NeuroImage 46 (2009) 559–568

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Origins of the BOLD post-stimulus undershoot

Jean J. Chen ⁎, G. Bruce PikeMcConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, 3801 University Street, WB325. Montreal, Quebec, H3A 2B4, Canada

⁎ Corresponding author. Fax: +1 514 398 2975.E-mail address: jean.chen@mail.mcgill.ca (J.J. Chen).

1053-8119/$ – see front matter © 2009 Elsevier Inc. Aldoi:10.1016/j.neuroimage.2009.03.015

a b s t r a c t

a r t i c l e i n f o

Article history:Received 21 November 2008Revised 18 February 2009Accepted 4 March 2009Available online 19 March 2009

Keywords:BOLD post-stimulus undershootCerebral oxygen metabolismNeurovascular couplingCerebral blood flowVenous cerebral blood volumefMRIVERVE

The interpretation of the blood-oxygenation level-dependent (BOLD) post-stimulus undershoot has been atopic of considerable interest, as the mechanisms behind this prominent BOLD transient may providevaluable clues on the neurovascular response process and energy supply routes of the brain. Biomechanicaltheories explain the origin of the BOLD undershoot through the passive ballooning of post-capillary vesselswhich leads to an increase in venous blood volume (CBVv, comprising deoxygenated blood in capillary,venular and arteriolar compartments), resulting in susceptibility-induced signal decrease. While there hasbeen substantial evidence supporting a role for venous ballooning, there have also been reports arguing for aprolonged post-stimulus elevation in cerebral oxygenation consumption (CMRo2) as the primary cause.Furthermore, a contribution of post-stimulus cerebral blood flow (CBF) undershoots has also beendemonstrated. To clarify the role of the venous compartment in causing the BOLD undershoot, we performedin vivo fMRI measurements of the transient ΔCBVv, ΔCBF and ΔBOLD responses in healthy humans. Weobserved a slow post-stimulus return to baseline in venous CBV which supports the existence of a passive“balloon” effect, implying that previous observations of a quicker recovery of the total CBV response may bedominated by arterial CBV change. Our findings also support a significant contribution from the CBFundershoots, which, combined with a slow venous CBV response, would account for much of the BOLDundershoot.

© 2009 Elsevier Inc. All rights reserved.

Introduction

Interpretation of the post-stimulus undershoot in the blood-oxygenation level-dependent (BOLD) signal has been a topic ofconsiderable interest. This BOLD undershoot typically starts between10 and 20 s after stimulus cessation (Fransson et al., 1999), and isobserved for up to 60 s (Bandettini et al., 1997; Mandeville et al.,1999a). This transient feature is present in both the gradient-echo andspin-echo BOLD signal (Jones, 1999; Yacoub et al., 2003). Insignificantinter-subject variationwas found in the duration of the undershoot fora given stimulus, but there is significant variation in its magnitude(Mildner et al., 2001). The mechanisms behind this prominent BOLDtransient may provide valuable clues on the neurovascular responseprocess and energy supply routes of the brain. In addition, the spatialspecificity of the undershoot has been suggested to reflect spatialvariability in neuronal activity (Yacoub and Hu, 2001; Yacoub et al.,2006). This discussion, however, is complicated by reports of contra-dictory observations as to themost likely origin of the undershoot, andthere is great interest in reconciling these differences by gainingaccess to more direct measures of hemodynamics and metabolicprocesses that govern the BOLD response.

The BOLD post-stimulus undershoot reflects a transient increase inthe local deoxy-hemoglobin (dHb) count, which could result from a

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number of possible scenarios. Biomechanical models, which attributethe local dHb accumulation to the temporal mismatch between theΔCBF and venous ΔCBV responses, include the Balloon Model,proposed by Buxton et al. (1998), as well as the Windkessel Model,proposed by Mandeville et al. (1999b). This biomechanical inter-pretation is supported by a number of human and animal imaging andmodeling studies (Friston et al., 2000; Kong et al., 2004; Mandeville etal., 1999a,b, 2001; Nakai et al., 2000; Obata et al., 2004). Notably, usingmonocrystalline iron-oxide nanocolloid (MION) enhanced CBV ima-ging (measuring total CBV change) simultaneously with BOLD andlaser Doppler flowmetry measurements, Mandeville et al. obtainedrobust, high signal-to-noise measurements which demonstrated adelayed compliance in CBV in a rat model at 2 T and 4.7 T in responseto both focal and global hyperemia (Mandeville et al., 1999a, 2001).Furthermore, there is accumulating evidence that the CBV post-stimulus response is in fact composed of a fast and a slow response,attributed to the elastic arterial recovery from stress and the morepassive venous recovery, respectively (Mandeville et al., 1999b; Obriget al., 1996; Silva et al., 2007).

