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Discriminant analysis of longitudinal cortical thickness changes in Alzheimer's disease using dynamic and network features.

Li Y, Wang Y, Wu G, Shi F, Zhou L, Lin W, Shen D; Alzheimer's Disease Neuroimaging Initiative.

Neurobiol Aging. 2012 Feb;33(2):427.e15-30. doi: 10.1016/j.neurobiolaging.2010.11.008. Epub 2011 Jan 26.


Baseline and longitudinal patterns of brain atrophy in MCI patients, and their use in prediction of short-term conversion to AD: results from ADNI.

Misra C, Fan Y, Davatzikos C.

Neuroimage. 2009 Feb 15;44(4):1415-22. doi: 10.1016/j.neuroimage.2008.10.031. Epub 2008 Nov 5.


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. Epub 2011 Oct 8.


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. Epub 2014 Oct 12.


Baseline MRI predictors of conversion from MCI to probable AD in the ADNI cohort.

Risacher SL, Saykin AJ, West JD, Shen L, Firpi HA, McDonald BC; Alzheimer's Disease Neuroimaging Initiative (ADNI).

Curr Alzheimer Res. 2009 Aug;6(4):347-61.


It is unclear if adjusting cortical thickness for changes in gray/white matter intensity ratio improves discrimination between normal aging, MCI, and AD.

Bauer CM, Cabral HJ, Killiany RJ; Alzheimer’s Disease Neuroimaging Initiative.

Brain Imaging Behav. 2014 Mar;8(1):133-40. doi: 10.1007/s11682-013-9268-6.


Locally linear embedding (LLE) for MRI based Alzheimer's disease classification.

Liu X, Tosun D, Weiner MW, Schuff N; Alzheimer's Disease Neuroimaging Initiative.

Neuroimage. 2013 Dec;83:148-57. doi: 10.1016/j.neuroimage.2013.06.033. Epub 2013 Jun 21.


Novel Cortical Thickness Pattern for Accurate Detection of Alzheimer's Disease.

Zheng W, Yao Z, Hu B, Gao X, Cai H, Moore P.

J Alzheimers Dis. 2015;48(4):995-1008. doi: 10.3233/JAD-150311.


Discerning mild cognitive impairment and Alzheimer Disease from normal aging: morphologic characterization based on univariate and multivariate models.

Liao W, Long X, Jiang C, Diao Y, Liu X, Zheng H, Zhang L; Alzheimer's Disease Neuroimaging Initiative.

Acad Radiol. 2014 May;21(5):597-604. doi: 10.1016/j.acra.2013.12.001. Epub 2014 Jan 13.


Sulcal morphology changes and their relationship with cortical thickness and gyral white matter volume in mild cognitive impairment and Alzheimer's disease.

Im K, Lee JM, Seo SW, Hyung Kim S, Kim SI, Na DL.

Neuroimage. 2008 Oct 15;43(1):103-13. doi: 10.1016/j.neuroimage.2008.07.016. Epub 2008 Jul 22.


Directed network motifs in Alzheimer's disease and mild cognitive impairment.

Friedman EJ, Young K, Tremper G, Liang J, Landsberg AS, Schuff N; Alzheimer's Disease Neuroimaging Initiative.

PLoS One. 2015 Apr 16;10(4):e0124453. doi: 10.1371/journal.pone.0124453. eCollection 2015.


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. Epub 2011 Jun 16.


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. eCollection 2014.


Regional magnetic resonance imaging measures for multivariate analysis in Alzheimer's disease and mild cognitive impairment.

Westman E, Aguilar C, Muehlboeck JS, Simmons A.

Brain Topogr. 2013 Jan;26(1):9-23. doi: 10.1007/s10548-012-0246-x. Epub 2012 Aug 14.


A Novel Grading Biomarker for the Prediction of Conversion From Mild Cognitive Impairment to Alzheimer's Disease.

Tong T, Gao Q, Guerrero R, Ledig C, Chen L, Rueckert D, Initiative ADN.

IEEE Trans Biomed Eng. 2017 Jan;64(1):155-165. doi: 10.1109/TBME.2016.2549363. Epub 2016 Apr 1.


Prediction of Alzheimer's disease in subjects with mild cognitive impairment from the ADNI cohort using patterns of cortical thinning.

Eskildsen SF, Coupé P, García-Lorenzo D, Fonov V, Pruessner JC, Collins DL; Alzheimer's Disease Neuroimaging Initiative.

Neuroimage. 2013 Jan 15;65:511-21. doi: 10.1016/j.neuroimage.2012.09.058. Epub 2012 Oct 2.


A novel cortical thickness estimation method based on volumetric Laplace-Beltrami operator and heat kernel.

Wang G, Zhang X, Su Q, Shi J, Caselli RJ, Wang Y; Alzheimer’s Disease Neuroimaging Initiative.

Med Image Anal. 2015 May;22(1):1-20. doi: 10.1016/ Epub 2015 Feb 3.


Thickness network features for prognostic applications in dementia.

Raamana PR, Weiner MW, Wang L, Beg MF; Alzheimer's Disease Neuroimaging Initiative.

Neurobiol Aging. 2015 Jan;36 Suppl 1:S91-S102. doi: 10.1016/j.neurobiolaging.2014.05.040. Epub 2014 Sep 6.


Cortical folding analysis on patients with Alzheimer's disease and mild cognitive impairment.

Cash DM, Melbourne A, Modat M, Cardoso MJ, Clarkson MJ, Fox NC, Ourselin S.

Med Image Comput Comput Assist Interv. 2012;15(Pt 3):289-96.


Longitudinal progression of Alzheimer's-like patterns of atrophy in normal older adults: the SPARE-AD index.

Davatzikos C, Xu F, An Y, Fan Y, Resnick SM.

Brain. 2009 Aug;132(Pt 8):2026-35. doi: 10.1093/brain/awp091. Epub 2009 May 4.

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