Robustly measuring vascular reactivity differences with breath-hold: Normalisingstimulus-evoked and resting state BOLD fMRI data
Kevin Murphy ⁎, Ashley D. Harris, Richard G. WiseCardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, CF10 3AT, UK
Article history:Received 27 April 2010Revised 6 July 2010Accepted 26 July 2010Available online 1 August 2010
Inter-subject differences in local cerebral blood flow (CBF) and cerebral blood volume (CBV) contribute todifferences in BOLD signal reactivity and, therefore, unmodelled variance in group level fMRI analyses. Asimple way of elevating blood CO2 concentrations to characterise subject differences in vascular reactivity isthrough breath-holds but two aspects of this measure are often neglected: (1) breath-holds are usuallymodelled as blocks even though CO2 accumulates over time and (2) increases in CO2 differ between subjects.This study demonstrates that the BOLD breath-hold response is best modelled by convolving the end-tidalCO2 trace with a standard haemodynamic response function and including its temporal derivative. Inclusionof the BOLD breath-hold response as a voxel-dependent covariate in a group level analysis increases thespatial extent of activation in stimulus evoked and resting state datasets. By expressing the BOLD breath-hold response as a percentage signal increase with respect to an absolute change in the partial pressure ofCO2 (expressed in mmHg), the spatial extent of stimulus-evoked activation is further improved. Thisdemonstrates that individual end-tidal CO2 increases to breath-hold should be accounted for to provide anaccurate measure of vascular reactivity resulting in more statistically active voxels in group level analyses.
Since initial reports in the early 1990s (Bandettini et al., 1992;Kwong et al., 1992; Ogawa et al., 1992), blood oxygenation leveldependent (BOLD) contrast fMRI has evolved into a widely usedtechnique to non-invasively map brain function. Interesting changesin the BOLD signal not only reflect increasedmetabolic demand due toneural activity but also activation-related differences in cerebral bloodflow (CBF) and cerebral blood volume (CBV) (Buxton et al., 2004,1998; Davis et al., 1998; Hoge et al., 1999). Vascular effects causeinterpretability problems for BOLD fMRI results because signalchanges cannot solely be attributed to increases in neural activity.Variations in baseline vascular state, for example, due to vasoactivesubstances like caffeine (Behzadi and Liu, 2006) and pharmacologicalagents (Iannetti and Wise, 2007; Iannetti et al., 2005), confound theBOLD response to stimuli by altering CBF. Furthermore, differences invascular reactivity (i.e., a vessel's ability to respond to a vasodilatorystimulus such as carbon dioxide (CO2)) caused by natural variations inlocal CBF and CBV affect the local capacity to mount a BOLD response,resulting in inconsistent activation measures for a given “unit” ofneural activity. Similarly, subject-specific, within-region differences invascular reactivity lead to varying activationmeasures across subjects.
Such unmodelled variance reduces statistical power when comparingbetween scan sessions and subjects in fMRI analyses.
Vascular reactivity to increased arterial CO2 produces elevated CBFlevels through vasodilatation (Poulin et al., 1996; Ramsay et al., 1993).In steady state, CBF increases by 4–6% per mmHg rise in the partialpressure of arterial CO2 (Corfield et al., 2001). Natural changes in depthand rate of breathing (~0.03 Hz) (Abbott et al., 2005; Birn et al., 2006)cause fluctuations in arterial CO2 (0–0.05 Hz) which affect the BOLDsignal (Wise et al., 2004). Regional differences in cerebral vascularresponse to CO2 have been measured by positron emission tomogra-phy (Ito et al., 2000) and can be largely attributed to increases in CBF(Rostrup et al., 2000). Taking advantage of the vessels' reaction to CO2,characterisation of BOLD signal vascular responsiveness is oftenachieved using hypercapnic challenges that are assumed to increaseCBF and CBV without a concomitant increase in the metabolic oxygenconsumption rate (CMRO2) (Bandettini and Wong, 1997; Davis et al.,1998; Hoge et al., 1999), although some recent evidence suggests thathypercapniamay induce subtle changes in neural activity (Zappe et al.,2008). The derived signal is used to calibrate BOLD responses to moredirectly reflect CMRO2 changes and thus neural activity. Suchnormalisation methods have been shown to improve spatial localisa-tion of the BOLD response to neuronal activity (Bandettini and Wong,1997; Cohen et al., 2004) in a reproducible way (Leontiev and Buxton,2007).
A simple, alternative method to elevate arterial blood CO2 con-centration, without administering exogenous CO2, is breath-holding
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(Markus and Harrison, 1992; Sasse et al., 1996). This has beensuccessfully employed to measure vascular reactivity with fMRI(Corfield et al., 2001; Kastrup et al., 1998; Li et al., 1999; Liu et al.,2002). Corrections for vascular reactivity differences using breath-hold derived measures reduce variability between subjects duringcognitive tasks (Thomason et al., 2007) and between groups withdiverse vasculature, for example, young/elderly (Handwerker et al.,2007) and adult/child (Thomason et al., 2005). The breath-hold taskhas many advantages over CO2 administration due to its simplicity,especially when used with clinical populations (van der Zande et al.,2005). Vascular reactivity measures derived from a breath-hold aresimilar to those obtained from CO2 inhalation (Kastrup et al., 2001).Regional CBF changes depend on breath-hold duration and techniquebut relatively small inter-individual variability exists for a singleparadigm (Kastrup et al., 1999b). BOLD responses can be detected torelatively short breath-holds but longer breath-holds produce morerobust signal changes reaching a plateau at approximately 20 s (Liuet al., 2002; Magon et al., 2009). Breath-hold after inspiration showsa biphasic change in CBF and BOLD signal (decrease followed byincrease) whereas breath-hold after expiration leads to an immediaterise in bothCBF andBOLD (Kastrup et al., 1999a; Li et al., 1999). Breath-hold after inspiration causes an increase in intrathoracic pressurewhich changes mean arterial pressure, transiently altering CBF, andthus the BOLD signal (Harper et al., 1998; Macefield et al., 2006).Controlling the inspiration depth can reduce the variance of the BOLDresponse to an end-inspiration breath-hold (Thomason and Glover,2008) but higher repeatability after end-expiration breath-holdsuggests it is the superior method (Scouten and Schwarzbauer, 2008).
