`a`
Mathematical Biosciences and Engineering (MBE)
 

Classification of Alzheimer's disease using unsupervised diffusion component analysis
Pages: 1119 - 1130, Issue 6, December 2016

doi:10.3934/mbe.2016033      Abstract        References        Full text (1066.8K)           Related Articles

Dominique Duncan - Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, United States (email)
Thomas Strohmer - Department of Mathematics, University of California, Davis, United States (email)

1 Alzheimer's Association: Alzheimer's disease facts and figures. Alzheimer's & Dementia, 9 (2013), 208-245.
2 N. Ahmed, T. Natarajan and K. R. Rao, Discrete cosine transform, IEEE Transactions on Computers, 23 (1974), 90-93.       
3 R. R. Coifman and S. Lafon, Diffusion maps, Appl. Comp. Harm. Anal., 21 (2006), 5-30.       
4 D. Duncan, R. Talmon, H. P. Zaveri and R. R. Coifman, Identifying preseizure state in intracranial EEG data using diffusion kernels, Math Biosci Eng, 10 (2013), 579-590.       
5 C. Habeck and Y. Stern, Alzheimer's disease neuroimaging initiative, Multivariate data analysis for neuroimaging data: Overview and application to Alzheimer's disease, Cell Biochem Biophys., 58 (2010), 53-67.
6 P. Hagmann, M. Kurant, X. Gigandet, P. Thiran, V. J. Wedeen, R. Meuli and J.-P. Thiran, Mapping human whole-brain structural networks with diffusion MRI, PLoS ONE, 2 (2007), e597.
7 P. Hagmann, L. Cammoun, X. Gigandet, R. Meuli, C. J. Honey, V. J. Wedeen and O. Sporns, Mapping the structural core of human cerebral cortex, PLoS Biol, 6 (2008), e159.
8 S. Norton, F. E. Matthews, D. Barnes, K. Yaffe and C. Brayne, Potential for primary prevention of Alzheimer's disease: an analysis of population-based data, Lancet Neurology, 13 (2014), 788-794.
9 C. Syms, Principal components analysis, Reference Module in Earth Systems and Environmental Sciences Encyclopedia of Ecology, (2008), 2940-2949.
10 R. C. Petersen, Mild cognitive impairment clinical trials, Nature Reviews Drug Discovery, 2 (2003), 646-653.
11 Y. Rubner, C. Tomasi and L. J. Guibas, A metric for distributions with applications to image databases, IEEE 6th International Conference on Computer Vision, (1998), 59-66.
12 R. Talmon and R. R. Coifman, Differential stochastic sensing: intrinsic modeling of random time series with applications to nonlinear tracking, PNAS, (2012), 1-14.
13 R. Talmon, D. Kushnir, R. R. Coifman, I. Cohen and S. Gannot, Parametrization of linear systems using diffusion kernels, IEEE Transactions on Signal Processing, 60 (2012), 1159-1173.       
14 W. Yang, R. L. Lui, J. H. Gao, T. F. Chan, S. T. Yau, R. A. Sperling and X. Huang, Independent component analysis-based classification of Alzheimer's disease MRI data, J. Alzheimers Dis, 24 (2011), 775-783.
15 J. Ye, M. Farnum, E. Yang, R. Verbeeck, V. Lobanov, N. Raghavan, G. Novak, A. DiBernardo and V. A. Narayan, Sparse learning and stability selection for predicting MCI to AD conversion using baseline ADNI data, BMC Neurology, 12 (2012), 1-12.

Go to top