AUTHOR=Bao Shasha , Liao Chengde , Xu Nan , Deng Ailin , Luo Yueyuan , Ouyang Zhiqiang , Guo Xiaobin , Liu Yifan , Ke Tengfei , Yang Jun 

TITLE=Prediction of brain age using quantitative parameters of synthetic magnetic resonance imaging

JOURNAL=Frontiers in Aging Neuroscience

VOLUME=Volume 14 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/aging-neuroscience/articles/10.3389/fnagi.2022.963668

DOI=10.3389/fnagi.2022.963668

ISSN=1663-4365

ABSTRACT=Objective: Brain tissue changes dynamically during aging. The purpose of this study was to use synthetic magnetic resonance imaging (syMRI) to evaluate the changes in relaxation values in different brain regions during brain aging and to construct a brain age prediction model.
Materials and methods: Quantitative MRI was performed on 1000 healthy people (≥ 18 years old) . T1, T2 and proton density (PD) values were simultaneously measured in 17 regions of interest . The relationship between the relaxation values and age was investigated. In addition, we analyzed the relationship between brain tissue values and sex. Finally, the participants were divided into two age groups: < 60 years old and ≥ 60 years old. Logistic regression analysis was carried out on the two groups of data. According to the weight of related factors, a brain age prediction model was established and verified.
Results: We obtained the specific reference value range of different brain regions of individuals in different age groups and found that there were differences in relaxation values in brain tissue between different sexes in the same age group. Moreover, the relaxation values of most brain regions in males were slightly higher than those in females. In the study of age and brain relaxation, it was found that brain relaxation values were correlated with age. The T1  values of the frontal lobe and centrum semioval  increased with age,  the  PD values of  centrum semioval increased with age, while the T2 values of the caudate nucleus and lentiform nucleus decreased with age. Seven brain age prediction models were constructed with high sensitivity and specificity, among which the combined T1, T2 and PD values showed the best prediction efficiency.
Conclusion: Our study provides specific reference ranges of T1, T2 and PD values in different brain regions from healthy adults of different ages. In addition, there are differences in brain relaxation values in some brain regions between different sexes, which help to provide new ideas for brain diseases that differ according to sex. The brain age model based on synthetic MRI is helpful to determine brain age.