Improved fMRI calibration: Precisely controlled hyperoxic versus hypercapnic stimuli

NeuroImage 54 (2011) 1102–1111

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Improved fMRI calibration: Precisely controlled hyperoxic versus hypercapnic stimuli Clarisse I. Mark a,⁎, Joseph A. Fisher b, G. Bruce Pike a a b

McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University, 3801 University Street, Montreal, Quebec, Canada H3A 2B4 Department of Anaesthesiology, University Health Network, University of Toronto, 200 Elizabeth Street, Toronto, Ontario, Canada M5G 2C4

a r t i c l e

i n f o

Article history: Received 23 April 2010 Revised 11 July 2010 Accepted 31 August 2010 Available online 7 September 2010 Keywords: fMRI Computer-controlled respiratory actuator Hypercapnia Hyperoxia BOLD-calibration Cerebral blood flow

a b s t r a c t The calibration of functional magnetic resonance imaging (fMRI) for the estimation of neuronal activationinduced changes in cerebral metabolic rate of oxygen (CMRO2) has been achieved through hypercapnicinduced iso-metabolic increases in cerebral blood flow (CBF). Hypercapnia (HC) has been traditionally implemented through alterations in the fixed inspired fractional concentrations of carbon dioxide (FICO2) without otherwise controlling end-tidal partial pressures of carbon dioxide (PETCO2) or oxygen (PETO2). There are several shortcomings to the use of this manual HC method that may be improved by using precise targeting of PETCO2 while maintaining iso-oxia. Similarly, precise control of blood gases can be used to induce isocapnic hyperoxia (HO) to reduce venous deoxyhaemoglobin (dHb) and thus increase BOLD signals, without appreciably altering CMRO2 or CBF. The aim of our study was to use precise end-tidal targeting to compare the calibration of BOLD signals under an isocapnic hyperoxic protocol (HOP) (rises in PETO2 to 140, 240 and 340 mm Hg from baseline) to that of an iso-oxic hypercapnic protocol (HCP) (rises in PETCO2 of 3, 5, 7 and 9 mm Hg from baseline). Nine healthy volunteers were imaged at 3 T while monitoring end-tidal gas concentrations and simultaneously measuring BOLD and CBF signals, via arterial spin labeling (ASL), during graded HCP and HOP, alternating with normocapnic states in a blocked experimental design. The variability of the calibration constant obtained under HOP (MHOP) was 0.3–0.5 that of the HCP one (MHCP). In addition, Mvariances with precise gas targeting (MHCP and MHOP) were less than those reported in studies using traditional FICO2 and FIO2 methods (MHC and MHO, respectively). We conclude that precise controlled gas delivery markedly improves BOLD-calibration for fMRI studies of oxygen metabolism with both the HCP and the more precise HOP-alternative. © 2010 Elsevier Inc. All rights reserved.

Introduction The change of cerebral metabolic rate of oxygen consumption (CMRO2) during neural activation can be estimated with MRI by employing the deoxyhaemoglobin (dHb) dilution model (Davis et al., 1998; Hoge et al., 1999a). The application of the model first requires the calibration of resting-state blood oxygen level dependent (BOLD) signal through the induction of iso-metabolic changes in cerebral blood flow (CBF). An increase in the partial pressure of carbon dioxide in arterial blood (PaCO2) has traditionally been used as the vasoactive maneuver. This hypercapnic (HC) stimulus has most commonly been administered as a fixed increase in the inspired fractional concentration of CO2 (FICO2) and O2 (FIO2) through a non-rebreathing face mask (Chiarelli et al., 2007a; Davis et al., 1998; Hoge et al., 1999a; Kim et al., 1999; Leontiev and Buxton, 2007; Stefanovic et al., 2006). This method is popular because it is inexpensive and fairly simple to apply. The underlying assumption is that fixed inspired gas concentrations produce reliable iso-oxic (i.e., constant ⁎ Corresponding author. McConnell Brain Imaging Center, Montreal Neurological Institute, 3801 University Street, WB-325, Montreal, Quebec, Canada H3A 2B4. Fax: + 1 514 398 2975. E-mail address: [email protected] (C.I. Mark). 1053-8119/$ – see front matter © 2010 Elsevier Inc. All rights reserved. doi:10.1016/j.neuroimage.2010.08.070

PaO2) changes in PaCO2, these partial pressures being the actual independent variables affecting blood O2 content and brain blood flow, respectively. That this assumption is inaccurate can be readily appreciated when considering that in everyday life, breathing a fixed mixture of CO2 and O2—i.e., room air—does not clamp either PaCO2 or PaO2; they are both a function of minute ventilation (Prisman et al., 2007). The same holds true when administering any other fixed mixture of CO2 and O2 via a non-rebreathing mask. Increases in ventilation, such as those that may be stimulated by raising the FICO2, tend to moderate the rise in PaCO2 and raise the PaO2. The new steady-state PaCO2 and PaO2 are therefore complex functions of both the inhaled gas concentrations and the ventilatory response, which vary from subject to subject and in the same subject over time. As a consequence, a fixed FICO2 in air or O2 will result in an inconsistent change in PaCO2, PaO2, and thereby CBF and BOLD signal responses (Floyd et al., 2003; Mark et al., 2010; Prisman et al., 2008), adding to the variability of measurements, and hence to the uncertainty in the interpretation of brain oxygen metabolic studies (Chiarelli et al., 2007a,b; Leontiev and Buxton, 2007). In most studies, PaCO2 and PaO2 are not measured, but their non-invasive surrogates, the partial pressures of CO2 and O2 in the end-tidal (end-exhaled) gas (Robbins et al., 1990) (PETCO2 and PETO2, respectively), are used for monitoring and calculation (Chiarelli et al., 2007a). Henceforth we will refer to the partial pressure of

