UNC-emory infant atlases for macaque brain image analysis: Postnatal brain development through 12 months

Yundi Shi, Francois Budin, Eva Yapuncich, Ashley Rumple, Jeffrey T. Young, Christa Payne, Xiaodong Zhang, Xiaoping Hu, Jodi Godfrey, Brittany Howell, Mar M. Sanchez, Martin A. Styner

Research output: Contribution to journalArticlepeer-review

24 Scopus citations


Computational anatomical atlases have shown to be of immense value in neuroimaging as they provide age appropriate reference spaces alongside ancillary anatomical information for automated analysis such as subcortical structural definitions, cortical parcellations or white fiber tract regions. Standard workflows in neuroimaging necessitate such atlases to be appropriately selected for the subject population of interest. This is especially of importance in early postnatal brain development, where rapid changes in brain shape and appearance render neuroimaging workflows sensitive to the appropriate atlas choice. We present here a set of novel computation atlases for structural MRI and Diffusion Tensor Imaging as crucial resource for the analysis of MRI data from non-human primate rhesus monkey (Macaca mulatta) data in early postnatal brain development. Forty socially-housed infant macaques were scanned longitudinally at ages 2 weeks, 3, 6, and 12 months in order to create cross-sectional structural and DTI atlases via unbiased atlas building at each of these ages. Probabilistic spatial prior definitions for the major tissue classes were trained on each atlas with expert manual segmentations. In this article we present the development and use of these atlases with publicly available tools, as well as the atlases themselves, which are publicly disseminated to the scientific community.

Original languageEnglish (US)
Article number617
JournalFrontiers in Neuroscience
Issue numberJAN
StatePublished - 2017

Bibliographical note

Funding Information:
We would like to thank Anne Glenn, Christine Marsteller, Dora Guzman, Caroline Fu, and the staff at the YNRC Field Station and Imaging Center. This research was supported by the following funding sources MH086633, P50 MH064065, MH070890, HD053000, Roadmap Grant U54 EB005149-01, P50 MH078105 and MH078105-S1, HD055255. NIH grants: UNC Intellectual and Developmental Disabilities Research Center P30 HD03110, MH091645, and Office of Research Infrastructure Programs/OD grant OD11132 (YNPRC Base grant). The YNPRC is fully accredited by the Association for the Assessment and Accreditation of Laboratory Care, International.

Publisher Copyright:
© 2017 Shi, Budin, Yapuncich, Rumple, Young, Payne, Zhang, Hu, Godfrey, Howell, Sanchez and Styner.


  • Automatic segmentation
  • Computational atlases
  • Diffusion tensor imaging
  • Macaque
  • Magnetic resonance imaging
  • Neuroimaging
  • Non-human primate
  • White matter pathways


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