Multimodal prediction of conversion to Alzheimer's disease based onincomplete biomarkers

Alzheimer's and Dementia: Diagnosis, Assessment and Disease Monitoring

Volumn 1, Issue 2, 2015, Pages 206-215

  Ritter, Kerstin ^a   Schumacher, Julia ^a   Weygandt, Martin ^a   Buchert, Ralph ^a   Allefeld, Carsten ^a   Haynes, John Dylan ^a  

^a CHARITÉ UNIVERSITÄTSMEDIZIN BERLIN   (Germany)

Author keywords

Alzheimer's dementia; Feature selection; Mild cognitive impairment; Missing data; Multimodal biomarker; Prognosis

Indexed keywords

EID: 84938676936     PISSN: None     EISSN: 23528729     Source Type: Journal    
DOI: 10.1016/j.dadm.2015.01.006     Document Type: Article

References (41)

1. Dal-Bianco P. Alzheimer-Differentialdiagnostik. Facharzt 2010, 3:4-9.
2. Petersen R. Mild cognitive impairment clinical trials. Nat Rev Drug Discov 2003, 2:646-653.
3. Hinrichs C., Singh V., Xu G., Johnson S.C. Predictive markers for AD in a multi-modality framework: an analysis of MCI progression in the ADNI population. Neuroimage 2011, 55:574-589. ADNI.
4. Young J., Modat M., Cardoso M.J., Mendelson A., Cash D., Ourselin S., et al. Accurate multimodal probabilistic prediction of conversion to Alzheimer's disease in patients with mild cognitive impairment. Neuroimage Clin 2013, 2:735-745.
5. Liu Y., Mattila J., Ruiz M.A.M., Paajanen T., Koikkalainen J., van Gils M., et al. Predicting AD conversion: comparison between prodromal AD guidelines and computer assisted predictAD tool. PLoS One 2013, 8:e55246.
6. Kloeppel S., Stonnington C.M., Barnes J., Chen F., Chu C., Good C.D., et al. Accuracy of dementia diagnosis-a direct comparison between radiologists and a computerized method. Brain 2008, 131:2969-2974.
7. Weiner M.W., Veitch D.P., Aisen P.S., Beckett L.A., Cairns N.J., Green R.C., et al. The Alzheimer's Disease Neuroimaging Initiative: a review of papers published since its inception. Alzheimers Dement 2013, 9:e111-e194.
8. Cui Y., Liu B., Luo S., Zhen X., Fan M., Liu1 T., et al. Identification of conversion from mild cognitive impairment to Alzheimer's disease using multivariate predictors. PLoS One 2011, 6:e21896.
9. Cheng B., Zhang D., Shen D. Domain transfer learning for MCI conversion prediction. Med Image Comput Comp Assist Interv 2012, 15:82-90.
10. Westman E., Muehlboeck J.S., Simmons A. Combining MRI and CSF measures for classification of Alzheimer's disease and prediction of mild cognitive impairment conversion. Neuroimage 2012, 62:229-238.
11. Donders A.R.T., van der Heijden G.J., Stijnen T., Moons K.G. Review: a gentle introduction to imputation of missing values. J Clin Epidemiol 2006, 59:1087-1091.
12. Dempster A.P., Laird N.M., Rubin D.B. Maximum likelihood from incomplete data via the em algorithm. J R Stat Soc Series B 1977, 1-38.
13. Schneider T. Analysis of incomplete climate data: estimation of mean values and covariance matrices and imputation of missing values. J Climate 2001, 14:853-871.
14. Kim S.H., Kim S.H. Fluctuation of estimates in an EM procedure for categorical data. J Stat Comput Simul 2005, 75:941-957.
15. Chen Y.W., Lin C.J. Combining SVMs with various feature selection strategies. Feature extraction; vol. 207 of studies in fuzziness and soft computing 2006, 315-324. Springer, Berlin Heidelberg. I. Guyon, M. Nikravesh, S. Gunn, L. Zadeh (Eds.).
16. Guyon I., Elisseeff A. An introduction to variable and feature selection. J Mach Learn Res 2003, 3:1157-1182.
17. Cortes C., Vapnik V. Support-vector networks. Mach Learn 1995, 20:273-297.
18. Shawe-Taylor J., Christianini N. Support vector machine and other Kernel-based learning methods 2000, Cambridge University Press.
19. Breiman L., Friedman J., Olshen R., Stone C. Classification and regression trees 1984, Wadsworth and Brooks.
20. Breiman L. Random Forests. Mach Learn 2001, 45:5-32.
