An Open Resource for Non-human Primate Imaging

Michael P. Milham, Lei Ai, Bonhwang Koo, Ting Xu, Céline Amiez, Fabien Balezeau, Mark G. Baxter, Erwin L.A. Blezer, Thomas Brochier, Aihua Chen, Paula L. Croxson, Christienne G. Damatac, Stanislas Dehaene, Stefan Everling, Damian A. Fair, Lazar Fleysher, Winrich Freiwald, Sean Froudist-Walsh, Timothy D. Griffiths, Carole Guedj, Fadila Hadj-Bouziane, Suliann Ben Hamed, Noam Harel, Bassem Hiba, Bechir Jarraya, Benjamin Jung, Sabine Kastner, P. Christiaan Klink, Sze Chai Kwok, Kevin N. Laland, David A. Leopold, Patrik Lindenfors, Rogier B. Mars, Ravi S. Menon, Adam Messinger, Martine Meunier, Kelvin Mok, John H. Morrison, Jennifer Nacef, Jamie Nagy, Michael Ortiz Rios, Christopher I. Petkov, Mark Pinsk, Colline Poirier, Emmanuel Procyk, Reza Rajimehr, Simon M. Reader, Pieter R. Roelfsema, David A. Rudko, Matthew F.S. Rushworth, Brian E. Russ, Jerome Sallet, Michael Christoph Schmid, Caspar M. Schwiedrzik, Jakob Seidlitz, Julien Sein, Amir Shmuel, Elinor L. Sullivan, Leslie Ungerleider, Alexander Thiele, Orlin S. Todorov, Doris Tsao, Zheng Wang, Charles R.E. Wilson, Essa Yacoub, Frank Q. Ye, Wilbert Zarco, Yong-di Zhou, Daniel S. Margulies, Charles E. Schroeder

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.