Journal of Shandong University(Engineering Science) ›› 2023, Vol. 53 ›› Issue (4): 65-73.doi: 10.6040/j.issn.1672-3961.0.2023.025

• Machine Learning & Data Mining • Previous Articles     Next Articles

The classification of mild cognitive impairment based on supervised graph regularization and information fusion

Ying LI1(),Jiankun WANG2,*()   

  1. 1. School of Information Engineering, Shandong Management University, Jinan 250357, Shandong, China
    2. Shandong Big Data Center, Jinan 250011, Shandong, China
  • Received:2023-02-07 Online:2023-08-20 Published:2023-08-18
  • Contact: Jiankun WANG E-mail:liying200606@163.com;wangjk0203@163.com

RichHTML

4

PDF (PC)

284

Abstract:

To precisely distinguish progressive and stable mild cognitive impairment (MCI). The projection matrix was learned from Alzheimer′s disease samples and normal control samples. The supervised graph regularization was used to optimize the local nearest neighbor relationship of the samples. Based on the projection matrix, the spatial transformation of the MCI samples was carried out to extract the discriminative features of progressive and stable MCI. The proposed features were fused with the scores of Mini-Mental State Examination and apolipoprotein E4. The SVM classifier was trained using the fused features for the MCI classification. The experiments were conduct on the Alzheimer′s Disease Neuroimaging Initiative (ADNI) database. The classification accuracy reached to 73.33%. Compared with the existing approaches, the proposed method significantly improved the classification accuracy, sensitivity and specificity.

Key words: mild cognitive impairment, supervised graph regularization, information fusion, feature extraction, classification

CLC Number: 

  • TP391

Fig.1

The framework of the proposed method"

Table 1

Demographic and clinical information of the subjects"

样本 人数 年龄/岁临床痴呆评分[34]mini精神状态检查评分
男 女 平均 标准差 均值 标准差
NC 78 87 76.40 5.37 0 29.19 0.96
sMCI 63 32 74.94 7.32 0.5 27.69 1.73
pMCI 73 53 73.40 9.25 0.5 26.49 1.70
AD 72 70 76.10 7.51 0.5/1 23.20 2.01

Table 2

Verification of classification performance of SGRIP features"

特征提取方式 分类精度/% 敏感度/% 特异度/% Auc
CT特征+CV特征+无监督图[27] 69.37 75.39 61.23 0.695 1
CV特征+无监督图 67.21 73.01 59.46 0.702 8
CV特征+有监督图+自适应近邻结构优化 69.15 77.81 57.57 0.709 2
CV特征+有监督图+自适应近邻结构优化+特征选择(SGRIP特征) 70.24 78.76 58.64 0.700 9

Table 3

Verification of classification performance of concatenative features"

特征 分类精度/% 敏感度/% 特异度/% Auc
MMSE 62.44 53.27 74.65 0.658 3
APOE4 60.67 63.50 56.84 0.417 2
MMSE-APOE4 59.17 57.78 61.15 0.685 0
SGRIP特征 70.24 78.76 58.64 0.700 9
SGRIP特征-MMSE 71.99 78.23 63.63 0.733 3
SGRIP特征-APOE4 69.83 77.95 59.03 0.721 1
SGRIP特征-MMSE-APOE4 73.33 78.84 65.93 0.761 2

Table 4

Comparison with existing methods"

特征 分类器 分类精度/% 敏感度/% 特异度/% Auc
MFN特征[6] SVM(高斯核) 65.61 70.63 58.95 0.667 0
分级生物特征[13] SVM(线性核) 67.43 70.04 63.96 0.698 1
基于子空间学习的生物特征[27] SVM(线性核) 69.37 75.39 61.23 0.695 1
SGRIP特征-MMSE-APOE4 SVM(高斯核) 72.79 81.71 60.88 0.754 2
SGRIP特征-MMSE-APOE4 SVM(线性核) 73.33 78.84 65.93 0.761 2

Fig.2

Visualization of all MCI samples in the SGRIP feature subspace"

