Journal
MEDICAL IMAGE ANALYSIS
Volume 61, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.media.2020.101652
Keywords
Early stage of Alzheimer's disease (AD); Brain network estimation; Self-calibration; Multi-modal classification; Joint non-convex multi-task learning
Categories
Funding
- National Natural Science Foundation of China [61871274, U1909209, 61801305, 81571758]
- Key Laboratory of Medical Image Processing of Guangdong Province [K217300003]
- Guangdong Pearl River Talents Plan [2016ZT06S220]
- Shenzhen Peacock Plan [KQTD2016053112051497, KQTD2015033016104926]
- Shenzhen Key Basic Research Project [JCYJ20180507184647636, JCYJ20170818094109846]
- OCEAN project - Engineering and Physical Sciences Research Council (EPSRC) [EP/M006328/1]
- MedIAN Network - Engineering and Physical Sciences Research Council (EPSRC) [EP/N026993/1]
- European Commission FP7 Project VPH-DARE@IT [FP7-ICT-2011-9-601055]
- Royal Academy of Engineering Chair in Emerging Technology
- EPSRC [EP/M000133/1, EP/N026993/1] Funding Source: UKRI
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Detection of early stages of Alzheimer's disease (AD) (i.e., mild cognitive impairment (MCI)) is important to maximize the chances to delay or prevent progression to AD. Brain connectivity networks inferred from medical imaging data have been commonly used to distinguish MCI patients from normal controls (NC). However, existing methods still suffer from limited performance, and classification remains mainly based on single modality data. This paper proposes a new model to automatically diagnosing MCI (early MCI (EMCI) and late MCI (LMCI)) and its earlier stages (i.e., significant memory concern (SMC)) by combining low-rank self-calibrated functional brain networks and structural brain networks for joint multi-task learning. Specifically, we first develop a new functional brain network estimation method. We introduce data quality indicators for self-calibration, which can improve data quality while completing brain network estimation, and perform correlation analysis combined with low-rank structure. Second, functional and structural connected neuroimaging patterns are integrated into our multi-task learning model to select discriminative and informative features for fine MCI analysis. Different modalities are best suited to undertake distinct classification tasks, and similarities and differences among multiple tasks are best determined through joint learning to determine most discriminative features. The learning process is completed by non-convex regularizer, which effectively reduces the penalty bias of trace norm and approximates the original rank minimization problem. Finally, the most relevant disease features classified using a support vector machine (SVM) for MCI identification. Experimental results show that our method achieves promising performance with high classification accuracy and can effectively discriminate between different sub-stages of MCI. (C) 2020 Elsevier B.V. All rights reserved.
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