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Publication Detail
Learning imaging biomarker trajectories from noisy Alzheimer's disease data using a Bayesian multilevel model
  • Publication Type:
    Conference
  • Authors:
    Oxtoby NP, Young AL, Daga P, Cash DM, Ourselin S, Schott JM, Fox NC, Alexander DC
  • Publisher:
    Springer
  • Publication date:
    18/09/2014
  • Pagination:
    85, 94
  • Published proceedings:
    Bayesian and grAphical Models for Biomedical Imaging
  • Volume:
    8677
  • Series:
    Lecture Notes in Computer Science
  • Editors:
    Cardoso MJ,Simpson I,Arbel T,Precup D,Ribbens A
  • ISBN-13:
    978-3-319-12288-5
  • Status:
    Published
  • Name of conference:
    MICCAI 2014 Workshop on Bayesian and Graphical Models for Biomedical Imaging
  • Conference place:
    Boston, USA
  • Conference start date:
    18/09/2014
  • Conference finish date:
    18/09/2014
  • Addresses:
    University College London
    Centre for Medical Image Computing, Department of Computer Science
    Malet Place
    London
    WC1E 6BT
    United Kingdom

    University College London
    Dementia Research Centre
    8-11 Queen Square
    London
    WC1N 3AR
    United Kingdom
Abstract
Characterising the time course of a disease with a protracted incubation period ultimately requires dense longitudinal studies, which can be prohibitively long and expensive. We consider what can be learned in the absence of such data. We estimate cohort-level biomarker trajectories by fitting cross-sectional data to a differential equation model, then integrating. These fits inform our stochastic differential equation model for synthesising individual-level biomarker trajectories for prognosis support. Our Bayesian multilevel regression model explicitly includes measurement noise estimation, which is not possible with traditional non-Bayesian mixed-effects models. Applicable to any disease, here we apply our approach to experiments on Alzheimer’s disease imaging biomarker data — volumes of regions of interest within the brain. We find that Alzheimer’s disease imaging biomarkers are dynamic over timescales from a few years to a few decades.
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