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gases in arterial blood (PaCO2 and PaO2) when addressing the independent variables affecting physiologic processes and to the end-tidal values (PETCO2 and PETO2) when addressing non-invasively measured surrogate values. In 2007, Chiarelli et al. (2007c) reported a marked improvement in the variability of M-value estimates by employing hyperoxic (HO) stimuli and measuring the relatively easily quantifiable signals, BOLD and PETO2. The theoretical advantage of this approach is that BOLD and PETO2 can be precisely quantified, compared to HC-induced CBF changes which are measured by perfusion imaging, a method with intrinsically low signal-to-noise ratio (SNR). Increases in BOLD signal induced by HO occur through flow- (Kolbitsch et al., 2002; Reinstrup et al., 2001) and metabolism- (Sicard and Duong, 2005) independent changes in dHb. The HO-induced increase of O2 dissolved in arterial blood plasma provides some of the O2 requirements of the tissues, reducing the extent of desaturation of oxyhaemoglobin in the venous blood (Berkowitz, 1997; Bulte et al., 2006; Kwong et al., 1995; Rostrup et al., 2005). However, in Chiarelli et al. (2007c), the HO stimuli were administered via a fixed inspired gas concentrations method that does not prevent variations in PETCO2 (see Figs. 3 and 4 in Bulte et al., 2007). The CBF could therefore not be assumed constant, and indeed, the authors observed mild vasoconstriction that required calculated corrections (Chiarelli et al., 2007c). The inability to maintain isocapnia introduces a potential bias in the M-estimates (MHO) and, if corrected for post hoc, adds an unknown degree of variability in the calculation. We recently reported that using precise end-tidal gas control for iso-oxic hypercapnic challenges to measure cerebrovascular reactivity, with BOLD and arterial spin labeling (ASL), minimized within- and across-subject variability compared to the traditional FICO2 methodology (Mark et al., 2010). The capability of providing rapid transitions to stable predictable PETCO2 improved the reliability of inducing changes in PaCO2 as the vasoactive stimulus. Similarly, maintaining strict iso-oxia reduces the flow-independent effects of PaO2 on the BOLD signal itself (Floyd et al., 2003; Prisman et al., 2008). Precise end-tidal gas control may likewise improve hyperoxic fMRI calibration by maintaining isocapnia during step changes in PETO2. Our aim was to use precise control of both PETCO2 and PETO2 to compare the precision of calibration with an isocapnic hyperoxic protocol (HOP) to that with an iso-oxic hypercapnic protocol (HCP). We also compared the variability of M-estimates under the HOP to that calculated by Chiarelli et al. (2007c) who used poikilocapnic HO (uncontrolled changes of PaCO2 with ventilation) as the stimulus. We hypothesized that M-values calculated using precise isocapnic hyperoxia (MHOP) would show much lower variability than those obtained during precisely targeted iso-oxic hypercapnia (MHCP) and those reported using poikilocapnic FIO2 (MHO) or poikilo-oxic FICO2 (MHC). Materials and methods The experimental protocol was approved by the Research Ethics Board of the Montreal Neurological Institute (MNI, Montreal, CA) and the computerized end-tidal gas targeting system (RespirAct™, Thornhill Research Inc, Toronto, CA) was approved for this study under an investigational device exemption by Health Canada. Signed informed consent was obtained from eleven non-smoking healthy adults. Subjects were instructed not to consume caffeine on the day of their scan. Two subjects were excluded from the study as their scans showed excessive head motion. We therefore included data from 9 subjects (5 females; mean age 26 years; range 18–30 years), of which 1 subject was excluded from hypercapnic analysis because significant activation was found under only two levels. Experimental protocol All MR scans were performed on a Siemens 3 T TIM Trio system (Siemens, Erlangen, Germany) with a 32-channel phased array head-

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coil. The functional acquisitions covered nine oblique axial slices (4 × 4 × 6 mm3; interslice gap of 1 mm) positioned to include both visual and motor cortices based on a high-resolution 3D T1-weighted data set (1 × 1 × 1 mm3) and a BOLD functional localization scan. During the localizer, subjects were instructed to perform voluntary bilateral cyclic finger tapping coincident with the presentation of a maximal contrast black/white checkerboard of 8 reversals per second in one OFF/ON/OFF block of 6-s/11-s/6-s. For the region of interest (ROI) definition (see Data analysis), these tasks were performed a second time in OFF/ON/OFF blocks of 24-s/48-s/24-s while acquiring ASL frames with the same MRI sequence (see MRI parameters) as under the respiratory challenges (see Automatic respiratory modulation).

Automatic respiratory modulation The computerized end-tidal gas targeting system (RespirAct™) was used to apply the following randomized ventilatory stimulations. The hyperoxic protocol (HOP) consisted of isocapnic (PETCO2 maintained at baseline) step increases in PETO2 of 140, 240 and 340 mm Hg from baseline PETO2. The hypercapnic protocol (HCP) consisted of isooxic (PETO2 maintained at baseline) increases in PETCO2 of 3, 5, 7 and 9 mm Hg from baseline. Each challenge was preceded by a 60-s steady-state level where PETCO2 and PETO2 were clamped at resting levels observed for each subject during spontaneous ventilation, prior to the stimulation associated with imaging, in order to establish an individual baseline. Imaging lasted 6 minutes and consisted of one OFF/ON/OFF block of 60-s/120-s/120-s. The control of end-tidal values is tolerant to a large range in breathing frequency, tidal volume and breathing pattern (Slessarev et al., 2007). Nevertheless, we cued ventilation frequency by setting a metronome at 12 breaths/min to maintain uniform effects of ventilation between subjects and to prevent slowed ventilation rates (Ito et al., 2008) as subjects relaxed in the magnet. Subjects were not constrained as to breath size as long as they at least emptied the inspiratory gas reservoir with every breath (Slessarev et al., 2007). Tidal PCO2 and PO2 sampled from inside the mask were recorded continuously by the RespirAct™. PETCO2, PETO2 and respiratory rate were identified by a computer algorithm in real time and were confirmed manually post hoc.

MRI parameters A multislice single-echo pulsed arterial spin labeling (PASL) echo planar imaging (EPI) sequence was used for simultaneous CBF and BOLD measurements, a sequence previously shown to result in BOLD signals equivalent to those obtained from standard BOLD sequences (Chen and Parrish, 2007). Preliminary tests also ensured that there would be no reduction in BOLD signal sensitivity, while the temporal resolution is improved, with this single-echo PASL sequence versus our interleaved version with a T2*-weighted gradient echo formerly used by our group for simultaneous BOLD and CBF measurements (e.g. Mark et al., 2010). The PASL sequence was based on QUIPSS II (Wong et al., 1997, 1998) with ASSIST background suppression (Ye et al., 2000) and two presaturation asymmetric BASSI pulses (Warnking and Pike, 2004, 2006) in the imaging region followed by an adiabatic BASSI inversion pulse in the labeling region (thickness of 150 mm, gap of 5 mm) and QUIPSS II inversion times (TI1) of 700 ms and (TI2) of 1400 ms. While changes in arterial transit time might occur over the range of vascular reactivity challenges employed herein, preliminary tests were carried out to select the parameters that minimized shortage of labeled blood for measuring CBF at the highest PETCO2. An EPI readout (2170 Hz/pixel) was employed with a single echo time (TE) of 25 ms for both CBF and BOLD. A repetition time (TR) of 3 s allowed the acquisition of 120 frames for each respiratory challenge and 128 frames for the ROI definition scan.