Two aspects of breath-hold derived vascular reactivity measuresare often neglected:
(1) arterial CO2 accumulates over time; and(2) increases in arterial CO2 due to breath-hold differ between
subjects (Sasse et al., 1996).
In the majority of studies, breath-hold periods are modelled asblocks. However, arterial CO2 increases over time during the breath-hold period (Sasse et al., 1996) which is likely to result in a BOLDresponse that less resembles a simple square block. Therefore, analternative method to model the breath-hold is required. Furthermore,by neglecting to account for inter-subject differences in arterial CO2
increases, vascular reactivity measures derived from breath-hold tasksare confounded by additional variability, which must be consideredwhen using vascular reactivity as an across-group normalisation tool.This study first determines the appropriate technique to model theBOLD response during breath-holds and then demonstrates howindividual differences in the CO2 response are reflected in such vascularreactivity measures. The relative benefits of accounting for vascularreactivityusingabreath-holdmeasurearedetermined in both stimulus-evoked and resting state activation datasets. Just as stimulus-evokedactivity measures can be contaminated by vascular reactivity differ-ences, similar noise is evident in resting state connectivity measures.
Methods
Subjects and data acquisition
Twelve volunteers (5 female) aged 29.2±4.6 (mean±SD)underwent gradient-echo echo-planar imaging at 3 T (GE HDx)using a BOLD weighted imaging sequence (TR=3 s, TE=35 ms,matrix=64×64, FOV/slice=20.5 cm/3.2 mm, flip=90°, 53 slices).Three separate scans were acquired: a breath-hold scan (BH), asensory scan in which a visual/auditory/motor task was presented(SENS) and a resting scan (REST) each lasting 110, 220 and 210volumes, respectively. End-tidal carbon dioxide (Petco2) and end-tidal oxygen (Peto2) traces were recorded throughout the experiment
using a nasal canula attached to a gas analyser (AEI Technologies, PA)as a representative measure of arterial partial pressures of both gases.A T1 weighted whole-brain structural scan was also acquired(1×1×1 mm voxels) for purposes of image registration. This studywas approved by the Cardiff University School of Psychology EthicsCommittee and all volunteers gave written informed consent.
Tasks
The breath-hold task consisted of 30 s of paced breathing followedby a 20-s breath-hold after expiration to a normal, unforced breathingdepth which ended in a quick, forced exhale prior to restarting pacedbreathing. The sensory task consisted of an 8 Hz flashing visualcheckerboard, an auditory 1 kHz beeping stimulus lasting 80 mspresented at 8 Hz and a self-paced fingertapping task (cued by acrosshair changing from yellow to red). For the first half of the scan,the stimuli were presented in 30 s blocks along with rest blocksserially in a pseudo-random order to allow for functional localisation(i.e., rest, visual, auditory, motor, rest, motor, visual, auditory, …).During the second half of the scan, the sensory stimuli were presentedin an event-related fashion in four blocks of 60 s each, interspersedwith 20 s of rest. For every second within the blocks, it was possiblefor each of the sensory tasks to be present. The probability of eachstimulus occurring in each 1 s period was 33%. The presentation orderwas optimised by randomly generating stimulus timings untilcollinearity between the three task types was minimised and theseoptimised timings were used for every participant.
Pre-processing
The functional MR data were motion corrected using MCFLIRTwithin the FMRIB Software Library (FSL—http://www.fmrib.ox.ac.uk/fsl (Smith et al., 2004)). Voxels from outside the brain were removedusing BET and the resulting data were smoothed with a 5-mm FWHMGaussian kernel. The smoothed data were temporally high-passfiltered (filter cut-off of 100 s=0.01 Hz) and were converted into apercentage change time series.
