Constraining Disease Progression Models Using Subject Specific Connectivity Priors

  • Anvar KurmukovEmail author
  • Yuji Zhao
  • Ayagoz Mussabaeva
  • Boris Gutman
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11848)


We propose a simple yet powerful extension for event-based progression disease model by exploiting the Network Diffusion Hypothesis. Our approach allows incorporating connectivity information derived from diffusion MRI data in the form of an informative prior on event ordering. This simple extension using a definition of transition probability based on network path length leads to improved reproducibility and discriminative power. We report experimental results on a subset of the Alzheimer’s Disease Neuroimaging Initiative data set (ADNI 2). Though trained solely on cross-sectional data, our model successfully assigns higher progression scores to patients converting to more severe stages of dementia.


Connectomes Disease Progression Model Alzheimer’s Disease 



Work by BG was supported by the Alzheimer’s Association grant 2018-AARG-592081, Advanced Disconnectome Markers of Alzheimer’s Disease. AK and AM were supported by the Russian Science Foundation under grant 17-11-01390.


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Anvar Kurmukov
    • 1
    • 2
    Email author
  • Yuji Zhao
    • 3
  • Ayagoz Mussabaeva
    • 1
  • Boris Gutman
    • 1
    • 3
  1. 1.Institute for Information Transmission ProblemsMoscowRussia
  2. 2.National Research University Higher School of EconomicsMoscowRussia
  3. 3.Department of Biomedical EngineeringIllinois Institute of TechnologyChicagoUSA

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