Dynamic Forcing of End-Tidal Carbon Dioxide and Oxygen Applied to Functional Magnetic Resonance Imaging

Abstract

Investigations into the blood oxygenation level-dependent (BOLD) functional MRI signal have used respiratory challenges with the aim of probing cerebrovascular physiology. Such challenges have altered the inspired partial pressures of either carbon dioxide or oxygen, typically to a fixed and constant level (fixed inspired challenge (FIC)). The resulting end-tidal gas partial pressures then depend on the subject's metabolism and ventilatory responses. In contrast, dynamic end-tidal forcing (DEF) rapidly and independently sets end-tidal oxygen and carbon dioxide to desired levels by altering the inspired gas partial pressures on a breath-by-breath basis using computer-controlled feedback. This study implements DEF in the MRI environment to map BOLD signal reactivity to CO₂. We performed BOLD (T2*) contrast FMRI in four healthy male volunteers, while using DEF to provide a cyclic normocapnichypercapnic challenge, with each cycle lasting 4 mins (Pet_CO2 mean±s.d., from 40.9 ± 1.8 to 46.4 ± 1.6 mm Hg). This was compared with a traditional fixed-inspired (Fi_CO2 = 5%) hypercapnic challenge (Pet_CO2 mean±s.d., from 38.2 ± 2.1 to 45.6 ± 1.4 mm Hg). Dynamic end-tidal forcing achieved the desired target Pet_CO2 for each subject while maintaining Pet_CO2 constant. As a result of CO₂-induced increases in ventilation, the FIC showed a greater cyclic fluctuation in Pet_CO2. These were associated with spatially widespread fluctuations in BOLD signal that were eliminated largely by the control of Pet_CO2 during DEF. The DEF system can provide flexible, convenient, and physiologically well-controlled respiratory challenges in the MRI environment for mapping dynamic responses of the cerebrovasculature.

Keywords

BOLD dynamic end-tidal forcing FMRI hypercapnia

Introduction

Respiratory challenges have been performed in humans simultaneously with magnetic resonance imaging (MRI) to measure cerebrovascular reserve (van der Zande et al, 2005), and they are now increasingly popular for investigating the complex contrast mechanisms of the blood oxygenation level-dependent (BOLD) functional MRI (FMRI) signal. Although MRI is not the only technique for examining cerebrovascular responses to respiratory challenges (others include transcranial Doppler ultrasound (TCD) and positron emission tomography (PET)), MRI does provide good spatial resolution with a temporal resolution that is well matched to the temporal response of the vasculature.

Hypercapnia, an elevated partial pressure of arterial carbon dioxide (Pa_CO2), is a convenient means of providing a cerebrovascular challenge and is therefore the most commonly applied challenge in FMRI. Carbon dioxide (CO₂) in arterial blood is a potent vasodilator producing a global increase in cerebral blood flow (CBF). Hypercapnia increases BOLD signal (Posse et al, 2001; Rostrup et al, 2000; Vesely et al, 2001). The CBF increase induced by hypercapnia results in increased venous oxyhemoglobin concentration and decreased venous deoxyhemoglobin concentration, producing a longer T2*.

Respiratory challenges that seek to elevate (hyperoxia) or decrease (hypoxia) arterial oxygen partial pressure (Pa_O2) have also been applied in conjunction with MRI. In studies of cerebral physiology, these have sought to investigate cerebrovascular responses, such as changes in blood flow, arising from modified oxygen levels (Bulte et al, 2007; Floyd et al, 2003; Kolbitsch et al, 2002). Functional magnetic resonance imaging studies have used modified oxygenation levels as a means of understanding and modeling BOLD contrast (Rostrup et al, 1995; Tuunanen et al, 2006).

To date, respiratory challenges presented in the MRI scanner have been simple and have not directly attempted to control the most important physiological parameters, namely arterial blood gas concentrations. Instead, the most common method of inducing a change of blood gas concentrations has been to deliver a fixed fraction of CO₂ or oxygen that is modified compared with air (FI_O₂ ≈20.7% and FI_CO₂ ≈0%), for example, to FI_CO₂ ≈;5% (see below; Hoge et al, 1999b). We will refer to this as a fixed-inspired challenge (FIC). Also common is the breath-hold. Aimed largely at causing hypercapnia, it inevitably reduces Pa_O₂, potentially confounding the interpretation of nominally hypercapnia-FMRI studies based on it. Although the FIC is convenient to deliver, the arterial CO₂ and oxygen partial pressures reached along with the rate of change of these partial pressures after any FIC transition are variable and unpredictable because they are determined by the individual's ventilatory response to CO₂. The ventilatory response will vary between subjects. Although it is known to depend on body weight, sex, environmental, and other factors such as drug treatment (Dahan and Teppema, 2003) it is to a large degree unpredictable.

Changes in arterial CO₂ and oxygen partial pressures are reflected closely in the end-tidal expired partial pressures of these gases (Pet_CO₂ and Pet_O₂, respectively) in healthy subjects, making end-tidal measurements a convenient surrogate for arterial partial pressures (Robbins et al, 1990). An additional concern with the FIC approach is the significant interaction between changes in Pet_CO₂ and Pet_O₂. In studies of hypercapnia, when the inspired fraction of carbon dioxide (Fi_CO₂) is increased, ventilation increases, causing a rise in Pet_O₂. In studies of hypoxia, if the inspired fraction of oxygen (Fi_O₂) is decreased sufficiently, the resulting increase in ventilation reduces the Pet_CO₂. Hence a pure change of only one of the arterial gases is achieved rarely using the FIC approach.

Dynamic end-tidal forcing (DEF) is a technique that largely overcomes these difficulties. The general technique was developed originally using fast-switching solenoid valves to control gas flow (Howson et al, 1987; Robbins et al, 1982a, b ; Swanson, 1972). It has since been developed for use with mass flow controllers of gas flow and adopted for rigorous investigation of ventilatory and cerebrovascular responses to changes in Pa_CO₂ and Pa_O₂ (Ainslie and Poulin 2004; Ide et al, 2003; Poulin et al, 1996, 1998; Poulin and Robbins, 1998). The system was further developed for use in the field at high altitude (Gamboa et al, 2003). It is a modified successor to this field system that we have developed for use in the MRI scanner.