1 2021 Alzheimer's disease facts and figures[J]. Alzheimers & Dementia, 2021, 17(3): 327-406.
2 MORRIS J C , CUMMINGS J . Mild cognitive impairment (MCI) represents early-stage Alzheimer's disease[J]. Journal of Alzheimer's Disease, 2005, 7 (3): 235- 239.
doi: 10.3233/JAD-2005-7306
3 PETERSEN R C , DOODY R , KURZ A , et al. Current concepts in mild cognitive impairment[J]. Archives of Neurology, 2001, 58 (12): 1985- 1992.
doi: 10.1001/archneur.58.12.1985
4 WARD A , TARDIFF S , DYE C , et al. Rate of conversion from prodromal Alzheimer's disease to Alzheimer's dementia: a systematic review of the literature[J]. Dementia & Geriatric Cognitive Disorders Extra, 2013, 3 (1): 320- 332.
5 RATHORE S , HABES M , IFTIKHAR M A , et al. A review on neuroimaging-based classification studies and associated feature extraction methods for Alzheimer's disease and its prodromal stages[J]. NeuroImage, 2017, 155, 530- 548.
doi: 10.1016/j.neuroimage.2017.03.057
6 ZHENG W , YAO Z , XIE Y , et al. Identification of Alzheimer's disease and mild cognitive impairment using networks constructed based on multiple morphological brain features[J]. Biol Psychiatry Cogn Neurosci Neuroimaging, 2018, 3 (10): 887- 897.
7 YANG D , MASURKAR A . Early-stage MRI volumetric differences in white matter hyperintensity and temporal lobe volumes between autopsy-confirmed Alzheimer's disease, cerebral small vessel disease, and mixed pathologies[J]. Dementia and Geriatric Cognitive Disorders Extra, 2022, (12): 69- 75.
8 陈全, 彭永, 甘棋心, 等. 阿尔茨海默病的影像学研究进展[J]. 国际神经病学神经外科学杂志, 2022, 49 (5): 60- 66.
doi: 10.16636/j.cnki.jinn.1673-2642.2022.05.013
CHEN Quan , PENG Yong , GAN Qixin , et al. Research advances in the imaging findings of Alzheimer's disease[J]. Journal of International Neurology and Neurosurgery, 2022, 49 (5): 60- 66.
doi: 10.16636/j.cnki.jinn.1673-2642.2022.05.013
9 张月芳, 邓红霞, 呼春香, 等. 融合残差块注意力机制和生成对抗网络的海马体分割[J]. 山东大学学报(工学版), 2020, 50 (6): 76- 81.
ZHANG Yuefang , DENG Hongxia , HU Chunxiang , et al. Hippocampal segmentation combining residual attention mechanism and generative adversarial networks[J]. Journal of Shandong University(Engineering Science), 2020, 50 (6): 76- 81.
10 MORADI E , PEPE A , GASER C , et al. Machine learning framework for early MRI-based Alzheimer's conversion prediction in MCI subjects[J]. NeuroImage, 2015, 104, 398- 412.
doi: 10.1016/j.neuroimage.2014.10.002
11 接标, 张道强. 面向脑网络的新型图核及其在MCI分类上的应用[J]. 计算机学报, 2016, 39 (8): 1667- 1680.
JIE Biao , ZHANG Daoqiang . The novel graph kernel for brain networks with application to MCI classification[J]. Chinese Journal of Computers, 2016, 39 (8): 1667- 1680.
12 姜世香, 杨艳杰. 轻度认知障碍的发展演化及识别诊断[J]. 中国临床心理学杂志, 2017, 25 (1): 88- 91.
doi: 10.16128/j.cnki.1005-3611.2017.01.020
JIANG Shixiang , YANG Yanjie . The development and the diagnosis of mild cognitive impairment[J]. Chinese Journal of Clinical Psychology, 2017, 25 (1): 88- 91.
doi: 10.16128/j.cnki.1005-3611.2017.01.020
13 TONG T , GAO Q , GUERRERO R , et al. A novel grading biomarker for the prediction of conversion from mild cognitive impairment to Alzheimer's disease[J]. IEEE Transactions on Biomedical Engineering, 2017, 64 (1): 155- 165.
doi: 10.1109/TBME.2016.2549363
14 FAN Y , BATMANGHELICH N , CLARK C M , et al. Spatial patterns of brain atrophy in MCI patients, identified via high-dimensional pattern classification, predict subsequent cognitive decline[J]. NeuroImage, 2008, 39 (4): 1731- 1743.
doi: 10.1016/j.neuroimage.2007.10.031
15 YOUNG J , MODAT M , CARDOSO M J , et al. Accurate multimodal probabilistic prediction of conversion to Alzheimer's disease in patients with mild cognitive impairment[J]. NeuroImage: Clinical, 2013, (2): 735- 745.