Regressors to model the breath-hold response
Nine GLM analyses were performed on the BH data using the3dDeconvolve function within AFNI (http://afni.nimh.nih.gov/afni(Cox, 1996)) to determine which set of regressors best fit the BOLDbreath-hold response (Fig. 1). The nine sets of regressors used were:
(1) [Block Only] The simple block task timing convolved with theSPM double gamma variate haemodynamic response function(HRF) (Friston et al., 1998), which is the standard approach tomodel breath-hold vascular reactivity (Abbott et al., 2005;Handwerker et al., 2007; Kastrup et al., 1999b; Scouten andSchwarzbauer, 2008; Thomason et al., 2005, 2007);
(2) [Block+td] The same block regressor along with its temporalderivative which allows for a shift in time between theregressor and the BOLD signal (Friston et al., 1998);
(3) [Block Lag] The block regressor delayed by 9 s (chosen bydetermining the time delay that maximises correlation betweentheglobal BOLDsignal and theblock regressor across all subjects);
(4) [Block Lag+td] The 9 s-delayed block regressor along with itstemporal derivative;
(5) [Sine–Cosine] A sine and a cosine wave at the task frequency(f=1/50 s=0.02 Hz) allowing phase flexibility;
(6) [CO2 Only] A regressor derived from the subject specific end-tidal CO2 trace by calculating the points of maximum CO2 valueat the end of each exhale, linearly interpolating between themand resampling the result to the TR timing. A similar linearinterpolation was performed during the breath-hold periods in
Fig. 1. Nine sets of regressors were used in separate GLMs to determine which bestexplained the BOLD response to a breath-hold. (1) Block Only: a simple block designconvolved with the standard SPM double gamma variate HRF; (2) Block+td: the BlockOnly regressor alongwith its temporal derivative; (3) Block Lag: the Block Only regressordelayed by 9 s; (4) Block Lag+td: the Block Lag regressor along with its temporalderivative; (5) Sine–Cosine: a sine and cosine wave at the task frequency of 0.02 Hz;(6) CO2 Only: the raw unprocessed subject specific end-tidal CO2 trace; (7) CO2+td: theCO2 Only regressor along with its temporal derivative; (8) CO2 HRF: the subject specificend-tidal CO2 trace convolved with the standard SPM double gamma variate HRF;(9) CO2 HRF+td: the CO2 HRF regressor along with its temporal derivative.
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which there are no end-tidal CO2 values (the interpolationrange was between the last exhale before breath-hold and thefirst after);
(7) [CO2+td] The subject specific CO2 trace from (6) along with itstemporal derivative;
(8) [CO2 HRF] The CO2 trace from (6) convolved with the standardSPM double gamma variate HRF; and
(9) [CO2 HRF+td] The HRF-convolved CO2 trace (7) along with itstemporal derivative.
Data analyses
The amount of variance explained in each voxel of the BH databy each of the 9 sets of regressors was calculated using AFNI's
Fig. 2. The left panel shows the average end-tidal CO2 traces for the 12 subjects with the stanvertical grey bars. During breath-holds, no end-tidal CO2 measures are recorded. The linear ibreath-hold is shown as a dotted line. The right panel depicts end-tidal changes during b(triangles) and the range of a phase-shifted sine wave (squares) fitted to the traces shown itidal O2 for each subject are displayed with diamonds (scale on the right-hand side of the g
3dDeconvolve function to perform multiple linear regressions onevery voxel. For each of the 9 sets of regressors, the absolute rangeof the fit to the model was used as the measure of breath-hold BOLDsignal increase in every voxel (i.e. non-responsive voxels wereincluded in subsequent analyses).
First-level data processing was carried out on both the blockand event-related data from the SENS scan using FSL's FEAT. Time-series analysis with local autocorrelation correction was performedusing FILM (Woolrich et al., 2001) with regressors for the threeconditions: visual, motor and auditory. The results were registered tothe high-resolution structural scan for each subject and convertedinto standard MNI space using FLIRT (Jenkinson et al., 2002). Group-level analyses were carried out for both the block and event-relateddata for the three conditions separately using FLAME stage 1, with asimple all 1 s contrast (Beckmann et al., 2003; Woolrich, 2008;Woolrich et al., 2004). Visual, motor and auditory functional ROIswere defined by liberally thresholding the block design group dataat ZN3.0 without further correction for multiple comparisons atthis stage.
To account for breath-hold BOLD increase differences acrosssubjects, voxel-dependent group level covariates were assembledfor each of the 9 sets of regressors. The breath-hold BOLD increasemapswere converted to standardMNI space using FLIRT, the resulting12 subjects' maps were concatenated and included in the group levelFEAT analyses of the event-related SENS data as voxel-dependentregressors. In this way, a different two regressor group-level modelwas fit to each voxel: the first determines the mean activationmeasure across all subjects and the second represents each subject'smeasure of BOLD increase to breath-hold for that voxel.
Vascular reactivity covariates
The importance of accounting for subject-specific increases in end-tidal CO2 when including the group level covariates in an analysis wasinvestigated. Baseline end-tidal CO2 was defined by averaging valuesacross the entire REST run. Individual subject increases in end-tidalCO2 during breath-hold were calculated in two ways for comparison:(1) the absolute range of the end-tidal CO2 trace was determined (i.e.maximumminus minimum value); and (2) a phase-shifted sine waveat the task frequency (f=0.02 Hz) was fitted to the end-tidal CO2
trace with its amplitude determining the increase. The voxel-dependent group level covariates from the previous paragraph were
dard deviation shown with grey lines. The timing of the breath-holds is indicated by thenterpolation between the last end-tidal CO2 value before breath-hold and the first afterreath-hold. Increases in end-tidal CO2 were defined in two ways: the absolute rangen A. Increases in end-tidal CO2 are uncorrelated with baseline levels. Decreases in end-raph).
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normalised by the subject-specific CO2 partial pressure increases (i.e.,per mmHg) to give vascular reactivity maps. These vascular reactivitymaps were included in separate group level analyses similar to thosedescribed in the previous paragraph.
