C. Soto and S. Pritzkow, Protein misfolding, aggregation, and conformational strains in neurodegenerative diseases, Nat Neurosci, vol.21, issue.10, pp.1332-1340, 2018.

M. Jucker and L. C. Walker, Self-propagation of pathogenic protein aggregates in neurodegenerative diseases, Nature, vol.501, issue.7465, p.45, 2013.

F. Carbonell, Y. Iturria-medina, and A. C. Evans, Mathematical modeling of protein misfolding mechanisms in neurological diseases: a historical overview, Frontiers in Neurology, vol.9, p.37, 2018.

A. Raj, A. Kuceyeski, and M. Weiner, A network diffusion model of disease progression in dementia, Neuron, vol.73, issue.6, pp.1204-1215, 2012.

N. P. Oxtoby, S. Garbarino, N. C. Firth, J. D. Warren, J. M. Schott et al., Alzheimers Disease Neuroimaging Initiative, 2017. Data-Driven sequence of changes to anatomical Brain connectivity in sporadic Alzheimers Disease. Frontiers in neurology, vol.8, p.580

J. Zhou, E. D. Gennatas, J. H. Kramer, B. L. Miller, and W. W. Seeley, Predicting regional neurodegeneration from the healthy brain functional connectome, Neuron, vol.73, issue.6, pp.1216-1227, 2012.

Y. Iturria-medina, F. M. Carbonell, R. C. Sotero, F. Chouinard-decorte, and A. C. Evans, Multifactorial causal model of brain (dis)organization and therapeutic intervention: Application to Alzheimers disease, Neuroimage, vol.152, pp.60-77, 2017.

F. Cauda, A. Nani, J. Manuello, E. Premi, S. Palermo et al., Brain structural alterations are distributed following functional, anatomic and genetic connectivity, Brain, vol.141, issue.11, pp.3211-3232, 2018.

A. L. Young, N. P. Oxtoby, P. Daga, D. M. Cash, N. C. Fox et al., A data-driven model of biomarker changes in sporadic Alzheimer's disease, Brain, vol.137, issue.9, pp.2564-2577, 2014.

M. Lorenzi, M. Filippone, G. B. Frisoni, D. C. Alexander, and S. Ourselin, Probabilistic disease progression modeling to characterize diagnostic uncertainty: application to staging and prediction in Alzheimer's disease, Neuroimage, 2017.

J. B. Schiratti, S. Allassonnire, O. Colliot, and S. Durrleman, A Bayesian mixed-effects model to learn trajectories of changes from repeated manifold-valued observations, The Journal of Machine Learning Research, vol.18, issue.1, pp.4840-4872, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01540367

M. C. Donohue, H. Jacqmin-gadda, M. Le-goff, R. G. Thomas, R. Raman et al., Estimating long-term multivariate progression from short-term data, vol.10, pp.400-410, 2014.
DOI : 10.1016/j.jalz.2013.10.003

URL : http://europepmc.org/articles/pmc4169767?pdf=render

M. Lorenzi and M. Filippone, Constraining the Dynamics of Deep Probabilistic Models, Proceedings of the 35th International Conference on Machine Learning, vol.80, pp.3233-3242, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01843006

K. Cutajar, E. V. Bonilla, P. Michiardi, and M. Filippone, Random feature expansions for deep Gaussian processes, Proceedings of the 34th International Conference on Machine Learning, vol.70, pp.884-893, 2017.

D. R. Thal, U. Rub, M. Orantes, and H. Braak, Phases of A-deposition in the human brain and its relevance for the development of AD, Neurology, vol.58, issue.12, pp.1791-1800, 2002.

G. B. Irvine, O. M. El-agnaf, G. M. Shankar, and D. M. Walsh, Protein aggregation in the brain: the molecular basis for Alzheimers and Parkinsons diseases, Molecular medicine, pp.451-464, 2008.