16 BRON E E , KLEIN S , PAPMA J M , et al. Cross-cohort generalizability of deep and conventional machine learning for mri-based diagnosis and prediction of Alzheimer's disease[J]. NeuroImage: Clinical, 2021, 102712.
17 LIN W , GAO Q , YUAN J , et al. Predicting Alzheimer's disease conversion from mild cognitive impairment using an extreme learning machine-based grading method with multimodal data[J]. Frontiers in Aging Neuroscience, 2020, (12): 1- 9.
18 SHEN H T , ZHU X , ZHANG Z , et al. Heterogeneous data fusion for predicting mild cognitive impairment conversion[J]. Information Fusion, 2021, 66 (1): 54- 63.
19 YE D H, POHL K M, DAVATZIKOS C. Semi-supervised pattern classification: application to structural MRI of Alzheimer's disease[C]//IEEE International Workshop on Pattern Recognition in Neuroimaging. Seoul, Korea: IEEE, 2011: 1-4.
20 FILIPOVYCH R , DAVATZIKOS C . Semi-supervised pattern classification of medical images: application to mild cognitive impairment (MCI)[J]. NeuroImage, 2011, 55 (3): 1109- 1119.
doi: 10.1016/j.neuroimage.2010.12.066
21 BATMANGHELICH K N, YE D H, POHL K M, et al. Disease classification and prediction via semi-supervised dimensionality reduction[C]//IEEE International Symposium on Biomedical Imaging. Chicago, USA: IEEE, 2011: 1086-1090.
22 CHENG B , LIU M , ZHANG D , et al. Domain transfer learning for MCI conversion prediction[J]. IEEE Transactions on Biomedical Engineering, 2015, 62 (7): 1805- 1817.
doi: 10.1109/TBME.2015.2404809
23 COUPÉ P , ESKILDSEN S F , MANJÓN J V , et al. Scoring by nonlocal image patch estimator for early detection of Alzheimer's disease[J]. NeuroImage: Clinical, 2012, 1 (1): 141- 152.
doi: 10.1016/j.nicl.2012.10.002
24 LI Y , FANG Y , ZHANG H , et al. Self-weighting grading biomarker based on graph-guided information propagation for the prediction of mild cognitive impairment conversion[J]. IEEE Access, 2019, (7): 116632- 116642.
25 LIU X , TOSUN D , WEINER M W , et al. Locally linear embedding (LLE) for MRI based Alzheimer's disease classification[J]. NeuroImage, 2013, (83): 148- 157.
26 ZHU X , SUK H I , LEE S W , et al. Subspace regularized sparse multitask learning for multiclass neurodegenerative disease identification[J]. IEEE Transactions on Biomedical Engineering, 2016, 63 (3): 607- 618.
doi: 10.1109/TBME.2015.2466616
27 LI Y , FANG Y , WANG J , et al. Biomarker extraction based on subspace learning for the prediction of mild cognitive impairment conversion[J]. BioMed Research International, 2021, 5531940.
28 MWANGI B , TIAN T S , SOARES J C . A review of feature reduction techniques in neuroimaging[J]. Neuroinformatics, 2014, 12 (2): 229- 244.
doi: 10.1007/s12021-013-9204-3
29 FOLSTEIN M F , FOLSTEIN S E , MCHUGH P R . "Mini-mental state": a practical method for grading the cognitive state of patients for the clinician[J]. Journal of Psychiatric Research, 1975, 12 (3): 189- 198.
doi: 10.1016/0022-3956(75)90026-6
30 LEHTOVIRTA M , SOININEN H , LAAKSO M P , et al. SPECT and MRI analysis in Alzheimer's disease: relation to apolipoprotein E ε4 allele[J]. Journal of Neurology Neurosurgery & Psychiatry, 1996, 60 (6): 644- 649.
31 VERGHESE P B , CASTELLANO J M , HOLTZMAN D M . Apolipoprotein E in Alzheimer's disease and other neurological disorders[J]. Lancet Neurology, 2011, 10 (3): 241- 252.
doi: 10.1016/S1474-4422(10)70325-2
32 HALL A , MUÑOZ-RUIZ M , MATTILA J , et al. Generalizability of the disease state index prediction model for identifying patients progressing from mild cognitive impairment to Alzheimer's disease[J]. Journal of Alzheimer's Disease, 2015, 44 (1): 79- 92.
