Advertisement

Temporally-Constrained Group Sparse Learning for Longitudinal Data Analysis

  • Daoqiang Zhang
  • Jun Liu
  • Dinggang Shen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7512)

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.

Keywords

Mild Cognitive Impairment Mini Mental State Examination Magnetic Resonance Imaging Data Group Lasso Sparse Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Liu, M., Zhang, D., Shen, D.: Ensemble sparse classification of Alzheimer’s disease. NeuroImage 60, 1106–1116 (2012)CrossRefGoogle Scholar
  2. 2.
    Tibshirani, R.: Regression shrinkage and selection via the Lasso. J. Roy. Stat. Soc. B. Met. 58, 267–288 (1996)MathSciNetzbMATHGoogle Scholar
  3. 3.
    Shen, L., Kim, S., Qi, Y., Inlow, M., Swaminathan, S., Nho, K., Wan, J., Risacher, S.L., Shaw, L.M., Trojanowski, J.Q., Weiner, M.W., Saykin, A.J.: Identifying Neuroimaging and Proteomic Biomarkers for MCI and AD via the Elastic Net. In: Liu, T., Shen, D., Ibanez, L., Tao, X. (eds.) MBIA 2011. LNCS, vol. 7012, pp. 27–34. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  4. 4.
    Ng, B., Abugharbieh, R.: Generalized sparse regularization with application to fMRI brain decoding. Inf. Process Med. Imaging 22, 612–623 (2011)CrossRefGoogle Scholar
  5. 5.
    Yuan, M., Lin, Y.: Model selection and estimation in regression with grouped variables. J. Roy Stat. Soc. B 68, 49–67 (2006)MathSciNetzbMATHCrossRefGoogle Scholar
  6. 6.
    Wang, H., Nie, F., Huang, H., Risacher, S., Saykin, A.J., Shen, L.: Identifying AD-Sensitive and Cognition-Relevant Imaging Biomarkers via Joint Classification and Regression. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011, Part III. LNCS, vol. 6893, pp. 115–123. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  7. 7.
    Zhang, D., Shen, D.: Multi-modal multi-task learning for joint prediction of multiple regression and classification variables in Alzheimer’s disease. NeuroImage 59, 895–907 (2012)CrossRefGoogle Scholar
  8. 8.
    Xu, S., Styner, M., Gilmore, J., Piven, J., Gerig, G.: Multivariate nonlinear mixed model to analyze longitudinal image data: MRI study of early brain development. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, pp. 1–8 (2008)Google Scholar
  9. 9.
    Liu, J., Yuan, L., Ye, J.: An efficient algorithm for a class of fused lasso problems. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 323–332. ACM, Washington, DC (2010)CrossRefGoogle Scholar
  10. 10.
    Beck, A., Teboulle, M.: A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems. SIAM J. Img. Sci. 2, 183–202 (2009)MathSciNetzbMATHCrossRefGoogle Scholar
  11. 11.
    Shen, D., Resnick, S.M., Davatzikos, C.: 4D HAMMER Image Registration Method for Longitudinal Study of Brain Changes. In: Proceedings of the Human Brain Mapping, New York City, USA (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Daoqiang Zhang
    • 1
    • 2
  • Jun Liu
    • 3
  • Dinggang Shen
    • 1
  1. 1.Dept. of Radiology and BRICUniversity of North Carolina at Chapel HillUSA
  2. 2.Dept. of Computer Science and EngineeringNanjing University of Aeronautics and AstronauticsNanjingChina
  3. 3.Imaging and Computer Vision Dept.Siemens Corporate ResearchPrincetonUSA

Personalised recommendations