Table of Contents Author Guidelines Submit a Manuscript
Computational and Mathematical Methods in Medicine
Volume 2018, Article ID 7429782, 13 pages
https://doi.org/10.1155/2018/7429782
Research Article

Linearized and Kernelized Sparse Multitask Learning for Predicting Cognitive Outcomes in Alzheimer’s Disease

1Computer Science and Engineering, Northeastern University, Shenyang, China
2Key Laboratory of Medical Image Computing of Ministry of Education, Northeastern University, Shenyang, China

Correspondence should be addressed to Peng Cao; nc.ude.uen.esc@gnepoac

Received 4 August 2017; Revised 18 December 2017; Accepted 26 December 2017; Published 24 January 2018

Academic Editor: Peng Li

Copyright © 2018 Xiaoli Liu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Alzheimer’s disease (AD) has been not only the substantial financial burden to the health care system but also the emotional burden to patients and their families. Predicting cognitive performance of subjects from their magnetic resonance imaging (MRI) measures and identifying relevant imaging biomarkers are important research topics in the study of Alzheimer’s disease. Recently, the multitask learning (MTL) methods with sparsity-inducing norm (e.g., -norm) have been widely studied to select the discriminative feature subset from MRI features by incorporating inherent correlations among multiple clinical cognitive measures. However, these previous works formulate the prediction tasks as a linear regression problem. The major limitation is that they assumed a linear relationship between the MRI features and the cognitive outcomes. Some multikernel-based MTL methods have been proposed and shown better generalization ability due to the nonlinear advantage. We quantify the power of existing linear and nonlinear MTL methods by evaluating their performance on cognitive score prediction of Alzheimer’s disease. Moreover, we extend the traditional -norm to a more general -norm (). Experiments on the Alzheimer’s Disease Neuroimaging Initiative database showed that the nonlinear -MKMTL method not only achieved better prediction performance than the state-of-the-art competitive methods but also effectively fused the multimodality data.