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Learning Biomarker Models for Progression Estimation of Alzheimer’s Disease

Author

Listed:
  • Alexander Schmidt-Richberg
  • Christian Ledig
  • Ricardo Guerrero
  • Helena Molina-Abril
  • Alejandro Frangi
  • Daniel Rueckert
  • on behalf of the Alzheimer’s Disease Neuroimaging Initiative

Abstract

Being able to estimate a patient’s progress in the course of Alzheimer’s disease and predicting future progression based on a number of observed biomarker values is of great interest for patients, clinicians and researchers alike. In this work, an approach for disease progress estimation is presented. Based on a set of subjects that convert to a more severe disease stage during the study, models that describe typical trajectories of biomarker values in the course of disease are learned using quantile regression. A novel probabilistic method is then derived to estimate the current disease progress as well as the rate of progression of an individual by fitting acquired biomarkers to the models. A particular strength of the method is its ability to naturally handle missing data. This means, it is applicable even if individual biomarker measurements are missing for a subject without requiring a retraining of the model. The functionality of the presented method is demonstrated using synthetic and—employing cognitive scores and image-based biomarkers—real data from the ADNI study. Further, three possible applications for progress estimation are demonstrated to underline the versatility of the approach: classification, construction of a spatio-temporal disease progression atlas and prediction of future disease progression.

Suggested Citation

  • Alexander Schmidt-Richberg & Christian Ledig & Ricardo Guerrero & Helena Molina-Abril & Alejandro Frangi & Daniel Rueckert & on behalf of the Alzheimer’s Disease Neuroimaging Initiative, 2016. "Learning Biomarker Models for Progression Estimation of Alzheimer’s Disease," PLOS ONE, Public Library of Science, vol. 11(4), pages 1-27, April.
  • Handle: RePEc:plo:pone00:0153040
    DOI: 10.1371/journal.pone.0153040
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    References listed on IDEAS

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    1. Eric Stallard & Bruce Kinosian & Arthur S. Zbrozek & Anatoliy I. Yashin & Henry A. Glick & Yaakov Stern, 2010. "Estimation and Validation of a Multiattribute Model of Alzheimer Disease Progression," Medical Decision Making, , vol. 30(6), pages 625-638, November.
    2. Chenxi Li & N. Maritza Dowling & Rick Chappell, 2015. "Quantile regression with a change‐point model for longitudinal data: An application to the study of cognitive changes in preclinical alzheimer's disease," Biometrics, The International Biometric Society, vol. 71(3), pages 625-635, September.
    3. Robin Wolz & Valtteri Julkunen & Juha Koikkalainen & Eini Niskanen & Dong Ping Zhang & Daniel Rueckert & Hilkka Soininen & Jyrki Lötjönen & the Alzheimer's Disease Neuroimaging Initiative, 2011. "Multi-Method Analysis of MRI Images in Early Diagnostics of Alzheimer's Disease," PLOS ONE, Public Library of Science, vol. 6(10), pages 1-9, October.
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