[1] Li Xuan, Lu Xuesong, Wang Haixian, Alzheimer’s disease classification based on sparse functionalconnectivity and non-negative matrix factorization [J]. Journal of Southeast University (English Edition), 2019, 35 (2): 147-152. [doi:10.3969/j.issn.1003-7985.2019.02.001]

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Alzheimer’s disease classification based on sparse functionalconnectivity and non-negative matrix factorization()

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Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:

35

Issue:

2019 2

Page:

147-152

Research Field:

Biological Science and Medical Engineering

Publishing date:

2019-06-30

Info

Title:

Alzheimer’s disease classification based on sparse functionalconnectivity and non-negative matrix factorization

Author(s):

Li Xuan¹;  Lu Xuesong²;  Wang Haixian¹;  3

¹Key Laboratory of Child Development and Learning Science of Ministry of Education, Research Center for Learning Science, Southeast University, Nanjing 210096, China
²Department of Rehabilitation, Zhongda Hospital, Southeast University, Nanjing 210009, China
³School of Mathematics and Big Data, Foshan University, Foshan 528000, China

Keywords:

Alzheimer’s disease;  sparse representation;  non-negative matrix factorization;  functional connectivity

PACS:

R318

DOI:

10.3969/j.issn.1003-7985.2019.02.001

Abstract:

A novel framework is proposed to obtain physiologically meaningful features for Alzheimer’s disease(AD)classification based on sparse functional connectivity and non-negative matrix factorization. Specifically, the non-negative adaptive sparse representation(NASR)method is applied to compute the sparse functional connectivity among brain regions based on functional magnetic resonance imaging(fMRI)data for feature extraction. Afterwards, the sparse non-negative matrix factorization(sNMF)method is adopted for dimensionality reduction to obtain low-dimensional features with straightforward physical meaning. The experimental results show that the proposed framework outperforms the competing frameworks in terms of classification accuracy, sensitivity and specificity. Furthermore, three sub-networks, including the default mode network, the basal ganglia-thalamus-limbic network and the temporal-insular network, are found to have notable differences between the AD patients and the healthy subjects. The proposed framework can effectively identify AD patients and has potentials for extending the understanding of the pathological changes of AD.

References:

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Memo

Memo:

Biographies: Li Xuan(1990—), female, Ph.D. candidate; Wang Haixian(corresponding author), male, doctor, professor, hxwang@seu.edu.cn.
Foundation items: The Foundation of Hygiene and Health of Jiangsu Province(No.H2018042), the National Natural Science Foundation of China(No.61773114), the Key Research and Development Plan(Industry Foresight and Common Key Technology)of Jiangsu Province(No.BE2017007-3).
Citation: Li Xuan, Lu Xuesong, Wang Haixian. Alzheimer’s disease classification based on sparse functional connectivity and non-negative matrix factorization[J].Journal of Southeast University(English Edition), 2019, 35(2):147-152.DOI:10.3969/j.issn.1003-7985.2019.02.001.

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