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Measurement of oxygen extraction fraction (OEF): An optimized BOLD signal model for use with hypercapnic and hyperoxic calibration

Merola, Alberto; Murphy, Kevin; Stone, Alan J.; Germuska, Michael A.; Griffeth, Valerie E.M.; Blockley, Nicholas P.; Buxton, Richard B.; Wise, Richard G.

Measurement of oxygen extraction fraction (OEF): An optimized BOLD signal model for use with hypercapnic and hyperoxic calibration Thumbnail


Authors

Alberto Merola

Kevin Murphy

Alan J. Stone

Michael A. Germuska

Valerie E.M. Griffeth

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NIC BLOCKLEY Nicholas.Blockley@nottingham.ac.uk
Assistant Professor

Richard B. Buxton

Richard G. Wise



Abstract

© 2016 The Authors. Several techniques have been proposed to estimate relative changes in cerebral metabolic rate of oxygen consumption (CMRO2) by exploiting combined BOLD fMRI and cerebral blood flow data in conjunction with hypercapnic or hyperoxic respiratory challenges. More recently, methods based on respiratory challenges that include both hypercapnia and hyperoxia have been developed to assess absolute CMRO2, an important parameter for understanding brain energetics. In this paper, we empirically optimize a previously presented "original calibration model" relating BOLD and blood flow signals specifically for the estimation of oxygen extraction fraction (OEF) and absolute CMRO2.To do so, we have created a set of synthetic BOLD signals using a detailed BOLD signal model to reproduce experiments incorporating hypercapnic and hyperoxic respiratory challenges at 3 T. A wide range of physiological conditions was simulated by varying input parameter values (baseline cerebral blood volume (CBV0), baseline cerebral blood flow (CBF0), baseline oxygen extraction fraction (OEF0) and hematocrit (Hct)).From the optimization of the calibration model for estimation of OEF and practical considerations of hypercapnic and hyperoxic respiratory challenges, a new "simplified calibration model" is established which reduces the complexity of the original calibration model by substituting the standard parameters α and β with a single parameter θ. The optimal value of θ is determined (θ = 0.06) across a range of experimental respiratory challenges. The simplified calibration model gives estimates of OEF0 and absolute CMRO2 closer to the true values used to simulate the experimental data compared to those estimated using the original model incorporating literature values of α and β. Finally, an error propagation analysis demonstrates the susceptibility of the original and simplified calibration models to measurement errors and potential violations in the underlying assumptions of isometabolism. We conclude that using the simplified calibration model results in a reduced bias in OEF0 estimates across a wide range of potential respiratory challenge experimental designs.

Citation

Merola, A., Murphy, K., Stone, A. J., Germuska, M. A., Griffeth, V. E., Blockley, N. P., …Wise, R. G. (2016). Measurement of oxygen extraction fraction (OEF): An optimized BOLD signal model for use with hypercapnic and hyperoxic calibration. NeuroImage, 129, 159-174. https://doi.org/10.1016/j.neuroimage.2016.01.021

Journal Article Type Article
Acceptance Date Jan 9, 2016
Online Publication Date Jan 20, 2016
Publication Date Apr 1, 2016
Deposit Date Dec 10, 2018
Publicly Available Date Jan 22, 2019
Journal NeuroImage
Print ISSN 1053-8119
Electronic ISSN 1095-9572
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 129
Pages 159-174
DOI https://doi.org/10.1016/j.neuroimage.2016.01.021
Keywords Cognitive Neuroscience; Neurology
Public URL https://nottingham-repository.worktribe.com/output/1379530
Publisher URL https://www.sciencedirect.com/science/article/pii/S1053811916000276
Additional Information This article is maintained by: Elsevier; Article Title: Measurement of oxygen extraction fraction (OEF): An optimized BOLD signal model for use with hypercapnic and hyperoxic calibration; Journal Title: NeuroImage; CrossRef DOI link to publisher maintained version: https://doi.org/10.1016/j.neuroimage.2016.01.021; Content Type: article; Copyright: Copyright © 2016 The Authors. Published by Elsevier Inc.
Contract Date Dec 10, 2018

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