doi: 10.3233/JAD-140942
33 杨小惠, 吴芹, 石京山. ApoE4在阿尔茨海默病中的研究进展[J]. 临床与病理杂志, 2017, 37 (3): 627- 631.
YANG Xiaohui , WU Qin , SHI Jingshan . Research progress of apolipoprotein E4 in Alzheimer's disease[J]. Journal of Clinical and Pathological Research, 2017, 37 (3): 627- 631.
34 MORRIS J C . The clinical dementia rating (CDR): current version and scoring rules[J]. Neurology, 1993, 43 (11): 2412- 2414.
35 FISCHL B , SERENO M I , DALE A M . Cortical surface-based analysis. Ⅱ: Inflation, flattening, and a surface-based coordinate system[J]. NeuroImage, 1999, 9 (2): 195- 207.
doi: 10.1006/nimg.1998.0396
36 DALE A M , FISCHL B , SERENO M I . Cortical surface-based analysis. I. Segmentation and surface reconstruction[J]. NeuroImage, 1999, 9 (2): 179- 194.
doi: 10.1006/nimg.1998.0395
37 FISCHL B , LIU A , DALE A M . Automated manifold surgery: constructing geometrically accurate and topologically correct models of the human cerebral cortex[J]. IEEE Transactions on Medical Imaging, 2001, 20 (1): 70- 80.
doi: 10.1109/42.906426
38 FISCHL B , DALE A M . Measuring the thickness of the human cerebral cortex from magnetic resonance images[J]. Proceedings of the National Academy of Sciences of the United States of America, 2000, 97 (20): 11050- 11055.
39 TZOURIO-MAZOYER N , LANDEAU B , PAPATHANASSIOU D , et al. Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain[J]. NeuroImage, 2002, 15 (1): 273- 289.
40 FAN R E , CHANG K W , HSIEH C J , et al. LIBLINEAR: a library for large linear classification[J]. Journal of Machine Learning Research, 2008, 9 (9): 1871- 1874.
41 CHANG C C , LIN C J . LIBSVM: a library for support vector machines[J]. ACM Transactions on Intelligent Systems and Technology, 2011, 2 (3): 27.
42 ZHOU Q , GORYAWALA M , CABRERIZO M , et al. An optimal decisional space for the classification of Alzheimer's disease and mild cognitive impairment[J]. IEEE Transactions on Biomedical Engineering, 2014, 61 (8): 2245- 2253.
[1] Jianqing WU,Yanqiang HUO,Jianzhu WANG,Hongyu GUO. Research review of highway differentiated toll collection [J]. Journal of Shandong University(Engineering Science), 2023, 53(4): 18-29.
[2] Caihui LIU,Qi ZHOU,Xiaowen YE. An intrusion detection model based on improved ReliefF algorithm [J]. Journal of Shandong University(Engineering Science), 2023, 53(2): 1-10.
[3] Chunhong CAO,Hongxuan DUAN,Ling CAO,Lele ZHANG,Kai HU,Fen XIAO. Real-time semantic segmentation of high-resolution remote sensing image based on multi-level feature cascade [J]. Journal of Shandong University(Engineering Science), 2021, 51(2): 19-25.
[4] ZHANG Qinyang, LI Xu, YAO Chunlong, LI Changwu. Aspect-level sentiment classification combined with syntactic dependency information [J]. Journal of Shandong University(Engineering Science), 2021, 51(2): 83-89.
[5] Zhuoyu XIAO,Pei HE,Guo CHEN,Yunbiao XU,Jie GUO. Design pattern classification mining with feature metrics constraints [J]. Journal of Shandong University(Engineering Science), 2020, 50(6): 48-58.
[6] HUO Bingqiang, ZHOU Tao, LU Huiling, DONG Yali, LIU Shan. Lung tumor benign-malignant classification based on multi-modal residual neural network and NRC algorithm [J]. Journal of Shandong University(Engineering Science), 2020, 50(6): 59-67.
[7] MA Changxia, ZHANG Chen. Pre-trained based joint model for intent classification and slot filling in Chinese spoken language understanding [J]. Journal of Shandong University(Engineering Science), 2020, 50(6): 68-75.
