Pattern recognition analysis of Alzheimer’s disease based on brain structure network_Journal of Biomedical Engineering

Authors：

 ZHAO Xin ¹ , WU Qiong ¹ , CHEN Yuanyuan ^2,3 , ZHANG Xiong ¹ , NI Hongyan ⁴ , MING Dong ^1,2

1. College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin 300072, P.R.China;
2. Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, P.R.China;
3. College of Microelectronics, Tianjin University, Tianjin 300072, P.R.China;
4. Department of Radiology, Tianjin First Center Hospital, Tianjin 300192, P.R.China;

Corresponding author：

ZHAO Xin, Email: zhaoxin@tju.edu.cn

Keywords：

Alzheimer’s disease; structure network; pattern recognition; support vector machine

DOI：

10.7507/1001-5515.201712066

Video：

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Abstract Full text Figures/Tables Video References Cited by

Alzheimer’ s disease is the most common kind of dementia without effective treatment. Via early diagnosis, early intervention after diagnosis is the most effective way to handle this disease. However, the early diagnosis method remains to be studied. Neuroimaging data can provide a convenient measurement for the brain function and structure. Brain structure network is a good reflection of the fiber structural connectivity patterns between different brain cortical regions, which is the basis of brain’s normal psychology function. In the paper, a brain structure network based on pattern recognition analysis was provided to realize an automatic diagnosis research of Alzheimer’s disease and gray matter based on structure information. With the feature selection in pattern recognition, this method can provide the abnormal regions of brain structural network. The research in this paper analyzed the patterns of abnormal structural network in Alzheimer’s disease from the aspects of connectivity and node, which was expected to provide updated information for the research about the pathological mechanism of Alzheimer’s disease.

Citation： ZHAO Xin, WU Qiong, CHEN Yuanyuan, ZHANG Xiong, NI Hongyan, MING Dong. Pattern recognition analysis of Alzheimer’s disease based on brain structure network. Journal of Biomedical Engineering, 2019, 36(1): 16-23. doi: 10.7507/1001-5515.201712066 Copy

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6.	Zhang Xiaoyuan, Qiu Daoyin, Chen F. Support vector machine with parameter optimization by a novel hybrid method and its application to fault diagnosis. Neurocomputing, 2015, 149(B): 641-651.
7.	Prasad G, Joshi S H, Nir T M, et al. Brain connectivity and novel network measures for Alzheimer's disease classification. Neurobiol Aging, 2015, 36(1): S121-S131.
8.	Chyzhyk D, Savio A, Grana M. Evolutionary ELM wrapper feature selection for Alzheimer's disease CAD on anatomical brain MRI. Neurocomputing, 2014, 128: 73-80.
9.	Moradi E, Pepe A, Gaser C, et al. Machine learning framework for early MRI-based Alzheimer's conversion prediction in MCI subjects. Neuroimage, 2015, 104: 398-412.
10.	Li Muwei, Qin Yuanyuan, Gao Fei, et al. Discriminative analysis of multivariate features from structural MRI and diffusion tensor images. Magn Reson Imaging, 2014, 32(8): 1043-1051.
11.	Dyrba M, Ewers M, Wegrzyn M, et al. Robust automated detection of microstructural white matter degeneration in Alzheimer's disease using machine learning classification of multicenter DTI data. PLoS One, 2013, 8(5): e64925.
12.	Lacalle-Aurioles M, Navas-Sánchez F, Alemán-Gómez Y, et al. The disconnection hypothesis in Alzheimer's disease studied through multimodal magnetic resonance imaging: structural, perfusion, and diffusion tensor imaging. Journal of Alzheimer's Disease, 2016, 50(4): 1051-1064.
13.	Dyrba M, Grothe M, Kirste T, et al. Multimodal analysis of functional and structural disconnection in Alzheimer's disease using multiple kernel SVM. Hum Brain Mapp, 2015, 36(6): 2118-2131.
14.	Wang Tao, Shi Feng, Jin Yan, et al. Multilevel deficiency of white matter connectivity networks in Alzheimer's disease: a diffusion MRI study with DTI and HARDI models. Neural Plast, 2016, (2): 2947136.
15.	Cui Yue, Wen Wei, Lipnicki D M, et al. Automated detection of amnestic mild cognitive impairment in community-dwelling elderly adults: a combined spatial atrophy and white matter alteration approach. Neuroimage, 2012, 59(2): 1209-1217.
16.	Jack C R. Alliance for aging research AD biomarkers work group: structural MRI. Neurobiol Aging, 2011, 32(Suppl 1): S48-S57.
17.	Dubois B, Feldman H H, Jacova C, et al. Research criteria for the diagnosis of Alzheimer's disease: revising the NINCDS-ADRDA criteria. The Lancet Neurology, 2007, 6(8): 734-746.
18.	员锐娟, 吴水才, 林仲志, 等. 基于大脑结构网络特征的遗忘型轻度认知障碍诊断模型. 中国生物医学工程学报, 2014, 33(5): 564-573.

