Multimodal manifold-regularized transfer learning for MCI conversion prediction

Bo Cheng; Mingxia Liu; Heung-Il Suk; Dinggang Shen; Daoqiang Zhang; Alzheimer’s Disease Neuroimaging Initiative

doi:10.1007/s11682-015-9356-x

Back

Multimodal manifold-regularized transfer learning for MCI conversion prediction

Journal article

Peer reviewed

Multimodal manifold-regularized transfer learning for MCI conversion prediction

Bo Cheng, Mingxia Liu, Heung-Il Suk, Dinggang Shen, Daoqiang Zhang and Alzheimer’s Disease Neuroimaging Initiative

Brain imaging and behavior, Vol.9(4), pp.913-926

12/2015

DOI: 10.1007/s11682-015-9356-x

PMCID: PMC4546576

PMID: 25702248

Files and links (1)

url

https://www.ncbi.nlm.nih.gov/pmc/articles/4546576View

Open Access

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.

Brain - diagnostic imaging

Prognosis

Humans

Middle Aged

Magnetic Resonance Imaging - methods

Alzheimer Disease - pathology

Alzheimer Disease - diagnosis

Diagnosis, Computer-Assisted - methods

Multimodal Imaging - methods

Aged, 80 and over

Cognitive Dysfunction - diagnosis

Databases, Factual

Alzheimer Disease - physiopathology

Alzheimer Disease - classification

Cognitive Dysfunction - pathology

Positron-Emission Tomography - methods

Cognitive Dysfunction - physiopathology

Disease Progression

Cognitive Dysfunction - classification

Brain - pathology

Least-Squares Analysis

ROC Curve

Aged

Supervised Machine Learning

Pattern Recognition, Automated - methods

Details

Title: Subtitle: Multimodal manifold-regularized transfer learning for MCI conversion prediction
Creators: Bo Cheng - School of Computer Science and Engineering, Chongqing Three Gorges University, Chongqing, 404000, China
Mingxia Liu - School of Information Science and Technology, Taishan University, Taian, 271021, China
Heung-Il Suk - Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea
Dinggang Shen - Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea. dgshen@med.unc.edu
Daoqiang Zhang - College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China. dqzhang@nuaa.edu.cn
Alzheimer’s Disease Neuroimaging Initiative
Contributors: Laura L Boles-Ponto (Contributor) - University of Iowa, Radiology
Resource Type: Journal article
Publication Details: Brain imaging and behavior, Vol.9(4), pp.913-926
DOI: 10.1007/s11682-015-9356-x
PMID: 25702248
PMCID: PMC4546576
NLM abbreviation: Brain Imaging Behav
ISSN: 1931-7557
eISSN: 1931-7565
Publisher: United States
Grant note: MH100217 / NIMH NIH HHS R01 EB006733 / NIBIB NIH HHS R01 AG042599 / NIA NIH HHS AG042599 / NIA NIH HHS EB006733 / NIBIB NIH HHS AG041721 / NIA NIH HHS P30 AG013846 / NIA NIH HHS EB008374 / NIBIB NIH HHS R01 EB008374 / NIBIB NIH HHS R01 AG041721 / NIA NIH HHS U01 AG024904 / NIA NIH HHS R01 EB009634 / NIBIB NIH HHS EB009634 / NIBIB NIH HHS R01 MH100217 / NIMH NIH HHS
Language: English
Date published: 12/2015
Academic Unit: Radiology; Pharmaceutical Sciences and Experimental Therapeutics
Record Identifier: 9984051790402771

Metrics

17 Record Views

69 Times Cited - Web of Science