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Low-rank dimensionality reduction for multi-modality neurodegenerative disease identification

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Abstract

In this paper, we propose a novel dimensionality reduction method of taking the advantages of the variability, sparsity, and low-rankness of neuroimaging data for Alzheimer’s Disease (AD) classification. We first take the variability of neuroimaging data into account by partitioning them into sub-classes by means of clustering, which thus captures the underlying multi-peak distributional characteristics in neuroimaging data. We then iteratively conduct Low-Rank Dimensionality Reduction (LRDR) and orthogonal rotation in a sparse linear regression framework, in order to find the low-dimensional structure of high-dimensional data. Experimental results on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset showed that our proposed model helped enhance the performances of AD classification, outperforming the state-of-the-art methods.

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Notes

  1. http://www.loni.usc.edu/ADNI.

  2. Besides, the remaining 84 MCI subjects include 33 subjects that did not convert in 24 months but converted in 36 months, and 51 subjects that were MCI at base line but were missed at any available time points among 0 – 96 months.

  3. http://mipav.cit.nih.gov/clickwrap.php.

  4. In this work, we extract the same number of features from MRI and PET as described in Section 2.1 and thus their feature dimensions are the same. However, it should be noted that the proposed method can be easily extended to multiple modalities with different numbers of features. Moreover, in the multi-modality case of this work, r < min{rank(Bi),rank(Ai)} or r < min{rank(Bi),rank(A)}, i = 1, 2.

  5. In our experiments, we used matlab function ‘floor’ to discritize the real values of r.

  6. In Tables 2–5, the boldface denotes the maximum performance in each column. (Symbols * and ◇, respectively, represent statistically significant difference between the proposed method and the comparison methods under p < 0.05 and p < 0.001, on the paired-sample t-tests at 95% significance level.)

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Acknowledgments

This work was supported in part by NIH grants (EB008374, AG041721, AG049371, AG042599, EB022880). X. Zhu was also supported by the National Natural Science Foundation of China (Grants No: 61573270 and 61876046); the Project of Guangxi Science and Technology (GuiKeAD17195062); the Guangxi Collaborative Innovation Center of Multi-Source Information Integration and Intelligent Processing; Strategic Research Excellence Fund at Massey University; and Marsden Fund of New Zealand (grant No: MAU1721). H.I. Suk was also supported by Institute for Information & Communications Technology Promotion (IITP) grant funded by the Korea government (No. 2017-0-00451).

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Authors and Affiliations

  1. Guangxi Key Lab of Multi-source Information Mining and Security, Guangxi Normal University, Guilin, 541004, Guangxi, People’s Republic of China

    Xiaofeng Zhu

  2. Institute of Natural and Mathematical Sciences, Massey University, Auckland, 0745, New Zealand

    Xiaofeng Zhu

  3. Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA

    Xiaofeng Zhu & Dinggang Shen

  4. Department of Brain and Cognitive Engineering, Korea University, Seoul, Korea

    Heung-Il Suk & Dinggang Shen

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  1. Xiaofeng Zhu
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Correspondence to Dinggang Shen.

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This article belongs to the Topical Collection: Special Issue on Deep vs. Shallow: Learning for Emerging Web-scale Data Computing and Applications

Guest Editors: Jingkuan Song, Shuqiang Jiang, Elisa Ricci, and Zi Huang

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Zhu, X., Suk, HI. & Shen, D. Low-rank dimensionality reduction for multi-modality neurodegenerative disease identification. World Wide Web 22, 907–925 (2019). https://doi.org/10.1007/s11280-018-0645-3

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