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

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

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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.

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。

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Authors and Affiliations

  1. School of Intelligent Technology and Engineering, Chongqing University of Science and Technology, Chongqing, 401331, China

    Yuchuan Liu  (刘玉川), Hao Li  (李 浩), Yulong Tang  (唐宇龙) & Yue Fu  (符 玥)

  2. Communication Sergeant School, Army Engineering University, Chongqing, 400035, China

    Dujuan Liang  (梁杜娟)

  3. CISDI Information Technology (Chongqing) Co., Ltd., Chongqing, 401329, China

    Jia Tan  (谭 佳)

  4. School of Microelectronics and Communication Engineering, Chongqing University, Chongqing, 400044, China

    Yongming Li  (李勇明)

Authors
  1. Yuchuan Liu  (刘玉川)
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  2. Hao Li  (李 浩)
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  3. Yulong Tang  (唐宇龙)
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  4. Dujuan Liang  (梁杜娟)
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  5. Jia Tan  (谭 佳)
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  6. Yue Fu  (符 玥)
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  7. Yongming Li  (李勇明)
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Correspondence to Yongming Li  (李勇明).

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Foundation item

the Natural Science Foundation of Chongqing (No. cstb2022nscq-msx1575), the Science and Technology Research Program of Chongqing Municipal Education Commission (Nos. KJQN202201512, KJQN202001523 and KJZD-M202101501), the Chongqing University of Science and Technology Research Funding Projects (Nos. CKRC2022019 and CKRC2019042), and the Open Foundation of the Chongqing Key Laboratory for Oil and Gas Production Safety and Risk Control (No. cqsrc202113)

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Liu, Y., Li, H., Tang, Y. et al. Brain Age Detection of Alzheimer’s Disease Magnetic Resonance Images Based on Mutual Information — Support Vector Regression. J. Shanghai Jiaotong Univ. (Sci.) (2023). https://doi.org/10.1007/s12204-023-2590-2

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  • DOI: https://doi.org/10.1007/s12204-023-2590-2

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