Abstract
In the early stage of Alzheimer’s disease (AD), mild cognitive impairment (MCI) has a higher risk of progression to AD, so the prediction of whether an MCI subject will progress to AD (known as progressive MCI, PMCI) or not (known as stable MCI, SMCI) within a certain period is particularly important in practice. It is known that such a task could benefit from jointly learning-related auxiliary tasks such as differentiating AD from PMCI or PMCI from normal control (NC) in order to take full advantage of their shared commonality. However, few existing methods along this line fully consider the correlations between the target and auxiliary tasks according to the clinical practice of AD pathology for diagnosis. To deal with this problem, in this paper, treating each task domain as a different one, we borrow the idea from transfer learning and propose a novel multi-auxiliary domain transfer learning (MaDTL) method, which explicitly utilizes the correlations between the target domain (task) and multi-auxiliary domains (tasks) according to the clinical practice. Specifically, the proposed MaDTL method incorporates two key modules. The first one is a multi-auxiliary domain transfer-based feature selection (MaDTFS) model, which can select a discriminative feature subset shared by the target domain and the multi-auxiliary domains. In the MaDTFS model, to combine more training data from multi-auxiliary domains and simultaneously suppress the negative effects resulting from the irrelevant parts of multi-auxiliary domains, we proposed a sparse group correlation Lasso that includes a proposed group correlation Lasso penalty (i.e., \({\Vert \mathbf{W}\mathbf{H}\Vert }_{\mathrm{2,1}}\)) and a proposed correlation Lasso penalty (i.e., \({\Vert \mathbf{W}\mathbf{H}\Vert }_{\mathrm{1,1}}\)). The second module in MaDTL is a multi-auxiliary domain transfer-based classification (MaDTC) model that improves the voting with linear weighting-based ensemble learning. This model extends the constraints of the linear weighting method so that it can simultaneously combine training data from multi-auxiliary domains and achieve a robust classifier by minimizing negative effects from the irrelevant part of multi-auxiliary domains. Experimental results on 409 subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database with the baseline magnetic resonance imaging (MRI) and cerebrospinal fluid (CSF) data validate the effectiveness of the proposed method by significantly improving the classification accuracy to 80.37% for the identification of MCI-to-AD conversion, outperforming the state-of-the-art methods.
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Data availability
The datasets used in this paper are from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) which are available at http://adni.loni.usc.edu/. Source code and binary programs developed in this paper are available via email, cb729@nuaa.edu.cn.
Notes
\(\Omega =\{0.0001, 0.0005, 0.0009, 0.001:0.001:0.009, 0.01:0.01:0.09, 0.1:0.1:2\}\)
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Acknowledgements
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. Hoffmann-La Roche, Schering-Plough, Synarc, Inc., as well as nonprofit partners the Alzheimer’s Association and Alzheimer’s Drug Discovery Foundation, with participation from the US 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 of Neuroimaging at the University of California, Los Angeles.
Funding
Data collection and sharing for this project were funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904). This work was supported by the National Natural Science Foundation of China (No. 61602072), the Chongqing Cutting-edge and Applied Foundation Research Program (No. cstc2018jcyjAX0502), the Scientific and Technological Research Program of Chongqing Municipal Education Commission (No. KJQN202001222), and the Chongqing Three Gorges University Research Program (No.19QN08).
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Cheng, B., Zhu, B. & Pu, S. Multi-auxiliary domain transfer learning for diagnosis of MCI conversion. Neurol Sci 43, 1721–1739 (2022). https://doi.org/10.1007/s10072-021-05568-6
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DOI: https://doi.org/10.1007/s10072-021-05568-6