In Human brain mapping
It is an essential task to construct brain templates and analyze their anatomical structures in neurological and cognitive science. Generally, templates constructed from magnetic resonance imaging (MRI) of a group of subjects can provide a standard reference space for analyzing the structural and functional characteristics of the group. With recent development of artificial intelligence (AI) techniques, it is desirable to explore AI registration methods for quantifying age-specific brain variations and tendencies across different ages. In this article, we present an AI-based age-specific template construction (called ASTC) framework for longitudinal structural brain analysis using T1-weighted MRIs of 646 subjects from 18 to 82 years old collected from four medical centers. Altogether, 13 longitudinal templates were constructed at a 5-year age interval using ASTC, and tissue segmentation and substructure parcellation were performed for analysis across different age groups. The results indicated consistent changes in brain structures along with aging and demonstrated the capability of ASTC for longitudinal neuroimaging study.
Gu Dongdong, Shi Feng, Hua Rui, Wei Ying, Li Yufei, Zhu Jiayu, Zhang Weijun, Zhang Han, Yang Qing, Huang Peiyu, Jiang Yi, Bo Bin, Li Yao, Zhang Yaoyu, Zhang Minming, Wu Jinsong, Shi Hongcheng, Liu Siwei, He Qiang, Zhang Qiang, Zhang Xu, Wei Hongjiang, Liu Guocai, Xue Zhong, Shen Dinggang
artificial intelligence, brain template, longitudinal template, magnetic resonance imaging (MRI), registration, statistical analysis