Brain Imaging and Behavior

, Volume 10, Issue 4, pp 1148–1159 | Cite as

Label-aligned multi-task feature learning for multimodal classification of Alzheimer’s disease and mild cognitive impairment

  • Chen Zu
  • Biao Jie
  • Mingxia Liu
  • Songcan Chen
  • Dinggang ShenEmail author
  • Daoqiang ZhangEmail author
  • the Alzheimer’s Disease Neuroimaging Initiative
Original Research


Multimodal classification methods using different modalities of imaging and non-imaging data have recently shown great advantages over traditional single-modality-based ones for diagnosis and prognosis of Alzheimer’s disease (AD), as well as its prodromal stage, i.e., mild cognitive impairment (MCI). However, to the best of our knowledge, most existing methods focus on mining the relationship across multiple modalities of the same subjects, while ignoring the potentially useful relationship across different subjects. Accordingly, in this paper, we propose a novel learning method for multimodal classification of AD/MCI, by fully exploring the relationships across both modalities and subjects. Specifically, our proposed method includes two subsequent components, i.e., label-aligned multi-task feature selection and multimodal classification. In the first step, the feature selection learning from multiple modalities are treated as different learning tasks and a group sparsity regularizer is imposed to jointly select a subset of relevant features. Furthermore, to utilize the discriminative information among labeled subjects, a new label-aligned regularization term is added into the objective function of standard multi-task feature selection, where label-alignment means that all multi-modality subjects with the same class labels should be closer in the new feature-reduced space. In the second step, a multi-kernel support vector machine (SVM) is adopted to fuse the selected features from multi-modality data for final classification. To validate our method, we perform experiments on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database using baseline MRI and FDG-PET imaging data. The experimental results demonstrate that our proposed method achieves better classification performance compared with several state-of-the-art methods for multimodal classification of AD/MCI.


Alzheimer’s disease Mild cognitive impairment Label alignment Multi-task learning Feature selection Multimodal classification 



This work was supported in part by the National Natural Science Foundation of China (Nos. 61422204, 61473149, 61170151), the Jiangsu Natural Science Foundation for Distinguished Young Scholar (No. BK20130034), the Specialized Research Fund for the Doctoral Program of Higher Education (No. 20123218110009), and the NUAA Fundamental Research Funds (No. NE2013105), and by NIH grants EB006733, EB008374, EB009634, MH100217, AG041721, and AG042599.

Compliance with ethical standards

Conflict of interest

All authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed consent

Informed consent was obtained from all individual participants included in the study.


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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Chen Zu
    • 1
  • Biao Jie
    • 1
    • 2
  • Mingxia Liu
    • 1
  • Songcan Chen
    • 1
  • Dinggang Shen
    • 3
    • 4
    Email author
  • Daoqiang Zhang
    • 1
    Email author
  • the Alzheimer’s Disease Neuroimaging Initiative
  1. 1.Department of Computer Science and EngineeringNanjing University of Aeronautics and AstronauticsNanjingChina
  2. 2.School of Mathematics and Computer ScienceAnhui Normal UniversityWuhuChina
  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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