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

• • 上一篇

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.

参考文献

[1] ZENG A, JIA L, PAN D, et al. Early prognosis of Alzheimer’s disease based on convolutional neural networks and ensemble learning [J]. Journal of Biomedical Engineering, 2019, 36(5): 711-719 (in Chinese).
[2] GUAN H, TAO C, TAO D. MRI-based Alzheimer’s disease prediction via distilling the knowledge in multimodal data [J]. NeuroImage, 2021, 244: 118586.
[3] TURKSON R E, QU H, MAWULI C B, et al. Classifi- cation of Alzheimer’s disease using deep convolutional spiking neural network [J]. Neural Processing Letters, 2021, 53(4): 2649-2663.
[4] WU H, LUO J, LU X, et al. 3D transfer learning network for classification of Alzheimer’s disease with MRI [J]. International Journal of Machine Learning and Cybernetics, 2022, 13(7): 1997-2011.
[5] ZHANG Y, TENG Q, LIU Y, et al. Diagnosis of Alzheimer’s disease based on regional attention with sMRI gray matter slices [J]. Journal of Neuroscience Methods, 2022, 365: 109376.
[6] RAMZAN F, KHAN M U G, REHMAT A, et al. A deep learning approach for automated diagnosis and multi-class classification of Alzheimer’s disease stages using resting-state fMRI and residual neural networks [J]. Journal of Medical Systems, 2019, 44(2): 37.
[7] LI Y, LI F, ZHU X, et al. Detection of brain age of Alzheimer’s disease based on separability distance criterion and MR image [J]. Journal of Southeast University (Natural Science Edition), 2016, 46(6): 1137-1142 (in Chinese).
[8] IRIMIA A, TORGERSON C M, GOH S, et al. Statistical estimation of physiological brain age as a descriptor of senescence rate during adulthood [J]. Brain Imaging and Behavior, 2015, 9(4): 678-689.
[9] SENDI M S E, SALAT D H, CALHOUN V D. Brain age gap difference between healthy and mild dementia subjects: Functional network connectivity analysis [C]//2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society. Online: IEEE, 2021: 1636-1639.
[10] BEHESHTI I, MISHRA S, SONE D, et al. T1-weighted MRI-driven brain age estimation in Alzheimer’s disease and Parkinson’s disease [J]. Aging and Disease, 2020, 11(3): 618-628.
[11] POLONI K M, FERRARI R J. A deep ensemble hippocampal CNN model for brain age estimation applied to Alzheimer’s diagnosis [J]. Expert Systems With Applications, 2022, 195: 116622.
[12] PALMA M, TAVAKOLI S, BRETTSCHNEIDER J, et al. Quantifying uncertainty in brain-predicted age using scalar-on-image quantile regression [J]. NeuroImage, 2020, 219: 116938.
[13] LY M, YU G Z, KARIM H T, et al. Improving brain age prediction models: Incorporation of amyloid status in Alzheimer’s disease [J]. Neurobiology of Aging, 2020, 87: 44-48.
[14] LOWE L C, GASER C, FRANKE K, et al. The effect of the APOE genotype on individual BrainAGE in normal aging, mild cognitive impairment, and Alzheimer’s disease [J]. PLoS ONE, 2016, 11(7): e0157514.
[15] NAKANO R, KOBASHI S, BINTE ALAM S, et al. Neonatal brain age estimation using manifold learning regression analysis [C]//2015 IEEE International Conference on Systems, Man, and Cybernetics. Hong Kong: IEEE, 2015: 2273-2276.
[16] DAVATZIKOS C, XU F, AN Y, et al. Longitudinal progression of Alzheimer’s-like patterns of atrophy in normal older adults: The SPARE-AD index [J]. Brain, 2009, 132(8): 2026-2035.
[17] GONNEAUD J, BARIA A T, PICHET BINETTE A, et al. Accelerated functional brain aging in pre-clinical familial Alzheimer’s disease [J]. Nature Communications, 2021, 12: 5346.
[18] LI Y, LI F, WANG P, et al. Estimating the brain pathological age of Alzheimer’s disease patients from MR image data based on the separability distance criterion [J]. Physics in Medicine and Biology, 2016, 61(19): 7162-7186.
[19] GANAIE M A, TANVEER M, BEHESHTI I. Brain age prediction using improved twin SVR [J]. Neural Computing and Applications, 2022. https://doi.org/10.1007/s00521-021-06518-1.
[20] SMOLA A J, SCHOLKOPF B. A tutorial on support vector regression [J]. Statistics and Computing, 2004,14(3): 199-222.
[21] YANG J, WANG S, WU T. Maximum mutual information for feature extraction from graphstructured data: Application to Alzheimer’s disease classification [J]. Applied Intelligence, 2022. https://doi.org/10.1007/s10489-022-03528-x.
[22] PANINSKI L. Estimation of entropy and mutual information [J]. Neural Computation, 2003, 15(6): 1191-1253.
[23] FRANKE K, ZIEGLER G, KLOPPEL S, et al. Estimating the age of healthy subjects from T1-weighted MRI scans using kernel methods: Exploring the influence of various parameters [J]. NeuroImage, 2010,50(3): 883-892.