The DEF technique uses computer feedback control of inspired P_CO₂ and P_O₂ (PI_CO₂ and PI_O₂) to rapidly (breath by breath) and independently achieve specified target levels of PET_CO₂ and PET_O₂. Dynamic end-tidal forcing has not until this study been applied to FMRI, partly because of the technical challenges involved. The principal challenge is to analyze end-tidal gases within the period of a single breath (∼4 secs) when the gas analyzers typically need to be sited several meters from the MRI scanner, necessitating a gas sampling tube of several meters in length with its associated gas transport delay.

However, the potential rewards of a DEF system for use with FMRI are significant. Computer control of PET_CO₂ and PET_O₂ would provide accurate, target-controlled, repeatable respiratory challenges that could be automatically synchronized with FMRI data acquisition. Dynamic end-tidal forcing is able to force fast changes in arterial gas partial pressures by transiently delivering greater excursions in inspired gas partial pressures than can be easily accomplished with traditional challenges. Confounds of ‘mixed’ CO₂ and oxygen challenges could be avoided, opening the way for better-controlled quantitative studies of BOLD and perfusion image contrast. Dynamic end-tidal forcing would also allow more advanced respiratory experiments aimed at mapping the central networks involved in the control of breathing (McKay et al, 2003).

This study aims to implement and hence show the feasibility of DEF for FMRI and to compare it to the traditional FIC. We have chosen to do this using a simple hypercapnic challenge, this being the most common respiratory challenge used in FMRI. We compare the behavior of respiratory parameters during the two techniques to show the differing degrees of control afforded by them. We evaluate the BOLD signal responsiveness to changes in PET_CO₂ and the effects of concomitant PET_O₂ changes, using both the DEF and FIC to illustrate the potential improvements in control of the BOLD signal offered by DEF.

Materials and methods

Implementation of Dynamic End-Tidal Forcing for Magnetic Resonance Imaging

Figure 1 gives a schematic representation of our experimental setup for DEF. A laptop personal computer controlled the system using BreatheDP software (v1.0, Department of Physiology, Anatomy and Genetics, Oxford, UK). Having specified the desired end-tidal partial pressures of O₂ and CO₂, the controlling computer calculates the required inspired partial pressures to achieve those targets throughout the course of the respiratory experiment, while also monitoring the end-tidal expired levels actually achieved. The gas mixtures were delivered through a fast gas mixing system in which CO₂, O₂, N₂, and air were mixed as closely as possible to the volunteer to minimize delays in delivery. The controlling computer sampled respiratory variables every 10 ms. The direction and timing of respiratory flow (inspiration or expiration) were measured using an MR-compatible turbine volume flow transducer (VMM-400, Interface Associates, Laguna Niguel, CA, USA).

Figure 1

The dynamic end-tidal forcing system. The system, controlled by a laptop PC running BreatheDP (v1.0), was used to deliver the hypercapnia challenges to target values of PET_CO₂ and PET_O₂. D to A, digital-to-analogue converter; A to D, analogue-to-digital converter; MFC, mass (gas) flow controller; VMM, ventilation measurement module. For delivery of the fixed inspired challenge during the same session, flow of CO₂, O₂, and N₂ were switched off and a mixture of 5% CO₂ (balance air) was supplied, alternated with air.

Tidal gases were constantly sampled from the volunteer's facemask, and the partial pressures of CO₂ and O₂ were measured using rapidly responding gas analyzers (Models CD-3A and S-3A; AEI Technologies, Pittsburgh, PA, USA). The BreatheDP control software measured the end-tidal partial pressures of CO₂ and O₂ (PET_CO₂ and PET_O₂, respectively) from these continuous measurements. It compared these with the desired PET_CO₂ and PET_O₂ and then modified, on a breath-by-breath basis, the inspired P_CO₂ and P_O₂ (I_CO₂ and PI_O₂). The feedback control was performed using an integral-proportional algorithm of the deviation of the desired end-tidal partial pressures from those measured (Robbins et al, 1982a, b ). The proportional control uses the difference between the target and the actual end-tidal gas values for each breath to modify the breathed mixture and is designed to reduce the effects of breath-to-breath variation, whereas the integral component uses the sum of these differences to reduce drift and steady-state error.

During DEF, the total gas delivery rate to the breathing system was maintained at 70 liters per minute (1pm) to avoid rebreathing of expired gases and to minimize the delay in supplying updated gas mixtures to the subject. The O₂, N₂, and air supplies were humidified for volunteer comfort (HC150 Respiratory Heated Humidifier, Fisher & Paykell, Maidenhead, UK). The CO₂ supply was not humidified because of its comparatively low flow rate. Gas delivery was controlled by four gas-specific mass flow controllers (Model MFC 1559A, MKS Instruments, Wilmington, MA, USA) powered by two two-channel power supplies (Model PR4000, MKS Instruments, Wilmington, MA, USA). BreatheDP software was built on Lab VIEW (National Instruments Corp., Austin, TX, USA). An analogue-to-digital (A to D) PCMCIA card (NI DAQCard-1200, National Instruments Corp., Austin, TX, USA) was used for acquisition of gas partial pressures from the gas analyzers and for digitization of the inspired and expired volumes from the volume flow transducer. This card also provided two analogue signal outputs to the mass (gas) flow controller power supplies, the other two channels being supplied by a second PCMCIA card (NI DAQCard-AO-2DC, National Instruments Corp., Austin, TX, USA). Air was supplied by a compressor (Model 150/500 fitted with a breathing air filter, Bambi Air Compressors Ltd, Birmingham, UK). Other gases were supplied from high-pressure cylinders or hospital piped gases.

For the feedback system to control end-tidal gases, it was necessary to minimize delays in gas mixture delivery and sampling. This was achieved by mixing the four gases in a home-built mixing chamber that rested on the chest of the volunteer. The gas sampling line (4 × 1.8 m, 1.2 mm internal diameter, monitoring line, Intersurgical Ltd, Wokingham, UK) from the tight-fitting facemask (Hans Rudolf, Kansas City, MO, USA) was kept as short as possible but needed to be long enough to pass out of the 3T magnet enclosure to the gas analyzers situated outside the scan room next to the penetration panel. The time (delay and dispersion) to register 95% of a step change in CO₂ partial pressure through the sampling line was measured to be 1.6 sec. Once factored into the evaluation of end-tidal gases by the control software, this delay proved short enough to allow feedback control of end-tidal gases. This short delay was achieved using a suction pump (R2 Flow Control, AEI Technologies, Pittsburgh, PA, USA) placed at the end of the sampling line.

