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bioRxivNeurosciencePreprint

Unbiased Age-Appropriate Structural Brain Atlases for Chinese Pediatrics

Tengda ZhaoiD, Xuhong LiaoiD, Vladimir FonoviD, Weiwei MeniD, Yanpei WangiD, Shaozheng QiniD, Shuping TaniD, Jia‐Hong GaoiD, Alan Charles EvansiD, Sha TaoiD, Qi DongiD, Yong He AiD

PDFDOI

Abstract

In magnetic resonance imaging (MRI) studies of children brain development, structural brain atlases usually serve as important references of pediatric population in which individual images are spatially normalized into a common or standard stereotactic space. However, the existing popular children brain atlases (e.g., National Institutes of Health pediatric atlases, NIH-PD atlases) are made mostly based on MR images from Western populations, and are thus insufficient to characterize the brains of Chinese children due to the neuroanatomical differences that are relevant to genetic and environmental factors. By collecting high-quality T1- and T2- weighted MR images from 328 typically developing Chinese children aged from 6 to 12 years old, we created a set of age-appropriate Chinese pediatric (CHN-PD) atlases using an unbiased template construction algorithm. The CHN-PD atlases included the head/brain templates, the symmetric brain template, the gender-specific brain templates and the corresponding tissue probability atlases. Moreover, the atlases contained multiple age-specific templates with a one-year interval. A direct comparison of the CHN-PD and the NIH-PD atlases revealed remarkable anatomical differences bilaterally in the lateral frontal and parietal regions and somatosensory cortex. While applying the CHN-PD atlases to two independent Chinese pediatric datasets (N = 114 and N = 71, respectively), machine-learning regression approaches revealed higher prediction accuracy on brain ages than the usage of NIH-PD atlases. These results suggest that the CHN-PD brain atlases are necessary and important for future typical and atypical developmental studies in Chinese pediatric population. Currently, the CHN-PD atlases have been released on the NITRC website (https://www.nitrc.org/projects/chn-pd).

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