Multi-Task Linear Programming Discriminant Analysis for the Identification of Progressive MCI Individuals

• Published in: PloS one Vol. 9; no. 5; p. e96458
• Main Authors: Yu, Guan, Liu, Yufeng, Thung, Kim-Han, Shen, Dinggang
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 12.05.2014; Public Library of Science (PLoS)
• Subjects: Aged; Aged, 80 and over; Algorithms; Alzheimer Disease - diagnosis; Alzheimer's disease; Alzheimers disease; Artificial intelligence; Biology and Life Sciences; Biomarkers; Biomedical research; Brain research; Classification; Cognitive ability; Cognitive Dysfunction - diagnosis; Computer and Information Sciences; Constraining; Discriminant Analysis; Electronic mail systems; Female; Gene expression; Humans; Image classification; Linear programming; Magnetic resonance; Magnetic Resonance Imaging; Male; Medical imaging; Medicine and Health Sciences; Metabolism; Methods; Missing data; Neurodegenerative diseases; Neuroimaging; Neurology; Neurosciences; NMR; Nuclear magnetic resonance; Operations research; Physical Sciences; Positron emission; Positron emission tomography; Programming, Linear; Tomography; North Carolina; United States > US
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0096458

Accurately identifying mild cognitive impairment (MCI) individuals who will progress to Alzheimer's disease (AD) is very important for making early interventions. Many classification methods focus on integrating multiple imaging modalities such as magnetic resonance imaging (MRI) and fluorodeoxyglucose positron emission tomography (FDG-PET). However, the main challenge for MCI classification using multiple imaging modalities is the existence of a lot of missing data in many subjects. For example, in the Alzheimer's Disease Neuroimaging Initiative (ADNI) study, almost half of the subjects do not have PET images. In this paper, we propose a new and flexible binary classification method, namely Multi-task Linear Programming Discriminant (MLPD) analysis, for the incomplete multi-source feature learning. Specifically, we decompose the classification problem into different classification tasks, i.e., one for each combination of available data sources. To solve all different classification tasks jointly, our proposed MLPD method links them together by constraining them to achieve the similar estimated mean difference between the two classes (under classification) for those shared features. Compared with the state-of-the-art incomplete Multi-Source Feature (iMSF) learning method, instead of constraining different classification tasks to choose a common feature subset for those shared features, MLPD can flexibly and adaptively choose different feature subsets for different classification tasks. Furthermore, our proposed MLPD method can be efficiently implemented by linear programming. To validate our MLPD method, we perform experiments on the ADNI baseline dataset with the incomplete MRI and PET images from 167 progressive MCI (pMCI) subjects and 226 stable MCI (sMCI) subjects. We further compared our method with the iMSF method (using incomplete MRI and PET images) and also the single-task classification method (using only MRI or only subjects with both MRI and PET images). Experimental results show very promising performance of our proposed MLPD method.