A standardised representation for non-parametric fMRI results

Camille Maumet 1 Guillaume Flandin 2 Martin Perez-Guevara 3 Jean-Baptiste Poline 4, 5 Justin Rajendra 6 Richard Reynolds 6 Bertrand Thirion 7 Thomas E. Nichols 8
1 VisAGeS - Vision, Action et Gestion d'informations en Santé
INSERM - Institut National de la Santé et de la Recherche Médicale : U1228, Inria Rennes – Bretagne Atlantique , IRISA-D5 - SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE
2 Wellcome Trust Centre for Neuroimaging
3 Criteo [Paris]
4 McGill University
5 LBNL - Lawrence Berkeley National Laboratory [Berkeley]
6 NIH - National Institutes of Health [Bethesda]
7 PARIETAL - Modelling brain structure, function and variability based on high-field MRI data
NEUROSPIN - Service NEUROSPIN, Inria Saclay - Ile de France
8 University of Oxford [Oxford]
Abstract : Introduction : Reuse of data collected and analysed at another site is becoming more prevalent in the neuroimaging community (cf. for example (Milham et al. 2017)) but this process usually relies on intensive data and metadata curation. Given the ever-increasing number of research datasets produced and shared, it is desirable to rely on standards that will enable automatic data and metadata retrieval for large-scale analyses (Wilkinson et al. 2016). We recently introduced NIDM-Results (Maumet et al. 2016), a data model to represent and publish data and metadata created as part of a mass univariate neuroimaging study (typically functional magnetic resonance imaging). Here we extend this model to allow for the representation of non-parametric analyses and we introduce a JSON API that will facilitate export into NIDM-Results.
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https://www.hal.inserm.fr/inserm-01828914
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Submitted on : Tuesday, July 3, 2018 - 3:27:50 PM
Last modification on : Monday, February 10, 2020 - 6:13:44 PM
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Camille Maumet, Guillaume Flandin, Martin Perez-Guevara, Jean-Baptiste Poline, Justin Rajendra, et al.. A standardised representation for non-parametric fMRI results. OHBM 2018 - Annual meeting of the Organization of Human Brain Mapping, Jun 2018, Singapore, Singapore. pp.1-4. ⟨inserm-01828914⟩

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