An Event-Based Disease Progression Model and Its Application to Familial Alzheimer’s Disease

  • Hubert M. Fonteijn
  • Matthew J. Clarkson
  • Marc Modat
  • Josephine Barnes
  • Manja Lehmann
  • Sebastien Ourselin
  • Nick C. Fox
  • Daniel C. Alexander
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6801)

Abstract

This study introduces a novel event-based model for disease progression. The model describes disease progression as a series of events. An event can consist of a significant change in symptoms or in tissue. We construct a forward model that relates heterogeneous measurements from a whole cohort of patients and controls to the event sequence and fit the model with a Bayesian estimation framework. The model does not rely on a priori classification of patients and therefore has the potential to describe disease progression in much greater detail than previous approaches. We demonstrate our model on serial T1 MRI data from a familial Alzheimer’s disease cohort. We show progression of neuronal atrophy on a much finer level than previous studies, while confirming progression patterns from pathological studies, and integrate clinical events into the model.

Keywords

Disease progression Alzheimer’s disease atrophy computational model 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Braak, H., Braak, E.: Neuropathological stageing of Alzheimer-related changes. Acta Neuropathologica 82(4), 239–259 (1991)CrossRefGoogle Scholar
  2. 2.
    Carbone, P., Kaplan, H., Musshoff, K., Smithers, D., Tubiana, M.: Report of the committee on Hodgkin’s disease staging classification. Cancer Research 31(11), 1860 (1971)Google Scholar
  3. 3.
    Dempster, A., Laird, N., Rubin, D., et al.: Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society. Series B (Methodological) 39(1), 1–38 (1977)MathSciNetMATHGoogle Scholar
  4. 4.
    Dickerson, B., Bakkour, A., Salat, D., Feczko, E., Pacheco, J., Greve, D., Grodstein, F., Wright, C., Blacker, D., Rosas, H., et al.: The cortical signature of Alzheimer’s disease: regionally specific cortical thinning relates to symptom severity in very mild to mild AD dementia and is detectable in asymptomatic amyloid-positive individuals. Cerebral Cortex 19(3), 497 (2009)CrossRefGoogle Scholar
  5. 5.
    Fischl, B., Salat, D., Busa, E., Albert, M., Dieterich, M., Haselgrove, C., van der Kouwe, A., Killiany, R., Kennedy, D., Klaveness, S., et al.: Whole Brain Segmentation: Automated Labeling of Neuroanatomical Structures in the Human Brain. Neuron 33(3), 341–355 (2002)CrossRefGoogle Scholar
  6. 6.
    Fischl, B., Van Der Kouwe, A., Destrieux, C., Halgren, E., Segonne, F., Salat, D., Busa, E., Seidman, L., Goldstein, J., Kennedy, D., et al.: Automatically parcellating the human cerebral cortex. Cerebral Cortex 14(1), 11 (2004)CrossRefGoogle Scholar
  7. 7.
    Folstein, M.F., Folstein, S.E., McHugh, P.R.: Mini-mental state. A practical method for grading the cognitive state of patients for the clinician. Journal of Psychiatric Research 12(3), 189 (1975)CrossRefGoogle Scholar
  8. 8.
    Freeborough, P., Fox, N.: Modeling brain deformations in Alzheimer disease by fluid registration of serial 3D MR images. Journal of Computer Assisted Tomography 22(5), 838 (1998)CrossRefGoogle Scholar
  9. 9.
    Gilks, W.R., Richardson, S., Spiegelhalter, D.J.: Markov chain Monte Carlo in practice. Chapman & Hall/CRC (1996)Google Scholar
  10. 10.
    Mannila, H., Meek, C.: Global partial orders from sequential data. In: Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, p. 168. ACM, New York (2000)Google Scholar
  11. 11.
    McLachlan, G., Peel, D.: Finite mixture models. Wiley Interscience, Hoboken (2000)CrossRefMATHGoogle Scholar
  12. 12.
    Modat, M., Ridgway, G., Taylor, Z., Lehmann, M., Barnes, J., Hawkes, D., Fox, N., Ourselin, S.: Fast free-form deformation using graphics processing units. Computer methods and programs in biomedicine 98(3), 278–284 (2010)CrossRefGoogle Scholar
  13. 13.
    Mueller, S., Weiner, M., Thal, L., Petersen, R., Jack, C., Jagust, W., Trojanowski, J., Toga, A., Beckett, L.: Ways toward an early diagnosis in Alzheimer’s disease: the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Alzheimer’s and Dementia: The Journal of the Alzheimer’s Association 1(1), 55–66 (2005)CrossRefGoogle Scholar
  14. 14.
    Puolamaki, K., Fortelius, M., Mannila, H.: Seriation in paleontological data using Markov chain Monte Carlo methods. PLoS Computational Biology 2(2), e6 (2006)CrossRefGoogle Scholar
  15. 15.
    Ridha, B.H., Barnes, J., Bartlett, J.W., Godbolt, A., Pepple, T., Rossor, M.N., Fox, N.C.: Tracking atrophy progression in familial Alzheimer’s disease: a serial MRI study. The Lancet Neurology 5(10), 828–834 (2006)CrossRefGoogle Scholar
  16. 16.
    Scahill, R.I., Schott, J.M., Stevens, J.M., Rossor, M.N., Fox, N.C.: Mapping the evolution of regional atrophy in Alzheimer’s disease: unbiased analysis of fluid-registered serial MRI. Proceedings of the National Academy of Sciences of the United States of America 99(7), 4703 (2002)CrossRefGoogle Scholar
  17. 17.
    Tabrizi, S., Langbehn, D., Leavitt, B., Roos, R., Durr, A., Craufurd, D., Kennard, C., Hicks, S., Fox, N., Scahill, R., et al.: Biological and clinical manifestations of Huntington’s disease in the longitudinal TRACK-HD study: cross-sectional analysis of baseline data. The Lancet Neurology 8(9), 791–801 (2009)CrossRefGoogle Scholar
  18. 18.
    Thompson, P., Mega, M., Woods, R., Zoumalan, C., Lindshield, C., Blanton, R., Moussai, J., Holmes, C., Cummings, J., Toga, A.: Cortical change in Alzheimer’s disease detected with a disease-specific population-based brain atlas. Cerebral Cortex 11(1), 1 (2001)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Hubert M. Fonteijn
    • 1
  • Matthew J. Clarkson
    • 2
  • Marc Modat
    • 2
  • Josephine Barnes
    • 3
  • Manja Lehmann
    • 3
  • Sebastien Ourselin
    • 2
  • Nick C. Fox
    • 3
  • Daniel C. Alexander
    • 1
  1. 1.Centre for Medical Image Computing, Department of Computer ScienceUniversity College LondonUK
  2. 2.Centre for Medical Image Computing, Department of Medical Physics and BioengineeringUniversity College LondonUK
  3. 3.Dementia Research Centre, Institute of NeurologyUniversity College LondonUK

Personalised recommendations