Four safety features were incorporated into the end-tidal forcing system (Figure 1) because of the potential for delivering severely hypoxic gas mixtures. First, the volunteer's heart rate and pulse oxygenation were monitored continuously using a finger pulse oximeter (In Vivo Research, Orlando, FL, USA). Second, the volunteer in the scanner was instructed to actively depress a switch mounted on a handle. Should the volunteer let go of the handle, a buzzer would sound in the control room. Third, an additional (backup) oxygen analyzer (Portable Oxygen Analyzer, Servomex Group Ltd, Crowborough, UK) sampled and provided a continuous display of oxygen partial pressure in the supply to the facemask. This sounded an alarm to the experimenter if the oxygen fraction should decrease below a specified level (15% in this study). Finally, a separate oxygen supply was attached to the mixing chamber in case of a need to provide emergency enrichment of oxygen, independent of the computer-controlled system. The subject's end-tidal gases were closely monitored by the experimenters.

The Fixed Inspired Challenge

For comparison with DEF, each volunteer underwent a fixed inspired challenge (FIC). To provide the FIC, the gas supply system shown in Figure 1 was modified by introducing a supply of premixed 5% CO₂ in a balance of air (19.7% O₂) (BOC (Special Gases) Ltd, UK). To cycle between periods of hypercapnia and normocapnia, the supply of gas to the volunteer was manually switched between air and 5% CO₂ (balance air), while the flow rate was maintained at 30 lpm. This is adequate for preventing rebreathing of exhaled gases and is consistent with the flow rates used in previous FMRI studies of hypercapnia.

Respiratory Challenges

Four healthy male volunteers (mean age = 37, range = 32 to 40) were studied under approval of the Oxfordshire Clinical Research Ethics Committee, having given written informed consent. The application of a facemask and introduction of the volunteer into the MRI scanner temporarily modifies breathing (Maxwell et al, 1985). Therefore, 10 mins of quiet air-breathing was allowed for acclimatization after introducing the volunteer into the scanner. This period was used for prescribing image locations.

The FIC was performed first in all volunteers to allow the end-tidal partial pressures from that challenge to be used as targets for the subsequent DEF challenge. The FIC run consisted of a period of 3 mins of normocapnia (normal air-breathing, F^I_CO₂ ≈0, F^I_O₂ ≈20.7%) followed by four cycles of hypercapnia (2mins, F^I_CO₂ ≈5%, F^I_O₂ ≈19.7%) and normocapnia (2 mins, air breathing), giving an experimental duration of 19 mins. The initial 3 mins period of scanning the FIC was used to measure resting PET_CO₂ and PET_O₂ required to set the targets for the DEF that followed shortly after FIC, while the volunteer remained in the scanner.

The DEF respiratory protocol was analogous to that for the FIC. After an initial 3 mins period of normocapnia, there were four cycles of 2 mins hypercapnia and 2 mins normocapnia. Target PET_CO₂ and PET_O₂ levels were specified in the DEF setup. The target PET_O₂ was set to the resting value for each volunteer. The ‘normocapnic’ PET_CO₂ target was set to be 1 mm Hg above the resting value for each volunteer (Table 1). This was to allow for the fact that DEF can in general only increase PET_CO₂ from basal levels. The hypercapnic PET_CO₂ target was set to the same value achieved at the end of hypercapnic periods of the FIC (average across cycles) for each volunteer (Table 1). Although these choices of target PET_CO₂ for DEF provide a smaller dynamic range, they were chosen to ensure that an easily tolerable maximum PET_CO₂ level was reached for each volunteer.

Table 1

Respiratory parameters and gray matter BOLD signal variation during periods of normocapnia and hypercapnia.

	Fixed inspired challenge		Dynamic end-tidal forcing
ΔS_BOLD(GM) (%)	1.97 ± 0.31		1.27^a ± 0.26
σ^t_Peto₂ (mmHg)	3.5 ± 1.1		1.7^a ± 0.6
σ^t_BOLD,O₂ (%)	0.13 ± 0.09		0.05 ± 0.03
Gray matter BOLD signal reactivity to CO₂ (%/mm Hg)	0.27 ± 0.04		0.24^b ± 0.05
	Normocapnia	Hypercapnia	Normocapnia^c	Hypercapnia

Target Petco₂ (mm Hg)	N/A	N/A	40.6 ± 1.9	46.8 ± 1.3
Petco₂ (mm Hg)^d	38.2 ± 2.1	45.6 ± 1.4	40.9^e ± 1.8	46.4^f ± 1.6
Pico₂ (mm Hg)^d	2.4 ± 0.5	32.5 ± 2.8	17.8^e ± 2.5	34.0^f ± 2.1
Target Peto₂ (mm Hg)	N/A	N/A	108.0 ± 3.9
Peto₂ (mm Hg)^d	110.3 ± 4.2	112.8 ± 1.2	107.8^a ± 3.5	108.2^a ± 4.7
Pio₂ (mm Hg)^d	144.5 ± 0.7	133.3 ± 0.9	132.5^a ± 3.4	124.7^a ± 4.2
Minute ventilation (l/min)^d	12.5 ± 1.6	17.7 ± 2.1	17.3^e ± 2.6	22.4^b ± 4.4

Group mean (n = 4) and s.d. are given.

DEF, dynamic end-tidal forcing; FIC, fixed inspired challenge; ΔS_BOld(gm), % change in cortical gray matter (GM) BOLD signal in response to the hypercapnic challenge compared with normocapnia; σ^t_BOLD,O₂, mean % BOLD signal variation (temporal s.d.) attributable to Peto₂ variation, derived from the fit of Peto₂ to the cortical gray matter BOLD signal; σ^t_Peto₂, (mm Hg) mean of temporal s.d. of Peto₂.

Indicates a significantly smaller value for DEF than FIC (P < 0.05, one-tailed, paired t-test).

