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Author Notes:

Correspondence: Martin A. Styner ; Email: styner@unc.edu

We would like to thank Anne Glenn, Christine Marsteller, Dora Guzman, Caroline Fu, and the staff at the YNRC Field Station and Imaging Center.

Study Design: MMS. Methods: MAS, YS. Project oversight: MAS, MMS. MR protocol design and acquisition: XZ, XH. Animal handling: CP, JG, BH. Initial data processing, quality control: YS, BH. Manual edits of brain masks and tissue segmentations: EY, AR, CP, JG. Atlas generation: YS, FB. Writing: YS, JY, MAS, MMS. Figure generation: YS, JY, MAS.

The YNPRC is fully accredited by the Association for the Assessment and Accreditation of Laboratory Care, International.

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Research Funding:

his 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).


  • Science & Technology
  • Life Sciences & Biomedicine
  • Neurosciences
  • Neurosciences & Neurology
  • neuroimaging
  • non-human primate
  • macaque
  • computational atlases
  • white matter pathways
  • magnetic resonance imaging
  • diffusion tensor imaging
  • automatic segmentation
  • MRI

UNC-Emory Infant Atlases for Macaque Brain Image Analysis: Postnatal Brain Development through 12 Months

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Journal Title:

Frontiers in Neuroscience


Volume 10


, Pages 617-617

Type of Work:

Article | Final Publisher PDF


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.

Copyright information:

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

This is an Open Access work distributed under the terms of the Creative Commons Attribution-NoDerivatives 4.0 International License (http://creativecommons.org/licenses/by-nd/4.0/).

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