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Data analysis CBF images were formed by subtracting adjacent non-selective (control) and selective (tag) frames while BOLD images were obtained from their average. Differences in frame timing were corrected using sinc interpolation, which also removed the potential contamination of ASL data by BOLD (Lu et al., 2006). Motion correction parameters were estimated using AFNI's 3dvolreg software (Cox, 1996). Frames with estimated translation exceeding 1 mm or rotation greater than 1° were excluded from the analysis, a situation never exceeding 3 frames at any challenge level. All data were spatially smoothed with a three-dimensional Gaussian filter of 6 mm full-width half-maximum (FWHM). Drift was removed by subtracting from each voxel's time course the low-frequency components of its discrete cosine transform, with a cutoff frequency of one-half of the stimulation paradigm frequency. Areas of statistically significant BOLD and CBF activation were identified based on the generalized linear model assuming a gammavariate hemodynamic response function using fMRIstats (Worsley et al., 2002). To avoid masking potential spatial differences in activation across challenge type, signal changes under HCP and HOP were calculated in separate ROI, each formed following the same procedure i.e., from statistically thresholded t-maps (at significance level of p b 0.05, corrected for multiple comparisons) overlapped at all challenge levels to provide a fair comparison across levels. Of the four levels acquired in the HCP, on average only three resulted in significant activation due to differences in individual vascular responsiveness (i.e., MR nonsignificant at low levels or displaying stimulus-correlated motion at high levels). To investigate specific brain regions, we further restricted for each subject the whole brain (WB) ROI described above to the visual (VC) and motor (MC) cortices by overlapping the statistically thresholded subject-specific BOLD and CBF t-maps obtained during the fMRI localizer. Given that the HOP was not expected to yield statistically significant CBF responses, BOLD and CBF ROIs were analyzed separately to ensure a fair comparison between challenge type. Time courses of CBF and BOLD percentage responses for each subject, obtained in all ROI (WB, VC and MC), were temporally lowpass-filtered (Hanning, FWHM 6 s). After removing the initial 60-s baseline frames in each data series, mean CBF and BOLD responses were normalized to their respective baseline values. The amplitude of the response to each challenge level was computed as the signal intensity averaged across only the time points representing the plateau portion of the response (i.e., rejecting frames in the initial 24-s of the ON period). This range selection, confirmed manually post hoc for every subject, excluded the rising portion of the MR responses at every level for either type of challenge. Mean changes in PETCO2 and PETO2 were calculated as the difference between averages of end-tidal values at steady-state levels and baseline. Calibration M-values We calculated calibration M-values, the maximum possible BOLD signal, under both HCP (Eq. (1)) and HOP (Eq. (2)), based, respectively, on the dHb dilution model (Hoge et al., 1999a) and an analogous formulation described earlier (Chiarelli et al., 2007c), ΔBOLD

MHCP =

.

BOLD0  . α−β CBF 1− CBF0

ð1Þ

Where α, the venous cerebral blood volume (CBV) contribution to the BOLD signal, is assumed to be 0.38 (Grubb et al., 1974); β, a constant linking blood oxygenation to relaxivity (Boxerman et al., 1995), is estimated to be 1.3 at 3 T (Buxton, 2003; Ogawa et al., 1993) and calculation of [dHb]ν / [dHb]ν0, the fractional reduction in deoxygenated haemoglobin in the venous vasculature, is detailed in Appendix A. Per-subject M-values were obtained from fitting data points across the three HOP and HCP challenge levels (MSubject_HOP and MSubject_HCP, respectively), with error reported as the 95% confidence interval of the mean (CIm). The M-values across subjects were calculated from fitting the entire set subjects BOLD and, in the case of HCP (MGroup_HCP), CBF responses, or, in the case of HOP (MGroup_HOP), PETO2 responses, rather than the responses averaged across subjects; the error was similarly reported as the CIm of the fit. Variability in Mvalues were quantitatively assessed and compared between methods based on the percent coefficient of variation (CoV) (i.e., 100 × error / mean). Results Physiologic responses Resting PETCO2 (PETCO2,0) averaged 37.8 ± 3.0 mm Hg for all subjects and baseline levels established by the system was 36.3 ± 2.4 mm Hg. Throughout the hyperoxic challenges, PETCO2 values were maintained, on average, within ±0.5 mm Hg of the individual resting level. Target PETCO2 levels were attained within 1 mm Hg in HCP, and PETO2 were within 12 mm Hg of target values in HOP (Tables 1A and 1B). These attained levels remained stable throughout the stimulation periods. The average standard deviations of PETCO2 and PETO2 during the plateau phase of each level were 0.3 and 1.9 mm Hg for HCP and 0.5 and 2.9 mm Hg for HOP, respectively (data not shown). MR responses While all HOP and HCP induced similar WB BOLD activated volumes across subjects (500.3 ± 130.5 cm3 and 448.2 ± 104.7 cm3, respectively, p = 0.44 paired two-tailed Student's t-test), hyperoxic challenges clearly lacked a CBF response compared to those of hypercapnia (0.5± 0.5 cm3 and 215.9 ± 100.8 cm3, respectively, p b 10−3 paired two-tailed Student's t-test). The group average MR responses under either challenge followed an expected linear trend with respect to the graded levels of stimulation (R2 = 0.97 for PETCO2-induced changes in CBF and BOLD signal, R2 = 0.91 for hyperoxic-induced BOLD changes) (Fig. 1). The linearity of individual MR responses across subjects for all respiratory conditions in VC and MC are listed in Tables 2A and 2B. Fig. 2 shows typical CBF and BOLD activation maps of each challenge type. While hyperoxia did not induce significant global blood flow changes, it induced a BOLD spatial coverage similar to hypercapnia, predominantly located in gray matter with a relatively larger portion of the signal located around large venous structures where blood, and therefore dHb, is concentrated. M-estimates Group M-values were (MGroup ± CIm) 7.3 ± 0.7% (R2 = 0.63) for the HCP and 5.5 ± 0.3% (R2 = 0.70) for HOP (Fig. 3). The MSubject_HCP- and MSubject_HOP-values obtained across subjects in all ROI are listed in Tables 3A and 3B.

and ΔBOLD

BOLD0



MHOP = 1−

½dHbν

Discussion

. .

