Multimodal manifold-regularized transfer learning for MCI conversion prediction.

Journal: Brain imaging and behavior
Published Date: Dec 1, 2015

Abstract

As the early stage of Alzheimer's disease (AD), mild cognitive impairment (MCI) has high chance to convert to AD. Effective prediction of such conversion from MCI to AD is of great importance for early diagnosis of AD and also for evaluating AD risk pre-symptomatically. Unlike most previous methods that used only the samples from a target domain to train a classifier, in this paper, we propose a novel multimodal manifold-regularized transfer learning (M2TL) method that jointly utilizes samples from another domain (e.g., AD vs. normal controls (NC)) as well as unlabeled samples to boost the performance of the MCI conversion prediction. Specifically, the proposed M2TL method includes two key components. The first one is a kernel-based maximum mean discrepancy criterion, which helps eliminate the potential negative effect induced by the distributional difference between the auxiliary domain (i.e., AD and NC) and the target domain (i.e., MCI converters (MCI-C) and MCI non-converters (MCI-NC)). The second one is a semi-supervised multimodal manifold-regularized least squares classification method, where the target-domain samples, the auxiliary-domain samples, and the unlabeled samples can be jointly used for training our classifier. Furthermore, with the integration of a group sparsity constraint into our objective function, the proposed M2TL has a capability of selecting the informative samples to build a robust classifier. Experimental results on the Alzheimer's Disease Neuroimaging Initiative (ADNI) database validate the effectiveness of the proposed method by significantly improving the classification accuracy of 80.1 % for MCI conversion prediction, and also outperforming the state-of-the-art methods.

Authors

  • Bo Cheng
    Department of Urology, The Affiliated Hospital of Southwest Medical University, Luzhou, China.
  • Mingxia Liu
    Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
  • Heung-Il Suk
    Biomedical Research Imaging Center (BRIC) and Department of Radiology, University of North Carolina, Chapel Hill, NC, 27599, USA, hsuk@med.unc.edu.
  • Dinggang Shen
    School of Biomedical Engineering, ShanghaiTech University, Shanghai, China.
  • Daoqiang Zhang
    College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China.

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