An Open Resource for Non-human Primate Imaging

2800 Neuroscience Primates 570 Connectome/methods QH301 Biology [SDV]Life Sciences [q-bio] Biological Psychology Information Dissemination/methods 150 Radboud University Medical Center Datasets as Topic Neuroimaging Article QH301 03 medical and health sciences SDG 17 - Partnerships for the Goals 0302 clinical medicine Research Support, N.I.H., Extramural Journal Article Connectome Psychology Animals Brain/anatomy & histology Neurology & Neurosurgery DI-BCB_DCC_Theme 2: Perception, Action and Control Radboudumc 7: Neurodevelopmental disorders DCMN: Donders Center for Medical Neuroscience Primate Action, intention, and motor control Information Dissemination General Neuroscience Research Support, Non-U.S. Gov't Neurosciences 500 Brain DAS Magnetic Resonance Imaging Biomedical Imaging Biological psychology Cognitive Sciences [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]
DOI: 10.1016/j.neuron.2018.08.039 Publication Date: 2018-09-27T14:49:47Z
ABSTRACT
ABSTRACTNon-human primate neuroimaging is a rapidly growing area of research that promises to transform and scale translational and cross-species comparative neuroscience.Unfortunately, the technological and methodological advances of the past two decades have outpaced the accrual of data, which is particularly challenging given the relatively few centers that have the necessary facilities and capabilities. The PRIMate Data Exchange (PRIME-DE) addresses this challenge by aggregating independently acquired non-human primate magnetic resonance imaging (MRI) datasets and openly sharing them via the International Neuroimaging Data-sharing Initiative (INDI). Here, we present the rationale, design and procedures for the PRIME-DE consortium, as well as the initial release, consisting of 13 independent data collections aggregated across 11 sites (total = 98 macaque monkeys). We also outline the unique pitfalls and challenges that should be considered in the analysis of the non-human primate MRI datasets, including providing automated quality assessment of the contributed datasets.
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