The impact of inspired oxygen levels on calibrated fMRI measurements of M, OEF and resting CMRO2 using combined hypercapnia and hyperoxia

Recent calibrated fMRI techniques using combined hypercapnia and hyperoxia allow the mapping of resting cerebral metabolic rate of oxygen (CMRO2) in absolute units, oxygen extraction fraction (OEF) and calibration parameter M (maximum BOLD). The adoption of such technique necessitates knowledge about the precision and accuracy of the model-derived parameters. One of the factors that may impact the precision and accuracy is the level of oxygen provided during periods of hyperoxia (HO). A high level of oxygen may bring the BOLD responses closer to the maximum M value, and hence reduce the error associated with the M interpolation. However, an increased concentration of paramagnetic oxygen in the inhaled air may result in a larger susceptibility area around the frontal sinuses and nasal cavity. Additionally, a higher O2 level may generate a larger arterial blood T1 shortening, which require a bigger cerebral blood flow (CBF) T1 correction. To evaluate the impact of inspired oxygen levels on M, OEF and CMRO2 estimates, a cohort of six healthy adults underwent two different protocols: one where 60% of O2 was administered during HO (low HO or LHO) and one where 100% O2 was administered (high HO or HHO). The QUantitative O2 (QUO2) MRI approach was employed, where CBF and R2* are simultaneously acquired during periods of hypercapnia (HC) and hyperoxia, using a clinical 3 T scanner. Scan sessions were repeated to assess repeatability of results at the different O2 levels. Our T1 values during periods of hyperoxia were estimated based on an empirical ex-vivo relationship between T1 and the arterial partial pressure of O2. As expected, our T1 estimates revealed a larger T1 shortening in arterial blood when administering 100% O2 relative to 60% O2 (T1LHO = 1.56±0.01 sec vs. T1HHO = 1.47±0.01 sec, P < 4*10−13). In regard to the susceptibility artifacts, the patterns and number of affected voxels were comparable irrespective of the O2 concentration. Finally, the model-derived estimates were consistent regardless of the HO levels, indicating that the different effects are adequately accounted for within the model.


