[1] Gaudreault R, Mousseau N. Mitigating Alzheimer’s Disease with Natural Polyphenols: A Review. Curr Alzheimer Res 2019;16:529–43. https://doi.org/10.2174/1567205016666190315093520.
[2] Association A. 2019 Alzheimer’s disease facts and figures. Alzheimer’s Dement 2019;15:321–87. https://doi.org/10.1016/j.jalz.2019.01.010.
[3] Mueller SG, Weiner MW, Thal LJ, Petersen RC, Jack CR, Jagust W, et al. Ways toward an early diagnosis in Alzheimer’s disease: The Alzheimer’s Disease Neuroimaging Initiative (ADNI). Alzheimer’s Dement 2005;1:55–66. https://doi.org/10.1016/j.jalz.2005.06.003.
[4] Zhang D, Wang Y, Zhou L, Yuan H, Shen D. Multimodal classification of Alzheimer’s disease and mild cognitive impairment. Neuroimage 2011;55:856–67. https://doi.org/10.1016/j.neuroimage.2011.01.008.
[5] Perry RJ, Watson P, Hodges JR. The nature and staging of attention dysfunction in early (minimal and mild) Alzheimer’s disease: Relationship to episodic and semantic memory impairment. Neuropsychologia 2000;38:252–71. https://doi.org/10.1016/S0028-3932(99)00079-2.
[6] Morris JC, Storandt M, Miller JP, McKeel DW, Price JL, Rubin EH, et al. Mild cognitive impairment represents early-stage Alzheimer disease. Arch Neurol 2001;58:397–405. https://doi.org/10.1001/archneur.58.3.397.
[7] Nestor PJ, Scheltens P, Hodges JR. Advances in the early detection of alzheimer’s disease. Nat Rev Neurosci 2004;10:S34. https://doi.org/10.1038/nrn1433.
[8] Duara R, Barker WW, Lopez-Alberola R, Loewenstein DA, Grau LB, Gilchrist D, et al. Alzheimer’s disease: Interaction of apolipoprotein E genotype, family history of dementia, gender, education, ethnicity, and age of onset. Neurology 1996;46:1575–9. https://doi.org/10.1212/WNL.46.6.1575.
[9] Selkoe DJ. The molecular pathology of Alzheimer’s disease. Neuron 1991;6:487–98. https://doi.org/10.1016/0896-6273(91)90052-2.
[10] Teipel S, Drzezga A, Grothe MJ, Barthel H, Chételat G, Schuff N, et al. Multimodal imaging in Alzheimer’s disease: Validity and usefulness for early detection. Lancet Neurol 2015;14:1037–53. https://doi.org/10.1016/S1474-4422(15)00093-9.
[11] Rathore S, Habes M, Iftikhar MA, Shacklett A, Davatzikos C. A review on neuroimaging-based classification studies and associated feature extraction methods for Alzheimer’s disease and its prodromal stages. Neuroimage 2017;155:530–48. https://doi.org/10.1016/j.neuroimage.2017.03.057.
[12] Jack CR, Knopman DS, Jagust WJ, Petersen RC, Weiner MW, Aisen PS, et al. Tracking pathophysiological processes in Alzheimer’s disease: An updated hypothetical model of dynamic biomarkers. Lancet Neurol 2013;12:207–16. https://doi.org/10.1016/S1474-4422(12)70291-0.
[13] Keihaninejad S, Zhang H, Ryan NS, Malone IB, Modat M, Cardoso MJ, et al. An unbiased longitudinal analysis framework for tracking white matter changes using diffusion tensor imaging with application to Alzheimer’s disease. Neuroimage 2013;72:153–63. https://doi.org/10.1016/j.neuroimage.2013.01.044.
[14] Jack CR, Holtzman DM. Biomarker modeling of alzheimer’s disease. Neuron 2013;80:1347–58. https://doi.org/10.1016/j.neuron.2013.12.003.
[15] Growdon JH. Incorporating biomarkers into clinical drug trials in Alzheimer’s disease. J Alzheimers Dis 2001;3:287–92. https://doi.org/10.3233/jad-2001-3303.
[16] Yang E, Farnum M, Lobanov V, Schultz T, Raghavan N, Samtani MN, et al. Quantifying the pathophysiological timeline of Alzheimer’s disease. J Alzheimer’s Dis 2011;26:745–53. https://doi.org/10.3233/JAD-2011-110551.
[17] William-Faltaos D, Chen Y, Wang Y, Gobburu J, Zhu H. Quantification of disease progression and dropout for Alzheimer’s disease. Int J Clin Pharmacol Ther 2013;51:120–31. https://doi.org/10.5414/CP201787.
