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

• Published in: Frontiers in neuroscience Vol. 10; p. 617
• Main Authors: Shi, Yundi, Budin, Francois, Yapuncich, Eva, Rumple, Ashley, Young, Jeffrey T., Payne, Christa, Zhang, Xiaodong, Hu, Xiaoping, Godfrey, Jodi, Howell, Brittany, Sanchez, Mar M., Styner, Martin A.
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Research Foundation 10.01.2017; Frontiers Media S.A
• Subjects: Autism; Brain research; Child development; Computational neuroscience; Cortex; Image processing; Laboratory animals; Macaca mulatta; Magnetic resonance imaging; Monkeys & apes; Neuroimaging; Neuroscience; Psychiatry; Research centers; Software; Studies; Atlanta Georgia; United States > US; macaque; magnetic resonance imaging; non-human primate; computational atlases; white matter pathways; diffusion tensor imaging; automatic segmentation; neuroimaging
• ISSN: 1662-453X; 1662-4548; 1662-453X
• DOI: 10.3389/fnins.2016.00617

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 ( ) 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.