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Items: 1 to 20 of 39

1.

A correlativity study of plasma APL1β28 and clusterin levels with MMSE/MoCA/CASI in aMCI patients.

Meng Y, Li H, Hua R, Wang H, Lu J, Yu X, Zhang C.

Sci Rep. 2015 Oct 27;5:15546. doi: 10.1038/srep15546.

2.

Cerebrospinal fluid volumetric MRI mapping as a simple measurement for evaluating brain atrophy.

De Vis JB, Zwanenburg JJ, van der Kleij LA, Spijkerman JM, Biessels GJ, Hendrikse J, Petersen ET.

Eur Radiol. 2016 May;26(5):1254-62. doi: 10.1007/s00330-015-3932-8. Epub 2015 Aug 30.

3.

Discriminative multi-task feature selection for multi-modality classification of Alzheimer's disease.

Ye T, Zu C, Jie B, Shen D, Zhang D; Alzheimer’s Disease Neuroimaging Initiative.

Brain Imaging Behav. 2015 Aug 27. [Epub ahead of print]

PMID:
26311394
4.

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.

5.

Imaging biomarkers associated with cognitive decline: a review.

McConathy J, Sheline YI.

Biol Psychiatry. 2015 Apr 15;77(8):685-92. doi: 10.1016/j.biopsych.2014.08.024. Epub 2014 Sep 16. Review.

6.

Multimodal neuroimaging feature learning for multiclass diagnosis of Alzheimer's disease.

Liu S, Liu S, Cai W, Che H, Pujol S, Kikinis R, Feng D, Fulham MJ; ADNI.

IEEE Trans Biomed Eng. 2015 Apr;62(4):1132-40. doi: 10.1109/TBME.2014.2372011. Epub 2014 Nov 20.

7.

Improving CSF Biomarkers' Performance for Predicting Progression from Mild Cognitive Impairment to Alzheimer's Disease by Considering Different Confounding Factors: A Meta-Analysis.

Ferreira D, Rivero-Santana A, Perestelo-Pérez L, Westman E, Wahlund LO, Sarría A, Serrano-Aguilar P.

Front Aging Neurosci. 2014 Oct 16;6:287. doi: 10.3389/fnagi.2014.00287. eCollection 2014. Review.

8.

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. Epub 2014 Oct 3.

9.

Meta-Review of CSF Core Biomarkers in Alzheimer's Disease: The State-of-the-Art after the New Revised Diagnostic Criteria.

Ferreira D, Perestelo-Pérez L, Westman E, Wahlund LO, Sarría A, Serrano-Aguilar P.

Front Aging Neurosci. 2014 Mar 24;6:47. doi: 10.3389/fnagi.2014.00047. eCollection 2014. Review.

10.

MTA index: a simple 2D-method for assessing atrophy of the medial temporal lobe using clinically available neuroimaging.

Menéndez-González M, López-Muñiz A, Vega JA, Salas-Pacheco JM, Arias-Carrión O.

Front Aging Neurosci. 2014 Mar 24;6:23. doi: 10.3389/fnagi.2014.00023. eCollection 2014.

11.

Semi-supervised multimodal relevance vector regression improves cognitive performance estimation from imaging and biological biomarkers.

Cheng B, Zhang D, Chen S, Kaufer DI, Shen D; Alzheimer’s Disease Neuroimaging Initiative.

Neuroinformatics. 2013 Jul;11(3):339-53. doi: 10.1007/s12021-013-9180-7.

12.

Predicting AD conversion: comparison between prodromal AD guidelines and computer assisted PredictAD tool.

Liu Y, Mattila J, Ruiz MÁ, Paajanen T, Koikkalainen J, van Gils M, Herukka SK, Waldemar G, Lötjönen J, Soininen H; Alzheimer's Disease Neuroimaging Initiative.

PLoS One. 2013;8(2):e55246. doi: 10.1371/journal.pone.0055246. Epub 2013 Feb 12.

13.

Magnetic resonance imaging and spectroscopy: how useful is it for prediction and prognosis?

Condon B.

EPMA J. 2011 Dec;2(4):403-10. doi: 10.1007/s13167-011-0086-x. Epub 2011 May 25.

14.

Injury markers predict time to dementia in subjects with MCI and amyloid pathology.

van Rossum IA, Vos SJ, Burns L, Knol DL, Scheltens P, Soininen H, Wahlund LO, Hampel H, Tsolaki M, Minthon L, L'italien G, van der Flier WM, Teunissen CE, Blennow K, Barkhof F, Rueckert D, Wolz R, Verhey F, Visser PJ.

Neurology. 2012 Oct 23;79(17):1809-16. doi: 10.1212/WNL.0b013e3182704056. Epub 2012 Sep 26.

15.

Short-term clinical outcomes for stages of NIA-AA preclinical Alzheimer disease.

Knopman DS, Jack CR Jr, Wiste HJ, Weigand SD, Vemuri P, Lowe V, Kantarci K, Gunter JL, Senjem ML, Ivnik RJ, Roberts RO, Boeve BF, Petersen RC.

Neurology. 2012 May 15;78(20):1576-82. doi: 10.1212/WNL.0b013e3182563bbe. Epub 2012 May 2.

16.

β-Amyloid (1-42) Levels in Cerebrospinal Fluid and Cerebral Atrophy in Mild Cognitive Impairment and Alzheimer's Disease.

Kaiser E, Thomann PA, Essig M, Schröder J.

Dement Geriatr Cogn Dis Extra. 2011 Jan;1(1):393-401. doi: 10.1159/000333082. Epub 2011 Nov 16.

17.

Predicting MCI outcome with clinically available MRI and CSF biomarkers.

Heister D, Brewer JB, Magda S, Blennow K, McEvoy LK; Alzheimer's Disease Neuroimaging Initiative.

Neurology. 2011 Oct 25;77(17):1619-28. doi: 10.1212/WNL.0b013e3182343314. Epub 2011 Oct 12.

18.

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. Epub 2011 Oct 4. Erratum in: Neuroimage. 2012 Sep;62(3):2179.

19.

Spatial patterns of brain amyloid-beta burden and atrophy rate associations in mild cognitive impairment.

Tosun D, Schuff N, Mathis CA, Jagust W, Weiner MW; Alzheimer's Disease NeuroImaging Initiative.

Brain. 2011 Apr;134(Pt 4):1077-88. doi: 10.1093/brain/awr044. Epub 2011 Mar 22.

20.

Automated hippocampal shape analysis predicts the onset of dementia in mild cognitive impairment.

Costafreda SG, Dinov ID, Tu Z, Shi Y, Liu CY, Kloszewska I, Mecocci P, Soininen H, Tsolaki M, Vellas B, Wahlund LO, Spenger C, Toga AW, Lovestone S, Simmons A.

Neuroimage. 2011 May 1;56(1):212-9. doi: 10.1016/j.neuroimage.2011.01.050. Epub 2011 Jan 25.

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