PREMISE: A database of 20 Macaca Fascicularis PET/MRI brain imaging available for research

Non-human primate (NHP) studies are unique in translational research, especially in neurosciences and neuroimaging approaches are a preferred method for scaling cross-species comparative neurosciences. In this regard, neuroimaging database development and sharing are encouraged to increase the number of subjects available to the community while limiting the number of animals used in research. We present here a simultaneous PET/MR dataset of 20 Macaca Fascicularis structured according to the Brain Imaging Data Structure (BIDS) standards. This database contains multiple MRI sequences (anatomical, diffusion and perfusion imaging notably), as well as PET perfusion and inflammation using respectively [15O]H2O and [11C]PK11195 radiotracers. We describe the pipeline method to assemble baseline data from various cohorts and qualitatively assessed all the data using signal-to-noise and contrast-to-noise ratios as well as the median of intensity. The database is stored and available through the PRIME-DE consortium repository.

[1]  Y. Seimbille,et al.  EANM guideline for harmonisation on molar activity or specific activity of radiopharmaceuticals: impact on safety and imaging quality , 2021, EJNMMI Radiopharmacy and Chemistry.

[2]  N. Subbaraman The US is boosting funding for research monkeys in the wake of COVID , 2021, Nature.

[3]  Krzysztof J. Gorgolewski,et al.  PET-BIDS, an extension to the brain imaging data structure for positron emission tomography , 2021, Scientific Data.

[4]  Tianzi Jiang,et al.  Imaging evolution of the primate brain: the next frontier? , 2020, NeuroImage.

[5]  N. Costes,et al.  CERMEP-IDB-MRXFDG: a database of 37 normal adult human brain [18F]FDG PET, T1 and FLAIR MRI, and CT images available for research , 2020, bioRxiv.

[6]  Essa Yacoub,et al.  Minimal specifications for non-human primate MRI: Challenges in standardizing and harmonizing data collection , 2020, NeuroImage.

[7]  Ulrich Dirnagl,et al.  The ARRIVE guidelines 2.0: Updated guidelines for reporting animal research* , 2020, BMC Veterinary Research.

[8]  O. Tillement,et al.  PET-MRI nanoparticles imaging of blood–brain barrier damage and modulation after stroke reperfusion , 2020, Brain communications.

[9]  N. Costes,et al.  A non-human primate model of stroke reproducing endovascular thrombectomy and allowing long-term imaging and neurological read-outs , 2020, Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.

[10]  Ciprian Catana,et al.  Guidelines for the content and format of PET brain data in publications and archives: A consensus paper , 2020, Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.

[11]  Ravi S. Menon,et al.  Accelerating the Evolution of Nonhuman Primate Neuroimaging , 2020, Neuron.

[12]  Daniel S. Margulies,et al.  An Open Resource for Non-human Primate Imaging , 2018, Neuron.

[13]  Kim Charles Aske,et al.  Expanding the 3R principles , 2017, EMBO reports.

[14]  Thomas E. Nichols,et al.  Scanning the horizon: towards transparent and reproducible neuroimaging research , 2016, Nature Reviews Neuroscience.

[15]  Guido Gerig,et al.  ITK-SNAP: An interactive tool for semi-automatic segmentation of multi-modality biomedical images , 2016, 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[16]  Satrajit S. Ghosh,et al.  The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments , 2016, Scientific Data.

[17]  Erik Schultes,et al.  The FAIR Guiding Principles for scientific data management and stewardship , 2016, Scientific Data.

[18]  William D Hopkins,et al.  Why primate models matter , 2014, American journal of primatology.

[19]  I. Cuthill,et al.  Reporting : The ARRIVE Guidelines for Reporting Animal Research , 2010 .

[20]  N. Otsu A threshold selection method from gray level histograms , 1979 .