Indicates a significantly different value for DEF and FIC (P < 0.05, two-tailed, paired t-test).

DEF necessitates a mildly elevated ‘baseline’ arterial level of CO₂ to hold the Peto₂ constant. The ‘normocapnia’ Peto₂ target was set 1 mm Hg above the measured resting level.

Mean across 2 mins period.

Indicates a significantly larger value for DEF than during the corresponding periods in FIC (P < 0.05, one-tailed, paired t-test).

Indicates no significant difference in the value for DEF and FIC (P > 0.05, two-tailed, paired t-test).

Functional Magnetic Resonance Imaging

Identical functional imaging acquisitions were performed for FIC and DEF with the same slice prescription. Imaging was performed at 3T using a Siemens Trio system. Gradient-echo echo-planar imaging was performed with T2*-weighted contrast: TE = 32 ms. One thousand one hundred and forty volumes were acquired with TR = 1 secs, flip angle 70°, and an image matrix of 64 × 64 voxels, giving in-plane voxel dimensions of 3 × 3 mm. Each volume comprised 16 contiguous 6-mm-thick axial slices. For each subject a T1-weighted structural scan was acquired (1 mm in plane voxel size, 3 mm slices). This was used to segment gray matter in subsequent analyses.

Data Analysis

Respiratory parameters (gas partial pressures and minute ventilation) were averaged within subject across each of the four periods of normocapnia and hypercapnia and then further averaged across subjects (Table 1). Note that for calculation of parameters in Table 1, the temporal blocks across which averaging was performed were delayed by 20 secs with respect to the nominal onset of each period. This approximate delay was chosen by inspection of the respiratory data (Figure 2) as the period after which the respiratory parameters had begun to show a clear response to the increase or decrease in CO₂. Experimentally, the delay of the responses from the nominal onset of each block occurs because of finite times taken for gas switching, mixing, inhalation, and finally physiological responses. As a measure of the oxygen control, the standard deviation of PET_O₂ across time was calculated for each subject (σ_{PET2_O₂}^t, Table 1). Statistical comparison of respiratory parameters between FIC and DEF was performed using the paired Student's t-test.

Figure 2

Functional magnetic resonance imaging BOLD and respiratory recordings. The left column presents data for the fixed inspired challenge (FIC) of 5% CO₂, whereas the right column shows the result of dynamic end-tidal forcing (DEF) from one representative volunteer. Mean quantities across cycles of hypercapnia within the imaging session are shown, and error bars represent the s.d. across those cycles. The 120secs duration of the hypercapnic challenge is indicated by the gray bar. The first row shows BOLD signal averaged across gray matter, whereas the second row displays PET_CO₂, the dotted line indicating the resting PET_CO₂ before the challenge began. The elevated inspired CO₂ (third row, PI_CO₂) causes the raised PET_CO₂. End-tidal oxygen remained constant during DEF but showed a repeatable variation during FIC (fourth row). Again, the dotted line indicates the resting PET_O₂ before the challenge began. Inspired oxygen PI_O₂ and minute ventilation (V, volume breathed per minute) are shown in rows 5 and 6, respectively.

Blood oxygen level-dependent contrast images were analyzed using tools from the FMRIB Software Library (FSL, http://www.fmrib.ox.ac.uk/fsl). The four-dimensional time series was motion corrected using MCFLIRT (Jenkinson et al, 2002), and nonlinear high-pass temporal filtering was performed (Gaussian-weighted least squares straight line fitting, with a high-pass filter cut-off at 360 secs). After coregistration of the FMRI volumes and the T1-weighted individual structural scan (FMRIB Linear Image Registration Tool, FLIRT) (Jenkinson and Smith, 2001), regions of gray and white matter were automatically identified from the T1-weighted scans (FMRIB Automated Segmentation Tool) (Zhang et al, 2001). The spatial-mean BOLD time course was extracted from gray matter voxels and then averaged across hypercapnia–normocapnia cycles to produce a mean BOLD response curve for display purposes and to estimate the fractional change in BOLD signal induced by the respiratory challenges (ΔS_BOLD(GM) (%)).

PET_CO₂ was the intended driver of changes in the BOLD signal. We estimated for each subject the time delay between changes in PET_CO₂ and the subsequent BOLD signal response by cross-correlation of these signals. For the FIC, the group mean (±standard deviation) lag of the BOLD signal after change in PET_CO₂ was 13 ± 3 secs, and for DEF this was 12 ± 5 secs (no significant difference in a paired t-test, P > 0.05). Pooling across both techniques, the group mean lag of BOLD signal after change in PET_CO₂ was 12 ± 4 secs. Therefore, this was used in subsequent analysis as the representative time of the BOLD signal to respond to changes in blood gas partial pressure. We then examined the degree of BOLD signal change explicable by the induced changes in PET_CO₂ and PET_O₂. A linear regression was performed (Matlab 7, The Math Works Inc., Natick, MA, USA) for the mean gray matter BOLD time course with the PET_CO₂ waveform, to yield gray matter estimates of BOLD signal responsiveness to CO₂. Included in this regression were also the PET_O₂ (as changes in venous oxygenation are the source of BOLD signal) and minute ventilation (volume breathed per minute) waveforms. In this model, minute ventilation was orthogonalized with respect to PET_CO₂ to remove the component of the ventilation signal that correlates with PET_CO₂ (Figure 2) and hence to ensure that where they correlate, BOLD signal changes would be assigned to changes in PET_CO₂. The BOLD–PET_O₂ regression coefficient was used to identify for each subject the temporal standard deviation of the BOLD signal accounted for by fluctuations in PET_O₂ (group mean σ_{BOLD, O₂}^t (%). Table 1).