Introduction
Recently, different groups have proposed that resting cerebral metabolic rate of O 2 consumption (CMRO 2 ) can be imaged using gas-based fMRI techniques [1][2][3]. Our team presented an approach, dubbed QUantitative O 2 (QUO2) based on respiratory calibration of the BOLD signal, using hypercapnia (HC), and hyperoxia (HO). During the gas manipulation, end-tidal O 2 (ETO 2 ) and CO 2 (ETCO 2 ) levels are constantly monitored and a dual-echo version of pseudocontinuous Arterial Spin Labeling (de-pCASL) is used to measure BOLD and cerebral blood flow (CBF) simultaneously. ETO 2 , BOLD and CBF then serve as inputs to the generalized calibration model (GCM) described in Gauthier and Hoge [4], which yields a system of two equations with solutions for the BOLD calibration parameter M, i.e. the maximum BOLD signal increase when venous O 2 saturation approaches 100%, and resting oxygen extraction fraction (OEF). The multiplication of OEF by baseline CBF and arterial O 2 content (estimated from ETO 2 monitoring and, optionally, blood testing) gives the estimated resting CMRO 2 in micromoles of oxygen extracted from the cerebral vasculature per minute, per 100g of tissue.
While the initial proof-of-concept of the method produced reliable results when spatially averaged within the brain and over multiple subjects, it suffered from a single-subject instability characterized by large fluctuations in the modeled values and a considerable lack of solution in certain regions [1]. In order to be considered a reliable method for within-subject longitudinal studies, there was a need to improve the single-subject image quality. Additionally, prior to being able to draw conclusion about differences in resting oxidative metabolism between populations or between states of a disease, knowledge about the precision and accuracy of the model-derived estimates was crucial. The breathing circuit and image analysis strategy were updated in previous work [5][6]. The repeatability of the respiratory responses as well as CBF and BOLD responses within gray matter (GM) has also been assessed [7]. Finally, the question of methodological precision was evaluated by assessing the regional intra-and inter-subject variability of QUO2 derived estimates [6].
The choice of O 2 and CO 2 concentration during respective periods of HO and HC may also have an impact on the accuracy and precision of QUO2 derived estimates, which remains to be assessed. Higher CO 2 concentration would have the advantage of increasing the image contrast-to-noise ratio due to higher CBF responses, however it can lead to anxiety and potentially alter brain physiology in ways other than the intended vasodilatory effect [8,9]. In a preliminary phase, it was agreed that the commonly employed 5% CO 2 during HC blocks was low enough to preserve participant's comfort, while high enough to yield significant cerebrovascular responses. As for the O 2 concentration, compared to slight HO levels (e.g. 50-60%), more extreme levels of HO may bring the BOLD responses closer to the maximum M value, therefore diminishing the measurement errors while increasing the SNR. However, due to the paramagnetic characteristic of oxygen molecule, the measured signal may be prone to more prominent susceptibility artifacts patterns in vulnerable regions such as the frontal sinuses and nasal cavity, thus yielding inaccurate or non-solution values in those regions. An additional potential impact of the O 2 concentration arises when changes in blood flow during HO are encompassed in the model, such as in the generalized calibrated model. Following a low HO level, CBF responses may be smaller than the inherent noise level of ASL acquisitions, making its measurement challenging. Furthermore, a decrease in CBF during periods of HO may reflect a combination of phenomena: a vasoconstrictive effect following a hyperventilationinduced decrease in ETCO 2 [10], a vasoconstriction due to increased O 2 per se, and an acceleration of arterial blood longitudinal relaxation (T 1 shortening) caused by the increase of dissolved molecular oxygen in blood plasma [11][12][13][14]. If not taken into account, this T 1 decay in arterial blood leads to an overestimation of CBF decrease during HO. As a consequence of those complications, it is common to assume a fixed, pre-determined CBF decrease [2,[15][16][17]. However, assuming a fixed CBF decrease contributes to the systematic errors and can affect the accuracy and repeatability of OEF and CMRO 2 estimates as reported in Lajoie et al [6]. Therefore, the application of a T 1 -correction on the measured CBF during HO is advocated.
Additionally, in theory, the QUO2-derived estimates should not depend on the level of hyperoxia induced, since the model is designed to account for this. In a previous study [6], the within-subject repeatability of the model-derived estimates was assessed based on very small variations of ETO 2 during periods of 60% O 2 hyperoxia. The effectiveness of the QUO2 model to obtain reproducible M, OEF and CMRO 2 despite considerable variations in hyperoxia ETO 2 is crucial and remains to be demonstrated.
The present study aims at exploring, in a small cohort of healthy individuals, the impact mentioned above, on QUO2 calibrated fMRI estimates, when providing 100% O 2 during periods of HO instead of the previously provided 60% O 2 , in addition to verifying the reproducibility of results regardless of the inspired oxygen levels.

Materials and methods
From the group of eight healthy adults that underwent the 24 hour QUO2 test-retest study mentioned previously [6], six of them repeated the experiment, but this time, instead of being given 60% O 2 during periods of HO (referred to as "lower HO levels protocol" (LHO)), the participants were given 100% O 2 ("higher HO levels protocol" (HHO)). Each HO protocol was repeated to assess repeatability of results at the different O 2 levels (referred to as "Test A" and "Test B"). To minimize effects of diurnal fluctuation in blood flow [18], all sessions were acquired between 2 PM and 6 PM. The participants were asked to abstain from caffeine 3 hours prior to scanning. All participants (3 females and 3 males, mean age: 30.5 ± 6.7 years) gave written informed consent and the project was approved by the Comité mixte d'éthique de la recherche du Regroupement Neuroimagerie/Québec.

