E. Karran and J. Hardy, Antiamyloid therapy for Alzheimer's disease--are we on the right road? The New England journal of medicine, pp.377-385, 2014.

E. Callaway, Alzheimer???s drugs take a new tack, Nature, vol.489, issue.7414, pp.13-17, 2012.
DOI : 10.1038/489013a

G. Frisoni, N. Fox, C. Jack, P. Scheltens, and P. Thompson, The clinical use of structural MRI in Alzheimer disease, Nature Reviews Neurology, vol.15, issue.2, pp.67-77, 2010.
DOI : 10.1038/nrneurol.2009.215

M. Braskie and P. Thompson, A Focus on Structural Brain Imaging in the Alzheimer???s Disease Neuroimaging Initiative, Biological Psychiatry, vol.75, issue.7, pp.527-560, 2014.
DOI : 10.1016/j.biopsych.2013.11.020

R. Cuingnet, E. Gerardin, J. Tessieras, G. Auzias, S. Lehericy et al., Automatic classification of patients with Alzheimer's disease from structural MRI: A comparison of ten methods using the ADNI database, NeuroImage, vol.56, issue.2, pp.766-81, 2011.
DOI : 10.1016/j.neuroimage.2010.06.013

G. Frisoni, N. Fox, C. Jack, J. Scheltens, P. Thompson et al., The clinical use of structural MRI in Alzheimer disease, Nature Reviews Neurology, vol.15, issue.2, pp.67-77, 2010.
DOI : 10.1038/nrneurol.2009.215

R. Wolz, V. Julkunen, J. Koikkalainen, E. Niskanen, D. Zhang et al., Multi-Method Analysis of MRI Images in Early Diagnostics of Alzheimer's Disease, PLoS ONE, vol.54, issue.3, pp.25446-25456, 2011.
DOI : 10.1371/journal.pone.0025446.s002

P. Coupe, S. Eskildsen, J. Manjon, V. Fonov, and D. Collins, Simultaneous segmentation and grading of anatomical structures for patient's classification: Application to Alzheimer's disease, NeuroImage, vol.59, issue.4, pp.3736-3783, 2012.
DOI : 10.1016/j.neuroimage.2011.10.080

P. Coupe, S. Eskildsen, J. Manjon, V. Fonov, J. Pruessner et al., Scoring by nonlocal image patch estimator for early detection of Alzheimer's disease The Alzheimer's Disease Neuroimaging Initiative (ADNI): MRI methods, NeuroImage Clinical. J Magn Reson Imaging, vol.127, issue.124, pp.141-52685, 2008.

G. Mckhann, D. Drachman, M. Folstein, R. Katzman, D. Price et al., Clinical diagnosis of Alzheimer's disease: report of the NINCDS-ADRDA Work Group under the auspices of Department of Health and Human Services Task Force on Alzheimer's Disease Group CS. Vascular factors and risk of dementia: design of the Three-City Study and baseline characteristics of the study populationMini-mental state". A practical method for grading the cognitive state of patients for the clinician, Neurology. Neuroepidemiology. J Psychiatr Res, vol.342212, issue.153, pp.939-44316, 1975.

C. Bernard, C. Helmer, B. Dilharreguy, H. Amieva, S. Auriacombe et al., Time course of brain volume changes in the preclinical phase of Alzheimer's disease, Alzheimer's & Dementia, vol.10, issue.2
DOI : 10.1016/j.jalz.2013.08.279

P. Coupe, J. Manjon, E. Gedamu, D. Arnold, M. Robles et al., Robust Rician noise estimation for MR images, Medical Image Analysis, vol.14, issue.4, pp.483-93, 2010.
DOI : 10.1016/j.media.2010.03.001
URL : https://hal.archives-ouvertes.fr/inserm-00486495

P. Coupe, P. Yger, S. Prima, P. Hellier, C. Kervrann et al., An optimized blockwise nonlocal means denoising filter for 3-D magnetic resonance images A nonparametric method for automatic correction of intensity nonuniformity in MRI data Automatic 3D intersubject registration of MR volumetric data in standardized Talairach space Unbiased average age-appropriate atlases for pediatric studies, IEEE Trans Med Imaging. IEEE Trans Med Imaging. Journal of computer assisted tomography. NeuroImage. NeuroImage, vol.2717185459, issue.233, pp.425-4187, 1994.

L. Nyul and J. Udupa, <title>Standardizing the MR image intensity scales: making MR intensities have tissue-specific meaning</title>, Medical Imaging 2000: Image Display and Visualization, pp.496-504, 2000.
DOI : 10.1117/12.383076

P. Coupe, J. Manjon, V. Fonov, J. Pruessner, M. Robles et al., Patch-based segmentation using expert priors: Application to hippocampus and ventricle segmentation, NeuroImage, vol.54, issue.2, pp.940-54, 2011.
DOI : 10.1016/j.neuroimage.2010.09.018
URL : https://hal.archives-ouvertes.fr/inserm-00541534

J. Dukart, M. Schroeter, K. Mueller, R. Scahill, C. Frost et al., Alzheimer's Disease Neuroimaging I Age correction in dementia--matching to a healthy brain A longitudinal study of brain volume changes in normal aging using serial registered magnetic resonance imaging, PLoS One. Archives of neurology, vol.660, issue.77, pp.22193-27989, 2003.