Resting state analysis
The influence of vascular reactivity differences on resting stateconnectivity measures was also determined. Slice timing correctionwas performed using a sinc interpolation to shift each time-series tothe same temporal origin. The data were smoothed with a 5-mmFWHM Gaussian kernel and high-pass filtered with a cut-offfrequency of 0.01 Hz. Seed region correlation analyses using ROIs inthe left motor cortex, the pre-supplementary motor area (pre-SMA)and the PCC were performed on a subject-by-subject basis. The leftmotor cortex and the pre-SMA ROIs were created by thresholding thegroup level motor block design data (ZN4.0 for left motor cortex andZN3.0 for pre-SMA) and combining with appropriate anatomicalmasks. The PCC seed region was defined by drawing a 12 mmdiameter sphere centred around the previously published Talairachcoordinate [5L, 49P, 40S] (Fox et al., 2005; Murphy et al., 2009;Shulman et al., 1997). Time courses from voxels in these regions wereaveraged separately and included in individual first-level FEATanalyses similar to those described above. Group level analyses withand without the previously described breath-hold covariates deter-mined the vascular reactivity influence on connectivity measures.
Results
Baseline levels of end-tidal CO2 concentration varied substantiallyacross subjects with a mean of 39.7 mmHg and a range of 35.8 to44.4 mmHg (Fig. 2). Increases in end-tidal CO2 over the course of the20 s breath-hold also vary greatly across subjects. Two methods wereused to quantify this: the absolute range and the range of a fitted sinewave. The mean increase in the absolute range was 13.4±2.2 mmHg(range: 9.5–17.3 mmHg) whereas the mean increase in the sine rangewas 8.86±1.2 mmHg (range: 7.5–11.5 mmHg). Neither were signif-icantly correlated with the baseline levels (p=0.14 and p=0.59,respectively) but tended towards significance with each other(p=0.07) as is to be expected. End-tidal O2 decreased after breath-
Fig. 3. The average variance explained over the whole brain by each of the nine sets of regrbreath-hold response better than any block based approach. Similar amounts of variance areHRF+td]. The right panel shows the average over motor cortex of the fits to the breath-hold dwere closest to the means across all subjects. The red line depicts the average BOLD responsthe top left of each graph. The Block Only regressor often fits negatively to the data due to thedata. The other three sets of regressors can account for this temporal lag, resulting in a mu
hold from baseline by 39.3±8.5 mmHg (range: 26.3–55.7 mmHg)which was uncorrelated with both measures of CO2 increase.
Regressors to model the breath-hold response
Modelling the breath-hold responsewith a simple block convolvedwith a HRF [Block Only] does not fit the data well since CO2
accumulates in the blood over time (Fig. 3). Although including atemporal derivate [Block+td] in the model improves the fit, anexplicitly defined lag in addition to the temporal derivative [Block Lag+td] forms the best block-based approach. Over the whole brain, astatistically significant increase in the average variance explained bythe model is observed across subjects when a lag of 9 s is introduced(paired t-test: t=5.01, pb0.0004). The Sine–Cosine modelling fits theBOLD breath-hold response better across subjects than the bestperforming block based approach (paired t-test: t=4.36, p=0.001).The most effective model based on end-tidal CO2 measures is theCO2 trace convolved with the HRF and its temporal derivative [CO2
HRF+td]. On average over the whole brain, there is no consistentimprovement in variance explained by the CO2 HRF+td model overthe Sine–Cosine model (Fig. 3). In both the visual and motor areas inisolation, there is no significant difference between these twomethods. However, the CO2 HRF+td modelling approach explainssignificantly more variance in the auditory cortex (paired t-test:t=2.83, pb0.02). As an example, Fig. 3B shows the average BOLDresponse to breath-holding over the motor cortex in a representativesubject along with the average of the corresponding fits. A GLMwith asimple block design regressor [Block Only] often results in a negativefit due to the temporal lag of the BOLD response. Therefore, theaverage fit over motor cortex is quite poor (r2=0.05). Explicitlyincluding a lag and a temporal derivative [Block Lag+td] in the modelimproves the average fit by accounting for this delay (r2=0.3).Further improvements are evident when fitting the Sine–Cosinemodel(r2=0.36) and the CO2 HRF+td model (r2=0.39).
Vascular reactivity covariates
To correct for vascular reactivity differences across subjects in thesensory tasks, voxel-dependent group covariates derived from the BHdata were included in a group analysis of the SENS data. BH-induced
essors is plotted for each subject (left panel). The Sine–Cosine modelling fits the BOLDexplained by modelling the HRF-convolved CO2 trace and its temporal derivative [CO2
ata for four of themodels for a single subject. This subject was chosen since the r2 valuese, the black shows the average fit time series and the variance explained is displayed intemporal lag of the BOLD response and so on average explains very little variance in thech larger amount of variance explained.
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signal changes derived from the three regressor sets were investigat-ed: the block design with temporal derivative [Block+td] (themethod most often used in the literature), the sine and cosineregressors [Sine–Cosine] and the CO2 trace convolved with the HRFalong with its temporal derivative [CO2 HRF+td] (regressor sets 2, 5and 9 in Fig. 1, respectively). As expected, there are widespread grey-matter correlations between the BH-induced signal changes and theindividual-subject estimates of end-tidal CO2 increase during breath-hold as measured by both the absolute and sine range methods(Fig. 4). Therefore, voxel-wise covariates of percentage BOLD signalchange per mmHg increase in CO2 were created to reflect vascularreactivity rather than the BOLD response to the specific breath-hold.