0

½dHbν

−β

ð2Þ

Reducing the variability in M-values (the maximum theoretical BOLD signal change (Hoge et al., 1999b)) is crucial due to its great impact on the determination CMRO2 and CBF coupling during neural

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Table 1A Physiologic measurements (m ± SEM) for all subjects under the iso-oxic hypercapnic protocol (HCP). Measurements include changes in end-tidal partial pressures in CO2 from baseline resting values (ΔPETCO2), baseline resting values (PETCO2,0) and levels reached in end-tidal O2 during the protocol (PETO2); respiratory rate, RR. Target ΔPETCO2 (mm Hg)

ΔPETCO2 (mm Hg)

PETCO2,0 (mm Hg)

PETO2 (mm Hg)

RR (breaths/min)

3a 5b 7a 9c

3.3 ± 0.8 4.7 ± 0.8 6.9 ± 0.9 8.8 ± 0.5

37.8 ± 3.6 38.2 ± 3.4 36.7 ± 2.7 38.4 ± 2.7

110.4 ± 4.3 109.5 ± 3.6 112.4 ± 2.4 110.2 ± 4.1

12.0 ± 0.2 11.9 ± 0.1 11.9 ± 0.0 11.9 ± 0.1

Note. Due to the selection of 3 out of 4 hypercapnic levels on a per-subject basis, the averages displayed at each of the 4 levels were obtained over aN = 6, bN = 7, and cN = 5 subjects, respectively.

activation tasks (Chiarelli et al., 2007b). This study demonstrates that precise control of end-tidal gases (Mark et al., 2010) and hyperoxiccalibration (Chiarelli et al., 2007c) each, independently, increases the precision of calculated M-values. Under traditional hypercapniccalibration with fixed inspired gases, variances ranging from onefifth to three-quarters of the MGroup_HC-value itself (i.e., ~20–75% coefficient of variation (CoV)) are typical in VC and MC (Chiarelli et al., 2007a; Kastrup et al., 2002; Leontiev and Buxton, 2007; Stefanovic et al., 2006). Our study employing HCP showed a markedly reduced CoV of MGroup_HCP-value of ~ 13–14% (Table 3A, VC and MC). We attribute the remaining variability to the natural variation in vascular reactivity between subjects (dispersed color lines in Fig. 3A, large range of MSubject_HCP in Table 3A) and to the low SNR perfusion measurements (large horizontal error bars in Fig. 3A). With HOP, the CoV further reduced to ~5–6% (Table 3B, and see also improved quality of fits). This reduction in variability was due to a combination of the high SNR of ΔPETO2 measurements (versus ASL under HCP) in calculating Mvalues (horizontal error bars within the marker size in Fig. 3B and small SEM of ΔPETO2 in Table 1B) and the strict maintenance of isocapnia that eliminates, physically rather than through post hoc corrections via calculations, the confounding effect of changes in CBF (tight color lines in Fig. 3B). In the only previously reported HOcalibration study, changes in the FIO2 were used as the provocative stimulus and the variability in M-values in VC and MC were reported as being ~10% (Chiarelli et al., 2007c). This low CoV was achieved despite not constraining ΔPETCO2 (poikilocapnia) during hyperoxia. Indeed, although the authors do not report on the ΔPETCO2 in their study, they have previously reported reductions in ΔPETCO2 of ~ 7.5 mm Hg with their hyperoxia method (see ΔPETCO2 in Figs. 3 and 4 in Bulte et al., 2007).

possible to achieve calibration with relatively brief stimulation. While we produced our M-estimates by fitting the results of several endtidal gas levels, the number of levels could be reduced to only one, without losing accuracy. This would enable the efficiency required to perform individual calibration on a per-brain-region basis, important for neurovascular coupling accuracy (Chiarelli et al., 2007b).

Prospective targeting Target PETCO2 or PETO2 were reproduced consistently across subjects providing fixed, stable and independent hypercapnic and hyperoxic stimuli. This removes variability in the test conditions that occur with the use of fixed inspired gases. It allows direct comparisons of MR signal responses within a subject over time, and between subjects, for a variety of physiological studies and clinical applications. The rapid transition (within 2–3 breaths) to a stable end-tidal partial pressure value with the RespirAct™, comparable to dynamic end-tidal forcing methods (Poulin et al., 1996; Wise et al., 2007), makes it

Table 1B Physiologic measurements (m ± SEM) for all subjects under the isocapnic hyperoxic protocol (HOP). Measurements include changes in end-tidal partial pressures in O2 from baseline resting values (ΔPETCO2 and ΔPETO2), baseline resting values (PETCO2,0 and PETO2,0) and levels reached in end-tidal CO2 during the protocol (PETCO2); respiratory rate, RR. Target ΔPETO2 (mm Hg)

ΔPETO2 (mm Hg)

PETO2,0 (mm Hg)

PETCO2 (mm Hg)

RR (breaths/min)

140 240 340

147.7 ± 4.0 240.8 ± 11.4 333.9 ± 8.6

110.8 ± 3.1 110.5 ± 3.0 111.6 ± 2.6

36.4 ± 2.5 36.2 ± 2.6 36.5 ± 2.4

11.8 ± 0.0 11.9 ± 0.1 11.9 ± 0.1

Note. Shown are averages for N = 9 in all cases.

Fig. 1. CBF and BOLD signal changes under the iso-oxic hypercapnic (HCP (A)) and isocapnic hyperoxic (HOP (B)) protocols plotted against measured PETCO2 and PETO2 in the whole brain region of interest. Error bars indicate the standard deviation of the steady-state values over the stimulation block. Data for individual subject (for HCP N = 8 and for HOP N = 9) are connected by lines of different colors. The black line represents the linear fit through the average values. The reactivity is given by the slope, with error margins representing the 95% confidence interval of the mean (CIm).