Respiratory paradigm
A gas timing schedule previously described by Bulte et al [2], with a total duration of 18 minutes, was applied, as in [6]. This involves two 2-min periods of hypercapnia (HC) and two 3-min periods of hyperoxia (HO), induced by administering gas mixtures enriched with CO 2 and O 2 respectively. Hypercapnia was followed by a 1-min normocapnic period and then the 3-min hyperoxic stimulus. Hyperoxia was followed by a 3-min period of normoxia. Periods of normocapnia and normoxia were long enough to ensure a return to baseline as shown by the CBF and BOLD time course in Tancredi et al, figure 3 [7]. Participants inhaled the gas mixtures via a breathing circuit developed in-house [5]. During the first test-retest experiment [6], the hyperoxia periods were induced with the subjects breathing a mixture of 50% pure oxygen balanced with air, yielding a fix inspired O 2 concentration of 60% O 2 . During the second testretest experiment, the participants were given 100% O 2 during periods of HO. Otherwise participants were given medical air to breath. Respiratory gases were continuously monitored using the CO2100C and O2100C modules of a BIOPAC MP150 system (BIOPAC Systems Inc., CA, USA). For additional details, see Lajoie et al. [6].

Respiratory data analysis
Analysis of the respiratory data was carried out using an in-house program developed in Matlab (MathWorks, Natick, MA, USA), as in Lajoie et al [6]. An automatic extraction of the end-tidal (ET) and end-inspiratory points from the continuous O 2 and CO 2 traces was performed. Each ET point was corrected to account for the low-pass filtering effect of the filter placed in series and to account for an expired partial pressure of water of 47 mmHg [20]. More details about the respiratory data analysis can be found in Lajoie et al [6].
The average values of ETO 2 at baseline and during both respiratory stimuli were used to compute arterial O 2 content (ml O 2 /ml blood) and change in the venous deoxygenated fraction ([dHb]/[dHb] 0 ) as in Chiarelli et al [14] and Gauthier et al [1]. The latter quantities are needed to obtain the BOLD calibrated value M, resting OEF and CMRO 2 as specified below.

Imaging data analysis
Preprocessing. Analysis of functional scans along with exclusion of artifact and non-paranchymal voxels were performed using in-house software implemented in C, as in Lajoie et al [6].
During hyperoxic manipulation, the longitudinal relaxation time (T 1 ) of blood is altered due to an increase in plasma concentration of paramagnetic O 2 [13]. To account for this change in blood T 1 , that would bias the measured CBF changes, a corrective factor using the approach described in Chalela et al [21] and Zaharchuk et al [22] was applied. First, estimates of the arterial blood T 1 values during hyperoxic periods were obtained based on the individual ETO 2 measurements, used as a surrogate for arterial partial pressure of O 2 (PaO 2 ), along with the R1 (1/ T 1 ) and PaO 2 relationship in rats' blood reported in Pilkinton et al [13]. Depending on whether our ETO 2 values were within or outside the range of values in Pilkinton et al's study, the T 1 values were either linearly interpolated or extrapolated. Then, the individual blood flow maps during HO were corrected by applying a slice-wise corrective factor based on the quantitative blood flow equation [23], the slice acquisition time and the adjusted T 1 value.
Computation of CMRO 2 . MRI measures of BOLD and CBF acquired during the hypercapnic manipulation, along with the changes in the venous deoxygenated fraction were used as inputs to the generalized calibration model (GCM), described in Gauthier and Hoge [4], yielding a functional curve (the "HC curve") of possible pairings of M and OEF. Repeating the procedure with the hyperoxia measurements yielded a second curve of possible M and OEF pairings (the "HO curve"). The intersection of these two curves provided the true values of M and OEF at each voxel. Finally, CMRO 2 was determined by multiplying OEF by O 2 delivery, computed as the product of resting CBF by arterial O 2 content. Since the small regional CBF responses to hyperoxia are difficult to measure due to the low SNR of ASL, a uniform change of CBF was assumed throughout the brain, based on the cortical gray matter change after T 1 correction. Additional information about the computation of CMRO 2 can be found in Lajoie et al [6].
Tissue segmentation. Automated segmentation of GM from the anatomical scans was carried out using the FMRIB Software Library (FSL) [24]. Structural images were extracted from T 1 -weighted scans using the brain extraction tool (FSL's BET). Finally, a probability mask of GM was created employing the automated segmentation tool (FSL's FAST), and was resampled to the resolution of the functional EPI scans.
Regions Of Interest (ROIs). The model-derived estimates were evaluated throughout cortical GM as well as within six ROIs selected from the ICBM OASIS-TRT-20 atlas [25] and presented in Lajoie et al [6], figure 1: the inferior parietal, superior parietal, precuneus, hippocampus, anterior (caudal and rostral) cingulate and posterior cingulate. Each ICBM threedimensional ROI was registered to the resolution of the functional EPI scans before being conjoined with the individual's GM probability mask excluding voxels with a GM probability lower than 50% as well as non-parenchymal voxels previously identified. Additionally, voxels where the QUO2 model could not be solved were excluded when performing the ROI analysis of M, OEF and CMRO 2 . The resultant ROI probability masks were used to perform weighted averaging of the different measurements and estimates.
Registration. Individual ΔR2 Ã HO , M, OEF and CMRO 2 maps were non-linearly registered to the ICBM152 template using the CIVET software package [26] via the CBRAIN tool [27] with 12 degrees of freedom, as in Lajoie et al [6]. Test-averaged maps of ΔR2 Ã HO were computed as arithmetic means using in-house software. Averaged maps of M, OEF and CMRO 2 were obtained excluding any voxels where the QUO2 model could not be solved.

