, frontotemporal dementia mutation carriers using multimodal MRI, NeuroImage Clin, vol.20, pp.188-96, 2018.

S. Meyer, K. Mueller, and K. Stuke, Predicting behavioral variant frontotemporal dementia with pattern classification in multi-center structural MRI data, NeuroImage Clin, vol.14, pp.656-62, 2017.

L. Zeng, H. Wang, and P. Hu, Multi-Site Diagnostic Classification of Schizophrenia Using Discriminant Deep Learning with, Functional Connectivity MRI. EBioMedicine, vol.30, pp.74-85, 2018.

Y. Chen, Y. Tang, and C. Wang, ADHD classification by dual subspace learning using resting-state functional connectivity, Artif Intell Med, p.103, 2020.

H. K. Van-der-burgh, R. Schmidt, H. Westeneng, M. A. De-reus, L. H. Van-den-berg et al., Deep learning predictions of survival based on MRI in amyotrophic lateral sclerosis, NeuroImage Clin, vol.13, pp.361-370, 2017.

K. Li, R. O'brien, M. Lutz, and S. Luo, A prognostic model of Alzheimer's disease relying on multiple longitudinal measures and time-to-event data, Alzheimers Dement J Alzheimers Assoc, vol.14, issue.5, pp.644-51, 2018.

I. Koval, J. Schiratti, and A. Routier, Spatiotemporal Propagation of the Cortical Atrophy: Population and Individual Patterns, Front Neurol, vol.9, p.235, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01910400

J. Schiratti, S. Allassonnière, O. Colliot, and S. Durrleman, A Bayesian Mixed-Effects Model to Learn Trajectories of Changes from Repeated Manifold-Valued Observations, J Mach Learn Res, vol.18, issue.133, pp.1-33, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01540367

E. Moradi, A. Pepe, C. Gaser, H. Huttunen, and J. Tohka, Alzheimer's Disease Neuroimaging Initiative. Machine learning framework for early MRI-based Alzheimer's conversion prediction in MCI subjects, NeuroImage, vol.104, pp.398-412, 2015.

L. Shen and P. M. Thompson, Brain Imaging Genomics: Integrated Analysis and Machine Learning, Proc IEEE Inst Electr Electron Eng, vol.108, issue.1, pp.125-62, 2020.

X. Da, J. B. Toledo, and J. Zee, 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, vol.4, pp.164-73, 2014.

Y. Gupta, R. K. Lama, and G. Kwon, Prediction and Classification of Alzheimer's Disease Based on Combined Features From Apolipoprotein-E Genotype, Cerebrospinal Fluid, MR, and FDG-PET Imaging Biomarkers, Front Comput Neurosci, vol.13, 2019.

S. Khanna, D. Domingo-fernández, and A. Iyappan, Using Multi-Scale Genetic, Neuroimaging and Clinical Data for Predicting Alzheimer's Disease and Reconstruction of Relevant Biological Mechanisms, Sci Rep, vol.8, issue.1, pp.1-13, 2018.

Y. Varatharajah, V. K. Ramanan, and R. Iyer, Predicting Short-term MCI-to-AD Progression Using Imaging, CSF, Genetic Factors, Cognitive Resilience, and Demographics. Sci Rep, vol.9, issue.1, p.2235, 201919.

*. Samper-gonzález, J. Burgos, N. Bottani, and S. , Reproducible evaluation of classification methods in Alzheimer's disease: Framework and application to MRI and PET data, Proposes a framework and sets standards for reproducible research, vol.183, pp.504-525, 2018.

K. J. Gorgolewski, T. Auer, and V. D. Calhoun, The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments, Sci Data, vol.3, p.160044, 2016.
URL : https://hal.archives-ouvertes.fr/inserm-01345616

S. Rathore, M. Habes, and M. A. Iftikhar, A review on neuroimaging-based classification studies and associated feature extraction methods for Alzheimer's disease and its prodromal stages, NeuroImage, vol.155, pp.530-578, 2017.