Alternatively, a transient post-stimulus decoupling between CBFand CMRo2 can also create a post-stimulus accumulation of deoxy-hemoglobin, producing a BOLD undershoot (Frahm et al., 1998; Krugeret al., 1998a,b). In support of this theory, a number of studies haveshown evidence of speedy post-stimulus recovery in total CBV, asmeasured using vascular space occupancy (VASO) as well as contrast-enhanced imaging, which, combined with a fast CBF recovery,

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suggests a post-stimulus CMRo2 elevation that is decoupled with thehemodynamic response (Frahm et al., 1996, 2008; Krüger et al., 1996;Lu et al., 2004).

In an effort to reconcile these alternate observations, more recentstudies (Jin and Kim, 2008; Yacoub et al., 2006; Zhao et al., 2007) haveexamined the spatial variability of post-stimulus CBF, CBV and BOLDbehaviours by imaging at high spatial resolution. Using MION at 9.4 T,Yacoub et al. showed that CBV changes in anesthetized cat corticaltissue persist after stimulus cessation while CBV changes in thesurface vessels quickly return to baseline levels, despite a concurrentundershoot in the BOLD signal in both the tissue and surface vesselareas (Yacoub et al., 2006). This was confirmed by later findings fromZhao et al. and Jin et al., who found the most prolonged post-stimulusCBV elevation to be the middle layer, corresponding to the location ofthe largest BOLD undershoot.

Interestingly, the main debate has not substantially involvedanother important theory, one that regards the role of CBF. A post-stimulus undershoot in CBF (Behzadi and Liu, 2005; Chen et al., 2007;Chen and Pike, 2008a; Friston et al., 2000; Hoge et al., 1999a; Irikura etal., 1994; Logothetis, 2003; Shmuel et al., 2006; Uludag et al., 2004),which would also result in local dHb accumulation, is a potentiallysignificant contributor. Hoge et al. observed post-stimulus CBFundershoots using human primary visual cortex stimulation at 1.5 T(Hoge et al., 1999a), a finding reproduced by Shmuel et al. in visualstimulation of anesthetized monkeys, and suggested to be associatedwith a post-stimulus decrease in neuronal activity as revealed incortical electrophysiological recordings (Shmuel et al., 2006).

Numerous optical studies found the post-stimulus period to becharacterized by an undershoot in the concentration of oxy-hemoglobin [Hb] and/or an overshoot in [dHb] (Huppert et al.,2006; Jones et al., 2005; MacIntosh et al., 2003; Schroeter et al., 2006).In particular, using arterial spin labeling (ASL) and BOLD inconjunction with near-infrared spectroscopy (NIRS), an elevationwas found in post-stimulus [dHb], mirroring the undershoot in theBOLD and CBF data, with the timing of the post-stimulus elevation intotal hemoglobin concentration coinciding with the overshoot in [Hb](Huppert et al., 2006; MacIntosh et al., 2003). In addition, usingoptical imaging and laser Doppler flowmetry (LDF) to estimate CBVand CBF changes, respectively, Jones et al. (2001) found delayed post-stimulus [dHb] recovery. However, this was contradicted by animmediate recovery in [Hb] observed by Schroeter et al. (2006).Notwithstanding the abundance of optical data, optical studies areintrinsically limited by penetration depth, spatial resolution and thefundamental differences in the measured parameters compared toMRI. Therefore, the above data, while adding valuable perspective,have not unequivocally resolved the debate.

To the present, there has not been data to address the simultaneouscontribution of CBF and venous CBV directly measured in humans. Inthe context of BOLD contrast, as is addressed in this work, the term“venous blood” refers to all partially deoxygenated blood, specifically,blood from a collective compartment encompassing capillaries andvenules, as well as, to a lesser extent, arterioles (Hillman et al., 2007;Hsu and W., 1992). In this study, we sought to clarify thesecontributions to the BOLD undershoot by performing human fMRImeasurements of in vivo transient responses in ΔCBVv, ΔCBF andΔBOLD evoked by graded visual and sensorimotor stimulation.