Analysis of sensitivity of model-derived QUO2 values to change in O 2 concentration
The end-tidal O 2 , blood flow and R2 Ã measurements during a hyperoxia manipulation depend on the employed O 2 concentration. It was discussed that hyperoxia may also perturb the metabolism [28], however, in our model, we consider HO as an isometabolism challenge as assumed in numerous previous calibrated BOLD studies [1][2][3]. In order to understand the impact of lower and higher levels of HO (respectively LHO and HHO) to QUO2, we performed an analysis of the sensitivity of its model-derived parameters, M, OEF and CMRO 2 , to changes in ETO 2

Statistical analysis
For each model-derived estimate (M, OEF and CMRO 2 ), we carried out a statistical analysis, using Matlab, on three different combinations of tests: 1) comparing Test A and Test B under the LHO protocol; 2) comparing Test A and Test B under the HHO protocol; 3) comparing tests A between both protocols. When needed, a two-tailed paired t-test was performed, considering a P < 0.05 level of significance, to detect any significant difference between tests and protocols. Within each protocol, we also investigated any difference across ROIs by pooling tests values and using family-wise error (FWE) correction for multiple comparisons, set at P < 0.05.
Prior to the analysis, statistical tests were performed on the data to ensure it satisfied the repeatability criteria: each distribution of difference between tests was evaluated for normality using the Shapiro-Wilk W-test, while the independence between the magnitude of difference and mean of measurements was verified using a rank correlation coefficient (Kendall's τ). If the difference distribution appeared to deviate from a normal distribution, or if the magnitude of difference increased with the mean of measurements, the data were transformed on the log 10 scale and the verification was repeated. In cases where the log 10 scaled data satisfied the criteria, the repeatability was assessed on these scaled values. Otherwise, assessment of repeatability was based on the original values, as done in previous studies [29][30][31][32].
The next metrics were evaluated: 1. dSD, the standard deviation of the difference between tests measurements.
3. wsCV, the within-subject (or intra-subject) coefficient of variation, as used in Floyd et al [30] and Chen et al [32]. wsCV = p [mean of the (wsSD/subject mean) 2 ]. wsCV provides an unbiased measure of variability expressed as a percent of the mean with a low wsCV indicating a high reproducibility/repeatability. When data were on the log 10 scale, wsCV was approximated by 10^(wsSD)-1 [33]. 4. bsCV, the between-subject (or inter-subject) coefficient of variation as computed in Tjandra et al [34]. bsCV = SD pooledData / mean pooledData Ã 100.