An analogous regression analysis of BOLD signal was performed on a voxel-by-voxel basis using FEAT (FMRIB Expert Analysis Tool, http://www.fmrib.ox.ac.uk/fsl version 5.1). The following pre-processing, in addition to the motion correction and temporal filtering described above, was applied to each subject's time-series of FMRI volumes: spatial smoothing using a Gaussian kernel of full-width-half-maximum 5 mm and subtraction of the mean of each voxel time-course from that time-course. The FMRI signal was then linearly modeled on a voxel-by-voxel basis using FILM (FMRIB's Improved Linear Model) with local autocorrelation correction (Woolrich et al, 2001). PET_CO₂ incorporating the empirically determined 12 secs BOLD time lag was the main regressor of interest. Further regressors were incorporated in the linear model including PET_O₂ and minute ventilation (orthogonalized with respect to the PET_CO₂ as described above). To account for breathing and other motion-related artifacts, inspired and expired volume and 6 motion parameters (rotations and translations each about 3 axes) were also included in the linear model. The images of the PET_CO₂ regression coefficient (or parameter estimate) were transformed into the standard space of the Montreal Neurological Institute (MNI) using FLIRT, and a mean value across subjects was calculated to yield maps of the mean percentage BOLD signal change per unit (mm Hg) change in PET_CO₂, that is BOLD reactivity to CO₂. The significance of the model fit to each voxel time series was calculated, yielding statistical parametric maps of contrasts for PET_CO₂, and residual BOLD signal changes explained by PET_O₂ and changes in minute ventilation. Z statistic images were thresholded using clusters determined by Z > 2.3 and a (corrected) cluster significance threshold of P = 0.05.

Results

Physiological Challenge

The administration of the FIC and DEF was tolerated well by all four volunteers. Good control of end-tidal CO₂ and oxygen was provided by the DEF system despite the adaptations necessary for using the system in the MRI environment. Dynamic end-tidal forcing produced a consistent hypercapnic challenge while holding end-tidal oxygen constant. Mean gray matter BOLD signal and respiratory parameters are shown for one representative volunteer in Figure 2. Table 1 also summarizes, across the group of four volunteers, changes in gray matter BOLD signal and respiratory parameters.

PET_CO₂ during the hypercapnic challenges of FIC was not significantly different from that during DEF. This confirms that the chosen PET_CO₂ targets were successfully met by DEF. During the normocapnic portions of each challenge, the PI_CO₂ and PET_CO₂ were significantly higher for DEF than for the FIC since the technique necessitates a mildly elevated ‘baseline’ level of CO₂ to hold PET_CO₂ constant by modifying PI_CO₂ on a breath-by-breath basis (see also Figure 2, second row). Residual hyperventilation in the FIC increases the PET_CO₂ difference in this period. The raised baseline level of PET_CO₂ in DEF also induced a general increase in minute-ventilation (Table 1).

In general, PET_CO₂ rose faster at the start of the hypercapnic challenge during DEF than FIC. PET_CO₂ shows a squarer waveform for DEF than for FIC. Dynamic end-tidal forcing aims to modify PI_CO₂ over a short timescale to elicit a particular PET_CO₂, whereas for FIC, PET_CO₂ is governed by PI_CO₂ and minute ventilation. In seeking a step increase in PET_CO₂ followed by a plateau, DEF initially chooses a sharp rise in PI_CO₂ (Figure 2, row 3). The undershoot of PET_CO₂ was evident immediately after the end of the FIC hypercapnic challenge (140 to 200 secs), also reflected in an undershoot of the gray matter BOLD signal (Figure 2, rows 1 and 2). The PET_CO₂ undershoot was caused by ‘over breathing’ at the end of the period of elevated PI_CO₂. Although PI_CO₂ returns to normal in the FIC, minute-ventilation (V) remains elevated for approximately 60 secs, causing a temporary reduction in Pa_CO₂ and hence PET_CO₂. By comparison, during the return to normocapnia, DEF avoids the PET_CO₂ undershoot by more gradually reducing the PI_CO₂.

DEF successfully maintains a constant PET_O₂, (Figure 2, row 4) by decreasing PI_O₂ when minute ventilation rises in response to hypercapnia. Importantly, PI_CO₂ remains lower than normal during the period of hyperventilation after the end of the hypercapnic challenge. This prevents the steep rise in PET_O₂ seen after 120 secs for the FIC, which only falls again once ventilation returns to normal. The maintenance of normoxia during DEF is also reflected in the lower values of PI_CO₂ and PET_O₂ during normocapnia and hypercapnia compared with the FIC in which PET_O₂ rises (Figure 2 row 4 and Table 1). The sharp decrease in PI_O₂ for the FIC results from the fractional displacement of oxygen in the 5% CO₂ (balance air) mixture (19.7% O₂). Nevertheless, this was not adequate to maintain PET_O₂ constant during the FIC while the minute ventilation varies in response to changing PET_CO₂.

CO₂ and O₂ Related Changes in Blood Oxygenation Level-Dependent Signal

The fractional BOLD signal increase (ΔS_BOLD(GM)) during hypercapnia, compared with baseline preceding the challenge, was significantly smaller during DEF than FIC (Table 1). This is likely to result largely from the smaller degree of change of PET_CO₂ in the normocapnia-hypercapnia cycles during DEF because of the slightly elevated baseline PET_CO₂. Furthermore, the undershoot of PET_CO₂ after hypercapnia provides a greater amplitude of PET_CO₂ fluctuation over the cycle. Dynamic end-tidal forcing significantly reduced the temporal variation in PET_O₂ compared with FIC during the normocapnia–hypercapnia cycles (Table 1).

Figure 3 illustrates the group mean (percentage) BOLD signal reactivity normalized to the induced changes in PET_CO₂. The spatial distribution of the BOLD signal reactivity is similar between the FIC and DEF techniques. This similarity is to be expected as both techniques provide a potent vasodilatory challenge through the same mechanism. The BOLD signal reactivity appears greatest in areas of high blood vessel density, for example sagittal sinus, where it is beyond 0.5% per mm Hg CO₂. It also appears higher in gray matter where cerebral blood volume is greater than in white matter (Rostrup et al, 2000). The comparison of the global gray matter BOLD signal reactivity from the linear regression indicates a slightly smaller reactivity when measured using DEF compared with FIC (Table 1), potentially arising from PET_O₂ changes during the FIC (discussed below).

Figure 3

BOLD signal reactivity per unit change in end-tidal carbon dioxide. Group mean results are shown after registration to the standard space of the Montreal Neurological Institute. The central group-mean anatomical image identifies the axial slices of fractional BOLD signal change per mm Hg change in PET_CO₂, shown on the left for the fixed-inspired challenge (FIC) and on the right for dynamic end-tidal forcing (DEF).