Materials and methods

Stimulation paradigm

Sixteen healthy adults (14/2 females/males, ages ranging from 22to 31 years) participated in the study, and gave informed consent priorto the scanning session. The subjects performed bilateral, sequentialfinger-to-thumb apposition at low (1.73 Hz) and high (3.46 Hz)frequency (cued by ametronome) while being presentedwith a radial

yellow/blue checkerboard at low (25%) and high (100%) contrast,reversing contrast at 8 Hz. The low apposition frequency wasaccompanied by the low contrast checkerboard, the high appositionfrequency by the high-contrast checkerboard. The radial checkerboardstimulus was implemented using locally developed software (GLStim)based on the OpenGL graphics library (Silicon Graphics, MountainView, CA), and was delivered via a back-projection mirror and using aLCD projector with a resolution of 1024×768 pixels and a refresh rateof 75 Hz. The visual stimulus occupied between 40° and 45° of thesubjects' visual field. The stimulation conditions were alternated witha uniform grey baseline (with luminance level matching that in thepresence of the checkerboard). During the resting condition, subjectswere requested to look at a white triangular fixation point to maintainattention. Two stimulation-“on” durations were used, namely 24 s and96 s, each flanked by 16 s and 120 s of pre- and post-stimulus baselineblocks. By varying the stimulation intensity and duration, we expect tomodulate the neuronal activity-induced metabolic deficit, therebyproviding the conditions to test the theory that the BOLD undershootis caused mainly by a post-stimulus elevation in CMRo2, which wassuggested to occur mainly to restore this deficit (Lu et al., 2004). Eachoff/on/off block was repeated twice, and jittered (Yang et al., 2000) toachieve an effective temporal resolution of 2 s. An additional 32 sresting state was inserted at the beginning of the paradigm to permitmore robust baseline estimation.

MRI acquisition

All examinations were performed on a Siemens 3 TMagnetom Triosystem (Siemens, Erlangen, Germany). Subjects were immobilizedusing a head holder assembly. An RF body coil was used fortransmission and an 8-channel phased array head coil for signalreception. The scanning protocol included a 3D RF-spoiled gradient-echo scanwith 1mm3 isotropic resolutionwhich served as anatomicalreference. An 80 smulti-slice BOLD visual and sensorimotor functionalscout was acquired to guide slice positioning. During the functionalscout, subjects were presented with the high-contrast visual stimuluswhile performing the high-frequency motor task described in theprevious section. Activation t-maps were produced using scannerconsole software, and the coordinates of the regions with the mostsignificant activation in both the visual and sensorimotor regions wereused in placing the imaging slice.

The common functional imaging parameters were: FOV/matrix/slice-thickness/TR=200×200 mm2/64×64/5 mm/4 s. The fMRIacquisitions were all single-slice, with a voxel size of 3.1×3.1×5mm3.The VERVE technique was used to measure changes in venous CBV(ΔCBVv). Cerebral-spinal fluid (CSF) suppression was performed atan inversion time of 1100 ms for a repetition time of 4 s. This wasfollowed by the VERVE magnetization preparation, composed of apair of non-selective 90° tip-down and tip-up pulses flanking anMLEV phase-cycled CPMG train made up of non-selective composite90°x−180°y−90°x refocusing pulses for either fast or slow refocusing.The corresponding τF180 and τS180 were 3 ms and 24 ms, respectively,forming a refocusing train with duration of 192 ms; this duration,combined with the echo-spacing of 6.8 ms for the TSE readoutresulted in an effective VERVE echo time (TE) of 198.8 ms. One cycle ofphase-chopping was used in the slice-selective excitation pulse.Venous blood oxygenation, which is necessary for VERVE calibration,was obtained for each subject in vivo based on internal jugularoximetry using a magnetization-prepared segmented EPI sequence(Foltz et al., 1999), cardiac-gated using a finger oxymeter.

CBF and BOLDdatawere simultaneously acquired using pulsed ASL,which was previously shown to provide BOLD measurementsequivalent to those acquired with standard BOLD (Chen and Parrish,2007). The QUIPSS II ASL technique (Wong et al., 1997) was used, withASSIST background suppression (Ye et al., 2000). The protocolcontained the following parameters: TI1/TI2/TE/labeling thickness/

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gap=700 ms/1300 ms/25 ms/100 mm/5 mm. Two asymmetricBASSI pre-saturation pulses (Warnking and Pike, 2004, 2006) in theimaging regionwere followed by an adiabatic BASSI inversion pulse inthe labeling region (gap of 5mm). EPI readout (bandwidth=2170Hz/pixel) was used.

Data processing

Motion correction of the CBF, CBVv and BOLD datasets wasperformed across all runs using AFNI's 2dimreg software (Cox, 1996).The frames with estimated translation exceeding 1 mm or rotationgreater than 1° were excluded from the analysis. The CBF and CBVv

data were then aligned to each other using MincTools (MontrealNeurological Institute, Canada), such that all functional data weremutually aligned. The functional data were spatially smoothed using atwo-dimensional Gaussian filter with a full-width at half-maximum(FWHM) of 6 mm. Drift was removed by subtracting from each voxel'stime course the low-frequency components of its discrete cosinetransform, with a cutoff frequency at half of the stimulation paradigmfrequency.

