Learning spatiotemporal trajectories from manifold-valued longitudinal data

Advances in Neural Information Processing Systems

Volumn 2015-January, Issue , 2015, Pages 2404-2412

  Schiratti, Jean Baptiste ^a,b   Allassonnière, Stéphanie ^b   Colliot, Olivier ^a   Durrleman, Stanley ^a  

^a SORBONNE UNIVERSITÉ   (France)

^b CENTRE DE MATHÉMATIQUES APPLIQUÉES   (France)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; APPROXIMATION ALGORITHMS; COMPUTER PROGRAMMING; GEOMETRY; GRAPHICAL USER INTERFACES; IMAGE SEGMENTATION; INFORMATION SCIENCE; MAXIMUM PRINCIPLE; NEURODEGENERATIVE DISEASES; RANDOM PROCESSES; STOCHASTIC SYSTEMS; TRAJECTORIES;

ALZHEIMER'S DISEASE; COGNITIVE IMPAIRMENT; EXPECTATION-MAXIMIZATION ALGORITHMS; MANIFOLD-VALUED DATUM; MIXED EFFECTS MODELS; REPEATED MEASUREMENTS; SPATIO-TEMPORAL TRAJECTORIES; STOCHASTIC APPROXIMATIONS;

STOCHASTIC MODELS;

EID: 84965116220     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (18)

1. The Alzheimer's Disease Neuroimaging Initiative, https://ida.loni.usc.edu
2. Allassonnière, S., Kuhn, E., Trouvé, A.: Construction of bayesian deformable models via a stochastic approximation algorithm: a convergence study. Bernoulli 16(3), 641-678 (2010)
3. Braak, H., Braak, E.: Staging of alzheimer's disease-related neurofibrillary changes. Neurobiology of aging 16(3), 271-278 (1995)
4. Delyon, B., Lavielle, M., Moulines, E.: Convergence of a stochastic approximation version of the em algorithm. Annals of statistics pp. 94-128 (1999)
5. Dempster, A. P., Laird, N. M., Rubin, D. B.: Maximum likelihood from incomplete data via the em algorithm. Journal of the royal statistical society. Series B (methodological) pp. 1-38 (1977)
6. Diggle, P., Heagerty, P., Liang, K. Y., Zeger, S.: Analysis of longitudinal data. Oxford University Press (2002)
7. Donohue, M. C., Jacqmin-Gadda, H., Le Goff, M., Thomas, R. G., Raman, R., Gamst, A. C., Beckett, L. A., Jack, C. R., Weiner, M. W., Dartigues, J. F., Aisen, P. S., the Alzheimer's Disease Neuroimaging Initiative: Estimating long-term multivariate progression from short-term data. Alzheimer's & Dementia 10(5), 400-410 (2014)
8. Durrleman, S., Pennec, X., Trouvé, A., Braga, J., Gerig, G., Ayache, N.: Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data. International Journal of Computer Vision 103(1234567 1), 22-59 (2013)
9. Fonteijn, H. M., Modat, M., Clarkson, M. J., Barnes, J., Lehmann, M., Hobbs, N. Z., Scahill, R. I., Tabrizi, S. J., Ourselin, S., Fox, N. C., et al.: An event-based model for disease progression and its application in familial alzheimer's disease and huntington's disease. NeuroImage 60(3), 1880-1889 (2012)
10. Girolami, M., Calderhead, B.: Riemann manifold langevin and hamiltonian monte carlo methods. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 73(2), 123-214 (2011)
11. Hirsch, M. W.: Differential topology. Springer Science & Business Media (2012)
12. Hyvärinen, A., Karhunen, J., Oja, E.: Independent component analysis, vol. 46. John Wiley & Sons (2004)
13. Jack, C. R., Knopman, D. S., Jagust, W. J., Shaw, L. M., Aisen, P. S., Weiner, M. W., Petersen, R. C., Trojanowski, J. Q.: Hypothetical model of dynamic biomarkers of the alzheimer's pathological cascade. The Lancet Neurology 9(1), 119-128 (2010)
14. Kuhn, E., Lavielle, M.: Maximum likelihood estimation in nonlinear mixed effects models. Computational Statistics & Data Analysis 49(4), 1020-1038 (2005)
15. Laird, N. M., Ware, J. H.: Random-effects models for longitudinal data. Biometrics pp. 963-974 (1982)
16. Singer, J. D., Willett, J. B.: Applied longitudinal data analysis: Modeling change and event occurrence. Oxford university press (2003)
17. Singh, N., Hinkle, J., Joshi, S., Fletcher, P. T.: A hierarchical geodesic model for diffeomorphic longitudinal shape analysis. In: Information Processing in Medical Imaging. pp. 560-571. Springer (2013)
18. Su, J., Kurtek, S., Klassen, E., Srivastava, A., et al.: Statistical analysis of trajectories on riemannian manifolds: Bird migration, hurricane tracking and video surveillance. The Annals of Applied Statistics 8(1), 530-552 (2014)