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 thatrequires 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.

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assist the diagnosis of alzheimerx0027s disease mild cognitive impairment italicgroup regularization termitalic discovering diseaserelated biomarkers objective function ie italicone fused smoothness adni database optimization algorithm longitudinal analysis methods analysis of brain images timepoints of data its prodromal stage temporallyconstrained group sparse learning method disease progression sparse learningbased studies italictwo smoothness regularization termsitalic sparse learningbased methods smooth changes italicoutput smoothness term brain imaging sparse linear regression model

Biao Jie,.Temporally Constrained Group Sparse Learning for Longitudinal Data Analysis in Alzheimer's Disease. 64 (1),238-249.

Biao Jie,"Temporally Constrained Group Sparse Learning for Longitudinal Data Analysis in Alzheimer's Disease" 64

Biao Jie,"Temporally Constrained Group Sparse Learning for Longitudinal Data Analysis in Alzheimer's Disease"