Figure 4A illustrates the mean gray matter BOLD signal for the same subject as shown in Figure 2 and the fitted contributions to this signal from PET_CO₂, PET_O₂, and minute ventilation. In the linear regression, the PET_CO₂ provides a good fit to the BOLD signal across most of gray and white matter (Figure 4B). This is consistent with the global vasodilatory effect of CO₂. The inclusion of PET_O₂ provides a significantly improved fit (the contribution being shown by the yellow line in Figure 4A) to the gray matter signal for FIC but not for DEF. This suggests a significant degree of PET_O₂-related signal change in FIC that is not accounted for by the changes in PET_CO₂. This is confirmed by the extensive regions of significant BOLD–PET_O₂ regression for FIC in Figure 4B. The degree of mean gray matter BOLD signal variation (temporal standard deviation σ_{BOLD, O₂}^t) explicable by the PET_O₂ regressor is greater for FIC than DEF with a trend toward significance (one-tailed t-test, P = 0.07). The variation of PET_O₂ helps to explain the higher BOLD signal than predicted by PET_CO₂ during the later phases of the hypercapnic period and the early phase of the recovery (Figure 4A). This increase in BOLD signal probably results from increased venous blood oxygenation when PET_O₂ is elevated. The additional BOLD signal changes accounted for by the inclusion of minute ventilation (and instantanteous breathed volumes, data not shown) are small and largely confined to image intensity boundaries. These changes in signal are likely to arise from breathing-related motion and B_o shifts of the image.

Figure 4

BOLD signal variation explicable by variation of end-tidal carbon dioxide, end-tidal oxygen levels, and minute ventilation (one representative volunteer). Results from the fixed inspired challenge are shown on the left, and those from dynamic end-tidal forcing are shown on the right. Data are from the same volunteer as represented in Figure 2. (A) The fractional change in mean gray matter BOLD signal (black line) is modeled by the PET_CO₂ waveform (blue), the PET_O₂ waveform (yellow), and the minute ventilation waveform (green). The total fit is given by the red line. The inclusion of the PET_O₂ waveform into the linear regression results in an improved fit for FIC but not DEF (B) Images show the Z scores indicating a significant linear regression (Z > 2.3, cluster corrected P < 0.05) of the PET_CO₂, PET_O₂, and minute ventilation with BOLD signal. Only BOLD signal changes for FIC and not for DEF correlated significantly with the PET_O₂ variation.

Discussion

We have shown the feasibility of DEF as a means of delivering respiratory challenges for FMRI. By adapting a previously described DEF system (Robbins et al, 1982a, b ) through minimizing expired gas sampling delays and using an MR-compatible turbine flow transducer, we achieved automated control of the partial pressure of end-tidal carbon dioxide (PET_CO₂) and oxygen (PET_O₂). In our example, a hypercapnic challenge aiming at a step change in PET_CO₂, DEF successfully achieved target PET_CO₂ values with a sharp temporal profile. This allowed the BOLD signal responsiveness to CO₂ to be mapped.

The simple hypercapnic challenge was chosen as the first test of feasibility of DEF for FMRI because of the widespread use of hypercapnia in FMRI. The principal benefit of the DEF over the FIC is that the target PET_CO₂ and PET_O₂ and their temporal profiles are independent and are under the control of the experimenter. This improved control feeds through to the temporal profile of the BOLD response, eliminating the undershoot seen at the end of the FIC as the elevated ventilation drives the PET_CO₂ transiently below normal. We have shown the significance of controlling PET_O₂ in such a simple hypercapnia FMRI study. In addition to the intended CO₂ related BOLD signal change, in the FIC there is a measurable BOLD signal change owing to additional unintended arterial oxygenation-related fluctuations. This effect was largely eliminated by the control of PET_O₂ by DEF.

Respiratory Challenges for Functional Magnetic Resonance Imaging

The need for flexible control of end-tidal gas partial pressures in MRI has been shown by a series of studies of human cerebrovascular physiology. Modulation of carbon dioxide levels has been most common because of the potent vasodilatory effects of CO₂. Some early studies measuring BOLD signal changes as a marker of cerebrovascular reactivity employed a simple breath-hold to elevate Pa_CO₂ (Kastrup et al, 1999; Li et al, 2000; Liu et al, 2002). Although a breath-hold is convenient, it is something of an ill-defined challenge. Partial pressure of arterial carbon dioxide rises with time, Pa_O₂ falls with time, and the duration is strictly limited. The majority of studies of hypercapnia have used a fixed and elevated FI_CO₂ (similar to the FIC experiment used here), typically by mixing 5% CO₂ with the remaining balance as air (Pattinson et al, 2006). The resulting rise in PET_CO₂ depends on the volunteer's ventilatory response to CO₂. The FIC approach has been used to map cerebrovascular reactivity using PET and FMRI (Lythgoe et al, 1999; Rostrup et al, 2000; van der Zande et al, 2005) and to measure the relationship between CBF and cerebral blood volume using PET (Rostrup et al, 2005). Hypercapnia has also been used as a convenient method of increasing baseline CBF to investigate the latter's effect on the measured stimulus-induced BOLD response. Such studies have produced mixed results with reports of decreased (Cohen et al, 2002), unchanged (Corfield et al, 2001), and increased (Posse et al, 2001) stimulus-induced BOLD responses. Hypercapnia, delivered by FIC, has been used as a means of calibrating the BOLD response by providing an increased CBF without a change in metabolic consumption of oxygen (Hoge et al, 1999b). The only report of a breathing system that does not use a fixed inspired fraction of CO₂ is that of Vesely et al (2001). The system was used in a study to measure cerebrovascular reactivity. It employed a rebreathing reservoir and required volunteers to match their breathed volume to the supply of fresh gas. Although this achieved a rapid transition to hypercapnia, it did rely on volunteers monitoring the volume of the fresh gas reservoir, a task that could interfere with other tasks during an FMRI scan and suppress the natural ventilatory responses. It should be noted that for studies of hypocapnia, voluntary hyperventilation is required. A reduced PET_CO₂ cannot be achieved by decreasing FI_CO₂ because this is normally close to zero in the basal state. Thus, DEF can be used in conjunction with hyperventilation to maintain a normal PET_O₂ and a steady, but below normal PET_CO₂.