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Table 2A Fractional percent change in BOLD and CBF signals with respect to baseline (m ± SEM) for all subjects under the iso-oxic hypercapnic protocol (HCP). Data shown for different regions of interest: visual cortex (VC) and motor cortex (MC). VC Target ΔPETCO2 (mm Hg) a

3 5b 7a 9c

MC

ΔBOLD (%)

ΔCBF (%)

ΔBOLD (%)

ΔCBF (%)

1.1 ± 0.6 1.6 ± 0.4 2.1 ± 0.6 2.9 ± 0.7

33.6 ± 7.8 44.3 ± 7.5 52.2 ± 7.5 67.6 ± 10.4

1.0 ± 0.4 1.3 ± 0.2 1.9 ± 0.5 2.4 ± 0.8

30.5 ± 8.1 37.7 ± 8.3 47.2 ± 7.3 63.4 ± 9.1

Note. Due to the selection of 3 out of 4 hypercapnic levels on a per-subject basis, the shown averages displayed for all 4 levels were obtained over aN = 6, bN = 7 and cN = 5 subjects.

MR responses MR responses were consistent across ROI under both respiratory challenges (Fig. 1 and Tables 2A and 2B). The slightly larger response in the VC versus MC, although not statistically significant (p = 0.26 for HCP and p = 0.39 for HOP, paired one-tailed Student's t-test), may reflect the greater vascularity of this region (Chiarelli et al., 2007a,c; Stefanovic et al., 2006). The average slope of the linear fits in Fig. 1 gives the average MR responsiveness to either ΔPETCO2 (0.35 ± 0.03% ΔBOLD/mm Hg and 7.76 ± 0.78% ΔCBF/mm Hg) or ΔPETO2 (0.005 ± 0.001% ΔBOLD/mm Hg). As expected, BOLD is ~ 70 times more sensitive to ΔPETCO2 than ΔPETO2 (Losert et al., 2002; Prisman et al., 2008). Although BOLD cerebrovascular reactivity depends on field strength, MRI sequences and parameters employed, our results are similar to those previously reported in response to hyperoxic challenges (Chiarelli et al., 2007c) as well as in response to hypercapnic challenges such as breath hold (Bulte et al., 2009), fixed inspired gas (Bulte et al., 2009; Hoge et al., 1999c; Mark et al., 2010; Stefanovic et al., 2006) or earlier versions of the prospective targeting system (Prisman et al., 2008). The values for CBF cerebrovascular reactivity to HC stimuli in our study are also consistent with published results, despite differences in techniques employed to obtain blood flow changes (Kemna et al., 2001; Rostrup et al., 2000) and MRI sequences used to derive CBF (Bulte et al., 2009; Hoge et al., 1999c; Mark et al., 2010; Rostrup et al., 2000; Stefanovic et al., 2006). We did not observe a significant reduction (or increase) in CBF with hyperoxia in any ROI. A number of previous studies of the effects of hyperoxia on CBF in spontaneously ventilating humans have demonstrated reductions in CBF, but these have been accompanied by reductions in PETCO2 as well (Bulte et al., 2007; Kety and Schmidth, 1948; Watson et al., 2000). As changes in CBF are very sensitive to changes in PaCO2 it has been very difficult to distinguish the independent effect of PaO2 on CBF without maintaining precise isocapnia. Kolbitsch et al. (2002) administered both room air and FIO2 of 1.0 (i.e., 100% inspired O2) to healthy volunteers and asked them to maintain isocapnia voluntarily by constraining their PETCO2 to 40 mm Hg using feedback from their PETCO2 displayed on a screen. CBF was estimated from middle cerebral artery flow velocity as measured by transcranial Doppler (TCD) and from contrast-enhanced MRI perfusion measurements. Even with this high level of hyperoxia, the TCD did not show any change in flow velocity and MRI showed only slight

heterogeneous reductions in CBF. Floyd et al. (2003) administered a series of gases with progressively higher concentrations of CO2 with the balance being either air or O2. This study showed that the maximally HO mixtures resulted in a downward offset of the PaCO2– CBF response curve, the latter measured by continuous ASL. However, we could not use the data to predict the effect of our moderate level of hyperoxia (ΔPETO2 ~ 340 mm Hg, versus ΔPaO2 ~ 500 mm Hg) on CBF as the relationship is thought to be non-linear (Floyd et al., 2003) and the model provides only two oxygen concentration levels (0.21 and 1.0). Nevertheless, we can presume that the effect of hyperoxia on CBF in our study would be considerably less than that seen with FIO2 1.0. Bulte et al. (2007) estimated that for FIO2 of 0.5 (i.e., 50% inspired O2), approximately equal to our maximal hyperoxic level, the flow changes would be too small to influence the BOLD-weighted signal. With the moderate change in PETO2 in our study, a change in CBF—if indeed it was present—was below the detection level of the ASL method. For a

Table 2B Fractional percent change in BOLD and CBF signals with respect to baseline (m ± SEM) for all subjects under the isocapnic hyperoxic protocol (HOP). Data shown for different regions of interest: visual cortex (VC) and motor cortex (MC). Target ΔPETO2 (mm Hg) 140 240 340

VC

MC

ΔBOLD (%)

ΔCBF (%)

ΔBOLD (%)

ΔCBF (%)

0.8 ± 0.1 1.3 ± 0.2 1.4 ± 0.2

– – –

0.8 ± 0.2 1.1 ± 0.2 1.5 ± 0.2

– – –

Note. Shown are averages for N = 9 in all cases.

Fig. 2. BOLD (top) and CBF (bottom) activation maps in a single slice in one subject with low, medium and high levels of the iso-oxic hypercapnic (HCP) and isocapnic hyperoxic (HOP) protocols. The color range represents the percentage signal change from baseline.

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Table 3B Summary of M-values (%) for all subjects under the isocapnic hyperoxic protocol (HOP) (N = 9). Data shown for different regions of interest: whole brain (WB), visual cortex (VC) and motor cortex (MC). Subject range represents the [min–max] across individual fits to the 3 challenge levels. The group values were obtained by linear fitting of the entire data set, with the error representing the 95% confidence interval of the mean (CIm) and the percent coefficient of variation (CoV), the ratio of the error to the mean.

