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. 2018 Oct 10;100(1):61-74.e2.
doi: 10.1016/j.neuron.2018.08.039. Epub 2018 Sep 27.

An Open Resource for Non-human Primate Imaging

Michael P Milham  1 Lei Ai  2 Bonhwang Koo  2 Ting Xu  2 Céline Amiez  3 Fabien Balezeau  4 Mark G Baxter  5 Erwin L A Blezer  6 Thomas Brochier  7 Aihua Chen  8 Paula L Croxson  5 Christienne G Damatac  9 Stanislas Dehaene  10 Stefan Everling  11 Damian A Fair  12 Lazar Fleysher  13 Winrich Freiwald  14 Sean Froudist-Walsh  15 Timothy D Griffiths  4 Carole Guedj  16 Fadila Hadj-Bouziane  16 Suliann Ben Hamed  17 Noam Harel  18 Bassem Hiba  17 Bechir Jarraya  10 Benjamin Jung  19 Sabine Kastner  20 P Christiaan Klink  21 Sze Chai Kwok  22 Kevin N Laland  23 David A Leopold  24 Patrik Lindenfors  25 Rogier B Mars  26 Ravi S Menon  11 Adam Messinger  19 Martine Meunier  16 Kelvin Mok  27 John H Morrison  28 Jennifer Nacef  4 Jamie Nagy  5 Michael Ortiz Rios  4 Christopher I Petkov  4 Mark Pinsk  20 Colline Poirier  4 Emmanuel Procyk  3 Reza Rajimehr  29 Simon M Reader  30 Pieter R Roelfsema  31 David A Rudko  27 Matthew F S Rushworth  32 Brian E Russ  33 Jerome Sallet  34 Michael Christoph Schmid  4 Caspar M Schwiedrzik  14 Jakob Seidlitz  35 Julien Sein  7 Amir Shmuel  27 Elinor L Sullivan  36 Leslie Ungerleider  19 Alexander Thiele  4 Orlin S Todorov  37 Doris Tsao  38 Zheng Wang  39 Charles R E Wilson  3 Essa Yacoub  18 Frank Q Ye  40 Wilbert Zarco  14 Yong-di Zhou  41 Daniel S Margulies  42 Charles E Schroeder  43
Affiliations

An Open Resource for Non-human Primate Imaging

Michael P Milham et al. Neuron. .

Abstract

Non-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 25 independent data collections aggregated across 22 sites (total = 217 non-human primates). We also outline the unique pitfalls and challenges that should be considered in the analysis of non-human primate MRI datasets, including providing automated quality assessment of the contributed datasets.

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Figures

Figure 1
Figure 1
Spatial Quality Metrics for Morphometry MRI Datasets Spatial quality metrics include: contrast-to-noise ratio (CNR), smoothness of voxels indexed as full width at half maximum (FWHM), signal-to-noise ratio (SNR), and artifactual voxel detection (Qi1). See Table 3 for details on this and the other quality metrics released. The colored scatterplots illustrate the quality metrics distribution for each data collection. The violin plots on the left of each panel represent a kernel density estimation of the distribution across all data collections for each quality metric. Starting from the bottom: each horizontal line marks the 1st, 5th, 25th, 50th, 75th, 95th, and 99th percentiles.
Figure 2
Figure 2
Spatial and Temporal Quality Metrics for Functional MRI Datasets Spatial quality metrics include: ghost-to-single ratio (GSR), smoothness of voxels indexed as full width at half maximum (FWHM), and signal-to-noise ratio (SNR). Temporal metrics are mean frame-wise displacement (Mean FD), standardized DVARS, global correlation (GCORR), and temporal signal-to-noise ratio (tSNR). See Table 3 for details on this and the other quality metrics released. The colored scatterplots illustrate the quality metrics distribution for each data collection. The violin plots on the left of each panel represent a kernel density estimation of the distribution across all data collections for each quality metric. Starting from the bottom: each horizontal line marks the 1st, 5th, 25th, 50th, 75th, 95th, and 99th percentiles.
Figure 3
Figure 3
Example Structural Images Example structural images aligned to the common space defined by the NMT template.
Figure 4
Figure 4
Example Functional Images Example functional images aligned to the common space defined by the NMT template.

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