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

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:...

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Autores principales: Bao, Shasha, Liao, Chengde, Xu, Nan, Deng, Ailin, Luo, Yueyuan, Ouyang, Zhiqiang, Guo, Xiaobin, Liu, Yifan, Ke, Tengfei, Yang, Jun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9705592/
https://www.ncbi.nlm.nih.gov/pubmed/36457759
http://dx.doi.org/10.3389/fnagi.2022.963668
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author Bao, Shasha
Liao, Chengde
Xu, Nan
Deng, Ailin
Luo, Yueyuan
Ouyang, Zhiqiang
Guo, Xiaobin
Liu, Yifan
Ke, Tengfei
Yang, Jun
author_facet Bao, Shasha
Liao, Chengde
Xu, Nan
Deng, Ailin
Luo, Yueyuan
Ouyang, Zhiqiang
Guo, Xiaobin
Liu, Yifan
Ke, Tengfei
Yang, Jun
author_sort Bao, Shasha
collection PubMed
description 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 1,000 healthy people (≥ 18 years old) from September 2020 to October 2021. T1, T2 and proton density (PD) values were simultaneously measured in 17 regions of interest (the cerebellar hemispheric cortex, pons, amygdala, hippocampal head, hippocampal tail, temporal lobe, occipital lobe, frontal lobe, caudate nucleus, lentiform nucleus, dorsal thalamus, centrum semiovale, parietal lobe, precentral gyrus, postcentral gyrus, substantia nigra, and red nucleus). 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 centrum semiovale increased with age, the PD values of the centrum semiovale 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. In the training set, the area under the curve (AUC), specificity and sensitivity were 0.959 [95% confidence interval (CI): 0.945–0.974], 91.51% and 89.36%, respectively. In the test cohort, the above indicators were 0.916 (95% CI: 0.882–0.951), 89.24% and 80.33%, respectively. 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.
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spelling pubmed-97055922022-11-30 Prediction of brain age using quantitative parameters of synthetic magnetic resonance imaging Bao, Shasha Liao, Chengde Xu, Nan Deng, Ailin Luo, Yueyuan Ouyang, Zhiqiang Guo, Xiaobin Liu, Yifan Ke, Tengfei Yang, Jun Front Aging Neurosci Neuroscience 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 1,000 healthy people (≥ 18 years old) from September 2020 to October 2021. T1, T2 and proton density (PD) values were simultaneously measured in 17 regions of interest (the cerebellar hemispheric cortex, pons, amygdala, hippocampal head, hippocampal tail, temporal lobe, occipital lobe, frontal lobe, caudate nucleus, lentiform nucleus, dorsal thalamus, centrum semiovale, parietal lobe, precentral gyrus, postcentral gyrus, substantia nigra, and red nucleus). 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 centrum semiovale increased with age, the PD values of the centrum semiovale 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. In the training set, the area under the curve (AUC), specificity and sensitivity were 0.959 [95% confidence interval (CI): 0.945–0.974], 91.51% and 89.36%, respectively. In the test cohort, the above indicators were 0.916 (95% CI: 0.882–0.951), 89.24% and 80.33%, respectively. 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. Frontiers Media S.A. 2022-11-15 /pmc/articles/PMC9705592/ /pubmed/36457759 http://dx.doi.org/10.3389/fnagi.2022.963668 Text en Copyright © 2022 Bao, Liao, Xu, Deng, Luo, Ouyang, Guo, Liu, Ke and Yang. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Bao, Shasha
Liao, Chengde
Xu, Nan
Deng, Ailin
Luo, Yueyuan
Ouyang, Zhiqiang
Guo, Xiaobin
Liu, Yifan
Ke, Tengfei
Yang, Jun
Prediction of brain age using quantitative parameters of synthetic magnetic resonance imaging
title Prediction of brain age using quantitative parameters of synthetic magnetic resonance imaging
title_full Prediction of brain age using quantitative parameters of synthetic magnetic resonance imaging
title_fullStr Prediction of brain age using quantitative parameters of synthetic magnetic resonance imaging
title_full_unstemmed Prediction of brain age using quantitative parameters of synthetic magnetic resonance imaging
title_short Prediction of brain age using quantitative parameters of synthetic magnetic resonance imaging
title_sort prediction of brain age using quantitative parameters of synthetic magnetic resonance imaging
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9705592/
https://www.ncbi.nlm.nih.gov/pubmed/36457759
http://dx.doi.org/10.3389/fnagi.2022.963668
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