The difference between the slow- and the fast-refocused imageswas calculated, and a calibration factor computed from the venousoxygenation was used to convert ΔVERVE to ΔCBVv on a per-subjectbasis (Stefanovic and Pike, 2005). As customary, ΔCBF was calculatedfrom the difference between the control and tag ASL images, whileΔBOLD was obtained as the average between tag and control. Finally,sinc interpolation was performed to correct the timing differencesbetween the control and tag images in the CBF and BOLD data, as wellas between the fast- and slow-refocused images in the VERVE data.This also permitted the correction of potential BOLD contamination inASL data due to this timing difference (Lu et al., 2006).

We used fMRIstat (Worsley et al., 2002) to identify areas ofstatistically significant task correlation based on the generalized linearmodel, with corrections for multiple comparisons. The gamma-variatehemodynamic response function, which has been found to providerobust activation correlation for all modalities, was used for theanalysis. The BOLD, CBF and CBVv ROIs were defined for each subjectby thresholding the t-maps (at the t-value corresponding to asignificance level of pb0.05 after correction for multiple compar-isons). Visual and sensorimotor cortex ROIs were differentiated basedon anatomical considerations, with sensorimotor ROIs constrained tothe banks of the Rolandic fissure, and the visual ROI in close proximityto the calcarine sulcus. The time course analysis was based on theoverlap between the significantly activated voxels in the CBF, CBVv andBOLD t-maps obtained from the functional run with the highestactivation t values. Theoretically, ASL is more sensitive to arteriolesand capillaries (Wong et al., 1997), whereas VERVE is more sensitive tointravascular changes in the capillaries and the post-capillarycompartment (i.e. venules and veins), and BOLD is sensitive to bothintra- and extra-vascular contributions, stemming from oxygenationchanges in capillaries, venules and veins. Therefore, although each

Fig. 1. Sample CBVv (a), CBF (b) and BOLD (c) activation t-maps from one subject during 100presenting significant activation in all three maps were used in the time course analysis.

voxel is likely to contain all compartments, it is likely that restrictingthe ROI to those activated in the CBVv, CBF and BOLD t-maps wouldreduce emphasis of the arterial and venous macrovasculature. Inaddition, the use of the overlapping ROIs targets the interaction of CBFand CBVv in the exactly the same location as the corresponding BOLDundershoot. Finally, to minimize overall bias towards the highest t-scores, the CBF–CBVv–BOLD overlapped ROI from the functional runwith the most robust activation (the highest t-scores) was applied toall other functional runs.

The CBF, CBVv and BOLD time courses derived from each ROI weretemporally low-pass-filtered (Hanning filter, FWHM=6 s). Piece-wise fitting of the time courses to a double-gamma hemodynamicresponse function (Glover, 1999) was performed using the uncon-strained nonlinear least-square method, i.e. the onset and offsettransient portions of the time courses were fit separately, such thatneither is biased by the temporal features of the other. The time-to-fall(Tf) of the time courses (the time taken to fall to the half-maximum)was estimated from the modeled time courses, as illustrated in Fig. 3.The statistically significant undershoots (at the pb0.05 level) wereidentified, and their durations quantified via their respective full-width at half-maximum (FWHMUS). Both Tf and FWHMUS werecorrected for the low-pass filter FWHM. Finally, the normalizedpost-stimulus response amplitude (APS) for each time course wascalculated as the measured post-stimulus response amplitude withinthe BOLD FWHMUS time frame divided by the corresponding positiveresponse amplitude. Thus, APS enabled us to assess the behaviours ofall measurements precisely when the BOLD post-stimulus undershootoccurs. The dependence of Tf, FWHMUS and APS on stimulus type,stimulation intensity and the stimulation-“on” duration was assessedusing 3-way ANOVA, while inter-modality differences were evaluatedusing Student's t-test. Finally, the correlation between undershootparameters in matching sets of time courses was assessed using linearregression.

Results and discussion

Results

CBVv, CBF and BOLD t-maps for one subject under high-intensityvisual and sensorimotor stimulation are shown in Fig. 1 for 96 sstimulus-“on” duration, overlaid with the corresponding anatomicalreference image. VERVE-CBV, ASL-CBF and BOLD have distinctsensitivities and specificities, as can be seen from the peaks in the t-maps. Although each voxel is likely to contain multiple vascularcompartments, it is likely that using the overlap between all three setsof t-maps would increase the probability of including voxels with highcapillary and venular weighting.

