Study of the relationship between smoking and brain aging using machine learning model based on MRI
10.3760/cma.j.cn112149-20220106-00015
- VernacularTitle:基于MRI的机器学习模型研究吸烟与大脑年龄的关系
- Author:
Xinyu GAO
1
;
Mengzhe ZHANG
;
Shaoqiang HAN
;
Zhengui YANG
;
Weijian WANG
;
Ke XU
;
Jingliang CHENG
;
Yong ZHANG
Author Information
1. 郑州大学第一附属医院磁共振科，郑州 450002
- Keywords:
Smoking;
Magnetic resonance imaging;
Brain age;
Machine learning
- From:
Chinese Journal of Radiology
2022;56(12):1347-1351
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To explore the value of machine learning models based on MRI predict the brain age of smokers and healthy controls, and further to explore the relationship between smoking and brain aging.Methods:This was a retrospective study. Dataset 1 consisted of 95 male smokers [20-50 (34±7) years old] and 49 healthy controls [20-50 (33±7) years old] recruited from August 2014 to October 2017 in First Affiliated Hospital of Zhengzhou University. Dataset 2 contained 114 healthy male volunteers [20-50 (34±11) years old] from the Southwestern University Adult Imaging Database from 2010 to 2015. All subjects underwent high-resolution 3D T 1WI scan. Gaussian process regression (GPR) model and support vector machine model were constructed to predict brain age based on structural MR images of healthy controls in dataset 1 and dataset 2. After the performance of the model was verified by the cross-validation method, the mean absolute error (MAE) between the predicted brain age and the actual age and the correlation ( r-value) between the actual age and the predicted brain age were calculated, and the best model was finally selected. The best models were applied to smokers and healthy controls to predict brain age. Finally, a general linear model was used to compare the differences in brain-predicted age difference (PAD) between smokers and healthy controls with age, taking years of education and total intracranial volume as covariates. Result:The performance of GPR model (MAE=5.334, r=0.747) in predicting brain age was better than support vector machine model (MAE=6.040, r=0.679). The GPR model predicted that PAD value of smokers in dataset 1 (2.19±6.64) was higher than that of healthy controls in dataset 1 (-0.80±8.94), and the difference was statistically significant ( F=8.52, P=0.004). Conclusion:GPR model based MRI has better performance in predicting brain age in smokers and healthy controls, and smokers show increased PAD values, further indicating that smoking accelerates brain aging.
