Brain, 138, 2701-2715. Oxford University Press Brain Brain, Vol. 138, No Pt 9 (2015) pp. 2701-2715 Toledo, J B, Zetterberg, H, van Harten, A C, Glodzik, L, Martinez-Lage, P, Bocchio-Chiavetto, L, Rami, L, Hansson, O, Sperling, R, Engelborghs, S, Osorio, R S, Vanderstichele, H, Vandijck, M, Hampel, H, Teipl, S, Moghekar, A, Albert, M, Hu, W T, Argiles, J A M, Gorostidi, A, Teunissen, C E, de Deyn, P P, Hyman, B T, Molinuevo, J L, Frisoni, G B, Linazasoro, G, de Leon, M J, van der Flier, W M, Scheltens, P, Blennow, K, Shaw, L M & Trojanowski, J Q 2015, ' Alzheimer's disease cerebrospinal fluid biomarker in cognitively normal subjects ', Brain, vol. 138, pp. 2701-2715 . https://doi.org/10.1093/brain/awv199 Brain 138(9), 2701-2715 (2015). doi:10.1093/brain/awv199
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.
Huang, M., W. Yang, Q. Feng, W. Chen, M. W. Weiner, P. Aisen, R. Petersen, et al. 2017. “Longitudinal measurement and hierarchical classification framework for the prediction of Alzheimer’s disease.” Scientific Reports 7 (1): 39880. doi:10.1038/srep39880. http://dx.doi.org/10.1038/srep39880.
Liu, H., X. Zhou, H. Jiang, H. He, X. Liu, M. W. Weiner, P. Aisen, et al. 2016. “A semi-mechanism approach based on MRI and proteomics for prediction of conversion from mild cognitive impairment to Alzheimer’s disease.” Scientific Reports 6 (1): 26712. doi:10.1038/srep26712. http://dx.doi.org/10.1038/srep26712.