Results
One participant reported a high level of anxiety during Test A of the LHO protocol, and the measured CBF response to CO 2 was found to be twice the standard deviation of the group mean. Data from this participant has been excluded from the present analysis (as in the previous related work [6]).

Susceptibility artifacts
Fig 2 shows a qualitative examination of R2 Ã changes during periods of HO (ΔR2 Ã ) through axial, sagittal and coronal views chosen in order to observe regions vulnerable to susceptibility artifacts. No masking, nor median filtering was performed on the functional maps prior to the non-linear registration to the ICBM template and maps average. The contrast window was chosen to facilitate the observation of increase in R2 Ã characterized by orange and red colors. An overall R2 Ã decrease (equivalent to a BOLD increase) in white and gray matter during HO is observed, which is more significant under the more extreme levels of HO. On the other hand, as a repercussion of the presence of paramagnetic oxygen molecules in inhaled air, both protocols presented comparable regions of susceptibility artifacts characterized by positive ΔR2 Ã in voxels surrounding the nasal cavity. Percent of voxels in GM characterized by this increase were found to be the same in both protocols, with 12.8% under the LHO protocol and 11.7% under the HHO protocol (P = 0.25), although the positive values were generally higher under the HHO protocol (shown by darker red color). Any voxel affected by the susceptibility artifacts, later results in a non-solution voxel for M, OEF and CMRO 2 , and were therefore excluded from the analysis as mentioned in the methodology section.

T 1 shortening
A value of 1.65 sec was assumed for the normoxic arterial blood T 1 [35], whereas the estimated blood T 1 shortening was larger during the high O 2 hyperoxia state than during the low

Analysis of sensitivity of model-derived QUO2 values to change in O 2 concentration
The individual impacts of changes in ETO2 HO , ΔR2 Ã HO and Δ%CBF HO , on M and OEF, as a function of the HO levels are examined by numerical simulations. These changes in ETO2 HO , ΔR2 Ã HO and Δ%CBF HO are dependent on one another and are examined in order to explain the combined impact on M and OEF. Results are summarized in Fig 4. Fig 4A shows   appear to cancel each other out, yielding a modest combined effect. The same conclusion stands for CMRO 2 , since it is the result of multiplying OEF by two measurements that are independent of the hyperoxic stimulus, i.e. the resting CBF and the resting arterial O 2 content. Therefore, in principle, one would expect M, OEF and CMRO 2 to remain stable, regardless of the O 2 concentration used to produce hyperoxia. The following sections explore this assumption using real values computed in different ROIs, but also on a voxel-wise basis.

Protocol-averaged estimates in ROIs
In Fig 5A are shown the ROI-averaged M, OEF and CMRO 2 in each protocol (red and blue bars) and over both protocols (green bars). For each combination of model-derived estimate The impact of inspired oxygen levels on calibrated fMRI measurements of M, OEF and resting CMRO 2 and ROI, we observe a good consistency between protocols with the lowest P values being: P = 0.17 in superior parietal for M, P = 0.37 in superior parietal for OEF and P = 0.06 in GM for CMRO 2 . Additionally, no apparent divergence was found in variance within each protocol. In Fig 5B are shown, for each estimate, the degree of difference between ROIs, when comparing the estimates averaged over both protocols and correcting for multiple comparisons (FWE set at P < 0.05). OEF estimates were found to be similar across ROIs, with the exception between hippocampus and anterior cingulate where a significant difference was detected (P = 0.04). Values of M and CMRO 2 in hippocampus were found to be the smallest compared with the other ROIs, with the exception of anterior cingulate (for M) and superior parietal (for CMRO 2 ).  The impact of inspired oxygen levels on calibrated fMRI measurements of M, OEF and resting CMRO 2 protocol (mean wsCV LHO = 16%, mean wsCV HHO = 25%, P = 0.006). On the other hand, within-subject variability of OEF and CMRO 2 were found unchanged regardless of the HO protocol (OEF: mean wsCV LHO = 15%, mean wsCV HHO = 16%, P = 0.2; CMRO 2 : mean wsCV LHO = 17%, mean wsCV HHO = 18%, P = 0.6).