To assess the efficacy of including the group level BH and vascularreactivity covariates, liberal ROIs in the motor, auditory and visualcortex were defined from the block-design data. The number ofsignificant voxels passing a specific statistical threshold was used asthe comparison metric. Figs. 5 and 6 demonstrate that any correctionfor BH-responsiveness/vascular reactivity increases the spatial extentof activation. In the motor and auditory cortices, the normalisations inwhich the measures are derived from the CO2 HRF+td modeloutperform those derived from the Sine–Cosine model or the block-based models. The spatial extent of activation is further improved byexpressing the covariates in terms of BOLD increase per unit mmHg(i.e. the vascular reactivity measures) especially at stricter statisticalthresholds. The absolute range of the end-tidal CO2 trace providesa relatively superior vascular reactivity measure than the sinewave range in terms of spatial extent of activation (data notshown). In the visual cortex, the Sine–Cosine fit to the BH dataprovides a superior normalisation technique to the CO2 HRF+td fit(Fig. 5). Accounting for differences in end-tidal CO2 increases onlyenhances the spatial extent of activation in the visual cortex at lowerstatistical thresholds.
Resting state data
Similar spatial extent increases are observed when resting stateconnectivity measures are corrected for vascular reactivity differencesacross subjects (Fig. 7). These results are dependent on the seedregion used. The number of significantly correlated voxels in the rightmotor cortex is increased by breath-hold normalisation when theseed time series is drawn from the pre-SMA. If the left motor cortex is
Fig. 4. These maps depict the correlation between the group level voxel-wise CO2-induced siincreases (estimated in 2 ways: absolute range—bottom row and sine wave modelling—topresulting model fit has been calculated to give the % BOLD change to breath-hold. Significcorrelates with the individual increases in end-tidal CO2 during breath-hold across the subjenormalise the BH-induced BOLD signal change by the individual increases in end-tidal COreactivity.
used as the seed region, spatial extent increases are observed aftercorrection at lower thresholds (ZN4.0) but all normalisation methodsreduce the spatial extent in the right motor cortex at higher statisticalthresholds (ZN4.5). Areas that exhibit a high correlation with the PCCseed region constitute the default mode network. Normalisation withBH-induced signal changes increases the spatial extent of this networkover the whole brain with the CO2 HRF+td measure performingslightly better than the Sine–Cosinemeasure. However, expressing thecovariates in terms of percentage BOLD increase per unit increase inCO2 reduces the efficacy of the normalisation.
Discussion
Correction for variations in BOLD signal responsiveness to CO2
across subjects using the response to a hypercapnic challengeimproves sensitivity in fMRI group level analyses of stimulus-evokedbrain activity. Normalisation for vascular reactivity differences usingsuch tasks is important when comparing groups with different vesselproperties (e.g., old vs. young). Advantages of using a breath-hold taskto induce hypercapnia over a gas challenge include ease of im-plementation and less discomfort for the participants — particularlyuseful when dealing with clinical populations and children/elderlysubjects (Handwerker et al., 2007; Thomason et al., 2005). Thisstudy addresses two aspects of breath-hold task analysis that areoften neglected: defining the correct approach to modelling theBOLD response to a breath-hold and investigating the influenceof inter-subject differences in breath-hold end-tidal CO2 (i.e., con-verting the BOLD increase to CO2 into a normalised vascular reactivitymeasure).
A common approach to modelling the BOLD response in a GLManalysis is to convolve a block design representing the breath-holdtask periodswith a gamma-variate HRF (referred to as the [Block Only]model in this paper) (Abbott et al., 2005; Handwerker et al., 2007;Kastrup et al., 1999b; Scouten and Schwarzbauer, 2008; Thomasonet al., 2007). The temporal derivative is often included in the modelto allow for temporal shifts between the model and the BOLD data[Block+td]. Fig. 3 demonstrates that although inclusion of a temporalderivative with the block design regressor results in a better fit, thetemporal shifts achieved by the model are not sufficient. Improve-ments in the variance explained are provided by delaying the blockregressor by 9 s. This value was determined using a cross-correlation
gnal change derived from the CO2 HRF+td regressors and the BH-induced end-tidal CO2
row). After fitting the CO2 HRF+td regressor to each voxel, the absolute range of theant voxels in these maps indicate where the percentage BOLD change to breath-holdcts. These correlations are observed mainly in grey matter and demonstrate the need to2 (%ΔBOLD/mmHg) to provide a more robust measure, that is, a measure of vascular
Fig. 6. A small area ofmotor cortex is displayed todemonstrate the spatial extent increasesshown in Fig. 5. The greatest growth in activated area occurs when the BH-induced BOLDsignal increases is expressed in terms of signal change per unit increase in end-tidal CO2.
Fig. 5. To assess the efficacy of the different group level vascular covariates, numbers of significant voxels in ROIs in themotor, auditory and visual cortex were defined. All group levelcorrections for vascular reactivity increase spatial extent of activation in the motor and auditory cortex. Expressing BH response derived from the CO2 HRF+td regressors in terms ofBOLD change per unit mmHg CO2 increase outperforms all other corrections, especially at stricter thresholds. The sine wave fit to the BH data provides a better measure of vascularreactivity in the visual cortex.
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analysis between the end-tidal CO2 regressor and the global BOLDsignal on a subject-by-subject basis. This average delay time is in broadagreementwith previousmeasurements of between 5 and 10 s (Mitsiset al., 2002; Panerai et al., 2000; Wise et al., 2004). The delay includesthe transport time of blood from the lungs to the brain and thehaemodynamic delay of the vasculature response. Longer delays canbe found in the literature but this is mainly with the use of end-inspiration breath-holds which produce a biphasic BOLD response (adecrease in BOLD signal followed by an increase), thus a longertemporal lag between the signal and the model. Including thetemporal derivative of the delayed block regressor in the model[Block lag+td] further increases the variance explained by allowingfor localized differences in vascular delay response to the breath-holdtask.
