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Items: 1 to 20 of 119

1.

Multi-source feature learning for joint analysis of incomplete multiple heterogeneous neuroimaging data.

Yuan L, Wang Y, Thompson PM, Narayan VA, Ye J; Alzheimer's Disease Neuroimaging Initiative..

Neuroimage. 2012 Jul 2;61(3):622-32. doi: 10.1016/j.neuroimage.2012.03.059.

2.

Multi-Source Learning for Joint Analysis of Incomplete Multi-Modality Neuroimaging Data.

Yuan L, Wang Y, Thompson PM, Narayan VA, Ye J.

KDD. 2012:1149-1157.

3.

Multi-task linear programming discriminant analysis for the identification of progressive MCI individuals.

Yu G, Liu Y, Thung KH, Shen D.

PLoS One. 2014 May 12;9(5):e96458. doi: 10.1371/journal.pone.0096458.

4.

Bi-level multi-source learning for heterogeneous block-wise missing data.

Xiang S, Yuan L, Fan W, Wang Y, Thompson PM, Ye J; Alzheimer's Disease Neuroimaging Initiative..

Neuroimage. 2014 Nov 15;102 Pt 1:192-206. doi: 10.1016/j.neuroimage.2013.08.015. Review.

5.

Multi-modality sparse representation-based classification for Alzheimer's disease and mild cognitive impairment.

Xu L, Wu X, Chen K, Yao L.

Comput Methods Programs Biomed. 2015 Nov;122(2):182-90. doi: 10.1016/j.cmpb.2015.08.004.

PMID:
26298855
6.

Machine learning framework for early MRI-based Alzheimer's conversion prediction in MCI subjects.

Moradi E, Pepe A, Gaser C, Huttunen H, Tohka J; Alzheimer's Disease Neuroimaging Initiative..

Neuroimage. 2015 Jan 1;104:398-412. doi: 10.1016/j.neuroimage.2014.10.002.

PMID:
25312773
7.

Multi-modal multi-task learning for joint prediction of multiple regression and classification variables in Alzheimer's disease.

Zhang D, Shen D; Alzheimer's Disease Neuroimaging Initiative..

Neuroimage. 2012 Jan 16;59(2):895-907. doi: 10.1016/j.neuroimage.2011.09.069. Erratum in: Neuroimage. 2012 Sep;62(3):2179.

8.

Comparison of neuroimaging modalities for the prediction of conversion from mild cognitive impairment to Alzheimer's dementia.

Trzepacz PT, Yu P, Sun J, Schuh K, Case M, Witte MM, Hochstetler H, Hake A; Alzheimer's Disease Neuroimaging Initiative..

Neurobiol Aging. 2014 Jan;35(1):143-51. doi: 10.1016/j.neurobiolaging.2013.06.018.

PMID:
23954175
9.

Manifold regularized multitask feature learning for multimodality disease classification.

Jie B, Zhang D, Cheng B, Shen D; Alzheimer's Disease Neuroimaging Initiative..

Hum Brain Mapp. 2015 Feb;36(2):489-507. doi: 10.1002/hbm.22642.

10.

Predicting cognitive decline in subjects at risk for Alzheimer disease by using combined cerebrospinal fluid, MR imaging, and PET biomarkers.

Shaffer JL, Petrella JR, Sheldon FC, Choudhury KR, Calhoun VD, Coleman RE, Doraiswamy PM; Alzheimer’s Disease Neuroimaging Initiative..

Radiology. 2013 Feb;266(2):583-91. doi: 10.1148/radiol.12120010.

11.

Hierarchical feature representation and multimodal fusion with deep learning for AD/MCI diagnosis.

Suk HI, Lee SW, Shen D; Alzheimer's Disease Neuroimaging Initiative..

Neuroimage. 2014 Nov 1;101:569-82. doi: 10.1016/j.neuroimage.2014.06.077.

12.

Longitudinal FDG-PET features for the classification of Alzheimer's disease.

Rodrigues F, Silveira M.

Conf Proc IEEE Eng Med Biol Soc. 2014;2014:1941-4. doi: 10.1109/EMBC.2014.6943992.

PMID:
25570360
13.

Studying ventricular abnormalities in mild cognitive impairment with hyperbolic Ricci flow and tensor-based morphometry.

Shi J, Stonnington CM, Thompson PM, Chen K, Gutman B, Reschke C, Baxter LC, Reiman EM, Caselli RJ, Wang Y; Alzheimer's Disease Neuroimaging Initiative..

Neuroimage. 2015 Jan 1;104:1-20. doi: 10.1016/j.neuroimage.2014.09.062.

14.
15.

ApoE4 effects on automated diagnostic classifiers for mild cognitive impairment and Alzheimer's disease.

Apostolova LG, Hwang KS, Kohannim O, Avila D, Elashoff D, Jack CR Jr, Shaw L, Trojanowski JQ, Weiner MW, Thompson PM; Alzheimer's Disease Neuroimaging Initiative..

Neuroimage Clin. 2014 Jan 4;4:461-72. doi: 10.1016/j.nicl.2013.12.012.

16.

Domain Transfer Learning for MCI Conversion Prediction.

Cheng B, Liu M, Zhang D, Munsell BC, Shen D.

IEEE Trans Biomed Eng. 2015 Jul;62(7):1805-17. doi: 10.1109/TBME.2015.2404809.

17.

Multimodal classification of Alzheimer's disease and mild cognitive impairment.

Zhang D, Wang Y, Zhou L, Yuan H, Shen D; Alzheimer's Disease Neuroimaging Initiative..

Neuroimage. 2011 Apr 1;55(3):856-67. doi: 10.1016/j.neuroimage.2011.01.008.

18.

Individual subject classification for Alzheimer's disease based on incremental learning using a spatial frequency representation of cortical thickness data.

Cho Y, Seong JK, Jeong Y, Shin SY; Alzheimer's Disease Neuroimaging Initiative..

Neuroimage. 2012 Feb 1;59(3):2217-30. doi: 10.1016/j.neuroimage.2011.09.085.

PMID:
22008371
19.

Multimodal manifold-regularized transfer learning for MCI conversion prediction.

Cheng B, Liu M, Suk HI, Shen D, Zhang D; Alzheimer’s Disease Neuroimaging Initiative..

Brain Imaging Behav. 2015 Dec;9(4):913-26. doi: 10.1007/s11682-015-9356-x.

20.

Local MRI analysis approach in the diagnosis of early and prodromal Alzheimer's disease.

Chincarini A, Bosco P, Calvini P, Gemme G, Esposito M, Olivieri C, Rei L, Squarcia S, Rodriguez G, Bellotti R, Cerello P, De Mitri I, Retico A, Nobili F; Alzheimer's Disease Neuroimaging Initiative..

Neuroimage. 2011 Sep 15;58(2):469-80. doi: 10.1016/j.neuroimage.2011.05.083.

PMID:
21718788
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