In FMRI, the ability to alter arterial blood oxygenation has been exploited less but should now be harnessed in humans because of the potential it offers for quantitative examination of BOLD contrast. A previous study using a hypoxic and hyperoxic FIC showed that a reduction in arterial oxygen saturation decreases T2*, whereas an increase in arterial oxygen saturation increases T2* (Rostrup et al, 1995). A recent study of BOLD signal and CBF measurement by arterial spin labeling has showed a spatially heterogeneously reduced degree of task-related signal change in regions engaged in a motor task during mild hypoxia (FI_O₂ =12%) (Tuunanen and Kauppinen, 2006). The visual stimulus-induced BOLD response appears to be reduced by hypoxic hypoxia probably as a result of arterial deoxygenation and increased cerebral perfusion. Mild hypoxic hypoxia has also been used to show a spatially heterogeneous dissociation between BOLD FMRI and CBF in the human visual cortex using quantitative FMRI (Tuunanen et al, 2006). Somewhat more extreme respiratory challenges can be administered to animals to quantify the effects of changing blood gases on BOLD, CBF, and CMRO₂. However, such studies highlight the interaction between changes in Pa_CO₂ and Pa_O₂ that would ideally be separated to provide a purer physiological challenge. Dynamic end-tidal forcing can achieve this separation.

Independent PET_CO₂ and PET_O₂ Control

To rigorously investigate cerebrovascular physiology using techniques other than MRI, such as TCD, the ability to independently control PET_CO₂, and PET_O₂ by DEF has proved invaluable (Ide et al, 2003; Poulin et al, 1996, 1998). With the spatial and temporal resolution afforded by FMRI techniques, DEF in the MRI scanner would allow detailed mapping of the dynamic blood flow response with rigorous temporal physiological control.

This study illustrates the usefulness of DEF. In an ideal hypercapnic challenge PET_O₂ remains constant while PET_CO₂ is elevated. An increase in Pa_CO₂ causes vasodilation and increased global CBF, without a change in cerebral oxygen usage. The resulting decrease in venous de-oxyhemoglobin within the image voxels increases T2* and elevates the BOLD signal during gradient–echo EPI. Hypercapnia also causes minute ventilation (breathed volume per minute) to rise appreciably as shown in Figure 2. The increased ventilation results in a rise of Pa_O₂ and hence PET_O₂ (Figure 2, left column). During the delivery of CO₂ this is somewhat mitigated by the slightly reduced FI_O₂ in the standard commercial mixture (from 20.7% to 19.7%). However, the persistently elevated minute ventilation after the return to normal FI_CO₂ (∼ 0%) and FI_O₂ (20.7%) causes another rise of Peto₂. In the example shown in Figure 2, PET_O₂ is elevated by approximately 15 mm Hg. The effect of this change in oxygen tension during hypercapnia has not been investigated previously in FMRI studies. This increase in PET_O₂ represents a small increase in the oxygen carried in arterial blood, both that dissolved in the plasma (because of the low solubility of oxygen) and the large majority that is carried by hemoglobin. An increase in Pa_O₂ from around 100 to 115 mm Hg would be expected to increase arterial oxygen saturation by approximately 1% (Severinghaus 1979). On the assumption that the amount of oxygen extracted as the blood passes through the capillaries is unchanged between FIC and DEF, the increase in venous oxygenation would parallel the small increase in arterial oxygenation for the FIC. A 1% increase in venous oxygenation is expected to translate to an approximate 0.3% increase in BOLD signal when applying the BOLD signal model of Hoge et al (1999a). This is consistent with our measured oxygen-related BOLD signal increase in the FIC, which is largely removed during control of PET_O₂ by the DEF. Changing oxygenation levels represent a source of systematic error in conventional FMRI hypercapnia studies, where they are not taken into account. In addition to the larger range of PET_CO₂ presented in the FIC, the PET_O₂-related BOLD signal changes are likely to contribute to the larger signal changes seen in the FIC compared with the DEF. The PET_O₂-related BOLD changes may also contribute to the slightly larger apparent BOLD signal reactivity to CO₂ measured during the FIC compared with the DEF.

A further example of the usefulness of DEF for MRI investigation studies lies in examining the effects of hyperoxia on CBF, which has recently been performed using FMRI (Bulte et al, 2007). When breathing 100% oxygen, CBF falls; however, this is accompanied by a fall in Pa_CO₂, and it is unclear whether this was wholly or partly responsible for the fall in CBF or whether there is an independent vasoconstrictive effect of elevated Pa_O₂. In an MRI study in which elevated inspired partial pressures of CO₂ were administered in concert with elevated oxygen levels, the independent Pa_O₂ vasoconstrictive effect was inferred from a regression analysis (Floyd et al, 2003). With the availability of DEF in the MRI environment to fix PET_CO₂ while elevating PET_O₂, such a study becomes more directly and easily performed. Similarly, in studies of mild hypoxia, DEF would allow the effect of hypoxia-induced ventilation increases in lowering PET_CO₂ to be removed.

Advantages and Disadvantages of DEF for Functional Magnetic Resonance Imaging

To fix PET_CO₂ during the ‘normocapnic’ condition, it is necessary to set the target value slightly higher (in this study, 1 mm Hg higher) than the mean PET_CO₂ recorded at rest, with the resulting anticipated effect of flattening out the normal resting fluctuations in PET_CO₂ (Harris et al, 2006; Van den Aardweg and Karemaker 2002; Wise et al, 2004). Although this keeps PET_CO₂ within a physiologically reasonable range, this consistent elevating effect on the PET_CO₂ during the baseline condition should be considered when designing and interpreting respiratory challenges. This method is adopted because reducing PET_CO₂ below resting values would require a voluntary increase in ventilation (hyperventilation), while a small increase in PET_CO₂ is easily achieved by slightly increasing the inspired partial pressure of CO₂ without the volunteer noticing. This necessary rise in DEF target PET_CO₂ during normocapnia is apparent in Table 1 (40.9 mm Hg) given that the mean resting PET_CO₂ before hypercapnic challenges began was 39.6 mm Hg. This value is higher than the mean value during the normocapnic periods of the FIC (38.2 mm Hg). The decreased PET_CO₂ during normocapnic periods relative to before the FIC, results from the hypercapnia-induced increase in ventilation that persists for approximately 1 min after PI_CO₂ returns to normal.

