H. Amieva, H. Jacqmin-gadda, J. Orgogozo, N. L. Carret, C. Helmer et al., The 9 year cognitive decline before dementia of the Alzheimer type: a prospective population-based study, Brain, vol.128, issue.5, pp.1093-1101, 2005.

H. Amieva, H. Mokri, M. L. Goff, C. Meillon, H. Jacqmingadda et al., Compensatory mechanisms in higher-educated subjects with Alzheimer's disease: a study of 20 years of cognitive decline, Brain, vol.137, issue.4, pp.1167-1175, 2014.

M. Ansart, S. Epelbaum, G. Gagliardi, O. Colliot, D. Dormont et al., Prediction of amyloidosis from neuropsychological and mri data for cost effective inclusion of pre-symptomatic subjects in clinical trials, Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, pp.357-364, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01578422

L. G. Apostolova, K. S. Hwang, D. Avila, D. Elashoff, O. Kohannim et al., Brain amyloidosis ascertainment from cognitive, imaging, and peripheral blood protein measures, Neurology, vol.84, issue.7, pp.729-737, 2015.
DOI : 10.1212/wnl.0000000000001231
URL : http://europepmc.org/articles/pmc4336101?pdf=render

M. R. Arbabshirani, S. Plis, J. Sui, and V. D. Calhoun, Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls, NeuroImage, vol.145, pp.137-165, 2017.

R. J. Bateman, C. Xiong, T. L. Benzinger, A. M. Fagan, A. Goate et al.,

P. R. Rossor, R. A. Schofield, S. Sperling, and J. C. Salloway, Morris. Clinical and Biomarker Changes in Dominantly Inherited Alzheimer's Disease, New England Journal of Medicine, vol.367, issue.9, pp.795-804, 2012.

R. E. Becker and N. H. Greig, A New Regulatory Road-Map for Alzheimer's Disease Drug Development, Current Alzheimer research, vol.11, issue.3, pp.215-220, 2014.

L. Breiman, Random forests. Machine learning, vol.45, pp.5-32, 2001.

G. Chetelat, R. La-joie, N. Villain, A. Perrotin, V. De-la-sayette et al., Amyloid imaging in cognitively normal individuals, at-risk populations and preclinical alzheimer's disease, Neuroimage Clin, vol.2, pp.356-365, 2013.

M. Chupin, A. Hammers, R. S. Liu, O. Colliot, J. Burdett et al., Automatic segmentation of the hippocampus and the amygdala driven by hybrid constraints: method and validation, NeuroImage, vol.46, issue.3, pp.749-761, 2009.
URL : https://hal.archives-ouvertes.fr/hal-00805390

M. Christopher, M. J. Clark, . Pontecorvo, G. Thomas, . Beach et al., Cerebral PET with florbetapir compared with neuropathology at autopsy for detection of neuritic amyloid-? plaques: a prospective cohort study, The Lancet Neurology, vol.11, issue.8, pp.669-678, 2012.

P. Domingos, A few useful things to know about machine learning, Communications of the ACM, vol.55, issue.10, p.78, 2012.

R. S. Doody, R. G. Thomas, M. Farlow, T. Iwatsubo, B. Vellas et al., Phase 3 trials of solanezumab for mild-to-moderate alzheimer's disease, New England Journal of Medicine, vol.370, issue.4, pp.311-321, 2014.

B. Dubois, H. Hampel, H. H. Feldman, P. Scheltens, P. Aisen et al., Alzheimer's disease: Definition, natural history, and diagnostic criteria, Alzheimer's & Dementia, vol.12, issue.3, pp.292-323, 2016.

S. Epelbaum, R. Genthon, E. Cavedo, M. O. Habert, F. Lamari et al., Preclinical Alzheimer's disease: A systematic review of the cohorts underlying the concept, Alzheimer's & Dementia: The Journal of the Alzheimer's Association, vol.13, issue.4, pp.454-467, 2017.

J. C. Foster, J. M. Taylor, and S. J. Ruberg, Subgroup identification from randomized clinical trial data, Statistics in Medicine, vol.30, issue.24, pp.2867-2880, 2011.
DOI : 10.1002/sim.4322
URL : http://europepmc.org/articles/pmc3880775?pdf=render

K. Franke, G. Ziegler, S. Klöppel, and C. Gaser, Estimating the age of healthy subjects from T1-weighted MRI scans using kernel methods: Exploring the influence of various parameters, NeuroImage, vol.50, issue.3, pp.883-892, 2010.

