TY - JOUR
T1 - Temporally constrained group sparse learning for longitudinal data analysis in Alzheimer's disease
AU - Jie, Biao
AU - Liu, Mingxia
AU - Liu, Jun
AU - Zhang, Daoqiang
AU - Shen, Dinggang
N1 - Funding Information:
This work was supported in part by National Natural Science Foundation of China (Nos. 61573023, 61422204, 61473149, 61473190, 61472005), the Jiangsu Natural Science Foundation for Distinguished Young Scholar (No. BK20130034), Natural Science Foundation of Anhui Province (No. 1508085MF125), the Open Projects Program of National Laboratory of Pattern Recognition (No. 201407361), the Specialized Research Fund for the Doctoral Program of Higher Education (No. 20123218110009), the NUAA Fundamental Research Funds (No. NE2013105), and NIH Grant EB006733, Grant EB008374, Grant EB009634, Grant MH100217, Grant AG041721, Grant AG049371, and Grant AG042599.
Publisher Copyright:
© 1964-2012 IEEE.
PY - 2017/1
Y1 - 2017/1
N2 - 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.
AB - 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.
KW - Alzheimer's Disease (AD)
KW - group sparsity
KW - longitudinal data analysis
KW - mild cognitive impairment (MCI)
KW - sparse learning
KW - temporal smoothness
UR - http://www.scopus.com/inward/record.url?scp=85008600022&partnerID=8YFLogxK
U2 - 10.1109/TBME.2016.2553663
DO - 10.1109/TBME.2016.2553663
M3 - Article
C2 - 27093313
AN - SCOPUS:85008600022
SN - 0018-9294
VL - 64
SP - 238
EP - 249
JO - IEEE Transactions on Biomedical Engineering
JF - IEEE Transactions on Biomedical Engineering
IS - 1
M1 - 7452361
ER -