Hexia Gan1, Quanzeng Zhang1, Han Zhang2, Yue Chen1, Jianzhong Lin3, TaiShan Kang3, Jiaxing Zhang1, Frederic A Troy4, Bing Wang5. 1. School of Medicine, Xiamen University, Fujian, China. 2. Center for Cognition and Brain Disorders, Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou Normal University, Hangzhou, China. 3. Department of Magnetic Resonance Imaging, ZhongShan affiliated Hospital of Xiamen University, Fujian, China. 4. Department of Biochemistry & Molecular Medicine, University of California School of Medicine, Davis, CA 95616 USA. 5. School of Medicine, Xiamen University, Fujian, China; School of Animal & Veterinary Sciences, Charles Sturt University, Wagga Wagga, Australia. Electronic address: biwang@csu.edu.au.
Abstract
PROPOSE: To design a set of brain templates for postnatal piglet brains based on high-resolution T1-weighted imaging for voxel-based morphometric analysis. MATERIALS AND METHODS: Using a 3.0 T magnetic resonance (MR) scanner, a population-based whole brain template was developed by averaging forty T1 images in the brains of postnatal piglets at 38 days of age. The templates for gray and white matter, and cerebrospinal fluid were designed based on the corresponding probability maps by adapting individual data sets using statistical parametric mapping. Anatomical labeling maps were generated from labeling propagation derived from the established Pig Brain Atlas. Differences in the coordinates from four significant structural landmarks in the template, plus an additional 12 normalized images and anatomical labeling maps were measured to validate the accuracy of the registration of the template. RESULTS: A whole brain template, a set of tissue-specific probability and anatomical labeling maps were developed. The location deviation of the four significant structural landmarks, including the anterior and posterior regions in the corpus callosum, and the left and right caudate nucleus, was found to be <0.25 cm, validating the sensitivity and resolution of the template. CONCLUSION: A whole brain template map and a set of tissue-specific probability and anatomical labeling maps were developed to analyze the morphometric imaging of the postnatal piglet brain, an animal model of the human infant.
PROPOSE: To design a set of brain templates for postnatal piglet brains based on high-resolution T1-weighted imaging for voxel-based morphometric analysis. MATERIALS AND METHODS: Using a 3.0 T magnetic resonance (MR) scanner, a population-based whole brain template was developed by averaging forty T1 images in the brains of postnatal piglets at 38 days of age. The templates for gray and white matter, and cerebrospinal fluid were designed based on the corresponding probability maps by adapting individual data sets using statistical parametric mapping. Anatomical labeling maps were generated from labeling propagation derived from the established Pig Brain Atlas. Differences in the coordinates from four significant structural landmarks in the template, plus an additional 12 normalized images and anatomical labeling maps were measured to validate the accuracy of the registration of the template. RESULTS: A whole brain template, a set of tissue-specific probability and anatomical labeling maps were developed. The location deviation of the four significant structural landmarks, including the anterior and posterior regions in the corpus callosum, and the left and right caudate nucleus, was found to be <0.25 cm, validating the sensitivity and resolution of the template. CONCLUSION: A whole brain template map and a set of tissue-specific probability and anatomical labeling maps were developed to analyze the morphometric imaging of the postnatal piglet brain, an animal model of the human infant.
Authors: Jidan Zhong; David Q Chen; Matthew Walker; Adam Waspe; Thomas Looi; Karolina Piorkowska; James M Drake; Mojgan Hodaie Journal: Front Neuroanat Date: 2016-09-27 Impact factor: 3.856
Authors: Stephano J Chang; Andrea J Santamaria; Francisco J Sanchez; Luz M Villamil; Pedro Pinheiro Saraiva; Jose Rodriguez; Yohjans Nunez-Gomez; Ioan Opris; Juan P Solano; James D Guest; Brian R Noga Journal: Front Neuroanat Date: 2020-11-13 Impact factor: 3.856