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

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Abstract

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.

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Acknowledgments

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.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China

    Chen Zu, Biao Jie, Mingxia Liu, Songcan Chen & Daoqiang Zhang

  2. School of Mathematics and Computer Science, Anhui Normal University, Wuhu, 241000, China

    Biao Jie

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

    Dinggang Shen

  4. Department of Brain and Cognitive Engineering, Korea University, Seoul, 136-701, Korea

    Dinggang Shen

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  1. Chen Zu
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the Alzheimer’s Disease Neuroimaging Initiative

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Correspondence to Dinggang Shen or Daoqiang Zhang.

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Zu, C., Jie, B., Liu, M. et al. Label-aligned multi-task feature learning for multimodal classification of Alzheimer’s disease and mild cognitive impairment. Brain Imaging and Behavior 10, 1148–1159 (2016). https://doi.org/10.1007/s11682-015-9480-7

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