Temporally constrained group sparse learning for longitudinal data analysis in Alzheimer's disease

Biao Jie, Mingxia Liu, Jun Liu, Daoqiang Zhang, Dinggang Shen

Research output: Contribution to journalArticle

20 Citations (Scopus)

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 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.

Original languageEnglish
Article number7452361
Pages (from-to)238-249
Number of pages12
JournalIEEE Transactions on Biomedical Engineering
Volume64
Issue number1
DOIs
Publication statusPublished - 2017 Jan 1

Fingerprint

Brain
Alzheimer Disease
Learning
Imaging techniques
Biomarkers
Linear regression
Linear Models
Prodromal Symptoms
Weights and Measures
Neuroimaging
Disease Progression
Cross-Sectional Studies
Databases

Keywords

  • Alzheimer's Disease (AD)
  • group sparsity
  • longitudinal data analysis
  • mild cognitive impairment (MCI)
  • sparse learning
  • temporal smoothness

ASJC Scopus subject areas

  • Biomedical Engineering

Cite this

Temporally constrained group sparse learning for longitudinal data analysis in Alzheimer's disease. / Jie, Biao; Liu, Mingxia; Liu, Jun; Zhang, Daoqiang; Shen, Dinggang.

In: IEEE Transactions on Biomedical Engineering, Vol. 64, No. 1, 7452361, 01.01.2017, p. 238-249.

Research output: Contribution to journalArticle

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