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Items: 1 to 20 of 54

1.

Machine learning and microsimulation techniques on the prognosis of dementia: A systematic literature review.

Dallora AL, Eivazzadeh S, Mendes E, Berglund J, Anderberg P.

PLoS One. 2017 Jun 29;12(6):e0179804. doi: 10.1371/journal.pone.0179804. eCollection 2017.

2.

Conversion Discriminative Analysis on Mild Cognitive Impairment Using Multiple Cortical Features from MR Images.

Guo S, Lai C, Wu C, Cen G; Alzheimer's Disease Neuroimaging Initiative.

Front Aging Neurosci. 2017 May 18;9:146. doi: 10.3389/fnagi.2017.00146. eCollection 2017.

3.

Prediction of Conversion to Alzheimer's Disease with Longitudinal Measures and Time-To-Event Data.

Li K, Chan W, Doody RS, Quinn J, Luo S; Alzheimer’s Disease Neuroimaging Initiative.

J Alzheimers Dis. 2017;58(2):361-371. doi: 10.3233/JAD-161201.

4.

A review on neuroimaging-based classification studies and associated feature extraction methods for Alzheimer's disease and its prodromal stages.

Rathore S, Habes M, Iftikhar MA, Shacklett A, Davatzikos C.

Neuroimage. 2017 Jul 15;155:530-548. doi: 10.1016/j.neuroimage.2017.03.057. Epub 2017 Apr 13. Review.

PMID:
28414186
5.

Optimizing Neuropsychological Assessments for Cognitive, Behavioral, and Functional Impairment Classification: A Machine Learning Study.

Battista P, Salvatore C, Castiglioni I.

Behav Neurol. 2017;2017:1850909. doi: 10.1155/2017/1850909. Epub 2017 Jan 31.

6.

Early Prediction of Alzheimer's Disease Using Null Longitudinal Model-Based Classifiers.

Gavidia-Bovadilla G, Kanaan-Izquierdo S, Mataró-Serrat M, Perera-Lluna A; Alzheimer’s Disease Neuroimaging Initiative.

PLoS One. 2017 Jan 3;12(1):e0168011. doi: 10.1371/journal.pone.0168011. eCollection 2017.

7.

Progression from normal cognition to mild cognitive impairment in a diverse clinic-based and community-based elderly cohort.

Chen Y, Denny KG, Harvey D, Farias ST, Mungas D, DeCarli C, Beckett L.

Alzheimers Dement. 2017 Apr;13(4):399-405. doi: 10.1016/j.jalz.2016.07.151. Epub 2016 Aug 30.

PMID:
27590706
8.

Multimodal prediction of conversion to Alzheimer's disease based on incomplete biomarkers.

Ritter K, Schumacher J, Weygandt M, Buchert R, Allefeld C, Haynes JD.

Alzheimers Dement (Amst). 2015 Apr 30;1(2):206-15. doi: 10.1016/j.dadm.2015.01.006. eCollection 2015 Jun.

9.

Prediction of Conversion from Mild Cognitive Impairment to Alzheimer's Disease Using MRI and Structural Network Features.

Wei R, Li C, Fogelson N, Li L.

Front Aging Neurosci. 2016 Apr 19;8:76. doi: 10.3389/fnagi.2016.00076. eCollection 2016.

10.

Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls.

Arbabshirani MR, Plis S, Sui J, Calhoun VD.

Neuroimage. 2017 Jan 15;145(Pt B):137-165. doi: 10.1016/j.neuroimage.2016.02.079. Epub 2016 Mar 21.

PMID:
27012503
11.

Predicting Progression from Mild Cognitive Impairment to Alzheimer's Dementia Using Clinical, MRI, and Plasma Biomarkers via Probabilistic Pattern Classification.

Korolev IO, Symonds LL, Bozoki AC; Alzheimer's Disease Neuroimaging Initiative.

PLoS One. 2016 Feb 22;11(2):e0138866. doi: 10.1371/journal.pone.0138866. eCollection 2016.

12.

BFLCRM: A BAYESIAN FUNCTIONAL LINEAR COX REGRESSION MODEL FOR PREDICTING TIME TO CONVERSION TO ALZHEIMER'S DISEASE.

Lee E, Zhu H, Kong D, Wang Y, Giovanello KS, Ibrahim JG.

Ann Appl Stat. 2015 Dec;9(4):2153-2178.

13.

A novel relational regularization feature selection method for joint regression and classification in AD diagnosis.

Zhu X, Suk HI, Wang L, Lee SW, Shen D; Alzheimer’s Disease Neuroimaging Initiative.

Med Image Anal. 2017 May;38:205-214. doi: 10.1016/j.media.2015.10.008. Epub 2015 Nov 10.

14.

Alzheimer's disease: Unique markers for diagnosis & new treatment modalities.

Aggarwal NT, Shah RC, Bennett DA.

Indian J Med Res. 2015 Oct;142(4):369-82. doi: 10.4103/0971-5916.169193. Review.

15.

Precuneus and Cingulate Cortex Atrophy and Hypometabolism in Patients with Alzheimer's Disease and Mild Cognitive Impairment: MRI and (18)F-FDG PET Quantitative Analysis Using FreeSurfer.

Bailly M, Destrieux C, Hommet C, Mondon K, Cottier JP, Beaufils E, Vierron E, Vercouillie J, Ibazizene M, Voisin T, Payoux P, Barré L, Camus V, Guilloteau D, Ribeiro MJ.

Biomed Res Int. 2015;2015:583931. doi: 10.1155/2015/583931. Epub 2015 Jun 17.

16.

A Subset of Cerebrospinal Fluid Proteins from a Multi-Analyte Panel Associated with Brain Atrophy, Disease Classification and Prediction in Alzheimer's Disease.

Khan W, Aguilar C, Kiddle SJ, Doyle O, Thambisetty M, Muehlboeck S, Sattlecker M, Newhouse S, Lovestone S, Dobson R, Giampietro V, Westman E, Simmons A; Alzheimer’s Disease Neuroimaging Initiative.

PLoS One. 2015 Aug 18;10(8):e0134368. doi: 10.1371/journal.pone.0134368. eCollection 2015.

17.

2014 Update of the Alzheimer's Disease Neuroimaging Initiative: A review of papers published since its inception.

Weiner MW, Veitch DP, Aisen PS, Beckett LA, Cairns NJ, Cedarbaum J, Green RC, Harvey D, Jack CR, Jagust W, Luthman J, Morris JC, Petersen RC, Saykin AJ, Shaw L, Shen L, Schwarz A, Toga AW, Trojanowski JQ; Alzheimer's Disease Neuroimaging Initiative.

Alzheimers Dement. 2015 Jun;11(6):e1-120. doi: 10.1016/j.jalz.2014.11.001. Review.

18.

Deep sparse multi-task learning for feature selection in Alzheimer's disease diagnosis.

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

Brain Struct Funct. 2016 Jun;221(5):2569-87. doi: 10.1007/s00429-015-1059-y. Epub 2015 May 21.

19.

Predicting Alzheimer's Disease Using Combined Imaging-Whole Genome SNP Data.

Kong D, Giovanello KS, Wang Y, Lin W, Lee E, Fan Y, Murali Doraiswamy P, Zhu H.

J Alzheimers Dis. 2015;46(3):695-702. doi: 10.3233/JAD-150164.

20.

Comparison of two methods for the analysis of CSF Aβ and tau in the diagnosis of Alzheimer's disease.

Faull M, Ching SY, Jarmolowicz AI, Beilby J, Panegyres PK.

Am J Neurodegener Dis. 2014 Dec 5;3(3):143-51. eCollection 2014.

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