编辑推荐 0

Metrics

阅读次数

全文

HTML			PDF

最新录用	在线预览	正式出版	最新录用	在线预览	正式出版
0	0	0	0	0	2

	来源	本网站

	次数	2
	比例	100%

摘要

236

最新录用	在线预览	正式出版

0	0	236

来源	本网站	其他网站

次数	10	226
比例	4%	96%

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

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐 0

Metrics

本文评价

[1]	赵艳飞1,2,3, 肖鹏4, 王景川1,2,3, 郭锐4. 基于局部语义地图的移动机器人半自主导航[J]. J Shanghai Jiaotong Univ Sci, 2025, 30(1): 27-33.
[2]	傅航1，许江长 1，李寅炜2,4，周慧芳2,4，陈晓军1,3. 基于视频图像增强现实的视神经管减压手术导航系统[J]. J Shanghai Jiaotong Univ Sci, 2025, 30(1): 34-42.
[3]	BALASUBRAMANIAN S1, NARUK Mahaveer Singh2, TEWARI Gaurav3. 基于经验小波变换优化自适应混合滤波器的心电信号去噪[J]. J Shanghai Jiaotong Univ Sci, 2025, 30(1): 66-80.
[4]	徐旺旺1,2，许良凤1,2，刘宁徽3，律娜3. 基于多注意力卷积神经网络的乳腺癌组织学图像诊断[J]. J Shanghai Jiaotong Univ Sci, 2025, 30(1): 91-106.
[5]	Sahaya Anselin Nisha1, NARMADHA R.1, AMIRTHALAKSHMI T. M.2, BALAMURUGAN V.1, VEDANARAYANAN V.1. LOBO优化的深度卷积神经网络用于脑肿瘤分类[J]. J Shanghai Jiaotong Univ Sci, 2025, 30(1): 107-114.
[6]	丁黎辉1,2，付立军1,3，杨光4,5,6，万林4,5，常志军7. 基于视频的婴儿癫痫性痉挛综合征检测：建模、检测与评估[J]. J Shanghai Jiaotong Univ Sci, 2025, 30(1): 1-9.
[7]	孔会扬1，王殊轶1，张璨2，陈赞2,3. 手术导板辅助增强现实技术与传统技术在椎弓根螺钉放置中的比较[J]. J Shanghai Jiaotong Univ Sci, 2025, 30(1): 10-17.
[8]	周苏, 钟泽滨. 基于车载智能手机的实时车辆及行人测距[J]. J Shanghai Jiaotong Univ Sci, 2024, 29(6): 1081-1090.
[9]	周成, 蒋祖华. 融入优质主题和注意力机制的设计规范命名实体识别方法[J]. J Shanghai Jiaotong Univ Sci, 2024, 29(6): 1169-1180.
[10]	鄢丛强1,2, 郭正玉3,4, 蔡云泽 1,2. 基于改进CycleGAN的SAR图像舰船尾迹数据增强[J]. J Shanghai Jiaotong Univ Sci, 2024, 29(4): 702-711.
[11]	陈旖旎，蒋祖华. 船舶舾装件立体仓储考虑车辆冲突的多AGV任务调度策略研究[J]. J Shanghai Jiaotong Univ Sci, 2024, 29(3): 492-508.
[12]	LONARE Savita1,2, BHRAMARAMBA Ravi2. 基于图卷积网络的联邦式隐私保护交通预测方法[J]. J Shanghai Jiaotong Univ Sci, 2024, 29(3): 509-517.
[13]	吕峰，王新彦，李磊，江泉，易政洋. 基于嵌入式YOLO轻量级网络的树木检测算法[J]. J Shanghai Jiaotong Univ Sci, 2024, 29(3): 518-527.
[14]	宋立博a，费燕琼b. 新型Lite YOLOv4-Tiny算法及其在裂纹智能检测中的应用[J]. J Shanghai Jiaotong Univ Sci, 2024, 29(3): 528-536.
[15]	顾星海，花豹，刘亚辉，孙学民，鲍劲松. 面向装配工艺文档的装配语义实体识别与关系构建方法[J]. J Shanghai Jiaotong Univ Sci, 2024, 29(3): 537-556.