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Annals of Biomedical Engineering

, Volume 41, Issue 11, pp 2296–2317 | Cite as

Model-based Quantification of Cerebral Hemodynamics as a Physiomarker for Alzheimer’s Disease?

  • V. Z. MarmarelisEmail author
  • D. C. Shin
  • M. E. Orme
  • R. Zhang
Article

Abstract

Previous studies have found that Alzheimer’s disease (AD) impairs cerebral vascular function, even at early stages of the disease. This offers the prospect of a useful diagnostic method for AD, if cerebral vascular dysfunction can be quantified reliably within practical clinical constraints. We present a recently developed methodology that utilizes a data-based dynamic nonlinear closed-loop model of cerebral hemodynamics to compute “physiomarkers” quantifying the state of cerebral flow autoregulation to pressure-changes (CA) and cerebral CO2 vasomotor reactivity (CVMR) in each subject. This model is estimated from beat-to-beat measurements of mean arterial blood pressure, mean cerebral blood flow velocity and end-tidal CO2, which can be made reliably and non-invasively under resting conditions. This model may also take an open-loop form and comparisons are made with the closed-loop counterpart. The proposed model-based physiomarkers take the form of two indices that quantify the gain of the CA and CVMR processes in each subject. It was found in an initial set of clinical data that the CVMR index delineates AD patients from control subjects and, therefore, may prove useful in the improved diagnosis of early-stage AD.

Keywords

Alzheimer’s disease Physiomarkers Modeling cerebral hemodynamics Closed-loop modeling Cerebral flow autoregulation Cerebral vasomotor reactivity 

Notes

Acknowledgments

This work was supported in part by the Biomedical Simulations Resource at the University of Southern California under NIH/NIBIB grant P41-EB001978 and NIA R01AG033106-01 grant to the UT-Southwestern Medical Center.

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Copyright information

© Biomedical Engineering Society 2013

Authors and Affiliations

  • V. Z. Marmarelis
    • 1
    Email author
  • D. C. Shin
    • 1
  • M. E. Orme
    • 2
  • R. Zhang
    • 3
  1. 1.University of Southern CaliforniaLos AngelesUSA
  2. 2.SonovationLos AngelesUSA
  3. 3.University of Texas, Southwestern Medical CenterDallasUSA

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