Parametric maps
In Fig 7, we present, for each combination of tests (1: Test A vs. B under the LHO protocol, 2: Test A vs. B under the HHO protocol, 3: Tests A between both HO protocols), mean tests, between-and within-subject CV maps of M, OEF and CMRO 2 . All functional maps were nonlinearly registered (NLreg) to the ICBM space. In addition to intrinsic physiological changes, errors in measurements and head movements occurring between the anatomical and the  Fig 5-B shows, for each estimate, any significant difference observed between ROIs after correcting for multiple comparisons (FWE, P < 0.05): dark blue indicates an absence of significant difference (P > 0.05), while light blue (P < 0.05), green (P < 0.005) and orange (P < 0.0005) illustrate a significant difference between two ROIs (represented in the X and Y axis). GM = gray matter, IP = inferior parietal, SP = superior parietal, PRE = precuneus, HIP = hippocampus, AC = anterior cingulate, PC = posterior cingulate. https://doi.org/10.1371/journal.pone.0174932.g005 The impact of inspired oxygen levels on calibrated fMRI measurements of M, OEF and resting CMRO 2 functional scans, a voxel-wise within-subject repeatability may be partly affected by random inaccuracies in registration. In order to evaluate any limitation on the voxel-wise repeatability caused by the registration to the ICBM space, we present the CVs maps for MPRAGE, and verify if any enhancement was possible thanks to the non-linearly registration of our maps ( Fig  7A), compared to the linearly registered MPRAGE (Fig 7B). All CVs maps are shown using a window level of 0-200%. At these levels, the passage from 20% to 30% is characterized by the transition from purple to blue, with 30% being an approximate upper limit for what is considered as low variability. Compared to the linearly registered maps (Lreg), the non-linearly registered (NLreg) MPRAGE maps presented a better defined gray matter region, while wholebrain between-and within-subject variability were found to be lower. WsCV values in NLreg were generally found to be <5% in WM, <10% in GM and exceptionally <20% in few small regions, whereas in Lreg wsCV, values were <10% in WM and GM and <20% in with few small regions. Mean maps of M, OEF and CMRO 2 (Fig 7C, 7D and 7E) qualitatively exhibited an absence of dependency on the O 2 protocol employed. CVs maps of M presented slightly less variability under the LHO than the HHO. All three estimates were found to have low GM within-subject variability for the three combinations of tests (<30%). M and CMRO 2 presented a clearer distinction between the population variance and the within-subject variability, The impact of inspired oxygen levels on calibrated fMRI measurements of M, OEF and resting CMRO 2 The impact of inspired oxygen levels on calibrated fMRI measurements of M, OEF and resting CMRO 2 whereas OEF was found to have a lower voxel-wise between-subject variability, approaching the within-subject variability.

