adoption of a common set of best practices in data acquisition and analysis.

Pubmed ID: 32065076

Research resources used in this publication

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Associated grants

  • Agency: NCATS NIH HHS, United States
    Id: UL1 TR001863
  • Agency: Intramural NIH HHS, United States
    Id: ZIA MH002852

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This is a list of tools and resources that we have found mentioned in this publication.


OpenNeuro (tool)

RRID:SCR_005031

Open platform for analyzing and sharing neuroimaging data from human brain imaging research studies. Brain Imaging Data Structure ( BIDS) compliant database. Formerly known as OpenfMRI. Data archives to hold magnetic resonance imaging data. Platform for sharing MRI, MEG, EEG, iEEG, and ECoG data.

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Brain Imaging Data Structure (BIDs) (tool)

RRID:SCR_016124

Standard specification for organizing and describing outputs of neuroimaging experiments. Used to organize and describe neuroimaging and behavioral data by neuroscientific community as standard to organize and share data. BIDS prescribes file naming conventions and folder structure to store data in set of already existing file formats. Provides standardized templates to store associated metadata in form of Javascript Object Notation (JSON) and tab-separated value (TSV) files. Facilitates data sharing, metadata querying, and enables automatic data analysis pipelines. System to curate, aggregate, and annotate neuroimaging databases. Intended for magnetic resonance imaging data, magnetoencephalography data, electroencephalography data, and intracranial encephalography data.

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