Temporally-constrained group sparse learning for longitudinal data analysis.

Daoqiang Zhang, Jun Liu, Dinggang Shen

Research output: Chapter in Book/Report/Conference proceedingChapter

15 Citations (Scopus)

Abstract

Sparse learning has recently received increasing attentions in neuroimaging research such as brain disease diagnosis and progression. Most existing studies focus on cross-sectional analysis, i.e., learning a sparse model based on single time-point of data. However, in some brain imaging applications, multiple time-points of data are often available, thus longitudinal analysis can be performed to better uncover the underlying disease progression patterns. In this paper, we propose a novel temporally-constrained group sparse learning method aiming for longitudinal analysis with multiple time-points of data. Specifically, for each time-point, we train a sparse linear regression model by using the imaging data and the corresponding responses, and further use the group regularization to group the weights corresponding to the same brain region across different time-points together. Moreover, to reflect the smooth changes between adjacent time-points of data, we also include two smoothness regularization terms into the objective function, i.e., one fused smoothness term which requires the differences between two successive weight vectors from adjacent time-points should be small, and another output smoothness term which requires the differences between outputs of two successive models from adjacent time-points should also be small. We develop an efficient algorithm to solve the new objective function with both group-sparsity and smoothness regularizations. We validate our method through estimation of clinical cognitive scores using imaging data at multiple time-points which are available in the Alzheimer's disease neuroimaging initiative (ADNI) database.

Original languageEnglish
Title of host publicationMedical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
Pages264-271
Number of pages8
Volume15
EditionPt 3
Publication statusPublished - 2012 Dec 1

Fingerprint

Learning
Neuroimaging
Disease Progression
Linear Models
Weights and Measures
Brain Diseases
Alzheimer Disease
Cross-Sectional Studies
Databases
Brain
Research

ASJC Scopus subject areas

  • Medicine(all)

Cite this

Zhang, D., Liu, J., & Shen, D. (2012). Temporally-constrained group sparse learning for longitudinal data analysis. In Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention (Pt 3 ed., Vol. 15, pp. 264-271)

Temporally-constrained group sparse learning for longitudinal data analysis. / Zhang, Daoqiang; Liu, Jun; Shen, Dinggang.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. Vol. 15 Pt 3. ed. 2012. p. 264-271.

Research output: Chapter in Book/Report/Conference proceedingChapter

Zhang, D, Liu, J & Shen, D 2012, Temporally-constrained group sparse learning for longitudinal data analysis. in Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. Pt 3 edn, vol. 15, pp. 264-271.
Zhang D, Liu J, Shen D. Temporally-constrained group sparse learning for longitudinal data analysis. In Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. Pt 3 ed. Vol. 15. 2012. p. 264-271
Zhang, Daoqiang ; Liu, Jun ; Shen, Dinggang. / Temporally-constrained group sparse learning for longitudinal data analysis. Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. Vol. 15 Pt 3. ed. 2012. pp. 264-271
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