Cerebrovascular reactivity (CVR) MRI with CO2 challenge: a technical review (2024)

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3.1 Fixed CO2 concentration in the inspired gas

This type of systems delivers room air to the subjects at the baseline state and hypercapnic gas with a fixed CO2 concentration (e.g., 5% CO2 with 21% O2 and balanced N2) at the challenged state.

Figure 2a shows the diagram of a representative gas delivery system (Lu et al., 2014; Yezhuvath et al., 2009). In this system, the pre-mixed hypercapnia gas (e.g., 5% CO2, 74% N2, and 21% O2) is contained in a large Douglas bag, e.g., 100 liters, that is pre-inflated and brought into the magnetic room. The gas is delivered to the subject through a mouthpiece where the inspired and expired air flows through different ports of a non-rebreathing valve, thus avoiding mixing between them. During the MRI scan, the subject wears a nose-clip and is instructed to breathe through the mouthpiece. End-tidal CO2 (Et-CO2) is sampled near the mouthpiece. A researcher stays inside the magnet room throughout the experiment to manually switch the valve to control the inspired air (either room air or CO2 bag) and to monitor the subject.

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Figure 2

Three systems of gas delivery apparatus with a fixed CO2 concentration in inspired air. (a) Breathing apparatus by Lu et al. (b) Breathing apparatus by Tancredi et al. (c) Breathing apparatus by Bulte et al.

In another system, a facemask is used instead of a mouthpiece (Figure 2b) (Lajoie et al., 2016; Tancredi et al., 2014). The advantage of the facemask is that the subject can maintain nose breathing, and thus, does not experience dry mouth during the scan. However, the facemask tends to require more space compared to the mouthpiece and may require more effort to fit in the head coil of the MRI, especially multi-array-type coils that are usually smaller in size. The Tancredi system is also different from the Lu system in that the gas mixture is delivered to the subject via a gas tank rather than a Douglas bag. The benefit of the gas tank delivery is that the incoming air is pressurized, and thus, there is less mechanical resistance in the delivery system and less effort is needed on the subject’s part to inhale the air. One disadvantage is that the gas flow rate needs to be optimized per subject based on their ventilation rate and, when a fixed rate is used, an additional outlet port of a sufficient length (e.g., 3.5 m) is necessary to serve as a gas reservoir. A similar system, with manual control of flow meters, has also been reported recently (Driver et al., 2016; Whittaker et al., 2016).

Another variant of the fixed inspired CO2 system is shown in Figure 3c (Bulte et al., 2009; Hare et al., 2013). Compared to the other two, this system does not use a non-rebreathing valve to explicitly avoid the mixing of inspired and expired air. Instead, it employs an excessive flow rate in the incoming air, e.g., twice the amount of the ventilation rate, to quickly dilute the expired air and minimize rebreathing. The absence of a non-rebreathing valve with this setup is expected to reduce the effort needed during inhalation. However, greater effort may be required for exhalation because of the high-pressure incoming air. In addition, for gas systems that use tanks, the tanks usually need to be moved in and out of the MRI suite for every study, which is less convenient compared to moving a Douglas bag.

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Figure 3

Example of CO2 time course during the experiment. (a) CO2 time course with 5% CO2 inhalation. Segments of the breath-by-breath CO2 content trace, as recorded by the CO2 monitor, are shown for a room-air breathing period (left inset) and a 5% CO2 inhalation period (right inset). (b) CO2 time course with 7.5% CO2 inhalation. Blue arrows indicate the periods where the inhaled CO2 concentration exceeded the exhaled CO2 concentration. Extracted Et-CO2 time courses are shown by the orange curve.

Common features of the fixed inspired CO2 systems are that they tend to be less expensive, involve less training and preparation for both the researcher and the participant, and troubleshooting is simpler. A potential limitation is that there may be some variations in baseline Et-O2 and challenge-induced Et-CO2 changes across subjects, due to individual differences in ventilation.

3.2 Fixed CO2 concentration in the expired gas

This type of systems uses feedback control or individualized pre-calibration to ensure that the expired CO2 level of the participant is maintained at desired levels. Two such systems have been reported thus far for use in MRI experiments (Slessarev et al., 2007; Wise et al., 2007).