[8] ZHAO Ningning, TANG Xuesong, ZHAO Mingbo. Depth segment classification algorithm based on convolutional neural network [J]. Journal of Shandong University(Engineering Science), 2020, 50(4): 22-27.
[9] Shiqi SONG,Yan PIAO,Zexin JIANG. Vehicle classification and tracking for complex scenes based on improved YOLOv3 [J]. Journal of Shandong University(Engineering Science), 2020, 50(2): 27-33.
[10] Chao FENG,Kunpeng XU,Lifei CHEN. LDA-based topic feature representation method for symbolic sequences [J]. Journal of Shandong University(Engineering Science), 2020, 50(2): 60-65.
[11] Chunyang LI,Nan LI,Tao FENG,Zhuhe WANG,Jingkai MA. Abnormal sound detection of washing machines based on deep learning [J]. Journal of Shandong University(Engineering Science), 2020, 50(2): 108-117.
[12] Mingxia GAO,Jingwei LI. Chinese short text classification method based on word2vec embedding [J]. Journal of Shandong University(Engineering Science), 2019, 49(2): 34-41.
[13] Jun FAN,Qiaolin YE,Ning YE. Face recognition based on improved prameter-free supervised localitypreserving projections [J]. Journal of Shandong University(Engineering Science), 2019, 49(1): 10-16.
[14] Qingtao QU,Qicheng LIU,Chunxiao MU. A parallel adaptive news topic tracking algorithm based on N-Gram language model [J]. Journal of Shandong University(Engineering Science), 2018, 48(6): 37-43.
[15] Yao LI,Zhihai WANG,Yan′ge SUN,Wei ZHANG. An adaptive ensemble classification method based on deep attribute weighting for data stream [J]. Journal of Shandong University(Engineering Science), 2018, 48(6): 44-55, 66.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
[1] WANG Su-yu,<\sup>,AI Xing<\sup>,ZHAO Jun<\sup>,LI Zuo-li<\sup>,LIU Zeng-wen<\sup> . Milling force prediction model for highspeed end milling 3Cr2Mo steel[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2006, 36(1): 1 -5 .
[2] ZHANG Yong-hua,WANG An-ling,LIU Fu-ping . The reflected phase angle of low frequent inhomogeneous[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2006, 36(2): 22 -25 .
[3] LI Kan . Empolder and implement of the embedded weld control system[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2008, 38(4): 37 -41 .
[4] SHI Lai-shun,WAN Zhong-yi . Synthesis and performance evaluation of a novel betaine-type asphalt emulsifier[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2008, 38(4): 112 -115 .
[5] KONG Xiang-zhen,LIU Yan-jun,WANG Yong,ZHAO Xiu-hua . Compensation and simulation for the deadband of the pneumatic proportional valve[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2006, 36(1): 99 -102 .
[6] LAI Xiang . The global domain of attraction for a kind of MKdV equations[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2006, 36(1): 87 -92 .
[7] YU Jia yuan1, TIAN Jin ting1, ZHU Qiang zhong2. Computational intelligence and its application in psychology[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2009, 39(1): 1 -5 .
[8] LI Liang, LUO Qiming, CHEN Enhong. Graph-based ranking model for object-level search
[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2009, 39(1): 15 -21 .
[9] CHEN Rui, LI Hongwei, TIAN Jing. The relationship between the number of magnetic poles and the bearing capacity of radial magnetic bearing[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2018, 48(2): 81 -85 .
[10] WANG Bo,WANG Ning-sheng . Automatic generation and combinatory optimization of disassembly sequence for mechanical-electric assembly[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2006, 36(2): 52 -57 .
Copyright 2018 © Editorial office of Journal of Shandong University (Engineering Science)
Supported by Beijing Magtech Co.Ltd