title: Multimodal manifold-regularized transfer learning for MCI conversion prediction.
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.affiliation: College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China.|Department of Radiology and BRIC, University of North Carolina, Chapel Hill, NC, 27599, USA.|School of Computer Science and Engineering, Chongqing Three Gorges University, Chongqing, 404000, China.
authors.forename: Bo
authors.initials: B
authors.lastname: Cheng
authors.affiliation: College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China.|School of Information Science and Technology, Taishan University, Taian, 271021, China.
authors.forename: Mingxia
authors.initials: M
authors.lastname: Liu
authors.affiliation: Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea.
authors.forename: Heung-Il
authors.initials: HI
authors.lastname: Suk
authors.affiliation: Department of Radiology and BRIC, University of North Carolina, Chapel Hill, NC, 27599, USA. dgshen@med.unc.edu.|Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea. dgshen@med.unc.edu.
authors.forename: Dinggang
authors.initials: D
authors.lastname: Shen
authors.affiliation: College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China. dqzhang@nuaa.edu.cn.
authors.forename: Daoqiang
authors.initials: D
authors.lastname: Zhang
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D000544:Alzheimer Disease / Q000145:classification / Q000175:diagnosis* / Q000473:pathology / Q000503:physiopathology; D001921:Brain / Q000000981:diagnostic imaging* / Q000473:pathology*; D060825:Cognitive Dysfunction / Q000145:classification / Q000175:diagnosis* / Q000473:pathology / Q000503:physiopathology; D016208:Databases, Factual; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D018450:Disease Progression; D006801:Humans; D016018:Least-Squares Analysis; D008279:Magnetic Resonance Imaging / Q000379:methods; D008875:Middle Aged; D064847:Multimodal Imaging / Q000379:methods; D010363:Pattern Recognition, Automated / Q000379:methods; D049268:Positron-Emission Tomography / Q000379:methods; D011379:Prognosis; D012372:ROC Curve; D000069553:Supervised Machine Learning
keywords: Manifold regularization; Mild cognitive impairment conversion; Multimodal classification; Sample selection; Semi-supervised learning; Transfer learning