The group-averages of the measured time courses for ΔCBVv, ΔCBFand BOLD are shown in Fig. 2 for the 96 s stimulation condition in thevisual and sensorimotor regions. The post-stimulus dynamics can beclearly seen in these measured time courses, particularly the slow

% contrast visual stimulus accompanied by 3.46 Hz bilateral finger tapping. Only voxels

Fig. 2.Group-averages of measured time courses for 96 s stimulation duration: (a) low-intensity sensorimotor stimulation (n=11); (b) low-intensity visual stimulation (n=13);(c) high-intensity sensorimotor stimulation (n=12); (d) high-intensity visual stimulation (n=14). The dashed lines indicate the standard error, and the stimulation-on periodis indicated by the shaded region. In order to enable better visualization of the general transient dynamics, these time courses were smoothed using a low-pass Hanning filterwith a 13 s FWHM.

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recovery of ΔCBVv, as well as the well-defined undershoots in thevisual ΔCBF and all BOLD responses. However, to enable thequantitative assessment of these features, modeling was performedon these time courses. The positive response amplitude is defined asthe average time course amplitude above the FWHM of the positiveresponse, and is therefore unbiased by stimulation duration. The post-stimulus response amplitudewas normalized by the positive responseamplitude to compare the transient responses between data acquiredusing different modalities.

A sample set of the measured CBVv, CBF and BOLD time coursesobtained under visual stimulation is shown in blue in Fig. 3, for short(Figs. 3a, c, e) and long (Figs. 3b, d, f) stimulation duration, along withtheir least-square fits to the double-gamma-variate model. As shownin Fig. 3g, all transient parameters were normalized to enable cross-modality and cross-subject comparison and categorization.

All transient parameter estimates were categorized as shown inFig. 4, and assessed for their dependence on stimulation intensity,duration and type. The rate of post-stimulus recovery was judgedbased on Tf, while the undershoot size was assessed based onFWHMUS and APS. The BOLD recovery rate and undershoot size werefound to be independent of stimulation intensity (Fig. 4a) andduration (Fig. 4b). However, the BOLD APS and FWHMUS were bothsignificantly influenced by the type of stimulation (i.e. visual vs.sensorimotor), with p=0.01 for both cases (Fig. 4c). The CBFundershoot was also independent of stimulation intensity andduration, but as in the BOLD case, both APS and FWHMUS weresignificantly different between the visual and sensorimotor cases(p=0.01 and 0.03, respectively), as shown in Fig. 4c. Lastly, nodependence on stimulation intensity, duration or type was observedin any of the CBVv transient parameters.

The transient parameters were compared according to data type(i.e. BOLD, CBF or CBVv). As significant differences were foundbetween the transient responses to visual and sensorimotor stimula-tion, the data were further divided according to the stimulation type,shown in Fig. 5. The first main observation is that Tf for CBVv (visual:16.4±2.6 s, sensorimotor: 19.1±3.4 s) was longer than either that of

CBF (visual: 5.0±0.3 s, sensorimotor: 10.0±1.2 s) or BOLD (visual:5.4±1.8 s, sensorimotor: 8.3±1.2 s). The CBF and BOLD Tf werestatistically indistinguishable. This was the case for both visual(pb0.01) and sensorimotor stimulation (pb0.05). The BOLD responseexhibited more prominent and consistent undershoot features thanboth CBF and CBVv, with the longest FWHMUS in all categories and thelargest APS. This was also common to both visual (pb0.001 for bothFWHMUS and APS) and sensorimotor stimulation (pb0.05 for FWHMUS

and APS). Another notable observation is that undershoots wereconsistently observed in the visual CBF responses, reflected by thenegative average CBF response (Fig. 5a). In the sensorimotor case, theCBF response did not show significant undershooting, and the averageCBF APS was statistically indistinguishable from zero, reflecting anearly return of CBF to baseline (Fig. 5b). However, under bothgroupings, the average APS for CBVv was consistently above zero,characteristic of an elevated venous CBV over the course of the BOLDundershoots.

Discussion

In this study, we sought to shed light on the possible mechanismsunderlying the BOLD post-stimulus undershoot by directly measuringvenous CBV changes, as well as ΔCBF and BOLD, in healthy humansubjects during graded visual and sensorimotor stimulation of varyingdurations. At 3 T, the VERVE technique provided robust measures(Chen and Pike, 2008b) which are in agreement with contrast-enhanced measurements of venous volume change in animals (Lee etal., 2001). The CBF and BOLD response amplitudes to gradedstimulation were in excellent agreement with the literature (Hogeet al., 1999a; Ito et al., 2001; Kastrup et al., 2002). Both the positiveBOLD CBF responses increased with stimulation intensity (Fig. 2).However, this was not the case for CBVv, likely because the CBF–CBVv

relationship is characterized by a shallow trend, with CBF variationsfar exceeding those in CBVv (Chen and Pike, 2008b; Lee et al., 2001).Our focus is, nonetheless, on the physiological and biomechanicalmechanism underlying the transient response. We have developed a

Fig. 3. Sample measured (open dots) and fitted (solid line) CBVv (a, b), CBF (c, d) and BOLD (e, f) time courses (normalized), for the high-intensity visual stimulation in one subject.The transient parameters measured from the modeled time courses are illustrated in (g).