J. Schott, N. Fox, C. Frost, R. Scahill, J. Janssen et al., Assessing the onset of structural change in familial Alzheimer's disease, Dietterich TG. Ensemble methods in machine learning. Multiple classifier systems, pp.181-189, 2000.
DOI : 10.1002/ana.10424

K. Franke, E. Luders, A. May, M. Wilke, and C. Gaser, Brain maturation: Predicting individual BrainAGE in children and adolescents using structural MRI, NeuroImage, vol.63, issue.3, pp.1305-1317, 2012.
DOI : 10.1016/j.neuroimage.2012.08.001

D. Zhang, D. Shen, C. Chu, S. Klöppel, C. Jack et al., Alzheimer's Disease Neuroimaging I. Predicting future clinical changes of MCI patients using longitudinal and multimodal biomarkers Predicting clinical scores from magnetic resonance scans in Alzheimer's disease, PLoS One. NeuroImage, vol.751, issue.34, pp.1405-1418, 2010.

S. Eskildsen, P. Coupé, V. Fonov, J. Pruessner, C. Dl-komlagan et al., Structural imaging biomarkers of Alzheimer's disease: predicting disease progression, Machine Learning in Medical Imaging, pp.23-31, 2014.
DOI : 10.1016/j.neurobiolaging.2014.04.034

T. Tong, R. Wolz, Q. Gao, R. Guerrero, J. Hajnal et al., Multiple instance learning for classification of dementia in brain MRI. Med Image Anal Prediction of conversion from mild cognitive impairment to Alzheimer's disease dementia based upon biomarkers and neuropsychological test performance, Ewers M Neurobiol Aging, vol.1833, issue.367, pp.808-181203, 2012.

H. Suk, S. Lee, and D. Shen, Hierarchical feature representation and multimodal fusion with deep learning for AD/MCI diagnosis, NeuroImage, vol.101, pp.569-82, 2014.
DOI : 10.1016/j.neuroimage.2014.06.077

E. Moradi, A. Pepe, C. Gaser, H. Huttunen, and J. Tohka, Machine learning framework for early MRI-based Alzheimer's conversion prediction in MCI subjects, NeuroImage, vol.104, pp.398-412, 2015.
DOI : 10.1016/j.neuroimage.2014.10.002

F. Falahati, E. Westman, and A. Simmons, Multivariate data analysis and machine learning in Alzheimer's disease with a focus on structural magnetic resonance imaging, Journal of Alzheimer's disease: JAD, vol.41, issue.3, pp.685-708, 2014.

A. Fellgiebel, P. Dellani, D. Greverus, A. Scheurich, P. Stoeter et al., Predicting conversion to dementia in mild cognitive impairment by volumetric and diffusivity measurements of the hippocampus al. Diffusion tensor imaging of the hippocampus and verbal memory performance: The RUN DMC Study, DWI predicts future progression to Alzheimer disease in amnestic mild cognitive impairment, pp.902-906, 2005.

. Hippocampal, S. Sun, C. Taylor, C. Ward, A. Gamst et al., Prediction of Alzheimer disease Volumetric MRI vs clinical predictors of Alzheimer disease in mild cognitive impairment, Neurology. Neurology, vol.6870, issue.113, pp.828-36191, 2007.

A. Addneuromed, Similar patterns of Alzheimer's atrophy and automated MRI classification accuracy in Europe and North America, NeuroImage, vol.58, issue.3, pp.818-846, 2011.

C. Jack, J. Barkhof, F. Bernstein, M. Cantillon, M. Cole et al., Steps to standardization and validation of hippocampal volumetry as a biomarker in clinical trials and diagnostic criterion for Alzheimer's disease. Alzheimer's & dementia : the journal of the Alzheimer's Association, pp.474-85, 2011.

M. Mapstone, A. Cheema, M. Fiandaca, X. Zhong, T. Mhyre et al., Plasma phospholipids identify antecedent memory impairment in older adults, Nature Medicine, vol.33, issue.4, pp.415-423, 2014.
DOI : 10.1038/nm.3466

O. Hansson, H. Zetterberg, P. Buchhave, E. Londos, K. Blennow et al., Association between CSF biomarkers and incipient Alzheimer's disease in patients with mild cognitive impairment: a follow-up study, The Lancet Neurology, vol.5, issue.3, pp.228-262, 2006.
DOI : 10.1016/S1474-4422(06)70355-6