B. Jie, Manifold regularized multitask feature learning for multimodality disease classification, Human Brain Mapping, vol.78, issue.2, pp.489-507, 2015.
DOI : 10.1016/j.neuroimage.2013.03.073

URL : https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4470367/pdf

D. Zhang, Multimodal classification of Alzheimer's disease and mild cognitive impairment, NeuroImage, vol.55, issue.3, pp.856-867, 2011.
DOI : 10.1016/j.neuroimage.2011.01.008

E. Bron, Standardized evaluation of algorithms for computer-aided diagnosis of dementia based on structural MRI: The CADDementia challenge, NeuroImage, vol.111, pp.562-579, 2015.
DOI : 10.1016/j.neuroimage.2015.01.048

URL : https://hal.archives-ouvertes.fr/hal-01220123

F. Falahati, 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.

G. Allen, Crowdsourced estimation of cognitive decline and resilience in Alzheimer's disease. Alzheimer's & Dementia, pp.645-653, 2016.

H. Yun, Multimodal Discrimination of Alzheimer???s Disease Based on Regional Cortical Atrophy and Hypometabolism, PLOS ONE, vol.34, issue.12, p.129250, 2015.
DOI : 10.1371/journal.pone.0129250.s001

J. Young, Accurate multimodal probabilistic prediction of conversion to Alzheimer's disease in patients with mild cognitive impairment, NeuroImage: Clinical, vol.2, pp.735-745, 2013.
DOI : 10.1016/j.nicl.2013.05.004

K. Gorgolewski, The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments, Scientific Data, vol.8, p.160044, 2016.
DOI : 10.3389/fnins.2013.00009

URL : https://hal.archives-ouvertes.fr/inserm-01345616

K. Gray, Random forest-based similarity measures for multi-modal classification of Alzheimer's disease, NeuroImage, vol.65, pp.167-175, 2013.
DOI : 10.1016/j.neuroimage.2012.09.065

M. Liu, Ensemble sparse classification of Alzheimer's disease, NeuroImage, vol.60, issue.2, pp.1106-1116, 2012.
DOI : 10.1016/j.neuroimage.2012.01.055

M. Sabuncu, Clinical Prediction from Structural Brain MRI Scans: A Large- Scale Empirical Study, Neuroinformatics, 2014.
DOI : 10.1007/s12021-014-9238-1

URL : https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4303550/pdf

R. Cuingnet, 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.766-781, 2011.
DOI : 10.1016/j.neuroimage.2010.06.013

R. Cuingnet, Spatial and Anatomical Regularization of SVM: A General Framework for Neuroimaging Data, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.35, issue.3, pp.682-696, 2013.
DOI : 10.1109/TPAMI.2012.142

URL : https://hal.archives-ouvertes.fr/hal-00790079

S. Haller, Principles of classification analyses in mild cognitive impairment (MCI) and Alzheimer disease, Journal of Alzheimer's disease: JAD, vol.26, issue.3, pp.389-394, 2011.

S. Rathore, A review on neuroimaging-based classification studies and associated feature extraction methods for Alzheimer's disease and its prodromal stages, NeuroImage, vol.155, 2017.
DOI : 10.1016/j.neuroimage.2017.03.057

T. Tong, Multiple instance learning for classification of dementia in brain MRI, Medical Image Analysis, vol.18, issue.5, pp.808-818, 2014.
DOI : 10.1016/j.media.2014.04.006

Y. Fan, Spatial patterns of brain atrophy in MCI patients, identified via high-dimensional pattern classification, predict subsequent cognitive decline, NeuroImage, vol.39, issue.4, pp.1731-1743, 2008.
DOI : 10.1016/j.neuroimage.2007.10.031