[18] Skinner J, Carvalho JO, Potter GG, Thames A, Zelinski E, Crane PK, et al. The Alzheimer’s Disease Assessment Scale-Cognitive-Plus (ADAS-Cog-Plus): An expansion of the ADAS-Cog to improve responsiveness in MCI. Brain Imaging Behav 2012;6:489–501. https://doi.org/10.1007/s11682-012-9166-3.
[19] Kueper JK, Speechley M, Montero-Odasso M. The Alzheimer’s Disease Assessment Scale-Cognitive Subscale (ADAS-Cog): Modifications and Responsiveness in Pre-Dementia Populations. A Narrative Review. J Alzheimer’s Dis 2018;63:423–44. https://doi.org/10.3233/JAD-170991.
[20] Zhang D, Shen D. Predicting Future Clinical Changes of MCI Patients Using Longitudinal and Multimodal Biomarkers. PLoS One 2012;7:e33182. https://doi.org/10.1371/journal.pone.0033182.
[21] Marinescu R V., Oxtoby NP, Young AL, Bron EE, Toga AW, Weiner MW, et al. TADPOLE Challenge: Accurate Alzheimer’s Disease Prediction Through Crowdsourced Forecasting of Future Data, 2019, p. 1–10. https://doi.org/10.1007/978-3-030-32281-6_1.
[22] Steenland K, Zhao L, Goldstein F, Cellar J, Lah J. Biomarkers for predicting cognitive decline in those with normal cognition. J Alzheimers Dis 2014;40:587–94. https://doi.org/10.3233/JAD-2014-131343.
[23] Benge JF, Balsis S, Geraci L, Massman PJ, Doody RS. How well do the ADAS-cog and its subscales measure cognitive dysfunction in Alzheimer’s disease? Dement Geriatr Cogn Disord 2009;28:63–9. https://doi.org/10.1159/000230709.
[24] Marinescu R V., Oxtoby NP, Young AL, Bron EE, Toga AW, Weiner MW, et al. TADPOLE Challenge: Prediction of Longitudinal Evolution in Alzheimer’s Disease 2018.
[25] Gómez-Sancho M, Tohka J, Gómez-Verdejo V. Comparison of feature representations in MRI-based MCI-to-AD conversion prediction. Magn Reson Imaging 2018;50:84–95. https://doi.org/10.1016/j.mri.2018.03.003.
[26] Voevodskaya O. The effects of intracranial volume adjustment approaches on multiple regional MRI volumes in healthy aging and Alzheimer’s disease. Front Aging Neurosci 2014;6. https://doi.org/10.3389/fnagi.2014.00264.
[27] Reuter M, Schmansky NJ, Rosas HD, Fischl B. Within-subject template estimation for unbiased longitudinal image analysis. Neuroimage 2012;61:1402–18. https://doi.org/10.1016/j.neuroimage.2012.02.084.
[28] Johnson KA, Sperling RA, Gidicsin CM, Carmasin JS, Maye JE, Coleman RE, et al. Florbetapir (F18-AV-45) PET to assess amyloid burden in Alzheimer’s disease dementia, mild cognitive impairment, and normal aging. Alzheimer’s Dement 2013;9. https://doi.org/10.1016/j.jalz.2012.10.007.
[29] Landau SM, Mintun MA, Joshi AD, Koeppe RA, Petersen RC, Aisen PS, et al. Amyloid deposition, hypometabolism, and longitudinal cognitive decline. Ann Neurol 2012;72:578–86. https://doi.org/10.1002/ana.23650.
[30] Landau SM, Lu M, Joshi AD, Pontecorvo M, Mintun MA, Trojanowski JQ, et al. Comparing positron emission tomography imaging and cerebrospinal fluid measurements of β-amyloid. Ann Neurol 2013;74:826–36. https://doi.org/10.1002/ana.23908.
[31] Mormino EC, Kluth JT, Madison CM, Rabinovici GD, Baker SL, Miller BL, et al. Episodic memory loss is related to hippocampal-mediated β-amyloid deposition in elderly subjects. Brain 2009;132:1310–23. https://doi.org/10.1093/brain/awn320.
[32] Landau SM, Harvey D, Madison CM, Koeppe RA, Reiman EM, Foster NL, et al. Associations between cognitive, functional, and FDG-PET measures of decline in AD and MCI. Neurobiol Aging 2011;32:1207–18. https://doi.org/10.1016/j.neurobiolaging.2009.07.002.
[33] Jagust WJ, Bandy D, Chen K, Foster NL, Landau SM, Mathis CA, et al. The Alzheimer’s Disease Neuroimaging Initiative positron emission tomography core. Alzheimer’s Dement 2010;6:221–9. https://doi.org/10.1016/j.jalz.2010.03.003.
