A Discriminative Event Based Model for Alzheimer’s Disease Progression Modeling
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The event-based model (EBM) for data-driven disease progression modeling estimates the sequence in which biomarkers for a disease become abnormal. This helps in understanding the dynamics of disease progression and facilitates early diagnosis by staging patients on a disease progression timeline. Existing EBM methods are all generative in nature. In this work we propose a novel discriminative approach to EBM, which is shown to be more accurate as well as computationally more efficient than existing state-of-the art EBM methods. The method first estimates for each subject an approximate ordering of events, by ranking the posterior probabilities of individual biomarkers being abnormal. Subsequently, the central ordering over all subjects is estimated by fitting a generalized Mallows model to these approximate subject-specific orderings based on a novel probabilistic Kendall’s Tau distance. To evaluate the accuracy, we performed extensive experiments on synthetic data simulating the progression of Alzheimer’s disease. Subsequently, the method was applied to the Alzheimer’s Disease Neuroimaging Initiative (ADNI) data to estimate the central event ordering in the dataset. The experiments benchmark the accuracy of the new model under various conditions and compare it with existing state-of-the-art EBM methods. The results indicate that discriminative EBM could be a simple and elegant approach to disease progression modeling.
KeywordsMild Cognitive Impairment Gaussian Mixture Model Probability Density Function Dirichlet Process Mixture Discriminative Approach
This work is part of the EuroPOND initiative, which is funded by the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 666992. The authors also thank Dr. Jonathan Huang for sharing the implementation of Huang’s EBM and Dr. Alexandra Young for the useful discussions on estimation of biomarker distributions as well as for sharing the implementation of the simulation system for biomarker evolution.
- 1.Bron, E.E., Steketee, R.M., Houston, G.C., Oliver, R.A., Achterberg, H.C., Loog, M., van Swieten, J.C., Hammers, A., Niessen, W.J., Smits, M., Klein, S., Alzheimer’s Disease Neuroimaging Initiative: Diagnostic classification of arterial spin labeling and structural MRI in presenile early stage dementia. Hum. Brain Mapp. 35(9), 4916–4931 (2014)Google Scholar
- 2.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., Alexander, D.C.: 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)CrossRefGoogle Scholar
- 4.Huang, J., Alexander, D.: Probabilistic event cascades for Alzheimer’s disease. In: Pereira, F., Burges, C.J.C., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems 25, pp. 3095–3103. Curran Associates, Inc., Red Hook (2012)Google Scholar
- 5.Iturria-Medina, Y., Sotero, R.C., Toussaint, P.J., Mateos-Prez, J.M., Evans, A.C., Alzheimer’s Disease Neuroimaging Initiative: Early role of vascular dysregulation on late-onset Alzheimer’s disease based on multifactorial data-driven analysis. Nat. Commun. 7, 11934 (2016)Google Scholar
- 6.Jack, C.R., Knopman, D.S., Jagust, W.J., Petersen, R.C., Weiner, M.W., Aisen, P.S., Shaw, L.M., Vemuri, P., Wiste, H.J., Weigand, S.D., Lesnick, T.G., Pankratz, V.S., Donohue, M.C., Trojanowski, J.Q.: Tracking pathophysiological processes in Alzheimer’s disease an updated hypothetical model of dynamic biomarkers. Lancet Neurol. 12(2), 207–216 (2013)CrossRefGoogle Scholar
- 7.Kumar, R., Vassilvitskii, S.: Generalized distances between rankings. In: Proceedings of the 19th International Conference on World Wide Web, WWW 2010, pp. 571–580. ACM, New York (2010)Google Scholar
- 10.Schmidt-Richberg, A., Ledig, C., Guerrero, R., Molina-Abril, H., Frangi, A., Rueckert, D., Alzheimers Disease Neuroimaging Initiative: Learning biomarker models for progression estimation of Alzheimers disease. PLoS One 11(04), 1–27 (2016)Google Scholar