Multimodal manifold-regularized transfer learning for MCI conversion prediction.
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...
| Published in: | Brain Imaging & Behavior Vol. 9; no. 4; pp. 913 - 927 |
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| Main Authors: | , , , , |
| Format: | research Journal Article |
| Published: |
Springer Nature
Dec2015
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=111244182&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 111244182 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 19317557 3GSC jtl: Brain Imaging & Behavior issn: 19317557 maglogo: N pubinfo: dt: Dec2015 vid: 9 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 111244182 111244182 NLM25702248 111244182 10.1007/s11682-015-9356-x NLM25702248 PMC4546576 111244182 ppf: 913 ppct: 14 formats: fmt: @attributes: type: P tig: atl: Multimodal manifold-regularized transfer learning for MCI conversion prediction. aug: au: Cheng, Bo Liu, Mingxia Suk, Heung-Il Shen, Dinggang Zhang, Daoqiang affil: Department of Brain and Cognitive Engineering, Korea University, Seoul Republic of Korea sug: subj: Brain Radiography Alzheimer's Disease Diagnosis Diagnosis, Computer Assisted Methods Brain Pathology Cognition Disorders Diagnosis Tomography, Emission-Computed Methods Magnetic Resonance Imaging Methods Cognition Disorders Pathology ROC Curve Alzheimer's Disease Classification Cognition Disorders Physiopathology Cognition Disorders Classification Regression Resource Databases Alzheimer's Disease Pathology Prognosis Aged, 80 and Over Middle Age Diagnostic Imaging Methods Alzheimer's Disease Physiopathology Information Science Methods Aged Disease Progression Human Validation Studies Comparative Studies Evaluation Research Multicenter Studies Funding Source Aged, 80 & over Middle Aged: 45-64 years Aged: 65+ years ab: 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. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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