|Table of Contents|

[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]

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

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

2019 2
Research Field:
Biological Science and Medical Engineering
Publishing date:


Alzheimer’s disease classification based on sparse functionalconnectivity and non-negative matrix factorization
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
Alzheimer’s disease sparse representation non-negative matrix factorization functional connectivity
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.


[1] Biswal B, Zerrin Yetkin F, Haughton V M, et al. Functional connectivity in the motor cortex of resting human brain using echoplanar MRI [J]. Magnetic Resonance in Medicine, 1995, 34(4): 537-541. DOI:10.1002/mrm.1910340409.
[2] Balthazar M L F, Pereira F R S, Lopes T M, et al. Neuropsychiatric symptoms in Alzheimer’s disease are related to functional connectivity alterations in the salience network[J]. Human Brain Mapping, 2014, 35(4): 1237-1246. DOI:10.1002/hbm.22248.
[3] Teipel S, Drzezga A, Grothe M J, et al. Multimodal imaging in Alzheimer’s disease: Validity and usefulness for early detection [J]. The Lancet Neurology, 2015, 14(10): 1037-1053. DOI:10.1016/s1474-4422(15)00093-9.
[4] Lee D D, Seung H S. Learning the parts of objects by non-negative matrix factorization[J]. Nature, 1999, 401(6755): 788-791. DOI:10.1038/44565.
[5] Padilla P, López M, Górriz J M, et al. NMF-SVM based CAD tool applied to functional brain images for the diagnosis of Alzheimer’s disease [J]. IEEE Transactions on Medical Imaging, 2012, 31(2): 207-216. DOI:10.1109/tmi.2011.2167628.
[6] Li X, Hu Z L, Wang H X. Overlapping community structure detection of brain functional network using non-negative matrix factorization [C]//International Conference on Neural Information Processing. Kyoto, Japan, 2016: 140-147.DOI:10.1007/978-3-319-46675-0_16.
[7] Li X, Wang H X. Identification of functional networks in resting state fMRI data using adaptive sparse representation and affinity propagation clustering [J]. Frontiers in Neuroscience, 2015, 9: 383-1-383-16.DOI:10.3389/fnins.2015.00383.
[8] Hoyer P O. Non-negative sparse coding [C]//Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing. Martigny, Switzerland, 2002: 557-565.
[9] Grave E, Obozinski G R, Bach F R. Trace lasso: A trace norm regularization for correlated designs [C]//Advances in Neural Information Processing Systems. Granada, Spain, 2011: 2187-2195.
[10] Yan C G, Zang Y F. DPARSF: A MATLAB toolbox for “pipeline” data analysis of resting-state fMRI [J]. Frontiers in Systems Neuroscience, 2010, 4:13-1-13-7. DOI:10.3389/fnsys.2010.00013.
[11] 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. DOI:10.1006/nimg.2001.0978.
[12] Klaassens B L, van Gerven J M A, van der Grond J, et al. Diminished posterior precuneus connectivity with the default mode network differentiates normal aging from Alzheimer’s disease [J]. Frontiers in Aging Neuroscience, 2017, 9: 97. DOI:10.3389/fnagi.2017.00097.
[13] Aggleton J P, Pralus A, Nelson A J D, et al. Thalamic pathology and memory loss in early Alzheimer’s disease: Moving the focus from the medial temporal lobe to Papez circuit [J]. Brain, 2016, 139(7): 1877-1890. DOI:10.1093/brain/aww083.
[14] Harrison T M, Burggren A C, Small G W, et al. Altered memory-related functional connectivity of the anterior and posterior hippocampus in older adults at increased genetic risk for Alzheimer’s disease[J]. Human Brain Mapping, 2016, 37(1): 366-380. DOI:10.1002/hbm.23036.
[15] 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.
[16] Bi X A, Jiang Q, Sun Q, et al. Analysis of alzheimer’s disease based on the random neural network cluster in fMRI[J]. Frontiers in Neuroinformatics, 2018, 12: 60. DOI:10.3389/fninf.2018.00060.
[17] Lian C F, Liu M X, Zhang J, et al. Hierarchical fully convolutional network for joint atrophy localization and alzheimer’s disease diagnosis using structural MRI[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018, 99:1-14. DOI:10.1109/tpami.2018.2889096.


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.
Last Update: 2019-06-20