Other models tested include a phase shifted sine wave at the taskfrequency [Sine–Cosine] and those derived individually for eachsubject from their end-tidal CO2 trace ([CO2 Only], [CO2+td], [CO2
HRF] and [CO2 HRF+td]). The Sine–Cosine model explains morevariance than any block design based approach. The frequency for thesine wave was chosen to be 0.02 Hz (=1/50 s) although rest andactive periods of the taskwere not equal (30 s paced breathing vs. 20 sbreath-hold). This temporal mismatch in the task periods is notstrongly evident in the BOLD signal where the primary frequency is0.02 Hz. Similar variance can be explained by regressors derived from
Fig. 7. The efficacy of BH correction of resting state connectivity measures is dependent on the resting state network under investigation and on the regions chosen to create the seed. The number of right motor cortex voxels that aresignificantly correlated with the average left motor cortex time series are shown (top left). At high statistical thresholds, correction with vascular reactivity measures does not improve results. However, if a region in the supplementary motorarea (pre-SMA) is chosen, correction with vascular activity measures increases the spatial extent (bottom left). This demonstrates that due to the symmetry of the vasculature, the left motor seed already accounts for vascular reactivitydifferences in the right motor cortex time series. When comparing correlations between areas with different vascular properties, such as those in the default mode network, correction increases spatial extent (top right). Areas in frontalregions far from the PCC seed grow in size after correction (bottom right).
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individual end-tidal CO2 traces. After convolution with the standarddouble gamma-variate HRF (Friston et al., 1998), the end-tidal CO2
regressor and its temporal derivative [CO2 HRF+td] explain similaramounts of variance to the Sine–Cosine across the whole-brain andcan perform better in specific areas, for example, the auditory cortex.Such regional differences in the time course to hypercapnia tasks havepreviously been found (Rostrup et al., 2000).
End-tidal CO2 traces have been used as a surrogate for arterialcarbon dioxide measures (Robbins et al., 1990; Wise et al., 2004;Young et al., 1991). A limitation of this method is that changes inarterial blood gas levels during the breath-hold are not measureddirectly. End-expiration breath-hold between paced breathing wasemployed since this approach offers superior BOLD repeatability(Scouten and Schwarzbauer, 2008). Another advantage is theavoidance of the complicated biphasic nature of arterial CO2
concentrations, blood pressure changes and the subsequent biphasicBOLD response after an end-inspiration breath-hold (Kastrup et al.,1999a; Li et al., 1999). From a position of normal expiration, ourparticipants were asked to expel some additional air from the lungsbefore returning to paced breathing after the breath-hold periods.This strategy allows the final arterial CO2 level to be estimated at theend of the breath-hold from that expired CO2. It can be seen in Fig. 2that we have assumed a linear increase in CO2 across the breath-holdperiod. Direct arterial measures by using radial artery sampling haveled to a logistic function model of arterial CO2 increases (Sasse et al.,1996). Modest rises in arterial CO2 concentration between 0 and 10 safter breath-hold were followed by a quicker, almost linear increasereaching a plateau after 25–30 s. Fig. 8 shows the average BOLDresponse to breath-holds along with a logistic function and a linear fit.The two fits are allowed to lag the initiation of the breath-hold to takeinto account any haemodynamic delay in the BOLD response to theincrease in arterial CO2. It can be clearly seen that there is noadvantage to modelling the missing end-tidal CO2 data with a logisticfunction. The strength of modelling the arterial CO2 concentrationwith a logistic function in the Sasse paper was the ability to model theCO2 level plateau after 25 s of breath-hold. Since the breath-holds inthis study were only 20 s long, the increase in arterial concentrations
Fig. 8. The BOLD response to breath-hold (averaged over the 6 breath-holds and thewhole brain) is shown for each subject in light grey. The mean time course across allsubjects is shown in black. During the breath-hold, no end-tidal CO2 measures can berecorded. Two methods of modelling the BOLD breath-hold response are plotted: alinear fit is depicted in blue and the best fit logistic function (described by Sasse et al.,1996) is shown in red. During fitting, a suitable lag was modelled for each to account forthe haemodynamic delay. The residual sum of squares for the logistic function fit tomean time course is 0.20 compared to 0.24 for the linear fit. No statistical difference infit is observed (F(2,4)=0.33, p=0.68), indicating that linear interpolation is sufficientto model the CO2 increase during breath-hold.
is in the approximately linear range of the function. Due to the shorterbreath-hold period (20 s) in which only 6 data points were collected,the non-linear CO2 increase seen by Sasse and colleagues is notevident in the BOLD signal. The expected response to the increase inCO2 is further blurred by the HRF of the BOLD response.
Paced breathing between breath-holds leads to slight hyperven-tilation and a quick reduction of end-tidal CO2 levels. A combination ofboth phenomena (CO2 increases during breath-hold and hyperven-tilation during paced breathing) could explain why the sine–cosinewave modelling approach can explain as much variance in the BOLDsignal during breath-holds as the end-tidal CO2 traces. However, fromFig. 8 the BOLD signal changes during breath-hold in this study appearto be approximately constant for 10 s followed by an almost linearincrease from 10 s to 30 s. By modelling the CO2 linearly during thebreath-hold and including the temporal derivative, CO2 HRF+td fitsthe data equally well.
