Network-guided sparse learning for predicting cognitive outcomes from MRI measures

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 8159 LNCS, Issue , 2013, Pages 202-210

  Yan, Jingwen ^a,b   Huang, Heng ^c   Risacher, Shannon L ^a   Kim, Sungeun ^a   Inlow, Mark ^d   Moore, Jason H ^e   Saykin, Andrew J ^a   Shen, Li ^a,b  

^a INDIANA UNIVERSITY SCHOOL OF MEDICINE   (United States)

^b INDIANA UNIVERSITY   (United States)

^c The University of Texas at Arlington   (United States)

^d ROSE HULMAN INSTITUTE OF TECHNOLOGY   (United States)

^e DARTMOUTH MEDICAL SCHOOL   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALZHEIMER'S DISEASE; BRAIN FUNCTIONS; COMPLEX RELATIONSHIPS; LEARNING METHODS; NEURODEGENERATION; OPTIMAL IMAGING; PREDICTION PERFORMANCE; SPARSE MODELS;

COMPLEX NETWORKS; IMAGE ANALYSIS; REGRESSION ANALYSIS;

FORECASTING;

EID: 84883268869     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-319-02126-3_20     Document Type: Conference Paper

References (11)

1. Aisen, P.S., Petersen, R.C., et al.: Clinical core of the Alzheimer's Disease Neuroimaging Initiative: progress and plans. Alzheimers Dement 6(3), 239-246 (2010)
2. Liu, M., Zhang, D., Yap, P.-T., Shen, D.: Tree-guided sparse coding for brain disease classification. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012, Part III. LNCS, vol. 7512, pp. 239-247. Springer, Heidelberg (2012)
3. Obozinski, G., Taskar, B., Jordan, M.: Multi-task feature selection. Technical Report, Statistics Department, UC Berkeley (2006)
4. Sabuncu, M.R., Van Leemput, K.: The relevance voxel machine (RVoxM): A bayesian method for image-based prediction. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011, Part III. LNCS, vol. 6893, pp. 99-106. Springer, Heidelberg (2011)
5. Tibshirani, R.: Regression shrinkage and selection via the lasso. J. R. Stat. Soc. Ser. B 58, 267-288 (1996)
6. Walhovd, K., Fjell, A., et al.: Multi-modal imaging predicts memory performance in normal aging and cognitive decline. Neurobiol Aging 31(7), 1107-1121 (2010)
7. Wan, J., et al.: Sparse Bayesian multi-task learning for predicting cognitive outcomes from neuroimaging measures in Alzheimer's disease. In: CVPR 2012, pp. 940-947 (2012)
8. Wang, H., Nie, F., et al.: Sparse multi-task regression and feature selection to identify brain imaging predictors for memory performance. In: ICCV 2011, pp. 557-562 (2011)
9. Wang, H., Nie, F., et al.: Identifying quantitative trait loci via group-sparse multitask regression and feature selection: an imaging genetics study of the ADNI cohort. Bioinformatics 28(2), 229-237 (2012)
10. Yan, J., et al.: Multimodal neuroimaging predictors for cognitive performance using structured sparse learning. In: Yap, P.-T., Liu, T., Shen, D., Westin, C.-F., Shen, L. (eds.) MBIA 2012. LNCS, vol. 7509, pp. 1-17. Springer, Heidelberg (2012)
11. Zhang, D., Shen, D.: Multi-modal multi-task learning for joint prediction of multiple regression and classification variables in Alzheimer's disease. Neuroimage 59(2), 895-907 (2012)