Advertisement

A Vertex Clustering Model for Disease Progression: Application to Cortical Thickness Images

  • Răzvan Valentin Marinescu
  • Arman Eshaghi
  • Marco Lorenzi
  • Alexandra L. Young
  • Neil P. Oxtoby
  • Sara Garbarino
  • Timothy J. Shakespeare
  • Sebastian J. Crutch
  • Daniel C. Alexander
  • for the Alzheimer’s Disease Neuroimaging Initiative
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10265)

Abstract

We present a disease progression model with single vertex resolution that we apply to cortical thickness data. Our model works by clustering together vertices on the cortex that have similar temporal dynamics and building a common trajectory for vertices in the same cluster. The model estimates optimal stages and progression speeds for every subject. Simulated data show that it is able to accurately recover the vertex clusters and the underlying parameters. Moreover, our clustering model finds similar patterns of atrophy for typical Alzheimer’s disease (tAD) subjects on two independent datasets: the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and a cohort from the Dementia Research Centre (DRC), UK. Using a separate set of subjects with Posterior Cortical Atrophy (PCA) from the DRC dataset, we also show that the model finds different patterns of atrophy in PCA compared to tAD. Finally, our model provides a novel way to parcellate the brain based on disease dynamics.

Keywords

Disease progression model Cortical thickness Vertex-wise measures Alzheimer’s disease Posterior Cortical Atrophy 

Notes

Acknowledgements

This work was supported by the EPSRC Centre For Doctoral Training in Medical Imaging with grant EP/L016478/1. AE received a McDonald Fellowship from the Multiple Sclerosis International Federation (MSIF, www.msif.org), and the ECTRIMS - MAGNIMS Fellowship. ALY was supported through EPSRC grant EP/J020990/01. NPO and SG received funding from the EU Horizon 2020 research and innovation programme under grant agreement No. 666992. SJC was supported by an Alzheimer’s Research UK Senior Research Fellowship and ESRC/NIHR (ES/L001810/1) and EPSRC (EP/M006093/1) grants. DCA’s work on this topic has funding from the EU Horizon 2020 research and innovation programme under grant agreement No. 666992, as well as EPSRC grants J020990, M006093 and M020533. Data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). The Dementia Research Centre is an ARUK coordination center.

References

  1. 1.
    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. Lancet Neurol. 9(1), 119–128 (2010)CrossRefGoogle Scholar
  2. 2.
    Bateman, R.J., Xiong, C., Benzinger, T.L., Fagan, A.M., Goate, A., Fox, N.C., Marcus, D.S., Cairns, N.J., Xie, X., Blazey, T.M., Holtzman, D.M.: Clinical and biomarker changes in dominantly inherited Alzheimer’s disease. N. Engl. J. Med. 367(9), 795–804 (2012)CrossRefGoogle Scholar
  3. 3.
    Schmidt-Richberg, A., Guerrero, R., Ledig, C., Molina-Abril, H., Frangi, A.F., Rueckert, D.: Multi-stage biomarker models for progression estimation in Alzheimer’s disease. In: Ourselin, S., Alexander, D.C., Westin, C.-F., Cardoso, M.J. (eds.) IPMI 2015. LNCS, vol. 9123, pp. 387–398. Springer, Cham (2015). doi: 10.1007/978-3-319-19992-4_30 CrossRefGoogle Scholar
  4. 4.
    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
  5. 5.
    Jedynak, B.M., Lang, A., Liu, B., Katz, E., Zhang, Y., Wyman, B.T., Raunig, D., Jedynak, C.P., Caffo, B., Prince, J.L., Initiative, A.D.N.: A computational neurodegenerative disease progression score: method and results with the Alzheimer’s Disease Neuroimaging Initiative cohort. Neuroimage 63(3), 1478–1486 (2012)CrossRefGoogle Scholar
  6. 6.
    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.: Estimating long-term multivariate progression from short-term data. Alzheimer’s & Dementia 10(5), S400–S410 (2014)CrossRefGoogle Scholar
  7. 7.
    Schiratti, J.-B., Allassonnière, S., Routier, A., Colliot, O., Durrleman, S.: A mixed-effects model with time reparametrization for longitudinal univariate manifold-valued data. In: Ourselin, S., Alexander, D.C., Westin, C.-F., Cardoso, M.J. (eds.) IPMI 2015. LNCS, vol. 9123, pp. 564–575. Springer, Cham (2015). doi: 10.1007/978-3-319-19992-4_44 CrossRefGoogle Scholar
  8. 8.
    Bilgel, M., Prince, J.L., Wong, D.F., Resnick, S.M., Jedynak, B.M.: A multivariate nonlinear mixed effects model for longitudinal image analysis: application to amyloid imaging. NeuroImage 134, 658–670 (2016)CrossRefGoogle Scholar
  9. 9.
    Seeley, W.W., Crawford, R.K., Zhou, J., Miller, B.L., Greicius, M.D.: Neurodegenerative diseases target large-scale human brain networks. Neuron 62(1), 42–52 (2009)CrossRefGoogle Scholar
  10. 10.
    Reuter, M., Schmansky, N.J., Rosas, H.D., Fischl, B.: Within-subject template estimation for unbiased longitudinal image analysis. Neuroimage 61(4), 1402–1418 (2012)CrossRefGoogle Scholar
  11. 11.
    Dickerson, B.C., Bakkour, A., Salat, D.H., Feczko, E., Pacheco, J., Greve, D.N., Grodstein, F., Wright, C.I., Blacker, D., Rosas, H.D., Sperling, R.A.: 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. Cereb. Cortex 19(3), 497–510 (2009)CrossRefGoogle Scholar
  12. 12.
    Crutch, S.J., Lehmann, M., Schott, J.M., Rabinovici, G.D., Rossor, M.N., Fox, N.C.: Posterior cortical atrophy. Lancet Neurol. 11(2), 170–178 (2012)CrossRefGoogle Scholar
  13. 13.
    Young, A.L., Oxtoby, N.P., Huang, J., Marinescu, R.V., Daga, P., Cash, D.M., Fox, N.C., Ourselin, S., Schott, J.M., Alexander, D.C.: Multiple orderings of events in disease progression. In: Ourselin, S., Alexander, D.C., Westin, C.-F., Cardoso, M.J. (eds.) IPMI 2015. LNCS, vol. 9123, pp. 711–722. Springer, Cham (2015). doi: 10.1007/978-3-319-19992-4_56 CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Răzvan Valentin Marinescu
    • 1
  • Arman Eshaghi
    • 1
    • 2
  • Marco Lorenzi
    • 1
    • 4
  • Alexandra L. Young
    • 1
  • Neil P. Oxtoby
    • 1
  • Sara Garbarino
    • 1
  • Timothy J. Shakespeare
    • 3
  • Sebastian J. Crutch
    • 3
  • Daniel C. Alexander
    • 1
  • for the Alzheimer’s Disease Neuroimaging Initiative
  1. 1.Centre for Medical Image Computing, Computer Science DepartmentUniversity College LondonLondonUK
  2. 2.Queen Square MS CentreUCL Institute of NeurologyLondonUK
  3. 3.Dementia Research CentreUCL Institute of Neurology, University College LondonLondonUK
  4. 4.University of Côte d’Azur, Inria Sophia Antipolis, Asclepios Research ProjectBiotFrance

Personalised recommendations