One system (Wise et al., 2007) uses a gas-mixing chamber where five gases (pressured air, O2, CO2, N2, and a backup O2) are mixed under the control of a laptop computer. The mixed gas is delivered to the subject’s face-mask at a constant rate, e.g., 70 liters per minute, and the exhaled air is sampled and Et-CO2 is measured. The controlling computer compares the desired Et-CO2 value with the measurements, and then, through feedback control, modifies the CO2 content in the mixed gas on a breath-by-breath basis, thereby ensuring a fixed CO2 level in the expired air.

Another system of fixed expired CO2 (Slessarev et al., 2007) achieves a targeted CO2 level by exploiting the fact that the expired CO2 level is directly related to the ventilation volume, when the inspired CO2 and the body’s metabolic production of CO2 are known. Therefore, by controlling the “effective” ventilation volume of the subject, the expired CO2 level can be fixed. Unlike the fixed inspiration systems that avoid rebreathing, this system utilizes the previously exhaled gas in addition to the incoming gas for the subject’s ventilation needs. Specifically, the volume of incoming fresh air is determined by the “effective” ventilation volume that the researcher determined during a preparation step. Then, any additional volume that the subject needs is met through rebreathing the exhaled air, which does not contribute to CO2 removal. The advantage of this system is that feedback control is not required, which simplifies the system. However, the preparation time for each study is relatively long and substantial training of the subject is needed to ensure a successful targeting of expired CO2.

Compared to the fixed inspired CO2 systems, an advantage of the fixed expired systems is that they allow precise control of exhaled CO2 across different subjects and, since brain hemodynamic response is directly related to the exhaled CO2 content rather than inhaled CO2, this scheme can potentially provide more harmonized data compared to the fixed inspired CO2 method. However, these systems are often expensive, require more training of the operators, and take a longer time to set up for each study. Furthermore, because of the CO2 targeting, the baseline CO2 level under which the measurement is performed is no longer the natural baseline CO2 level that the subject’s body usually operates under. Thus, it remains to be shown whether the measured CVR is relevant to the subject’s vascular health in daily functioning.

To date, there has been no study that has systematically compared these gas delivery systems in the same patients. The selection of a specific gas delivery system is often based on factors such as the study requirement (Fierstra et al., 2013), the project budget, and the skills of the personnel, for which a consensus for the field would be desirable.

Table 1

Examples of CVR imaging protocol.

CVR=Δ(MRI measurement)/ΔEtCO2,

Eq. [1]

7.1 General linear model assuming a hom*ogeneous bolus arrive time across the brain

This analysis is conceptually similar to that used in task-evoked fMRI, except that the regressor variable is the Et-CO2 time course. Figure 6a shows a flow chart of the processing steps. Before applying the voxel-wise linear model analysis, the Et-CO2 time course is temporally aligned to the MRI signal time course to account for the time it takes for the CO2 gas bolus to travel from the lung to the heart, then to the brain, and elicit a hemodynamic response. This delay time between Et-CO2 and the MRI signal can be determined by shifting the Et-CO2 time course one TR at a time, and, for every shift, calculating the cross-correlation coefficient (CC) between the two time courses. The MRI time course used here is usually based on a large ROI or on the whole brain where the signal is reliable. The shift value that yields the highest CC or smallest residual signal is considered the “lung-to-brain” delay. Typical delay time in young subjects was found to be approximately 15 seconds (Thomas et al., 2014; Yezhuvath et al., 2009). Older individuals tend to have a longer delay (Thomas et al., 2014). Once the Et-CO2 time course is shifted based on the optimal delay, a voxel-wise linear regression is performed:

where β_l, β₂, and β₀ are estimated coefficients. The term β₂ · t is added to account for any linear drift in MRI signal and ε indicates residual error. CVR, in the unit of percent signal change per mmHg of Et-CO2 change, is computed by:

The term min(EtCO2) · β₁ is included in the denominator so that the measured reactivity is in reference to a state corresponding to the baseline Et-CO2, rather than on a non-physiological state of Et-CO2=0. A voxel-wise CVR map is obtained with this procedure. In addition to the MRI signal drift, other factors may also be included in Eq.[2], such as motion factors and other physiological noise. However, caution should be taken when adding factors to the model, as inclusion of factors that are correlated with Et-CO2 time course (e.g., motion due to breathing) may result in an underestimation of CVR.

