Temporally Constrained Group Sparse Learning for Longitudinal Data Analysis in Alzheimer's Disease.

ABSTRACT: 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 m

SUBMITTER: Jie B 

PROVIDER: S-EPMC5179317 | biostudies-literature | 2017 Jan

REPOSITORIES: biostudies-literature