[34] Shaw LM, Vanderstichele H, Knapik-Czajka M, Clark CM, Aisen PS, Petersen RC, et al. Cerebrospinal fluid biomarker signature in alzheimer’s disease neuroimaging initiative subjects. Ann Neurol 2009;65:403–13. https://doi.org/10.1002/ana.21610.
[35] Battista P, Salvatore C, Castiglioni I. Optimizing neuropsychological assessments for cognitive, behavioral, and functional impairment classification: A machine learning study. Behav Neurol 2017;2017. https://doi.org/10.1155/2017/1850909.
[36] Folstein MF, Folstein SE, McHugh PR. “Mini-mental state”. A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res 1975;12:189–98. https://doi.org/10.1016/0022-3956(75)90026-6.
[37] Rey A. L’examin clinique en psychologie. Press Univ Fr 1958. https://psycnet.apa.org/record/1959-03776-000 (accessed December 30, 2019).
[38] Pfeffer RI, Kurosaki TT, Harrah CH, Chance JM, Filos S. Measurement of functional activities in older adults in the community. Journals Gerontol 1982;37:323–9. https://doi.org/10.1093/geronj/37.3.323.
[39] Prencipe M, Casini AR, Ferretti C, Lattanzio MT, Fiorelli M, Culasso F. Prevalence of dementia in an elderly rural population: effects of age, sex, and education. J Neurol Neurosurg Psychiatry 1996;60:628–33. https://doi.org/10.1136/jnnp.60.6.628.
[40] Michaelson DM. APOE ε4: The most prevalent yet understudied risk factor for Alzheimer’s disease. Alzheimer’s Dement 2014;10:861–8. https://doi.org/10.1016/j.jalz.2014.06.015.
[41] Mohs RC, Knopman D, Petersen RC, Ferris SH, Ernesto C, Grundman M, et al. Development of cognitive instruments for use in clinical trials of antidementia drugs: Additions to the Alzheimer’s disease assessment scale that broaden its scope. Alzheimer Dis Assoc Disord 1997;11. https://doi.org/10.1097/00002093-199700112-00003.
[42] Raghavan N, Samtani MN, Farnum M, Yang E, Novak G, Grundman M, et al. The ADAS-Cog revisited: Novel composite scales based on ADAS-Cog to improve efficiency in MCI and early AD trials. Alzheimer’s Dement 2013;9. https://doi.org/10.1016/j.jalz.2012.05.2187.
[43] Krishnan A, Williams LJ, McIntosh AR, Abdi H. Partial Least Squares (PLS) methods for neuroimaging: A tutorial and review. Neuroimage 2011;56:455–75. https://doi.org/10.1016/j.neuroimage.2010.07.034.
[44] Zhang D, Shen D. Multi-modal multi-task learning for joint prediction of multiple regression and classification variables in Alzheimer’s disease. Neuroimage 2012;59:895–907. https://doi.org/10.1016/j.neuroimage.2011.09.069.
[45] Gray KR, Aljabar P, Heckemann RA, Hammers A, Rueckert D. Random forest-based similarity measures for multi-modal classification of Alzheimer’s disease. Neuroimage 2013;65:167–75. https://doi.org/10.1016/j.neuroimage.2012.09.065.
[46] Leardi R, Boggia R, Terrile M. Genetic algorithms as a strategy for feature selection. J Chemom 1992;6:267–81. https://doi.org/10.1002/cem.1180060506.
[47] Lewis JD, Evans AC, Tohka J. T1 white/gray contrast as a predictor of chronological age, and an index of cognitive performance. Neuroimage 2018;173:341–50. https://doi.org/10.1016/j.neuroimage.2018.02.050.
[48] de Jong S. SIMPLS: An alternative approach to partial least squares regression. Chemom Intell Lab Syst 1993;18:251–63. https://doi.org/10.1016/0169-7439(93)85002-X.
[49] Breiman L. Bagging predictors. Mach Learn 1996;24:123–40. https://doi.org/10.1007/BF00058655.
[50] Breiman L. Random forests. Mach Learn 2001;45:5–32. https://doi.org/10.1023/A:1010933404324.
[51] R. Leardi and A. Lupiáñez. Genetic algorithms applied to feature selection in PLS regression: how and when to use them. Chemom Intell Lab Syst 1998;41:95–207.
[52] Leyhe T, Reynolds CF, Melcher T, Linnemann C, Klöppel S, Blennow K, et al. A common challenge in older adults: Classification, overlap, and therapy of depression and dementia. Alzheimer’s Dement 2017;13:59–71. https://doi.org/10.1016/j.jalz.2016.08.007.
[53] Chouldechova A, Hastie T. Generalized Additive Model Selection. ArXiv Prepr ArXiv150603850 2015.