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Figure 6

Illustration of the CVR data analysis pipelines.

7.2 General linear model considering voxel-specific bolus arrival time

The strength of the above method is that the algorithm is relatively robust and the computation demand is modest. However, this method relies on the assumption that the “lung-to-brain” delay time is the same for every voxel in the brain, which has recently been called into question. For example, the white matter was found to manifest a substantially longer delay (by 19 seconds), compared to the gray matter, even in healthy subjects (Thomas et al., 2014). Moreover, some studies have also shown that patients with cerebrovascular diseases have a longer delay time in affected regions and that the delay time itself can be a useful index of disease severity (e.g., intracranial stenosis (Christen et al., 2015; Donahue et al., 2016; Liu et al., 2017b)). Therefore, algorithms have been proposed to conduct linear modeling using a voxel-specific delay time, which can provide an estimation of both CVR and a map of bolus arrival time.

To obtain a voxel-wise map of bolus arrival time (BAT), the most intuitive approach is to extend the ROI method proposed above to voxel-wise analysis, by shifting the Et-CO2 time course relative to the MRI time course of each voxel and determining the time shift that provides the best correlation, using either maximum CC or minimal residual. However, a delay map using such an approach is usually noisy, especially for the white matter and disease-affected areas where the CVR is low. Liu et al. proposed an improved method using a reference MRI signal as an intermediate to determine the voxel-wise CO2 delay (Liu et al., 2017b). In this method (Figure 6b), a reference signal is generated by averaging MRI time courses from a large reference ROI, e.g., whole-brain for healthy volunteers and unaffected brain regions for patients with disease. The voxel-wise time shift between the reference signal and each voxel in the brain is then calculated. The summation of this shift and the time delay between the reference signal and the Et-CO2 time course then gives a voxel-wise BAT map. Compared to directly aligning Et-CO2 to the voxel-wise BOLD signal, the advantages of using the reference signal as an intermediate are that the reference signal is measured in the brain, has accounted for any dispersion of the CO2 bolus, and is measured at the same sampling rate as the voxel-wise signal. Thus, this signal has a higher correlation with the voxel-wise MRI signal. This approach yields more reliable results for voxel-wise maps of bolus delay. Once the voxel-wise delay map is obtained, the Et-CO2 time courses are shifted accordingly and linear regression similar to that described in Eqs. [2] and [3] is used to obtain a CVR map.

An iterative algorithm, dubbed Regressor Interpolation at Progressive Time Delays (RIPTiDe), has also shown promise in calculating the CO2 bolus delay in a voxel-wise fashion (Donahue et al., 2016; Tong et al., 2011). This algorithm (Figure 6c) first examines the temporal cross-correlations between the Et-CO2 time course, referred to as a probe regressor, and the voxel-wise MRI signals. This step results in a preliminary map of bolus delay. Then, MRI signal from each voxel is shifted accordingly, and a principal component analysis (PCA) is applied to the aligned time courses to compute a single time course that accounts for the highest shared variance. This new time course then replaces the Et-CO2 time course as the new probe regressor and the above-described procedure is repeated until convergence. The outcome of this algorithm is a response delay map and an estimated CO2 response waveform, which can then undergo linear regression analysis to estimate CVR. The advantage of this method is that the estimated CO2 response waveform accounts for CO2 bolus dispersion, and, therefore, has a higher correlation with the voxel-wise MRI signal, which improves the estimation of CVR. A potential limitation is that, when the voxel-wise data are noisy, e.g., in patient with a large region of diminished CVR, the algorithm may not converge to the correct delay time.

In addition to temporal delay characterized by bolus arrival time, studies have suggested that the hemodynamic response to blood CO2 changes may be smoother and broader in the white matter and in disease-affected brain regions (Duffin et al., 2015; Poublanc et al., 2015; Thomas et al., 2014). Characterization of this change in hemodynamic response may also be useful and worth investigating.

