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

Nørgaard, Martin Agge; Matheson, Granville J.; Hansen, Hanne D.; Thomas, Adrian L. R.; 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, Chris; 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:10.1038/s41597-022-01164-1

Cited by 21 publications

(10 citation statements)

References 23 publications

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“…We anticipate that this method will make it possible to study more clinically-relevant research questions which could not previously be answered with sufficient power in these datasets, and thereby to improve the clinical relevance of PET imaging. However, the potential benefits of retrospective re-analysis of existing data is considerably augmented in the context of recent steps taken within the field to promote data sharing, as well as to harmonise data storage, reporting and analysis procedures ( Knudsen et al, 2020 ; Norgaard et al, 2022 ). Combined with the pooling of smaller datasets from individual research centres, we anticipate that the potential for SiMBA to reveal new, clinically-relevant associations will be even greater.…”

Section: Discussionmentioning

confidence: 99%

Simultaneous multifactor Bayesian analysis (SiMBA) of PET time activity curve data

Matheson

Ogden

2022

NeuroImage

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Section: Discussionmentioning

confidence: 99%

Simultaneous multifactor Bayesian analysis (SiMBA) of PET time activity curve data

Matheson

Ogden

2022

NeuroImage

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“…Recent analytic advances have opened new opportunities to perform neurophysiological time-series phenotyping by computing comprehensive feature sets that go beyond power spectral measures, including measures of signal amplitude distribution, entropy, fractal scaling and autocorrelation [35][36][37][38][39][40]. Concomitant advances in imaging technologies and data sharing offer new ways to measure brain structure with unprecedented detail and depth [41][42][43], including gene expression [44], myelination [45,46], neurotransmitter receptors [47][48][49][50][51][52][53][54], cytoarchitecture [55][56][57], laminar differentiation [56,58], cell type composition [44,59,60], metabolism [61,62] and evolutionary expansion [63,64].…”

Section: Introductionmentioning

confidence: 99%

Neurophysiological signatures of cortical micro-architecture

Shafiei

Fulcher

Voytek

et al. 2023

Preprint

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Systematic spatial variation in micro-architecture is observed across the cortex. These micro-architectural gradients are reflected in neural activity, which can be captured by neurophysiological time-series. How spontaneous neurophysiological dynamics are organized across the cortex and how they arise from heterogeneous cortical micro-architecture remains unknown. Here we extensively profile regional neurophysiological dynamics across the human brain by estimating over 6,800 time-series features from the resting state magnetoencephalography (MEG) signal. We then map regional time-series profiles to a comprehensive multi-modal, multi-scale atlas of cortical micro-architecture, including microstructure, metabolism, neurotransmitter receptors, cell types and laminar differentiation. We find that the dominant axis of neurophysiological dynamics reflects characteristics of power spectrum density and linear correlation structure of the signal, emphasizing the importance of conventional features of electromagnetic dynamics while identifying additional informative features that have traditionally received less attention. Moreover, spatial variation in neurophysiological dynamics is co-localized with multiple micro-architectural features, including genomic gradients, intracortical myelin, neurotransmitter receptors and transporters, and oxygen and glucose metabolism. Collectively, this work opens new avenues for studying the anatomical basis of neural activity.

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“…The initial BIDS proposal covered anatomical, functional, and diffusion MRI 11 . Subsequently, extensions for magnetoencephalography (MEG) 12 , electroencephalography (EEG) 13 , intracranial EEG (iEEG) 14 , and Positron Emission Tomography 15 have been incorporated, and several other extension proposals are in development.…”

Section: Introductionmentioning

confidence: 99%

ASL-BIDS, the brain imaging data structure extension for arterial spin labeling

et al. 2022

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Arterial spin labeling (ASL) is a non-invasive MRI technique that allows for quantitative measurement of cerebral perfusion. Incomplete or inaccurate reporting of acquisition parameters complicates quantification, analysis, and sharing of ASL data, particularly for studies across multiple sites, platforms, and ASL methods. There is a strong need for standardization of ASL data storage, including acquisition metadata. Recently, ASL-BIDS, the BIDS extension for ASL, was developed and released in BIDS 1.5.0. This manuscript provides an overview of the development and design choices of this first ASL-BIDS extension, which is mainly aimed at clinical ASL applications. Discussed are the structure of the ASL data, focussing on storage order of the ASL time series and implementation of calibration approaches, unit scaling, ASL-related BIDS fields, and storage of the labeling plane information. Additionally, an overview of ASL-BIDS compatible conversion and ASL analysis software and ASL example datasets in BIDS format is provided. We anticipate that large-scale adoption of ASL-BIDS will improve the reproducibility of ASL research.

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

Cited by 21 publications

References 23 publications

Simultaneous multifactor Bayesian analysis (SiMBA) of PET time activity curve data

Simultaneous multifactor Bayesian analysis (SiMBA) of PET time activity curve data

Neurophysiological signatures of cortical micro-architecture

ASL-BIDS, the brain imaging data structure extension for arterial spin labeling

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