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Fig. 4. The group-average post-stimulus transient parameters, time-to-fall (Tf), normalized post-stimulus response amplitude (APS) and undershoot full-width-half-maximum(FWHMUS) are shown for CBVv, CBF and BOLD responses, classified by stimulus intensity (a), duration (b) and type (c). Only the CBF and BOLD data showed statistically significantundershoots, with CBVv being characterized by the longest Tf. No stimulation intensity or duration dependence was found in any of the parameters (a, b), but both the BOLD and CBFundershoots (APS and FWHMUS) were significantly larger in the visual than the sensorimotor case. In addition, the CBF Tf were both significantly longer in the visual than thesensorimotor region. Asterisks indicate significant difference (pb0.05).

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set of measures, namely the time-to-fall (Tf), the normalized post-stimulus response (APS) and the FWHM of the post-stimulus under-shoots (FWHMUS), which characterize multi-modal post-stimulustransient response without being biased by the vastly differentresponse amplitudes.

CBF post-stimulus undershootThe first important finding of this study is that the CBF response, as

measured using QUIPSS II ASL, exhibited post-stimulus undershoots,the amplitude of which was correlated with the BOLD undershootamplitude. The use of surround subtraction (Lu et al., 2006)minimized the potential of the CBF transients being biased byoxygenation effects, and hence precluded the possibility of the CBFundershoots being solely the result of BOLD contamination. CBFundershoots have been observed in both MRI and optical imagingliterature (Hoge et al., 1999a,b; Huppert et al., 2006; Shmuel et al.,2006; Uludag et al., 2004). However, a number of studies reported theabsence of CBF undershoots under stimulation conditions similar tothose studied here and using similar analysis techniques (Kruger et al.,1998b; Lu et al., 2004). While the exact origin of this difference is

Fig. 5. All post-stimulus transient parameters were summarized under the visual (a) andensemble visualization. The CBVv data was characterized by a Tf that was significantly longer(pb0.05), as well as post-stimulus elevation. The CBF and BOLD Tf were mutually indistinundershoot features than both CBF and CBVv, with the longest FWHMUS in all categoriesFWHMUS and APS) and sensorimotor stimulation (pb0.05 for both FWHMUS and APS). The Cdifference (pb0.05).

unclear, it has been suggested that the precise type of stimulus orparadigm influence the occurrence of the CBF transients (Hoge et al.,1999a). Although the positive CBF change has been found to beindependent of the stimulus type, delayed and stimulus-dependentfiner-scaled flow adjustments (Kruger et al., 1998b) may contribute todiverging post-stimulus responses.

The CBF undershoot has been suggested to be the result of anautoregulatory CBF feedbackmechanism of neuronal origin (Friston etal., 2000; Uludag et al., 2004). Using cortical electrophysiologicalrecordings, Shmuel et al. demonstrated post-stimulus undershoots inneuronal activity following visual stimulation, providing furthersupport for a neuronal origin to the CBF undershoot (Devor et al.,2007; Hamel, 2006; Shmuel et al., 2006; Zonta et al., 2003), which isalso in concordance with predictions based on the “arteriolarcompliance model”, in which neural activity modulates the compli-ance of arteriolar smooth muscle, and hence the initiation of the CBFresponse (Behzadi and Liu, 2005). In our study, the dependence of theCBF post-stimulus response (in terms of Tf, FWHMUS and APS) onstimulation type may be a reflection of the differences in the neuronalactivities elicited by visual and sensorimotor activation. However, we

sensorimotor (b) categories. Tf, APS and FWHMUS were rescaled differently for betterthan that of either CBF or BOLD for both visual (pb0.01) and sensorimotor stimulation

guishable. Furthermore, the BOLD response exhibited more prominent and consistentand the most negative APS. This was also common to both visual (pb0.001 for bothBF and CBVv undershoot sizes did not differ significantly. Asterisks indicate significant

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do not preclude regional variability in vascular structure andhemodynamic response, which could have also contributed to thevisual-sensorimotor CBF response difference.