21. Kloeppel S., Stonnington C.M., Chu C., Draganski B., Scahill R.I., Rohrer J.D., et al. Automatic classification of MR scans in Alzheimer's disease. Brain 2008, 131:681-689.
22. Weygandt M., Hackmack K., Pfueller C., Bellmann-Strobl J., Paul F., Zipp F., et al. MRI pattern recognition in multiple sclerosis normal-appearing brain areas. PLoS One 2011, 6:e21138.
23. Hackmack K., Paul F., Weygandt M., Allefeld C., Haynes J.D. Multi-scale classification of disease using structural MRI and wavelet transform. Neuroimage 2012, 62:48-58.
24. Chang C.C., Chih-Jen L. LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol 2011, 2:1-27.
25. Rokach L., Maimon O. Data mining with decision trees: theory and applications 2008, World Scientific Publishing Company, Singapore.
26. Gray K.R., Aljabar P., Heckemann R.A., Hammers A., Rueckert D. Random forest-based similarity measures for multi-modal classification of Alzheimer's disease. Neuroimage 2013, 65:167-175. ADNI.
27. Cuingnet R., Gerardin E., Tessieras J., Auzias G., Lehericy S., Habert M.O., et al. Automatic classification of patients with Alzheimer's disease from structural MRI: a comparison of ten methods using the ADNI database. Neuroimage 2011, 56:766-781.
28. Zhang D., Wang Y., Zhou L., Yuan H., Shen D. Multimodal classification of Alzheimer's disease and mild cognitive impairment. Neuroimage 2011, 55:856-867. ADNI.
29. Fawcett T. An introduction to ROC analysis. Pattern Recognit Lett 2006, 27:861-874.
30. Nichols T.E., Holmes A.P. Nonparametric permutation tests for functional neuroimaging: a primer with examples. Hum Brain Mapp 2002, 15:1-25.
31. Noirhomme Q., Lesenfants D., Gomez F., Soddu A., Schrouff J., Garraux G., et al. Biased binomial assessment of cross-validated estimation of classification accuracies illustrated in diagnosis predictions. Neuroimage Clin 2014, 4:687-694.
32. Statnikov A., Wang L., Aliferis C. A comprehensive comparison of random forests and support vector machines for microarray-based cancer classification. BMC Bioinformatics 2008, 9:319.
33. Kotsiantis S. Supervised machine learning: a review of classification techniques. Informatica 2007, 31:249-268.
34. Morlini I. On multicollinearity and concurvity in some nonlinear multivariate models. Stat Methods Appt 2006, 15:3-26.
35. Dormann C.F., Elith J., Bacher S., Buchmann C., Carl G., Carre G., et al. Collinearity: a review of methods to deal with it and a simulation study evaluating their performance. Ecography 2013, 36:27-46.
36. Musil C.M., Warner C.B., Yobas P.K., Jones S.L. A comparison of imputation techniques for handling missing data. West J Nurs Res 2002, 24:815-829.
37. Devanand D.P., Liu X., Tabert M.H., Pradhaban G., Cuasay K., Bell K., et al. Combining early markers strongly predicts conversion from mild cognitive impairment to Alzheimer's disease. Biol Psychiatry 2008, 64:871-879.
38. Li H., Liu Y., Gong P., Zhang C., Ye J. Hierarchical interactions model for predicting mild cognitive impairment (MCI) to Alzheimer's disease (AD) conversion. PLoS One 2014, 9:e82450. ADNI.
39. Shaffer J.L., Petrella J.R., Sheldon F.C., Choudhury K.R., Calhoun V.D., Coleman R.E., et al. Predicting cognitive decline in subjects at risk for Alzheimer disease by using combined cerebrospinal fluid, MR imaging, and PET biomarkers. Radiology 2013, 266:583-591.
40. Filippone M., Marquandt A., Blain C., Williams S., Mourao-Miranda J., Girolami M. Probabilistic prediction of neurological disorders with a statistical assessment of neuroimaing data modalities. Ann Appl Stat 2013, 6:1883-1905.
41. Da X., Toledo J.B., Zee J., Wolk D.A., Xie S.X., Ou Y., et al. Integration and relative value of biomarkers for prediction of MCI to AD progression: spatial patterns of brain atrophy, cognitive scores, APOE genotype and CSF biomarkers. Neuroimage Clin 2014, 4:164-173.