基于互信息-支持向量回归的阿尔兹海默症磁共振影像脑年龄检测

doi:10.1007/s12204-023-2590-2

J Shanghai Jiaotong Univ Sci ›› 2025, Vol. 30 ›› Issue (1): 130-135.doi: 10.1007/s12204-023-2590-2

基于互信息-支持向量回归的阿尔兹海默症磁共振影像脑年龄检测

刘玉川1，李浩1，唐宇龙1，梁杜娟2，谭佳3，符玥1，李勇明4

（1. 重庆科技学院智能技术与工程学院，重庆401331；2. 陆军工程大学通信士官学校，重庆400035；3. 中冶赛迪信息技术（重庆）有限公司，重庆401329；4. 重庆大学微电子与通信工程学院，重庆400044)

收稿日期:2022-11-11 接受日期:2022-12-01 出版日期:2025-01-28 发布日期:2025-01-28

Brain Age Detection of Alzheimer’s Disease Magnetic Resonance Images Based on Mutual Information - Support Vector Regression

LIU Yuchuan¹ (张轶伦), LI Hao¹ (徐思坤), TANG Yulong¹ (徐捷), LIANG Dujuan² (曾学奇), TAN Jia³ (李铮), FU Yue¹ (谢驰), LI Yongming^4∗ (谢驰)

(1. School of Intelligent Technology and Engineering, Chongqing University of Science and Technology, Chongqing 401331, China; 2. Communication Sergeant School, Army Engineering University, Chongqing 400035, China; 3. CISDI Information Technology (Chongqing) Co., Ltd., Chongqing 401329, China; 4. School of Microelectronics and Communication Engineering, Chongqing University, Chongqing 400044, China)

Received:2022-11-11 Accepted:2022-12-01 Online:2025-01-28 Published:2025-01-28

摘要/Abstract

摘要： 脑年龄是一种有效诊断阿尔兹海默症（AD）的生物标记物。针对现有的脑年龄检测方法与AD是大脑加速老化的生物学假说相悖的问题，提出了一种互信息-支持向量回归（MI-SVR）的脑年龄检测模型。首先，根据AD的生物学假说引入年龄偏差；其次，基于互信息准则设计了适应度函数；然后，支持向量回归和适应度函数分别用于获取受试者的脑年龄和适应度值，最佳年龄偏差则通过查找最大适应度值获得；最后，比较于现有的一些脑年龄检测方法。实验结果表明，提出的方法所获得的脑年龄具有更好的可分性，能更好反映AD的加速衰老进程，更有助于提升AD的诊断准确率。

关键词: 脑年龄，阿尔兹海默症，互信息-支持向量回归，年龄偏差

Abstract: Brain age is an effective biomarker for diagnosing Alzheimer’s disease (AD). Aimed at the issue that the existing brain age detection methods are inconsistent with the biological hypothesis that AD is the accelerated aging of the brain, a mutual information - support vector regression (MI-SVR) brain age prediction model is proposed. First, the age deviation is introduced according to the biological hypothesis of AD. Second, fitness function is designed based on mutual information criterion. Third, support vector regression and fitness function are used to obtain the predicted brain age and fitness value of the subjects, respectively. The optimal age deviation is obtained by maximizing the fitness value. Finally, the proposed method is compared with some existing brain age detection methods. Experimental results show that the brain age obtained by the proposed method has better separability, can better reflect the accelerated aging of AD, and is more helpful for improving the diagnostic accuracy of AD.

Key words: brain age, Alzheimer’s disease (AD), mutual information - support vector regression (MI-SVR), age deviation

中图分类号:

TP391
R318

刘玉川1，李浩1，唐宇龙1，梁杜娟2，谭佳3，符玥1，李勇明4. 基于互信息-支持向量回归的阿尔兹海默症磁共振影像脑年龄检测[J]. J Shanghai Jiaotong Univ Sci, 2025, 30(1): 130-135.

LIU Yuchuan¹ (刘玉川), LI Hao¹ (李浩), TANG Yulong¹ (唐宇龙), LIANG Dujuan² (梁杜娟), TAN Jia³ (谭佳), FU Yue¹ (符玥), LI Yongming^4∗ (李勇明). Brain Age Detection of Alzheimer’s Disease Magnetic Resonance Images Based on Mutual Information - Support Vector Regression[J]. J Shanghai Jiaotong Univ Sci, 2025, 30(1): 130-135.

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基于互信息-支持向量回归的阿尔兹海默症磁共振影像脑年龄检测

Brain Age Detection of Alzheimer’s Disease Magnetic Resonance Images Based on Mutual Information - Support Vector Regression

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