7.3 Frequency-based analysis

In the previous analyses, the bolus delay time and CVR were computed in separate steps. Frequency-based methods have been proposed to compute these parameters in one analysis. In this type of analysis, the signals are transformed into the frequency domain and the phase and magnitude of the signals are further analyzed. Specifically, the phase of the MRI signals at the CO2 modulation frequency can be used to obtain the timing of the CO2 bolus arrival (Blockley et al., 2011; Blockley et al., 2017; Duffin et al., 2015), and the ratio of the magnitude between CO2 and BOLD signals represents the CVR. These methods are expected to demonstrate excellent performance when the signal waveforms are close to a sinusoid function (Blockley et al., 2011; Blockley et al., 2013). For other types of signal waveforms, however, the power spectrum of the signal of interest is expected to be more widespread and attention will be required to identify the optimal frequency values to include for the best performance (Duffin et al., 2015).

7.4 Computation of functional connectivity from BOLD-CVR data

The primary feature of the BOLD time course in a CVR experiment is that the signal is modulated by hemodynamic responses to blood CO2 changes. However, the signal is also expected to contain spontaneous fluctuations due to neural activity, similar to those in a resting-state fMRI. Therefore, it was recently proposed that, after factoring out signal modulations attributed to the gas challenge (i.e., the term β₁ · EtCO2 in Eq.[2]), the residual signal (i.e., the term β₀ in Eq.[2]) can be used to analyze functional connectivity (Liu et al., 2017b). Standard tools for resting-state fMRI can be readily applied to the CVR residual signal and both seed-based and independent component analysis (ICA) approaches have been used (Hou et al., 2017). The functional connectivity information can thus be extracted from a CO2-CVR scan without additional cost in scan duration.

9.1 Concomitant CO2 and O2 inhalation

CO2 can be combined with an O2 gas challenge to measure multiple physiological parameters. O2 inhalation can provide an evaluation of venous cerebral blood volume (CBVv) (Blockley et al., 2013; Bulte et al., 2007). Recently, a block-design-based paradigm was proposed to modulate Et-CO2 and Et-O2 concomitantly, but independently, which allows the measurement of CVR and CBVv simultaneously within the time of a single scan (Liu et al., 2017b). In this paradigm, the timing of CO2 and O2 inhalation were designed such that Et-CO2 and Et-O2 were modulated at different frequencies with a temporal correlation of close to zero. Then, the BOLD signal change to Et-CO2 change yields a CVR map, while the BOLD signal change to Et-O2 change yields a CBVv map. The time delay between physiological recordings and the BOLD signal yields a BAT map, and the residual BOLD signal can be analyzed to yield functional connectivity networks. This approach allows the measurement of multiple hemodynamic parameters within a clinically acceptable imaging time. Figure 7 shows four parametric maps obtained from this type of integrated vascular (iVas) MRI that employs concomitant CO2 and O2 challenges. It was found that all physiological maps derived from this method were of good quality and the image contrasts were comparable to current methods that require separate scans (Liu et al., 2017b).

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Figure 7

CVR, CBVv, BAT and functional connectivity networks (FCNs) obtained in a healthy subject (28-year-old female) using concomitant CO2 and O2 inhalation. DMN: Default mode network. FPN: frontal-parietal network.

In addition, with biophysical modeling of the BOLD signal, combined CO2 and O2 challenges can also be used to estimate baseline parameters for the oxygen consumption of the brain, including oxygen extraction fraction (OEF) and cerebral metabolic rate of oxygen (CMRO2) (Bulte et al., 2012; Gauthier et al., 2012; Merola et al., 2016; Wise et al., 2013).

9.2 CVR mapping without CO2 inhalation

CVR mapping with CO2 inhalation provides reliable results and has shown great promise in clinical applications (Chan et al., 2015; Fierstra et al., 2016; Han et al., 2008; Kenney et al., 2016; Mandell et al., 2008; Mikulis et al., 2005; Zhao et al., 2009). However, the requirement of inhaling a special gas mixture inside the MRI still presents a practical limitation to its application in clinical practice. There have been some efforts to perform CVR mapping without gas inhalation. These techniques include breath-hold, hyperventilation, and resting-state fMRI.

