Abstract
Alzheimer’s disease (AD) is characterized by gradual neurodegeneration and loss of brain function, especially for memory during early stages. Regression analysis has been widely applied to AD research to relate clinical and biomarker data such as predicting cognitive outcomes from MRI measures. In particular, sparse models have been proposed to identify the optimal imaging markers with high prediction power. However, the complex relationship among imaging markers are often overlooked or simplified in the existing methods. To address this issue, we present a new sparse learning method by introducing a novel network term to more flexibly model the relationship among imaging markers. The proposed algorithm is applied to the ADNI study for predicting cognitive outcomes using MRI scans. The effectiveness of our method is demonstrated by its improved prediction performance over several state-of-the-art competing methods and accurate identification of cognition-relevant imaging markers that are biologically meaningful.
For the Alzheimer’s Disease Neuroimaging Initiative. Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database adni.loni.ucla.edu . As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.ucla.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf
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References
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)
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)
Obozinski, G., Taskar, B., Jordan, M.: Multi-task feature selection. Technical Report, Statistics Department, UC Berkeley (2006)
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)
Tibshirani, R.: Regression shrinkage and selection via the lasso. J. R. Stat. Soc. Ser. B 58, 267–288 (1996)
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)
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)
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)
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)
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)
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)
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Yan, J. et al. (2013). Network-Guided Sparse Learning for Predicting Cognitive Outcomes from MRI Measures. In: Shen, L., Liu, T., Yap, PT., Huang, H., Shen, D., Westin, CF. (eds) Multimodal Brain Image Analysis. MBIA 2013. Lecture Notes in Computer Science, vol 8159. Springer, Cham. https://doi.org/10.1007/978-3-319-02126-3_20
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DOI: https://doi.org/10.1007/978-3-319-02126-3_20
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