Unbiased age-specific structural brain atlases for Chinese pediatric population

Tengda Zhao; Xuhong Liao; Vladimir S Fonov; Qiushi Wang; Weiwei Men; Yanpei Wang; Shaozheng Qin; Shuping Tan; Jia-Hong Gao; Alan Evans; Sha Tao; Qi Dong; Yong He

doi:10.1016/j.neuroimage.2019.01.006

Unbiased age-specific structural brain atlases for Chinese pediatric population

Neuroimage. 2019 Apr 1:189:55-70. doi: 10.1016/j.neuroimage.2019.01.006. Epub 2019 Jan 6.

Authors

Tengda Zhao¹, Xuhong Liao², Vladimir S Fonov³, Qiushi Wang¹, Weiwei Men⁴, Yanpei Wang⁵, Shaozheng Qin¹, Shuping Tan⁶, Jia-Hong Gao⁷, Alan Evans⁸, Sha Tao⁵, Qi Dong⁵, Yong He⁹

Affiliations

¹ National Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, 100875, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China.
² National Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, 100875, China; School of Systems Science, Beijing Normal University, Beijing, 100875, China.
³ McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada.
⁴ Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, 100871, China; Beijing City Key Laboratory for Medical Physics and Engineering, Institute of Heavy Ion Physics, School of Physics, Peking University, Beijing, 100871, China.
⁵ National Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China.
⁶ Psychiatry Research Center, Beijing HuiLongGuan Hospital, Peking University, Beijing, 100096, China.
⁷ Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, 100871, China; Beijing City Key Laboratory for Medical Physics and Engineering, Institute of Heavy Ion Physics, School of Physics, Peking University, Beijing, 100871, China; McGovern Institute for Brain Research, Peking University, Beijing, 100871, China.
⁸ McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada; McGill Centre for Integrative Neuroscience, Montreal Neurological Institute, McGill University, Montreal, Canada; Ludmer Centre for NeuroInformatics and Mental Health, McGill University, Montreal, Canada.
⁹ National Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, 100875, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China. Electronic address: yong.he@bnu.edu.cn.

PMID: 30625395
DOI: 10.1016/j.neuroimage.2019.01.006

Abstract

In magnetic resonance (MR) imaging studies of child brain development, structural brain atlases usually serve as important references for the pediatric population, in which individual images are spatially normalized into a common or standard stereotactic space. However, the popular existing pediatric brain atlases (e.g., National Institutes of Health pediatric atlases, NIH-PD) are mostly based on MR images obtained from Caucasian populations and thus are not ideal for the characterization of the brains of Chinese children due to neuroanatomical differences related to genetic and environmental factors. Here, we use an unbiased template construction algorithm to create a set of age-specific Chinese pediatric (CHN-PD) atlases based on high-quality T1-and T2-weighted MR images from 328 cognitively normal Chinese children aged 6-12 years. The CHN-PD brain atlases include asymmetric and symmetric templates, sex-specific templates and tissue probability templates, and contain multiple age-specific templates at one-year intervals. A direct comparison of the CHN-PD and NIH-PD atlases reveals dramatic anatomical differences mainly in the bilateral frontal and parietal regions. After applying the CHN-PD and NIH-PD atlases to two independent Chinese pediatric datasets (N = 114 and N = 71), we find that the CHN-PD atlases result in significantly higher accuracy than the NIH-PD atlases in both predicting "brain age" and guiding brain tissue segmentation. These results suggest that the CHN-PD brain atlases are necessary for studies of the typical and atypical development of the Chinese pediatric population. These CHN-PD atlases have been released on the Neuroimaging Informatics Tools and Resources Clearinghouse (NITRC) website (https://www.nitrc.org/projects/chn-pd).

Keywords: Brain age; Brain template; Development; MRI; Machine learning.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Asian People
Atlases as Topic*
Brain / anatomy & histology*
Brain / diagnostic imaging
Child
China
Female
Humans
Magnetic Resonance Imaging
Male
Neuroimaging / methods*