Baseline end-tidal CO2 values vary between subjects with a meanof 39.7±2.7 mmHg (see Fig. 2) which is in general agreement withother MR studies (Bright et al., 2009; Lu et al., 2010) but do notinfluence the amplitude of the BOLD signal increase to breath-hold.Increases in end-tidal CO2 to breath-hold challenges have notpreviously been measured during MR scanning but Sasse andcolleagues determined the increase in arterial CO2 to be approxi-mately 9 mmHg after 20 s (Sasse et al., 1996). Difficulties arise whendefining the actual end-tidal CO2 increases to the breath-hold task.Absolute range of change, that is, the minimum to the maximum,provides an attractive solution since it can be directly compared to theabsolute range of the BOLD signal to the task. Unfortunately, it isinherently an unstable measure which can easily be skewed byoutliers in the end-tidal CO2 trace such as erroneous readings andpartial breaths. The average change in our study is higher thanpreviously reported (13.4±2.2 mmHg, corresponding to a hypercap-nic challenge of ~6% based on values reported by Rostrup andcolleagues (Rostrup et al., 2000)). However, due to the inducedhyperventilation between successive breath-holds, in practice theactual increase in end-tidal CO2 from a baseline level is lower. Asdiscussed above, arterial CO2 concentrations were well characterisedby a sine wave. Our alternative range measure was calculated basedon a sine wave fitting that yields values closer to those previouslypublished (8.86±1.2 mmHg). This is less prone to outliers thanabsolute range, but may not correctly capture all relevant character-istics of the end-tidal CO2 traces, for example, where there arevariations in performance between one breath-hold and another. Thisis particularly important in populations that may find the breath-holdtask and repeated breath-holds difficult (e.g. children, elderly andclinical populations). Poor compliance to even a single breath-holdevent (e.g. by resuming breathing too early) will affect the Sine–Cosinewave fit; however, this information would be available and incorpo-rated when using the recorded end-tidal CO2 measurements. Sincehealthy, young subjects participated in this study, the variation ofthe BOLD response to the 6 individual breath-holds is quite small:the average range of responses across the group is 2.84 mmHgrepresenting a range of 6.4% of the baseline end-tidal CO2 values.Anecdotally, these subjects did not experience any discomfort duringbreath-holds or have any difficulty performing the task. Although, achange of 13.4 mmHg may be associated with some discomfort in ahypercapnic condition, during breath-hold this represents the end-point of a linear ramp, thus participants spend relatively little time athigher hypercapnic levels (especially since this measure is a rangefrom a hypocapnic to a hypercapnic condition). A greater range ofBOLD responses to individual breath-holds would be expected inpoorly complying populations including situations in which thesubjects can not hold their breath for long enough. In this case, theadvantages of using the CO2 HRF+td model over the Sine–Cosinemodel would likely be more evident. It is not necessary that fullbreath-holds are performed when using the CO2 HRF+td model
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rather that there is a perturbation in the system that can be quantifiedin both the end-tidal CO2 measures and the BOLD signal.
Vascular reactivity differences across voxels/subjects lead to BOLDsignal variations that may not be related to neuronal differences(Bandettini and Wong, 1997). Hypercapnic normalization of the BOLDsignal uses CO2-related BOLD/CBF increases as a physiological referencestandard determining how much the BOLD signal will increase for agiven amount of CO2 (Bandettini and Wong, 1997; Corfield et al., 2001;Kastrup et al., 1999a; Posse et al., 2001). Thus, the BOLD signal can becalibrated to more closely reflect energy consumption or CMRO2 (Daviset al., 1998; Hoge et al., 1999). Correcting for vascular reactivitydifferences across a group leads to increases in spatial extent of grouplevel activation by reducing variance and increasingpower (see Fig. 5). Aproblem arises when using breath-hold as the hypercapnic challengesince increases in CO2 are not tightly controlled. Variations in CO2
increases due to breath-hold are quite large (see Fig. 2). Fig. 4demonstrates that group level covariates built from BOLD signalincreases during breath-holds are contaminated by this subject-to-subject variation in CO2 increases. Expressing these BOLD signal changesas a percentage increase with respect to the absolute increase in thepartial pressure of CO2 (per mmHg) produces a vascular reactivitymeasure that reduces this variability and further increases the spatialextent of activation (see the motor and auditory cortices in Fig. 5).Similar variability related to subject differences in end-tidalO2 decreasesduring breath-hold is not evident. Sasse and colleagues have shown thatnot only does breath-hold increase arterial CO2 but also reduces arterialO2 (Sasse et al., 1996). It would be expected that end-tidal O2 decreaseswould be anti-correlated with end-tidal CO2 increases across the group.Also, this breath-hold induced hypoxia will directly affect the venoushaemoglobin saturation, will lead to an increase in CBF (Poulin et al.,1996) and thus lead to an increase in BOLD signal. However, the pacedbreathing before each breath-hold leads to slight hyperventilation and,therefore, each participant is slightly hyperoxic (and hypocapnic) at thestart of the breath-hold. During the 20-s breath-hold, the participantswill go through normoxia towards hypoxia. The degree of hyperoxia atthe start of each breath-hold is subject-dependent, therefore affectingthe degree of hypoxia at the end of the breath-hold. This explains thedissociation between end-tidal O2 decreases and end-tidal CO2 increaseacross thegroup. The fact thatnovariability related to subjectdifferencesin end-tidal O2 increases is observed in the BOLD activity measuressuggests that these reductions in O2 play a less significant role in BOLDsignal changes than CO2 increases during breath-hold.