MSubject_HOP [range] MGroup_HOP (m ± CIm) CoV (% CIm/m)

WB

VC

MC

[4.6–6.9] 5.5 ± 0.3 (R2 = 0.70) 5.8

[4.6–5.7] 5.2 ± 0.3 (R2 = 0.71) 5.4

[4.2–6.5] 5.0 ± 0.3 (R2 = 0.70) 6.0

resampled onto the lower resolution functional images. Binary GM masks were then created for each subject by selecting pixels above a threshold of 0.75 in the probability maps, yielding an average GM volume of 281.9 ± 130.7 cm3 across subjects. Hyperoxic-induced CBF changes in these voxels were shadowed by the standard error at all but the highest challenge level (−2.3 ± 4.0, −7.2 ± 7.8 and −10.9 ± 4.7% for low, medium and high levels, respectively). Still, neglecting a potentially mild, non-statistically significant localized vasoconstriction (arose in the proximity of large venous sinuses only, data not shown) is unlikely to bias our MHOP-estimates. As a side note, a potential confound of perfusion calculation from ASL data under HOP is the tendency for a reduction in the longitudinal relaxation time of arterial blood (T1,a) (Bulte et al., 2007). However, the QUIPSS II model (Wong et al., 1998) indicates that failure to account for a decrease in T1,a leads to an overestimation of ΔCBF and has, therefore, not masked any possible vasoconstriction. Limitations

Fig. 3. Change in BOLD signal for the whole brain with the iso-oxic hypercapnic (HCP (A)) and isocapnic hyperoxic (HOP (B)) protocols. Error bars indicate the standard deviation of the steady-state values over the stimulation block (within the marker size for x-axis for HOP). Subject color code is consistent with Fig. 1. Lines indicate Msubject from fit to Eqs. (1) and (2) in text (values in Tables 3A and 3B). Gray shaded region represents the 95% confidence interval of the mean (CIm) for the entire data set (Group averages in Tables 3A and 3B).

robust evaluation of the lack of vasoaction under the HOP, irrespective of statistical significance, signals were also calculated on an individual basis in anatomical gray matter (GM) masks, generated from highresolution structural scans. After registering each subject's anatomical images to the average MNI 305 brain (Evans et al., 1993), automatic tissue classification was performed using a Bayesian classification scheme (Bezdek et al., 1993) and the resulting probability maps

Table 3A Summary of M-values (%) for all subjects under the iso-oxic hypercapnic protocol (HCP) (N = 8). Data shown for different regions of interest: whole brain (WB), visual cortex (VC) and motor cortex (MC). Subject range represents the [min–max] across individual fits to the 3 challenge levels. The group values were obtained by linear fitting of the entire data set, with the error representing the 95% confidence interval of the mean (CIm) and the percent coefficient of variation (CoV), the ratio of the error to the mean.

MSubject_HCP[range] MGroup_HCP (m ± CIm) CoV (% CIm/m)

WB

VC

MC

[5.7–9.6] 7.3 ± 0.7 (R2 = 0.63) 9.3

[4.7–9.4] 6.4 ± 0.8 (R2 = 0.48) 12.8

[4.3–9.6] 5.8 ± 0.8 (R2 = 0.38) 14.3

Assumption on flow–volume relationship The parameter α in Eq. (1), which accounts for the relative change in blood volume versus blood flow that occurs with hypercapnic provocations, was assumed to have a value of 0.38 (Grubb et al., 1974). Recent studies reported on a lower α-value reflecting only venous rather than total volume changes under neuronal activation (α = 0.23 (Chen and Pike, 2009a)) and likewise under hypercapnia and hypocapnia (α = 0.19 (Chen and Pike, 2009b)), a similarity in αvalue across challenge type also seen in PET studies (Ito et al., 2003, 2001). In our study, assuming a lower α (value of 0.20 instead of 0.38) reduces hypercapnic MGroup_HCP-values by 13–14% across all ROI (i.e., 6.3 ± 0.6%, 5.5 ± 0.7% and 5.0 ± 0.7% in WB, VC and MC, respectively). This is in agreement with the published results of a 12–17% decrease in hypercapnia-induced M-values in VC and MC under smaller α (value of 0.23 instead of 0.38) (Chen and Pike, 2009a). The magnitudes of our MGroup-values showed a trend (p = 0.05 in VC and p = 0.08 in MC, paired one-tailed Student's t-test) to higher values (~15–24%) under hypercapnic compared to hyperoxic stimulation when the original α of 0.38 was used (Tables 3A and 3B). This trend may be an indication of increased accuracy provided by the hyperoxia model because of its lack of dependence on the value of α, which is uncertain. Part of the difference that remains between HC- and HOinduced M-values when using an α representing venous volume changes might reside in the lack of a separate consideration for arterial volume changes in the current HC model (Kim et al., 2007; Lee et al., 2001; Vazquez et al., 2010; Zhao et al., 2007). Having made these arguments on the advantages of using HO for calibration, it is also quite possible that the effect of α-uncertainties in the calculation of M-values based on HC may cancel out in the calculation of ΔCMRO2 under neuronal activation, as α also directly impacts these estimates for each brain region (Ances et al., 2008; Chen and Pike, 2009a). The extent of this cancellation effect is unclear as the physiological mechanisms involved in local vasodilation in response

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C.I. Mark et al. / NeuroImage 54 (2011) 1102–1111 Table 4B Summary of group M-values (m ± SEM, in %) on a per level basis (rather than a fit across all levels, as given in Table 3B) for the isocapnic hyperoxic protocol (HOP) (N = 9). Data shown for different regions of interest: whole brain (WB), visual cortex (VC) and motor cortex (MC).