J. Friedman, Greedy function approximation: A gradient boosting machine, The Annals of Statistics, vol.29, issue.5, pp.1189-1232, 2001.

J. Friedman, T. Hastie, and R. Tibshirani, Additive logistic regression: a statistical view of boosting. The annals of statistics, vol.28, pp.337-407, 2000.
DOI : 10.1214/aos/1016218223
URL : https://doi.org/10.1214/aos/1016218223

J. Friedman, T. Hastie, and R. Tibshirani, Regularization paths for generalized linear models via coordinate descent, Journal of statistical software, vol.33, issue.1, p.1, 2010.
DOI : 10.18637/jss.v033.i01
URL : https://www.jstatsoft.org/index.php/jss/article/view/v033i01/v33i01.pdf

J. A. Hardy and G. A. Higgins, Alzheimer's disease: the amyloid cascade hypothesis, Science, vol.256, issue.5054, pp.184-185, 1992.

J. Hardy and D. Allsop, Amyloid deposition as the central event in the aetiology of Alzheimer's disease, Trends in Pharmacological Sciences, vol.12, pp.383-388, 1991.

J. Hardy and D. J. Selkoe, The Amyloid Hypothesis of Alzheimer's Disease: Progress and Problems on the Road to Therapeutics, Science, vol.297, issue.5580, pp.353-356, 2002.

G. Hughes, On the mean accuracy of statistical pattern recognizers, IEEE Transactions on Information Theory, vol.14, issue.1, pp.55-63, 1968.

A. Hyvarinen, Fast and robust fixed-point algorithms for independent component analysis, IEEE Transactions on Neural Networks, vol.10, issue.3, pp.626-634, 1999.
DOI : 10.1109/72.761722
URL : http://www.cs.helsinki.fi/u/ahyvarin/papers/TNN99_reprint.pdf

P. S. Insel, S. Palmqvist, R. S. Mackin, R. L. Nosheny, O. Hansson et al., Assessing risk for preclinical ?-amyloid pathology with APOE, cognitive, and demographic information, vol.4, pp.76-84, 2016.
DOI : 10.1016/j.jalz.2016.06.1089

C. Jack, D. Knopman, W. Jagust, L. Shaw, P. Aisen et al., Hypothetical model of dynamic biomarkers of the alzheimer's pathological cascade, Lancet Neurology, vol.9, issue.1, p.119, 2010.

C. R. Jack, D. A. Bennett, K. Blennow, M. C. Carrillo, B. Dunn et al., Creighton Statistical Methods in Medical Research, 2019.

K. P. Phelps, C. C. Rankin, P. Rowe, E. Scheltens, H. M. Siemers et al., NIA-AA Research Framework: Toward a biological definition of Alzheimer's disease, Alzheimer's & Dementia, vol.14, issue.4, pp.535-562, 2018.

C. R. Jack, H. J. Wiste, S. D. Weigand, D. S. Knopman, M. M. Mielke et al., Different definitions of neurodegeneration produce similar amyloid/neurodegeneration biomarker group findings, Brain, vol.138, issue.12, pp.3747-3759, 2015.

E. Karran, M. Mercken, and B. D. Strooper, The amyloid cascade hypothesis for Alzheimer's disease: an appraisal for the development of therapeutics, Nature Reviews Drug Discovery, vol.10, issue.9, pp.698-712, 2011.

C. Lopez-lopez, . Caputo, . Liu, . Me-riviere, . Ml-rouzade-dominguez et al., The alzheimer's prevention initiative generation program: Evaluating cnp520 efficacy in the prevention of alzheimer's disease. The journal of prevention of Alzheimer's disease, vol.4, pp.242-246, 2017.

M. M. Mielke, H. J. Wiste, S. D. Weigand, D. S. Knopman, V. J. Lowe et al., Indicators of amyloid burden in a populationbased study of cognitively normal elderly, Neurology, vol.79, issue.15, pp.1570-1577, 2012.

