Discriminative multi-task feature selection for multi-modality classification of Alzheimer’s disease
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Recently, multi-task based feature selection methods have been used in multi-modality based classification of Alzheimer’s disease (AD) and its prodromal stage, i.e., mild cognitive impairment (MCI). However, in traditional multi-task feature selection methods, some useful discriminative information among subjects is usually not well mined for further improving the subsequent classification performance. Accordingly, in this paper, we propose a discriminative multi-task feature selection method to select the most discriminative features for multi-modality based classification of AD/MCI. Specifically, for each modality, we train a linear regression model using the corresponding modality of data, and further enforce the group-sparsity regularization on weights of those regression models for joint selection of common features across multiple modalities. Furthermore, we propose a discriminative regularization term based on the intra-class and inter-class Laplacian matrices to better use the discriminative information among subjects. To evaluate our proposed method, we perform extensive experiments on 202 subjects, including 51 AD patients, 99 MCI patients, and 52 healthy controls (HC), from the baseline MRI and FDG-PET image data of the Alzheimer’s Disease Neuroimaging Initiative (ADNI). The experimental results show that our proposed method not only improves the classification performance, but also has potential to discover the disease-related biomarkers useful for diagnosis of disease, along with the comparison to several state-of-the-art methods for multi-modality based AD/MCI classification.
KeywordsAlzheimer’s disease Multi-task feature selection Multi-modality based classification Discriminative regularization Group-sparsity regularizer
This work is supported in part by National Natural Science Foundation of China (Nos. 61422204, 61473149), the Jiangsu Natural Science Foundation for Distinguished Young Scholar (No. BK20130034), the Specialized Research Fund for the Doctoral Program of Higher Education (No. 20123218110009), the NUAA Fundamental Research Funds (No. NE2013105), and NIH grants (EB006733, EB008374, EB009634, and AG041721).
For this project, the dataset we collected and used was provided by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904). ADNI is funded by non-profit partners the Alzheimer’s Association and Alzheimer’s Drug Discovery Foundation and 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., with participation from the U.S. Food and Drug Administration. What’s more, Private sector contributions to ADNI are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The Northern California Institute for Education and Research is the grantee organization, as well as the Alzheimer’s Disease Cooperative Study at the University of California, San Diego coordinate the study. ADNI data are disseminated by the Laboratory for Neuron Imaging at the University of California, Los Angeles.
Conflicts of interest
The authors declare that they have no conflict of interest.
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