Fig. 4. Sensitivity analysis of the hyperoxic model to plausible variations in parameters (A) β, (B) OEF0 and (C) [Hb]. The original values employed in this study where β = 1.3, OEF0 = 0.3 and [Hb] = 15 gHb/dlblood (shown by the arrows).

to neuronal activation differ from those of global vasodilation due to HC. While the former is mainly mediated by the release of a neurotransmitter (Iadecola, 1996), the latter involves a change in the hydrogen ion concentration (Iadecola and Zhang, 1994). Each calculation would require distinct model considerations, introducing its own calculation uncertainty, and thus once again an argument for a much simplified HO-calibration model. Assumption of iso-metabolism The BOLD-calibration model assumes no change in CMRO2 with both HCP and HOP. Iso-metabolism is more certain for hyperoxia (Sicard and Duong, 2005) than hypercapnia (Chen and Pike, 2010; Kety and Schmidth, 1948; Kliefoth et al., 1979; Sicard and Duong, 2005; Zappe et al., 2008). Hence, it may be that larger M-values arise from a depression of CMRO2 under HCP but not HOP. We examined our data by comparing MGroup-values, obtained as the average across subjects, at each level of HCP (Table 4A) and HOP (Table 4B). Interestingly, higher PETCO2 levels resulted in increases in MGroup_HCPvalues magnitudes in all ROI (WB: p b 10−3, VC: p = 0.0068 and MC: p = 0.0019, one-way repeated measures analysis of variance (RM_ANOVA), N = 8 balanced design) while MGroup_HOP-values did not vary with PETO2 levels (WB: p = 0.55, VC: p = 0.50 and MC: p = 0.70, oneway RM_ANOVA, N = 9 balanced design). If this reflects diminished CMRO2 with HCP, it violates the assumption of the calibration model and is another aspect in favor of HOP. The issue needs to be further investigated especially if single, higher, levels of hypercapnia are to be used for calibration. Region of interest definition The fact that separate ROIs were employed for each challenge type might also explain a portion of the difference between MGroup_HCP-

Table 4A Summary of group M-values (m ± SEM) on a per level basis (rather than a fit across all levels, as given in Table 3A) under the iso-oxic hypercapnic protocol (HCP) (N = 8). Data shown for different regions of interest: whole brain (WB), visual cortex (VC) and motor cortex (MC). HCP level

WB

VC

MC

Low Medium High

5.9 ± 0.4 7.5 ± 0.4 8.1 ± 0.3

5.3 ± 0.4 6.1 ± 0.4 6.9 ± 0.3

4.7 ± 0.4 5.8 ± 0.4 6.5 ± 0.3

HOP level

WB

VC

MC

Low Medium High

5.4 ± 0.5 5.6 ± 0.4 5.6 ± 0.3

5.1 ± 1.1 5.4 ± 0.7 5.0 ± 0.5

4.9 ± 0.5 5.0 ± 0.4 5.1 ± 0.3

and MGroup_HOP-values. While it might appear that both calibration protocols induced similar BOLD spatial activation (Fig. 2), subtle differences would not be perceivable due to the somewhat coarse resolution of these images. For example, the HCP-response might extend to areas in the proximity of arterial structures responsible for vasoaction. Hence, if present, such heterogeneities would warrant registering the separate HCP- and HOP-ROI for proper comparison across challenge type. Hyperoxic model sensitivity analysis The sensitivity of M-estimates to the assumed β, OEF0 and [Hb] constants in the HOP-calibrated model (see Calibration M-values and Appendix A) was considered based on experimental data in the WB ROI, following the same analysis for MGroup-calculations (i.e., fitting the entire set—all subjects and hyperoxic levels—of BOLD and PETO2 responses). Plausible ranges of β and OEF0 were chosen (Figs. 4A and B), as employed in a similar analysis by Chiarelli et al. (2007c), where OEF0 covered physiologically relevant values (Leenders et al., 1990). The span of [Hb] (Fig. 4C) was selected to encompass measures found across gender as well as mild anemia to polycythemia (Gustard et al., 2003). Our original MGroup_HOP- and MGroup_HCP-estimates were obtained assuming a β-value of 1.3 although many studies performed at 3 T kept the commonly used value of 1.5 for a field strength of 1.5 T (Chiarelli et al., 2007c; Hoge et al., 1999a; Uludag et al., 2004). The sensitivity of the HO-calibration model to this parameter (Fig. 4A and Chiarelli et al., 2007c) is similar to that of the HC-calibration model (Ances et al., 2008; Davis et al., 1998; Hoge et al., 1999a). Hence, assuming β = 1.5 does not change our conclusions as doing so scales down the magnitudes of M-values uniformly by ~ 12% for both challenges (i.e., in WB ROI, MGroup_HCP = 6.2 ± 0.6%, R2 = 0.62 and MGroup_HOP = 4.9 ± 0.3%, R2 = 0.70). The principal source of hyperoxia model-based variation in Mvalue is OEF0 (Fig. 4B and Chiarelli et al., 2007c), the baseline oxygen extraction fraction used to calculate fractional changes in deoxyhaemoglobin ([dHb]ν / [dHb]ν0) from measurements of PETO2 (see Eq. (2) and Appendix A). Although OEF0 is strikingly constant throughout the brain (Ashkanian et al., 2008; Frackowiak et al., 1980; Raichle and Gusnard, 2002), large variations in venous oxygen saturation (Yv) have been found across subjects (Lu et al., 2008) due to combined effects of differences in SaO2 and [Hb] levels (Gustard et al., 2003). Future investigations of HO-calibration would benefit from individual MR measurements of baseline Yv (Lu and Ge, 2008) or setting OEF0 as a free model parameter (Stephan et al., 2007). Accounting for variations in [Hb] between individuals by using measured values would also improve the accuracy of per-subject HOP-calibration, given the dependence of MGroup_HOP-values on this parameter (Fig. 4C). Introducing individual measures of OEF0 and [Hb] under the HOP-calibration might have increased the inter-subject variability (i.e., MSubject_HOP) of our study (Fig. 3B, Table 3B). But according to the sensitivity analysis, the spread of this variation for our healthy cohort would have most likely still been smaller than that seen under the HCP-calibration (Fig. 3A and Table 3A). For a clinical use of HOP-calibration, this needs to be investigated further.