Venous complianceAnother important finding of this study is that the CBVv post-

stimulus responses were clearly characterized by a slower return tobaseline relative to either CBF or BOLD. This observation is thus insupport of a passive and lagging dilatory component to the CBVresponse as described by Buxton et al. (1998) and observed byMandeville et al. (1999a,b). However, in this case, the definition of“venous blood” encompasses all deoxygenated vascular compart-ments, and our measurement of ΔCBVv is a weighted average of theCBV response in all these vascular compartments, expected to includecapillaries and venules (Hillman et al., 2007; Hutchison et al., 2006),and to a lesser extent, arterioles and veins. The nature of the venousblood volume response was also reflected by previous observations ofa passive dilatory component of CBV decrease (Mandeville et al., 1998;Shen et al., 2008). Furthermore, as the ΔCBVv transient parameterswere uninfluenced by the intensity, duration or type of thestimulation, our findings support a primarily biomechanical responsein venous blood volume to neuronal activation, hence a responsemodulated largely by changes in blood flow.

CBVv manifested significantly longer Tf than both CBF and BOLD, aswas also observed in previous fMRI studies (Leite et al., 2002;Mandeville et al., 1999a). Having reduced the contribution of drainingveins from the analysis (by using the CBF–CBVv–BOLD t-map overlap),our findings likely reflect primarily venular response, and supportbiomechanical theories regarding the contribution of the venous CBVto the BOLD undershoot (Aubert and Costalat, 2002; Buxton et al.,1998;Mandeville et al., 1999b). Furthermore, inMandeville's modifiedWindkessel Model, the temporal evolution of total CBV was describedas a rapid elastic response of the postarteriolar compartment,approximately synchronized with the CBF response, followed byslow venous relaxation of stress (Mandeville et al., 1999b), the latterbeing the chief cause of the BOLD undershoot. This bi-phasic CBVbehaviour was experimentally confirmed by Silva et al. (2007) andShen et al. (2008). The close agreement between our CBVv Tfmeasurements and the characteristics of the slow component(Mandeville et al., 1999b) is evidence that the fast component islikely more arterially weighted. Indeed, this was postulated by thearteriolar compliance model, in which baseline vascular state affectsthe dynamic response by changing the relative contributions of theactive smooth muscle component and the passive connective tissuecomponent to the overall vessel compliance. When coupled with thepassive Balloon Model, the arterial compliance model was able topredict the observed CBF and BOLD response at various baselineconditions (Behzadi and Liu, 2005).

Our findings of delayed venous compliance are in agreement withoptical imaging spectroscopy studies (Hoge et al., 2005; Malonek etal., 1997; Martindale et al., 2003). Malonek et al. noted an increase in[dHb] accompanied by a simultaneous increase in total hemoglobinconcentration [tHb], presumably reflecting an early total bloodvolume increase. Their observation of a CBF change lagging [tHb]changes by 1 to 2 s supports the notion of active neurovascularregulation of blood volume in the capillary bed and the existence of adelayed, passive process of capillary filling (Malonek et al., 1997). Apost-stimulus elevation in [tHb] and overshoot in [dHb] was alsoobserved by Hoge et al. using diffuse optical tomography (Hoge et al.,2005), in line with a prolonged CBV recovery observed by Martindaleet al. (2003). The relative timings of these parameters were found tobe consistent between the sensorimotor and the visual systems(Boden et al., 2007), in line with our observations.

The current results differ from findings of a more instantaneousCBV recovery observed in studies measuring total ΔCBV using thevascular space occupancy (VASO) and gadolinium-enhanced bolus

tracking techniques (Frahm et al., 2008; Lu et al., 2004; Yang et al.,2004). Notably, VASOmeasured a CBV response that decreased earlierthan BOLD or CBF, but lagged behind that of BOLD by 5 to 10 s in itseventual return to baseline (Lu et al., 2003, 2004). This is in contrastwith our finding of a slower initial CBVv drop-off relative to BOLD andCBF. Also in contrast with these earlier findings, our data show thatvenous CBV remains elevated over the whole duration of the BOLDundershoot, as indicated by positive CBVv APS, and is likely to play arole in inducing the BOLD undershoot. These observations likelyreflect dynamic differences between venous and total CBV change.

As described earlier, total ΔCBV transitions, involving both thearterial and venous compartments, have on several occasions beenfound to be biphasic. Venous blood volume change has been shown tocontribute to only 40% to total ΔCBV (Lee et al., 2001), implying thatarterial dilation is likely to contribute most to total percent CBVchange, as was reported by various groups (Lee et al., 2001; Okazawaet al., 2001; Zhao et al., 2007). Thus, arterial CBV is expected tocontribute substantially to the “fast” phase of total CBV responsedescribed above, and total ΔCBV, being the weighted average of itsarterial and venous components, is likely to exhibit a faster initialpost-stimulus recovery reflective of CBF recovery. As we observed adecidedly slower post-stimulus response in CBVv, we can attribute theslow component in total CBV change to primarily capillary and venularcontribution, as was observed in recent cortical two-photon micro-scopy studies (Hillman et al., 2007; Hutchison et al., 2006).

