Sources of systematic error in calibrated BOLD based mapping of baseline oxygen extraction fraction

Abstract

Recently a new class of calibrated blood oxygen level dependent (BOLD) functional magnetic resonance imaging (MRI) methods were introduced to quantitatively measure the baseline oxygen extraction fraction (OEF). These methods rely on two respiratory challenges and a mathematical model of the resultant changes in the BOLD functional MRI signal to estimate the OEF. However, this mathematical model does not include all of the effects that contribute to the BOLD signal, it relies on several physiological assumptions and it may be affected by intersubject physiological variability. The aim of this study was to investigate these sources of systematic error and their effect on estimating the OEF. This was achieved through simulation using a detailed model of the BOLD signal. Large ranges for intersubject variability in baseline physiological parameters such as haematocrit and cerebral blood volume were considered. Despite this the uncertainty in the relationship between the measured BOLD signals and the OEF was relatively low. Investigations of the physiological assumptions that underlie the mathematical model revealed that OEF measurements are likely to be overestimated if oxygen metabolism changes during hypercapnia or cerebral blood flow changes under hyperoxia. Hypoxic hypoxia was predicted to result in an underestimation of the OEF, whilst anaemic hypoxia was found to have only a minimal effect.

Graded Hypercapnia-Calibrated BOLD: Beyond the Iso-metabolic Hypercapnic Assumption

“…In terms of studies calculating the relative change in CMRO 2 to a task, this overestimation in M would result in an overestimation in the CMRO 2 task response (see Equation 1, replacing hypercapnia terms with the equivalent task response terms). In terms of studies investigating absolute CMRO 2 measurements, the overestimation in M would lead to an overestimation of absolute CMRO 2 (Blockley et al, 2015). …”

“…The steady-state variant of the CMRO 2 measurement used in that work may also be biased by not accounting for intravascular 15 O 2 (Lammertsma and Jones, 1983; Lammertsma et al, 1983; Ter-Pogossian and Herscovitch, 1985). Whilst our method is constrained by the accuracy of the assumptions associated with the calibrated BOLD technique (Hoge et al, 1999; Chiarelli et al, 2007b; Chen and Pike, 2010b; Blockley et al, 2015; Croal et al, 2017), it is non-invasive, not requiring use of radioactive tracers. It also has the potential for finer temporal and spatial resolution than O-15 PET.…”

“…Despite this limited sensitivity, recent calibrated BOLD studies have begun to correct for an assumed reduction in basal CMRO 2 with hypercapnia (Bulte et al, 2012), based on these literature values (Xu et al, 2011). An appropriate choice of CMRO 2 response (or lack thereof) to hypercapnia is important for calibrated BOLD experiments due to the sensitivity of the method to propagation of errors in M through to the endpoint CMRO 2 calculation (Hoge et al, 1999; Chiarelli et al, 2007b; Blockley et al, 2015). …”

Prospects for investigating brain oxygenation in acute stroke: Experience with a non‐contrast quantitative BOLD based approach

“…In practise, the information provided by sqBOLD could aid the interpretation of ASL CBF measurements, since low flow does not always progress to infarction in regions experiencing benign oligaemia (Kidwell, Alger, & Saver, ). Although it is beyond the scope of this study, the combination of CBF and [dHb] measurements allows for the calculation of the cerebral metabolic rate of oxygen consumption (CMRO 2 ) (Blockley, Griffeth, Stone, Hare, & Bulte, ). This has been shown to improve tissue outcome prediction and may partly explain the variability seen in the presenting sqBOLD oxygenation measurements (An et al, ).…”

“…Although it is beyond the scope of this study, the combination of CBF and [dHb] measurements allows for the calculation of the cerebral metabolic rate of oxygen consumption (CMRO 2 ) (Blockley, Griffeth, Stone, Hare, & Bulte, 2015). This has been shown to improve tissue outcome prediction and may partly explain the variability seen in the presenting sqBOLD oxygenation measurements .…”