The breath-hold method induces hypercapnia by having the subjects hold their breath for a short period of time (usually 15–30 seconds), which leads to an increase in blood CO2 concentration (Bright and Murphy, 2013; Chan et al., 2015; de Boorder et al., 2004; Geranmayeh et al., 2015; Magon et al., 2009; Tancredi and Hoge, 2013; Zaca et al., 2014). In a breath-hold CVR experiment, MRI images are acquired during alternating periods of breath-holding and normal breathing (with a guided pace in some studies (Bright and Murphy, 2013; Tancredi and Hoge, 2013)). One difference between the breath-hold and CO2 inhalation is that, during the breath-hold period, CO2 concentration in the lung cannot be measured, and thus, there is a gap in the CO2 time course. Modeled stimulus time course (e.g., ramps and gamma-variate function) can be used to estimate CO2 levels during this gap and applied as a regressor in the general linear model analysis. The advantage of the breath-hold CVR method is that no gas delivery system is required, which substantially simplifies the experimental procedure. A disadvantage is that this task requires considerable cooperation from the participant, which is particularly difficult for the elderly (Jahanian et al., 2017), and variations in adherence to the instruction can affect the data quality and reliability of this method (Magon et al., 2009). In contrast to the breath-hold method, which induces hypercapnia, hyperventilation that induces hypocapnia has also been used to measure CVR occasionally (Bright et al., 2009; Krainik et al., 2005). However, hyperventilation may lower Et-CO2 to a level where the change in CBF is no longer linear to the change in Et-CO2, resulting in underestimation of CVR (Bhogal et al., 2014; Tancredi and Hoge, 2013). For both breath-hold and hyperventilation tasks, subject motion is another potential concern. In addition, since breath-hold and hyperventilation could be considered motor tasks, the concomitant motor activation may affect the CVR estimation across the task-activated brain regions.

Recently, resting-state fMRI data, which are often acquired for functional connectivity mapping, have been shown to provide an estimation of CVR maps (Golestani et al., 2016; Jahanian et al., 2014; Liu et al., 2017a). Rather than manipulating the Et-CO2 level by hypercapnia breathing or breath-holding, this approach utilizes spontaneous fluctuations in the breathing pattern, and thus the CO2 level, to extract CVR information from the resting-state data. The BOLD signal fluctuations attributed to CO2 variations can be extracted by either applying a temporal filtering (e.g., 0.02–0.04 Hz (Liu et al., 2017a) or <0.025 Hz (Jahanian et al., 2014)), or by factoring out other physiological signals from the resting state data (Golestani et al., 2015). Figure 8 shows two examples of CVR maps derived from resting-state BOLD data, one in a healthy volunteer and the other in a patient with cerebrovascular disease. Good agreement between resting-state CVR results and hypercapnia CVR results have been reported (Liu et al., 2017a). There have also been studies using the resting state fluctuation of amplitude (RSFA) and the amplitude of low-frequency fluctuation (ALFF) (Di et al., 2013; Kannurpatti and Biswal, 2008; Kannurpatti et al., 2014; Lipp et al., 2015; Tsvetanov et al., 2015) to approximate CVR. In general, the resting-state CVR method requires minimal subject compliance, and can be performed in patients with more severe conditions, such as acute stroke. A limitation of the resting-state methods is that, if the subject breathes regularly with minimal fluctuation in their Et-CO2 level, there will not be enough CO2-related signal variations for CVR evaluation. Nonetheless, CVR mapping with resting-state data has great potential in many clinical applications in which hypercapnia challenge is not feasible.

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Figure 8

CVR maps derived from resting-state BOLD data and hypercapnia BOLD data. The healthy subject was a 25-year-old male. The Moyamoya patient was a 24-year-old female with left ICA stenosis.

There are other methods to measure CVR, such as those that use an acetazolamide challenge together with single-photon emission computed tomography (SPECT) or transcranial Doppler ultrasound (TCD) (See (Settakis et al., 2003) for review). Those methods are beyond the scope of this review, which focuses on MRI techniques to measure CO2-induced hemodynamic responses.

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