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
AUTHORS (70)
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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