C.I. Mark et al. / NeuroImage 54 (2011) 1102–1111

Lastly, the calculation of CaO2 with the HOP-model avoids issues related to the O2 affinity of Hb and shifts to the oxyhaemoglobin dissociation curve (ODC). At hyperoxic levels (PaO2 N 100 mm Hg), haemoglobin is fully saturated (SaO2 = 1) so that bound O2 content becomes a constant that acts as an offset to a linear function of plasma solubility and PaO2 (Eq. (A2) and Fig. 5A). In the lower range (PaO2 b 100 mm Hg) where the portion of O2 dissolved in plasma is small compared to that bound to Hb (Fig. 5A), CaO2 is a linear function of SaO2 (Eq. (A2) and Fig. 5B). A given reduction in blood O2 content due to tissue extraction of O2 (assumed constant under HOP) results in a consistent change in O2 saturation of Hb, regardless of the initial SaO2 or any shift to the ODC. Thus in hypoxic hypoxia patients, calculations of CaO2 depend only on the measure of SaO2 and [Hb]. In patients who are hypoxic due to ventilation/perfusion mismatch (i.e., “pulmonary shunt”), PaO2, and hence SaO2, become insensitive to inspired O2 levels (Comroe, 1979), making it very difficult to implement a significant increase in blood O2 content. However, although precluded from HOP-calibration, these patients could undergo HCP-calibration if able to tolerate increases in PaCO2.

Reproducibility Given the already long duration of the scanning sessions, it was beyond the scope of this study to evaluate intra- or inter-session reproducibility. However, accurate prospective gas targeting which provides a) high repeatability in PETCO2 (PETCO2) values that are b) closely linked to PaCO2 (PaO2) (Ito et al., 2008), while c) tightly controlling PETO2 (PETCO2), would optimize repeatability. Our study indicates that the precision of gas administration, particularly the maintenance of isocapnia, will further improve the already high reproducibility of hyperoxic stimulation techniques (Chiarelli et al., 2007c).

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Conclusions The present study is the first to use the precise control of partial pressures of end-tidal gases to compare the effectiveness of iso-oxic hypercapnia and isocapnic hyperoxia for calibration of the BOLD MRI signal to enable CMRO2 measurement. Hyperoxia under precise respiratory control produced a smaller M-value variability than hypercapnia, while being better tolerated by subjects. The maintenance of precise isocapnia during hyperoxia improved the precision of M-estimates compared to those from poikilocapnic studies. We also demonstrated a marked improvement in M-value precision with isooxic hypercapnic stimuli compared to those historically calculated from the administration of fixed inspired concentrations of CO2. The shorter calibration periods induced with the computerized prospective gas targeting can result in faster and more precise per-subject and per-brain-region calibration, diminishing potential biases currently encountered in fMRI oxygen metabolism studies. Acknowledgments This work was supported by Le Fonds de la Recherche en Santé du Québec (FRSQ) and the Canadian Institute of Health Research (CIHR). The authors would like to thank Rick Hoge for insightful discussion concerning the formulation of the hyperoxic model. Appendix A In Eq. (2), the fractional reduction in deoxygenated haemoglobin in the venous vasculature ([dHb]ν / [dHb]ν0) is estimated through the following series of equations. Starting from the arterial partial pressure of O2 (PaO2) inferred via PETO2 measurements (in mm Hg), the fractional oxygen saturation of arterial haemoglobin (SaO2) is calculated from the modified Hill's equation describing the standard (pH = 7.4 and T = 37 °C) human blood O2 dissociation curve (Severinghaus, 1979) as follows: 

SaO2 = 1+

23 400

.

1



ððPaO2 Þ

3

ðA1Þ

+ 150⋅PaO2 Þ

Alternatively, it could be measured non-invasively and accurately (within 1% in a clinical range) by a pulse oximeter (Van de Louw et al., 2001). The arterial oxygen content (CaO2; in mlO2/dlblood) is then given as the combination of O2 bound to Hb and O2 dissolved in the plasma, CaO2 = φ⋅½Hb⋅SaO2 + PaO2 ⋅ε |fflfflfflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflfflfflffl} |fflfflfflffl{zfflfflfflffl} Bound O2

ðA2Þ

Plasmatic O2

where, the O2 carrying capacity of haemoglobin (φ) is 1.34 mlO2/gHb for humans, the solubility coefficient of O2 in blood (ε), is 0.0031 mlO2/ dlblood/mm Hg (Severinghaus, 1979) and [Hb], in lieu of measurement, is assumed to be 15 gHb/dlblood (Gustard et al., 2003; Leenders et al., 1990). The venous oxygen outflow can be obtained from the mass balance of arterial O2 supply and metabolic O2 extraction, the latter assumed constant (i.e., iso-metabolic, as with the HCP model), under HOP: CBF⋅CvO2 = CBF⋅CaO2 − CBF0 ⋅CaO2;0 ⋅OEF0 |fflfflfflfflfflfflffl{zfflfflfflfflfflfflffl} |fflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl} Arterial O2 supply

Fig. 5. The arterial blood oxygen content (CaO2) as a function of (A) the arterial partial pressure of oxygen (PaO2) and (B) the fractional oxygen saturation of arterial haemoglobin (SaO2). The equations employed are described in Appendix A.

ðA3aÞ

Metabolic O2 extraction

where, the baseline oxygen extraction fraction (OEF0) is estimated to be around 0.3 from global CBF and CMRO2 measures of a 15O2-labeled radiotracer study (Leenders et al., 1990; Permutter et al., 1985). The venous oxygen content (CvO2), in mlO2/dlblood, can be calculated by rearranging the terms (Eq. (A3b)). If strict isocapnia is maintained

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C.I. Mark et al. / NeuroImage 54 (2011) 1102–1111

during HOP, changes in CBF can be minimized, or avoided, further simplifying the relationship (Eq. (A3c)): CvO2 = CaO2 −

CaO2;0 ⋅OEF0 . CBF

ðA3bÞ

CBF0

= CaO2 −CaO2;0 ⋅OEF0

ðA3cÞ

The fractional oxygen saturation of venous haemoglobin (SvO2) is determined in a manner similar to Eq. (A2): CvO2 = φ⋅½Hb⋅SvO2 + PvO2 ⋅ε |fflfflfflfflffl{zfflfflfflfflffl} |fflfflfflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflfflfflffl} Bound O2

ðA4aÞ

Plasmatic O2

In the venous vasculature, virtually all of the O2 is bound to Hb, irrespective of HOP levels, so the contribution of dissolved O2 can be neglected: SvO2 =

CvO2 φ⋅½Hb

ðA4bÞ

The fractional reduction in deoxygenated Hb in the veins, to be used in Eq. (2), is calculated from: ½dHbν

. ½dHbν0

=

1−SvO2

. 1−SvO2;0

ðA5Þ

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