In this study, the FIC always preceded the DEF challenge. This was because we chose to use the values of PET_CO₂ and PET_O₂ reached in FIC as the fixed targets for the DEF. At the higher PET_CO₂ levels, this provides a fairer comparison between the techniques as it limits the increase in ventilation in response to CO₂. Choosing instead the PET_CO₂ target for DEF to maintain the dynamic range of PET_CO₂ seen for the FIC would have necessitated a mean target value of around 48 mm Hg because of the undershoot of PET_CO₂ seen in the FIC. This would generally shift the PET_CO₂ and BOLD signal values for DEF to a higher range. Our present approach of basing the DEF targets on the FIC provided a demonstration that DEF could achieve the requested targets. Using this method, we were confident that the DEF approach would offer a tolerable hypercapnic challenge for volunteers within one scan session. One disadvantage of our approach is that a smaller dynamic range of PET_CO₂ and therefore BOLD signal change was investigated with the DEF than the FIC, potentially biasing comparison of BOLD signal change from DEF and FIC. This difference in the range of PET_CO₂ studied with DEF and FIC could contribute to the smaller apparent BOLD signal reactivity for DEF than FIC if the BOLD– PET_CO₂ relationship is nonlinear.

The DEF technique does involve considerably more expense and setup time than a typical FIC. It is quite possible therefore that for some studies the FIC may remain preferable, for example, studies in which a tight-fitting face mask could discourage volunteers or those that do not benefit greatly from control of PET_CO₂ and PET_O₂. Although we achieved adequate control of end-tidal gas partial pressures, the performance of the DEF system is likely to be limited by the gas sampling delay of 1.6 secs imposed by the need to site the gas analyzers several meters from the MRI magnet. Compared with earlier DEF systems developed for use in a standard (non-MRI) laboratory environment (Robbins et al, 1982a, b ), the response capability of the MR-compatible system is likely to be reduced. In the experiments presented here, it was necessary to extend the gas sampling delays assumed in the gas control software to ensure that end-tidal values were correctly estimated. Because of the extended sampling delay, we also anticipate that for volunteers whose breathing rates increase greatly during hypercapnia, it may be necessary in the future to reduce the gas partial pressure feedback gain to avoid oscillations in end-tidal levels.

Despite the complexities of implementing DEF in the MRI environment, its development provides new opportunities for quantitative FMRI investigations. It has the capacity to fix end-tidal gas partial pressures, not only within an individual subject's scan session but also between sessions and subjects. The changes in PET_CO₂ and PET_O₂ demanded by the experimenter, since they are target driven, become largely independent of the individual subject's ventilatory response to hypercapnia, hypoxia, and hyperoxia. The computer control of end-tidal gas partial pressures facilitates synchronization of the respiratory challenge with MRI data acquisition. Previously, many FIC approaches have relied on manual switching of inspired gas mixtures. Dynamic end-tidal forcing enables more complex respiratory challenges to be implemented in the MRI environment than previously attempted, such as multiple levels of PET_CO₂ within a single scan session and rapidly or slowly ramping end-tidal gas partial pressures. Figure 2 provides a hint that DEF may be useful for quantitative modeling and mapping of the temporal response parameters of CBF. The slope of the PET_CO₂ rise was steeper than its BOLD signal counterpart in DEF, in contrast to the FIC approach. This may reveal a limitation in the speed of response of the cerebral vasculature worthy of further investigation with rapid changes in arterial gas partial pressures (Poulin et al, 1996).

A potential new application for DEF in FMRI lies in controlling end-tidal gases, especially PET_CO₂, as a means of reducing Pa_CO₂-related fluctuations in CBF. It has recently been shown that the natural low-frequency fluctuations in Pa_CO₂ arising from slow changes in breathing give rise to low-frequency noise (0 to 0.05 Hz) in the BOLD signal (Wise et al, 2004). Reducing the fluctuations in PET_CO₂ by DEF has also been shown to reduce the variability in CBF (Harris et al, 2006), which gives rise to these BOLD signal changes. Dynamic end-tidal forcing therefore has the potential to reduce low-frequency breathing-related physiological noise in FMRI. Although the confounds of using a face mask may outweigh the benefits of this approach for many FMRI studies, it is likely to be beneficial in studies where breathing patterns may correlate with the stimulation presented to the subject. Such studies may otherwise have CO₂-related BOLD signal changes that are falsely identified as task-related neuronal activity. Dynamic end-tidal forcing may also have similar applications in stabilizing end-tidal gases during a drug challenge, especially when respiration is stimulated, as has previously been observed in an FMRI study of cocaine (Breiter et al, 1997). If the CBF–PET_CO₂ relationship is already characterized for a volunteer, DEF could also be harnessed to compensate by modifying PET_CO₂ for potential pharmacological modulation of CBF and the confound that this presents in interpreting stimulus-induced BOLD signal changes.

In summary, we have showed the feasibility of DEF for FMRI for a hypercapnic challenge in a small cohort of volunteers. The control of end-tidal gases afforded to the experimenter can now be applied in quantitative studies of FMRI BOLD contrast as well as studies aimed at probing cerebrovascular physiology across the full range of respiratory challenges in human volunteers.

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Dynamic Forcing of End-Tidal Carbon Dioxide and Oxygen Applied to Functional Magnetic Resonance Imaging

Abstract

Keywords

Introduction

Materials and methods

Implementation of Dynamic End-Tidal Forcing for Magnetic Resonance Imaging

The Fixed Inspired Challenge

Respiratory Challenges

Functional Magnetic Resonance Imaging

Data Analysis

Results

Physiological Challenge

CO2 and O2 Related Changes in Blood Oxygenation Level-Dependent Signal

Discussion

Respiratory Challenges for Functional Magnetic Resonance Imaging

Independent PETCO2 and PETO2 Control

Advantages and Disadvantages of DEF for Functional Magnetic Resonance Imaging

References

CO₂ and O₂ Related Changes in Blood Oxygenation Level-Dependent Signal

Independent PET_CO₂ and PET_O₂ Control