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Neuroinformatics

, Volume 12, Issue 3, pp 381–394 | Cite as

Identifying Informative Imaging Biomarkers via Tree Structured Sparse Learning for AD Diagnosis

  • Manhua Liu
  • Daoqiang Zhang
  • Dinggang ShenEmail author
  • the Alzheimer’s Disease Neuroimaging Initiative
Original Article

Abstract

Neuroimaging provides a powerful tool to characterize neurodegenerative progression and therapeutic efficacy in Alzheimer’s disease (AD) and its prodromal stage—mild cognitive impairment (MCI). However, since the disease pathology might cause different patterns of structural degeneration, which is not pre-known, it is still a challenging problem to identify the relevant imaging markers for facilitating disease interpretation and classification. Recently, sparse learning methods have been investigated in neuroimaging studies for selecting the relevant imaging biomarkers and have achieved very promising results on disease classification. However, in the standard sparse learning method, the spatial structure is often ignored, although it is important for identifying the informative biomarkers. In this paper, a sparse learning method with tree-structured regularization is proposed to capture patterns of pathological degeneration from fine to coarse scale, for helping identify the informative imaging biomarkers to guide the disease classification and interpretation. Specifically, we first develop a new tree construction method based on the hierarchical agglomerative clustering of voxel-wise imaging features in the whole brain, by taking into account their spatial adjacency, feature similarity and discriminability. In this way, the complexity of all possible multi-scale spatial configurations of imaging features can be reduced to a single tree of nested regions. Second, we impose the tree-structured regularization on the sparse learning to capture the imaging structures, and then use them for selecting the most relevant biomarkers. Finally, we train a support vector machine (SVM) classifier with the selected features to make the classification. We have evaluated our proposed method by using the baseline MR images of 830 subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, which includes 198 AD patients, 167 progressive MCI (pMCI), 236 stable MCI (sMCI), and 229 normal controls (NC). Our experimental results show that our method can achieve accuracies of 90.2 %, 87.2 %, and 70.7 % for classifications of AD vs. NC, pMCI vs. NC, and pMCI vs. sMCI, respectively, demonstrating promising performance compared with other state-of-the-art methods.

Keywords

Alzheimer’s disease diagnosis Tree-structured sparse learning Biomarker identification Mild cognitive impairment Group sparse learning 

Notes

Acknowledgments

This work was supported in part by NIH grants EB006733, EB008374, EB009634 and AG041721, MH100217, and AG042599, and by National Natural Science Foundation of China (NSFC) grants (No. 61375112, No. 61005024) and Medical and Engineering Foundation of Shanghai Jiao Tong University (No. YG2012MS12). This work was also partially supported by the National Research Foundation grant (No. 2012-005741) funded by the Korean government, and supported by the Open Project Program of the National Laboratory of Pattern Recognition (NLPR), and by Jiangsu Natural Science Foundation for Distinguished Young Scholar (No. BK20130034),and NUAA Fundamental Research Funds under grant (No. NE2013105). Data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: Abbott, AstraZeneca AB, Bayer Schering Pharma AG, Bristol-Myers Squibb, Eisai Global Clinical Development, Elan Corporation, Genentech, GE Healthcare, GlaxoSmithKline, Innogenetics, Johnson and Johnson, Eli Lilly and Co., Medpace, Inc., Merck and Co., Inc., Novartis AG, Pfizer Inc., F. Hoffman-La Roche, Schering-Plough, Synarc, Inc., as well as non-profit partners the Alzheimer’s Association and Alzheimer’s Drug Discovery Foundation, with participation from the U.S. Food and Drug Administration. Private sector contributions to ADNI are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of California, Los Angeles.

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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Manhua Liu
    • 1
    • 3
  • Daoqiang Zhang
    • 2
    • 3
  • Dinggang Shen
    • 3
    • 4
    Email author
  • the Alzheimer’s Disease Neuroimaging Initiative
  1. 1.Department of Instrument Science and Engineering, SEIEEShanghai Jiao Tong UniversityShanghaiChina
  2. 2.Department of Computer Science and EngineeringNanjing University of Aeronautics & AstronauticsNanjingChina
  3. 3.Department of Radiology and BRICUniversity of North Carolina at Chapel HillChapel HillUSA
  4. 4.Department of Brain and Cognitive EngineeringKorea UniversitySeoulKorea

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