PET-BIDS, an extension to the brain imaging data structure for positron emission tomography

Nørgaard, Martin Agge; Matheson, Granville J.; Hansen, Hanne D.; Thomas, Adam G.; Searle, Graham; Rizzo, Gaia; Veronese, Mattia; Giacomel, Alessio; Yaqub, Maqsood; Tonietto, Mattéo; Funck, Thomas; Gillman, Ashley; Boniface, Hugo; Routier, Alexandre; Dalenberg, Jelle R.; Betthauser, Tobey J.; Feingold, Franklin; Markiewicz, Christopher J.; Gorgolewski, Krzysztof J.; Blair, Ross; Appelhoff, Stefan; Gau, Rémi; Salo, Taylor; Niso, Guiomar; Pernet, Cyril; Phillips, Christophe; Oostenveld, Robert; Gallezot, Jean‐Dominique; Carson, Richard E.; Knudsen, Gitte Moos; Innis, Robert B.; Ganz, Melanie

doi:10.1101/2021.06.16.448390

Cited by 11 publications

(7 citation statements)

References 22 publications

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“…The brain PET imaging community has lacked data format standards, and only very recently it has been recognised the need of more standardized data structure (Innis et al 2007; Knudsen et al 2020). The hope is that initiatives like PET-BIDS will address the heterogeneity of data organization by following the FAIR principles (findability, accessibility, interoperability, and reusability) (Nørgaard et al 2021).…”

Section: Discussionmentioning

confidence: 99%

Digital data repository and automatic analysis framework for FDOPA PET neuroimaging

Nordio

Easmin

Giacomel

et al. 2022

Preprint

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IntroductionFDOPA PET has been used extensively to image the human brain in many clinical disorders and has the potential to be used for patient stratification and individualized treatment. However, to reach its full and effective clinical translation, FDOPA PET requires both a robust data infrastructure and analytical protocol that are capable of ensuring high quality data and metadata, accurate biological quantification, and replicable results. In this study we evaluate a digital data repository and automated analytical framework for FDOPA PET neuroimaging that can produce an individualised quantification of dopamine synthesis capacity in the living human brain.MethodsThe imaging platform XNAT was used to store the King’s College London institutional brain FDOPA PET imaging archive, alongside individual demographics and clinical information. A fully automated analysis pipeline for imaging processing and data quantification was developed in Python and integrated in XNAT using the Docker technology. Reproducibility was assessed in test-retest datasets both in controls and patients with psychosis. The agreement between the automated analysis estimates and the results derived by the manual analysis were compared. Finally, using a sample of healthy controls (N=115), a sensitivity analysis was performed to explore the impact of experimental and demographic variables on the FDOPA PET measures.ResultsThe final data repository includes 892 FDOPA PET scans organized from 23 different studies, collected at five different imaging sites. After removing commercials studies, the infrastructure consisted of 792 FDOPA PET scans from 666 individuals (female 33.9%, healthy controls 29.1%) collected from four different imaging sites between 2004-2021. The automated analysis pipeline provided results that were in agreement with the results from the manual analysis, with a Pearson’s correlation that ranged from 0.64 to 0.99 for Kicer, and from 0.79 to 1.00 for SUVR. The mean absolute difference between the two pipelines ranges from 3.4% to 9.4% for Kicer, and from 2.5% to 12.4% for SUVR. Moreover, we found good reproducibility of the data analysis by the automated pipeline (in the whole striatum for the Kicer: ICC for the controls = 0.71, ICC for the psychotic patients = 0.88). From the demographic and experimental variables assessed, gender was found to most influence striatal dopamine synthesis capacity (F = 10.7, p <0.001), with women showing greater dopamine synthesis capacity than men, while the effects of weight, age, injected radioactivity, and scanner, varied by brain region and parameter of interest.ConclusionsCombining information from different neuroimaging studies has allowed us to test comprehensively the automated pipeline for quantification of dopamine synthesis capacity using FDOPA PET data and to validate its replicability and reproducibility performances on a large sample size. This validation process is a necessary methodological step for the development of the clinical application of FDOPA PET as precision medicine biomarker. The proposed infrastructure is generalisable behind the FDOPA radiotracer.

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Section: Discussionmentioning

confidence: 99%

Digital data repository and automatic analysis framework for FDOPA PET neuroimaging

Nordio

Easmin

Giacomel

et al. 2022

Preprint

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“…One of the important features of BIDS is its extensibility; using a scheme inspired by open source software projects, community members can propose extensions to BIDS that encompass new data types. To date, modality extensions include magnetoencephalography (Niso et al, 2018), scalp electroencephalography (Pernet et al, 2019), intracranial EEG (Holdgraf et al, 2019), positron emission tomography (Norgaard et al, 2021), and arterial spin labeling MRI. In addition to standards for raw data, the BIDS community has also developed a standard for the organization of the outputs of processing operations (known as "BIDS Derivatives"), providing a framework for sharing processed as well as raw data.…”

Section: Standards-focused Data Sharingmentioning

confidence: 99%

The OpenNeuro resource for sharing of neuroscience data

Markiewicz

Gorgolewski

Feingold

et al. 2021

eLife

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128

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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.

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“…To this end, it is necessary to obtain patients’ consent to share their data in a secured, controlled manner, ensuring GDPR compliance [ 130 ], and using the standardized BIDS format [ 131 ] following existing extensions for e.g. PET [ 132 ] or ASL [ 133 ]. Finally, it is important to consider that brains already differ in their capacity to cope with therapy-related damage prior to cancer therapy, which can be considered a traumatic event.…”

Section: Recommendationsmentioning

confidence: 99%

A systematic review on the use of quantitative imaging to detect cancer therapy adverse effects in normal-appearing brain tissue

Petr

Hogeboom

Nikulin

et al. 2021

Magn Reson Mater Phy

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Cancer therapy for both central nervous system (CNS) and non-CNS tumors has been previously associated with transient and long-term cognitive deterioration, commonly referred to as ‘chemo fog’. This therapy-related damage to otherwise normal-appearing brain tissue is reported using post-mortem neuropathological analysis. Although the literature on monitoring therapy effects on structural magnetic resonance imaging (MRI) is well established, such macroscopic structural changes appear relatively late and irreversible. Early quantitative MRI biomarkers of therapy-induced damage would potentially permit taking these treatment side effects into account, paving the way towards a more personalized treatment planning.This systematic review (PROSPERO number 224196) provides an overview of quantitative tomographic imaging methods, potentially identifying the adverse side effects of cancer therapy in normal-appearing brain tissue. Seventy studies were obtained from the MEDLINE and Web of Science databases. Studies reporting changes in normal-appearing brain tissue using MRI, PET, or SPECT quantitative biomarkers, related to radio-, chemo-, immuno-, or hormone therapy for any kind of solid, cystic, or liquid tumor were included. The main findings of the reviewed studies were summarized, providing also the risk of bias of each study assessed using a modified QUADAS-2 tool. For each imaging method, this review provides the methodological background, and the benefits and shortcomings of each method from the imaging perspective. Finally, a set of recommendations is proposed to support future research.

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PET-BIDS, an extension to the brain imaging data structure for positron emission tomography

Cited by 11 publications

References 22 publications

Digital data repository and automatic analysis framework for FDOPA PET neuroimaging

Digital data repository and automatic analysis framework for FDOPA PET neuroimaging

The OpenNeuro resource for sharing of neuroscience data

A systematic review on the use of quantitative imaging to detect cancer therapy adverse effects in normal-appearing brain tissue

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