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所属单位：计算机科学与技术学院/人工智能学院/软件学院
发表刊物：IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
关键字：Alzheimer's Disease (AD) group sparsity longitudinal data analysis mild cognitive impairment (MCI) sparse learning temporal smoothness
摘要：Sparse learning has been widely investigated for analysis of brain images to assist the diagnosis of Alzheimer's disease and its prodromal stage, i.e., mild cognitive impairment. However, most existing sparse learning-based studies only adopt cross-sectional analysis methods, where the sparse model is learned using data from a single time-point. Actually, multiple time-points of data are often available in brain imaging applications, which can be used in some longitudinal analysis methods to better uncover the disease progression patterns. Accordingly, in this paper, we propose a novel temporallyconstrained group sparse learning method aiming for longitudinal analysis with multiple time-points of data. Specifically, we learn a sparse linear regression model by using the imaging data from multiple time-points, where a group regularization term is first employed to group the weights for the same brain region across different time-points together. Furthermore, to reflect the smooth changes between data derived from adjacent time-points, we incorporate two smoothness regularization terms into the objective function, i.e., one fused smoothness term that requires that the differences between two successive weight vectors from adjacent time-points should be small, and another output smoothness term thatrequires the differences between outputs of two successive models from adjacent time-points should also be small. We develop an efficient optimization algorithm to solve the proposed objective function. Experimental results on ADNI database demonstrate that, compared with conventional sparse learning-based methods, our proposed method can achieve improved regression performance and also help in discovering disease-related biomarkers.
ISSN号：0018-9294
是否译文：否
发表时间：2017-01-01
合写作者：Jie, Biao,Liu, Jun,张道强,Shen, Dinggang
通讯作者：刘明香