Discussion
Performing an analysis of individual impacts, on M and OEF, of variation in ETO2 HO , ΔR2 Ã HO and Δ%CBF HO , we have shown how little M is affected by the O 2 concentration in GM, and how the individual impacts on OEF were practically cancelling out, yielding a nearly nonexistent combined impact on OEF and therefore on CMRO 2 . Exploring the within-subject reproducibility in different ROIs as well as on a voxel-wise basis, we observed an unchanged reproducibility for OEF and CMRO 2 regardless of differences in ETO2 HO , ΔR2 Ã HO and Δ% CBF HO caused by a distinct O 2 concentration in inhaled gas. On the other hand, the M withinsubject repeatability was found to be slightly enhanced under the LHO protocol. No significant difference was found between protocol-averaged values.
In certain situations, the differences between subjects' brain anatomy are such that a linear transformation is insufficient to register their brain maps on to standard spaces. The local deformations produced by the non-linear registration improve the match. The comparison of linearly versus non-linearly registered individual MPRAGE images provides a qualitative example of the improvement brought by the non-linear registration. The method produced sharper group-averaged maps, characterized by more distinct sulci and more accentuated grey/white matter contrast. Quantitatively, the non-linear co-registration afforded lower CV values.
The presence of paramagnetic molecular oxygen in inhaled air produces susceptibility artifacts. We examined regions vulnerable to those artifacts such as the frontal sinuses and nasal cavity of our ΔR2 Ã HO maps. However, no evidence of enlarged patterns of susceptibility artifacts under inhalation of 100% O 2 (HHO) compared to 60% (LHO) was found, thus yielding a comparable percent of non-solution voxels in GM for both protocols.
Rather than assuming a fixed value of CBF change during HO, the individual T 1 -corrected Δ%CBF HO averaged in GM was used, therefore capturing any intra-subject variation between Test A and Test B in blood flow during HO. Our T 1 values were extrapolated from experimentally-determined values in animal model, which is a common practice in calibrated fMRI approaches. Human blood constitution is similar to that of bovine and rat blood and is likely to experience comparable T 1 shortening during the hyperoxia stimulus [13,36,37]. This is of course an assumption and represents a potential source of confounds in our blood flow changes calculations.
In CBF quantification, so long as the PLD is equal to or higher than the arterial transit time (ATT), the exact ATT value does not matter. In our 2D acquisition, the first and last slices are acquired after a delay of 900 msec and 1986 msec respectively, resulting in a brain-averaged PLD of 1443 msec. Donahue et al. [38] applied a pCASL in a cohort of healthy volunteers (mean age of 30 ± 4 years) and obtained a group-averaged ATT lower than 900 msec within each lobe, including within the occipital lobe with 834 ± 29 msec. We therefore believe that in the large majority of cases, the acquired ASL signal was accurately reflecting CBF and that an increase in our PLD would have resulted in a loss in SNR, especially during hypercapnic where the ATT is known to diminish [38]. Additionally, the ATT increase during HO should be minor as our data indicates that the CBF decreases induced by hyperoxia, even at high O 2 concentrations, are not substantial. When using a 2D acquisition in a population of elderly or and CMRO 2 (E) are shown in one axial slice. Maps were non-linearly registered to the ICBM152 template. As a reference, the equivalent information is presented for MPRAGE maps non-linearly (A) and linearly registered (B) to the template.
https://doi.org/10.1371/journal.pone.0174932.g007 unhealthy patients, it would be recommended to increase the PLD slightly while also imaging a lower number of thicker slices, as in the study De Vis et al. (2015) where a nominal PLD of 1550 msec and 11 slices with 7 mm slice thickness were employed.
Small cohort sizes like that of the present study have been common in recent years, particularly for complex fMRI protocols with greater physiological specificity than the classic BOLD contrast. Despite the relatively small sample size, which limits confidence in the statistical significance of our findings, the present study provides new information on the impact of inspired oxygen levels on calibrated fMRI technique.
To conclude, it was revealed that the pattern of susceptibility artifacts under hyperoxia was comparable regardless of the HO levels. We also demonstrated that variations in ETO2 HO, CBF HO and R2 Ã HO were accounted for within the QUO2 model, resulting in an unchanged ROI-averaged M, OEF and CMRO 2 estimates. We observed that the within-subject repeatability was either unchanged (for OEF and CMRO 2 ) or slightly enhanced under the LHO protocol (for M). In summary, the use of a higher hyperoxic challenge revealed no beneficial impact on the calibrated fMRI measurements, while a reduced concentration of 60% O 2 was shown to maintain sufficient BOLD contrast and to produce consistent model-derived results.