In motor and auditory cortices, the breath-hold BOLD signalincrease measure derived directly from end-tidal CO2 producessuperior corrected group level results to those derived from theSine–Cosine fit (see Fig. 5). This is not the case in the visual cortexwhich, in humans, has twice the density of neurons as other areas andthus markedly different vascular properties (Logothetis, 2008). Largedraining veins also contribute to unique temporal characteristics of theBOLD signal in the visual cortex such as a pronounced undershoot(Handwerker et al., 2004). These differences between the visual andmotor/auditory cortex may explain the results whereby Sine–Cosinederived vascular reactivity measures provide the superior correctionmethod in the visual cortex. The BOLD response to a regularlyalternating stimulus (such as the BH task) would appear moresinusoidalwith amore pronounced undershoot than if the undershootwas absent. A better fit of the Sine–Cosine model in the visual cortexwould lead to a more accurate estimate to the BOLD response tobreath-hold and thus a superior normalisation method. However,since both models improve the spatial extent, use of the CO2 HRF+tdmodel over the Sine–Cosinemodel is still recommended in populationsthat are likely to comply poorlywith the task for the reasonsmentionedabove.
An area of fMRI that has sparked much interest in recent years isthat of resting state connectivity (Fox and Raichle, 2007). Spontaneousfluctuations in the BOLD signal are often correlated between
functionally related areas, first observed between motor cortices(Biswal et al., 1995). It is hypothesised that these correlatedfluctuations reflect synchronised variations in neuronal activity.However, fluctuations in arterial CO2 also occur at low frequenciesand can confound resting state connectivity measures (Birn et al.,2008; Murphy et al., 2009). Similarly, alterations in baseline vascularCBF lead to connectivity differences (Biswal et al., 1997; Rack-Gomeret al., 2009). The results shown here demonstrate that vascularreactivity differences across subjects can also contaminate connectiv-ity measures (see Fig. 7). Increases in the spatial extent of the defaultmode network are seen when normalisation for BOLD signal increaseto CO2 is performed. The same is true in the motor cortex network butis dependent on the seed region chosen. The number of right motorvoxels significantly correlated with the pre-SMA increases when BHnormalisation is performed. However, when the left motor cortex ischosen as the seed, the spatial extent of right motor cortex voxelsincreases after normalisation at lower thresholds but is reduced athigher thresholds (ZN4.5). This discrepancy between the PCC/pre-SMA results versus the left motor cortex result may be explained bysymmetry. In the motor cortex, the vasculature in the left and righthemispheres is similar and there is no reason to expect differentvascular reactivity. Therefore, by deriving a seed time series from theleft motor cortex to correlate with the right, we have taken intoaccount the averagemotor cortex vascular reactivity differences acrosssubjects. When normalising with these measures, the reduction in thealready small number of degrees-of-freedom leads to a reduction in Zvalues across the region. However, there still remains a voxel-specificcomponent of vascular reactivity that is accounted for by thecorrection, thus broadening the distribution of Z-scores. Thus, atlower thresholds (Zb4.0), the spatial extent increases after correctionbut at higher thresholds (ZN4.5), the spatial extent is reduced. In thedefault mode network, the relationship between vascular reactivity inthe PCC and frontal regions is not likely to be coherent i.e. the PCC is notlikely to be as good a predictor of CO2-related signal change in frontalcortex in a given subject as left motor cortex is for right motor cortex.Fig. 7 (bottom right) also demonstrates that the defaultmode networkspatial extent increases are mainly evident in the frontal cortex whichlends further credence to this theory. Similarly, vascular reactivity inthe pre-SMAwould not likely predict that in the rightmotor cortex andso the number of significantly correlated voxels increases in the rightmotor cortex with normalisation.
For both the Sine–Cosine and CO2 HRF+td models, expressing theBOLD increase to breath-hold as a percentage increase per mmHg CO2
increase (i.e. as a vascular reactivity measure rather than a simple %BOLD increase to BH) does not further improve the spatial extent ofthe connectivity measures in the default mode network. For example,the number of voxels significantly correlated with the PCC (ZN5.0) is~5000 without any correction and ~8300 after CO2 HRF+tdcorrection which reduces to ~7300 after CO2 HRF+td per mmHgcorrection. This implies that a component of the inter-subject restingstate correlations measured with BOLD is CO2-related. However, theconnectivity measure in each voxel across subjects is more closelyrelated to percentage BOLD changes than to percentage BOLD permmHg changes from breath-hold. This suggests two components tothe correlation values, one related to CO2 fluctuations in a voxel andone that is not. The relative contribution of the two normalisationtechniques may suggest a way of separating the neuronal from theCO2-related fluctuations in resting state data.
Conclusions
Accounting for variations in vascular reactivity differences usingBOLD signal increases to breath-hold in group level analyses increasesstatistical significance. Variance in breath-hold data is explained bestby fitting either a sine wave at the task frequency or the end-tidal CO2
trace convolved with a HRF and its temporal derivative. The absolute
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increase in end-tidal CO2 during breath-hold should be factored in toprovide a measure of vascular reactivity to CO2 (percentage BOLDincreases per mmHg rise in CO2) which results in more statisticallyactive voxels in task-related analyses at the group level. Correctionwith breath-hold measures can increase the spatial extent of restingstate correlations, but is dependent on the shared vascular reactivityproperties of the seed and target regions.
Acknowledgments
Funded by Pfizer Ltd (KM, RW), the UK Medical Research Council(RW) and the Natural Sciences and Engineering Research Council ofCanada (AH).
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