Journal
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
Volume 62, Issue 7, Pages 1805-1817Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TBME.2015.2404809
Keywords
Alzheimer's disease (AD); domain transfer learning; feature selection; mild cognitive impairment converters (MCI-C); sample selection
Categories
Funding
- NIH [EB006733, EB008374, EB009634, MH100217, AG041721, AG042599]
- National Natural Science Foundation of China [61422204, 61473149, 61473190]
- Jiangsu Natural Science Foundation for Distinguished Young Scholar [BK20130034]
- Specialized Research Fund for the Doctoral Program of Higher Education [20123218110009]
- NUAA Fundamental Research Funds [NE2013105]
- Scientific and Technological Research Program of Chongqing Municipal Education Commission [KJ131108]
- Alzheimer's Disease Neuroimaging Initiative (ADNI)
- National Institutes of Health [U01 AG024904]
- National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering
- 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.
- Alzheimer's Association
- Alzheimer's Drug Discovery Foundation
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Machine learning methods have successfully been used to predict the conversion of mild cognitive impairment (MCI) to Alzheimer's disease (AD), by classifying MCI converters (MCI-C) from MCI nonconverters (MCI-NC). However, most existing methods construct classifiers using data from one particular target domain (e.g., MCI), and ignore data in other related domains (e.g., AD and normal control (NC)) that may provide valuable information to improve MCI conversion prediction performance. To address is limitation, we develop a novel domain transfer learning method for MCI conversion prediction, which can use data from both the target domain (i.e., MCI) and auxiliary domains (i.e., AD and NC). Specifically, the proposed method consists of three key components: 1) a domain transfer feature selection component that selects the most informative feature-subset from both target domain and auxiliary domains from different imaging modalities; 2) a domain transfer sample selection component that selects the most informative sample-subset from the same target and auxiliary do-mains from different data modalities; and 3) a domain transfer support vector machine classification component that fuses the selected features and samples to separate MCI-C and MCI-NC patients. We evaluate our method on 202 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) that have MRI, FDG-PET, and CSF data. The experimental results show the proposed method can classify MCI-C patients from MCI-NC patients with an accuracy of 79.4%, with the aid of additional domain knowledge learned from AD and NC.
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