S. Minsker, Y. Zhao, and G. Cheng, Active Clinical Trials for Personalized Medicine, Journal of the American Statistical Association, vol.111, issue.514, pp.875-887, 2016.

K. R. Muller, S. Mika, G. Ratsch, K. Tsuda, and B. Scholkopf, An introduction to kernel-based learning algorithms, IEEE Transactions on Neural Networks, vol.12, issue.2, pp.181-201, 2001.

A. Nakamura, N. Kaneko, V. L. Villemagne, T. Kato, J. Doecke et al., High performance plasma amyloid-? biomarkers for Alzheimer's disease, Nature, vol.554, issue.7691, pp.249-254, 2018.

J. T. O'brien and K. Herholz, Amyloid imaging for dementia in clinical practice, BMC Medicine, p.13, 2015.

M. Qian and S. A. Murphy, Performance guarantees for individualized treatment rules, The Annals of Statistics, vol.39, issue.2, pp.1180-1210, 2011.
DOI : 10.1214/10-aos864
URL : https://doi.org/10.1214/10-aos864

S. Salloway, R. Sperling, N. C. Fox, K. Blennow, W. Klunk et al., Two Phase 3 Trials of Bapineuzumab in Mild-to-Moderate Alzheimer's Disease, New England Journal of Medicine, vol.370, issue.4, pp.322-333, 2014.

A. Satlin, J. Wang, V. Logovinsky, S. Berry, C. Swanson et al., Design of a Bayesian adaptive phase 2 proofof-concept trial for BAN2401, a putative disease-modifying monoclonal antibody for the treatment of Alzheimer's disease, Statistical Methods in Medical Research, vol.2, issue.1, pp.1-12, 2016.

L. M. Shaw, H. Vanderstichele, M. Knapik-czajka, C. M. Clark, P. S. Aisen et al., Cerebrospinal Fluid Biomarker Signature in Alzheimer's Disease Neuroimaging Initiative Subjects, Annals of neurology, vol.65, issue.4, pp.403-413, 2009.
DOI : 10.1002/ana.21610
URL : http://europepmc.org/articles/pmc2696350?pdf=render

A. Reisa, P. S. Sperling, L. A. Aisen, D. A. Beckett, S. Bennett et al., Toward defining the preclinical stages of Alzheimer's disease: Recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease, vol.7, pp.280-292, 2011.

A. Mara-ten-kate, E. Redolfi, I. Peira, S. J. Bos, R. Vos et al., MRI predictors of amyloid pathology: results from the EMIF-AD Multimodal Biomarker Discovery study. Alzheimer's Research & Therapy, vol.10, p.100, 2018.

S. Duygu-tosun, M. W. Joshi, and . Weiner, Neuroimaging Predictors of Brain Amyloidosis in Mild Cognitive Impairment, Annals of neurology, vol.74, issue.2, pp.188-198, 2013.

J. L. Watson, L. Ryan, N. Silverberg, V. Cahan, and M. A. Bernard, Obstacles And Opportunities In Alzheimer's Clinical Trial Recruitment, vol.33, pp.574-579, 2014.
DOI : 10.1377/hlthaff.2013.1314
URL : https://www.healthaffairs.org/doi/pdf/10.1377/hlthaff.2013.1314

M. Michael, N. L. Witte, A. S. Foster, M. M. Fleisher, K. Williams et al., Clinical use of amyloid-positron emission tomography neuroimaging: Practical and bioethical considerations, Assessment & Disease Monitoring, vol.1, issue.3, pp.358-367, 2015.

H. David and . Wolpert, The Supervised Learning No-Free-Lunch Theorems, Soft Computing and Industry, pp.25-42, 2002.

H. David, W. Wolpert, and . Macready, No Free Lunch Theorems for Optimization, IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, vol.1, issue.1, p.16, 1997.

Y. Zhao, D. Zeng, A. J. Rush, and M. R. Kosorok, Estimating Individualized Treatment Rules Using Outcome Weighted Learning, Journal of the American Statistical Association, vol.107, issue.499, pp.1106-1118, 2012.
DOI : 10.1080/01621459.2012.695674
URL : http://europepmc.org/articles/pmc3636816?pdf=render