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10.

Predicting brain activity using a Bayesian spatial model.
by Derado, Gordana

Statistical methods in medical research, August 2013, Vol.22(4), pp.382-397

14.

Standardization of analysis sets for reporting results from ADNI MRI data.
by Wyman, Bradley T

Alzheimer's & dementia : the journal of the Alzheimer's Association, May 2013, Vol.9(3), pp.332-337

15.

Mining Outcome-relevant Brain Imaging Genetic Associations via Three-way Sparse Canonical Correlation Analysis in Alzheimer’s Disease
by Hao, Xiaoke

Hao, X., C. Li, L. Du, X. Yao, J. Yan, S. L. Risacher, A. J. Saykin, et al. 2017. “Mining Outcome-relevant Brain Imaging Genetic Associations via Three-way Sparse Canonical Correlation Analysis in Alzheimer’s Disease.” Scientific Reports 7 (1): 44272. doi:10.1038/srep44272. http://dx.doi.org/10.1038/srep44272.

17.

Cascaded Multi-view Canonical Correlation (CaMCCo) for Early Diagnosis of Alzheimer’s Disease via Fusion of Clinical, Imaging and Omic Features
by Singanamalli, Asha

Singanamalli, A., H. Wang, A. Madabhushi, M. Weiner, P. Aisen, R. Petersen, C. Jack, et al. 2017. “Cascaded Multi-view Canonical Correlation (CaMCCo) for Early Diagnosis of Alzheimer’s Disease via Fusion of Clinical, Imaging and Omic Features.” Scientific Reports 7 (1): 8137. doi:10.1038/s41598-017-03925-0. http://dx.doi.org/10.1038/s41598-017-03925-0.

18.

Pattern Discovery in Brain Imaging Genetics via SCCA Modeling with a Generic Non-convex Penalty
by Du, Lei

Du, L., K. Liu, X. Yao, J. Yan, S. L. Risacher, J. Han, L. Guo, et al. 2017. “Pattern Discovery in Brain Imaging Genetics via SCCA Modeling with a Generic Non-convex Penalty.” Scientific Reports 7 (1): 14052. doi:10.1038/s41598-017-13930-y. http://dx.doi.org/10.1038/s41598-017-13930-y.

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