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Multi-stage Biomarker Models for Progression Estimation in Alzheimer’s Disease

  • Alexander Schmidt-Richberg
  • Ricardo Guerrero
  • Christian Ledig
  • Helena Molina-Abril
  • Alejandro F. Frangi
  • Daniel Rueckert
  • on behalf of the Alzheimers Disease Neuroimaging Initiative
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9123)

Abstract

The estimation of disease progression in Alzheimer’s disease (AD) based on a vector of quantitative biomarkers is of high interest to clinicians, patients, and biomedical researchers alike. In this work, quantile regression is employed to learn statistical models describing the evolution of such biomarkers. Two separate models are constructed using (1) subjects that progress from a cognitively normal (CN) stage to mild cognitive impairment (MCI) and (2) subjects that progress from MCI to AD during the observation window of a longitudinal study. These models are then automatically combined to develop a multi-stage disease progression model for the whole disease course. A probabilistic approach is derived to estimate the current disease progress (DP) and the disease progression rate (DPR) of a given individual by fitting any acquired biomarkers to these models. A particular strength of this method is that it is applicable even if individual biomarker measurements are missing for the subject. Employing cognitive scores and image-based biomarkers, the presented method is used to estimate DP and DPR for subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Further, the potential use of these values as features for different classification tasks is demonstrated. For example, accuracy of 64 % is reached for CN vs. MCI vs. AD classification.

Keywords

Mild Cognitive Impairment Quantile Regression Cognitive Score Cognitive Normal Disease Progression Rate 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007 2013) under grant agreement no. 601055, VPH-DARE@IT.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Alexander Schmidt-Richberg
    • 1
  • Ricardo Guerrero
    • 1
  • Christian Ledig
    • 1
  • Helena Molina-Abril
    • 2
  • Alejandro F. Frangi
    • 2
  • Daniel Rueckert
    • 1
  • on behalf of the Alzheimers Disease Neuroimaging Initiative
    • 1
  1. 1.Biomedical Image Analysis GroupImperial College LondonLondonUK
  2. 2.Center for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB)University of SheffieldSheffieldUK

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