1. Johansenberg H, Behrens T. Diffusion MRI: from quantitative measurement to in vivo neuroanatomy. Second Edition. United State of America , Elsevier science publishers, 2013: 1-614.
2. Mcmillan C T, Avants B B, Cook P, et al. The power of neuroimaging biomarkers for screening frontotemporal dementia. Hum Brain Mapp, 2014, 35(9): 4827-4840.
3. Xu Lele, Wu Xia, Chen Kewei, et al. Multi-modality sparse representation-based classification for Alzheimer's disease and mild cognitive impairment. Comput Methods Programs Biomed, 2015, 122(2): 182-190.
4. O'Dwyer L, Lamberton F, Bokde A L, et al. Using support vector machines with multiple indices of diffusion for automated classification of mild cognitive impairment. PLoS One, 2012, 7(2): e32441.
5. Roher A E, Weiss N, Kokjohn T A, et al. Increased Aβ peptides and reduced cholesterol and myelin proteins characterize white matter degeneration in Alzheimer's disease. Biochemistry, 2002, 41(37): 11080-11090.
6. Zhang Xiaoyuan, Qiu Daoyin, Chen F. Support vector machine with parameter optimization by a novel hybrid method and its application to fault diagnosis. Neurocomputing, 2015, 149(B): 641-651.
7. Prasad G, Joshi S H, Nir T M, et al. Brain connectivity and novel network measures for Alzheimer's disease classification. Neurobiol Aging, 2015, 36(1): S121-S131.
8. Chyzhyk D, Savio A, Grana M. Evolutionary ELM wrapper feature selection for Alzheimer's disease CAD on anatomical brain MRI. Neurocomputing, 2014, 128: 73-80.
9. Moradi E, Pepe A, Gaser C, et al. Machine learning framework for early MRI-based Alzheimer's conversion prediction in MCI subjects. Neuroimage, 2015, 104: 398-412.
10. Li Muwei, Qin Yuanyuan, Gao Fei, et al. Discriminative analysis of multivariate features from structural MRI and diffusion tensor images. Magn Reson Imaging, 2014, 32(8): 1043-1051.
11. Dyrba M, Ewers M, Wegrzyn M, et al. Robust automated detection of microstructural white matter degeneration in Alzheimer's disease using machine learning classification of multicenter DTI data. PLoS One, 2013, 8(5): e64925.
12. Lacalle-Aurioles M, Navas-Sánchez F, Alemán-Gómez Y, et al. The disconnection hypothesis in Alzheimer's disease studied through multimodal magnetic resonance imaging: structural, perfusion, and diffusion tensor imaging. Journal of Alzheimer's Disease, 2016, 50(4): 1051-1064.
13. Dyrba M, Grothe M, Kirste T, et al. Multimodal analysis of functional and structural disconnection in Alzheimer's disease using multiple kernel SVM. Hum Brain Mapp, 2015, 36(6): 2118-2131.
14. Wang Tao, Shi Feng, Jin Yan, et al. Multilevel deficiency of white matter connectivity networks in Alzheimer's disease: a diffusion MRI study with DTI and HARDI models. Neural Plast, 2016, (2): 2947136.
15. Cui Yue, Wen Wei, Lipnicki D M, et al. Automated detection of amnestic mild cognitive impairment in community-dwelling elderly adults: a combined spatial atrophy and white matter alteration approach. Neuroimage, 2012, 59(2): 1209-1217.
16. Jack C R. Alliance for aging research AD biomarkers work group: structural MRI. Neurobiol Aging, 2011, 32(Suppl 1): S48-S57.
17. Dubois B, Feldman H H, Jacova C, et al. Research criteria for the diagnosis of Alzheimer's disease: revising the NINCDS-ADRDA criteria. The Lancet Neurology, 2007, 6(8): 734-746.
18. 员锐娟, 吴水才, 林仲志, 等. 基于大脑结构网络特征的遗忘型轻度认知障碍诊断模型. 中国生物医学工程学报, 2014, 33(5): 564-573.

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