Variations in the spatial specificity of the CBVmeasurements couldalso result in different observation of the CBV transient. Using MIONimaging at high spatial resolution, Yacoub et al. showed that the post-stimulus CBV response was distinctly slower in the deeper parench-yma than near the cortical surface (Yacoub et al., 2006). Their findingsare consistent with a CBV response with primarily capillary andvenular weighting, and are in remarkable agreement with ourobservations, derived from ROIs with high microvascular weighting.As the BOLD signal is modulated by partially deoxygenated volumeinstead of total CBV, the quantification of CBV contributions to theBOLD undershoot should be based primarily upon CBVv measure-ments. The fractional CBV changes associatedwith neuronal activationare expected to be weighted more towards deeper cortical layers,while the BOLD response is generally greater in the draining pial veinson the cortical surface, as reflected by the spatial profile analysis inprevious works (Shen et al., 2008; Zhao et al., 2007). Nonetheless,given the spatial resolution employed in most in vivo human fMRIstudies, including the current study, it is not possible to spatiallyresolve the various vascular compartments or cortical layers reliably.Thus, the BOLD and CBVv responses observed in this study are bothweighted averages of the contributions from capillaries, venules andveins (pial and intra-cortical) across multiple cortical layers.

BOLD post-stimulus responseBOLD showed significant and pronounced undershoots, as

expected. The BOLD APS estimates were significantly larger thanthose of CBF in all cases (pb0.05), showing that the undershootfigures most prominently in the BOLD time course. The BOLDundershoot amplitude did not vary significantly with the positiveΔCBF, in agreement with previous observations (Hoge et al., 1999a).

Significant correlation was found between the BOLD and CBF APS,strengthening support for the existence of a non-negligible CBFcontribution to the BOLD undershoot. In addition, the BOLD under-shoot amplitude was significantly larger in the visual region, inagreement with recent studies (Buckner et al., 1998; Chiarelli et al.,2007). The same was true for FWHMUS, and both the undershootamplitude and duration dependency coincide with findings with theCBF transients, while no such dependency was observed with CBVv.Given that the CBF response is likely to have a significant neuronalcorrelation (Hamel, 2006; Hyder et al., 1998), which is likely to varywith stimulus type, this visual-sensorimotor difference in the BOLD

566 J.J. Chen, G.B. Pike / NeuroImage 46 (2009) 559–568

undershoot not only adds support for a strong contribution of the CBFundershoot to the BOLD undershoot, but further implicates the role ofa neuronal contribution in producing the BOLD undershoots. How-ever, the BOLD Tf was not significantly different between the visualand sensorimotor categories, while that of CBF was (Fig. 4c). Thisobservation may be attributed to the concurrent and significant CBVv

contribution, as the CBVv Tf was also found to be independent ofstimulus type. This finding further indicates that neuronally mediatedfactors such as CBF and biomechanical factors such as CBVv bothcontribute to the formation of the BOLD undershoots, though indifferent fashions.

Differences in visual and sensorimotor neuronal activity are likelyto result from fundamental differences in the stimuli, as well as fromthe neural and glial heterogeneity that characterizes the cerebralcortex (Jezzard et al., 2003). The cortex is also characterized byheterogeneity in vascular density (Cavaglia et al., 2001). It was foundthat surface layers of the cortex (layers I to III), which are associatedwith the highest level of vascularization, are also the locations of thelargest BOLD changes and undershoots (Shen et al., 2008; Silva et al.,2007). In addition, the occipital lobe surface is more vascularizedthan the surface of the sensorimotor areas (Duvernoy et al., 1981).These facts may also attribute the BOLD undershoot differencebetween visual and sensorimotor stimulation to the regionallyspecific capillary density. However, given that no significant distinc-tion was made between the visual and sensorimotor transient CBVv

responses hints that this regional difference in hemodynamics is notsolely the result of local vascular construct, but may have a neuronalcontributor that is determined by the nature of the stimulithemselves. Indeed, Hoge et al. extensively studied the visualstimulus-dependence in the BOLD and CBF dynamic responses, andfound that certain visual patterns, such as the polychromatic radialcheckerboard used in this study, produced more robust andpronounced BOLD undershoots than others (Hoge et al., 1999a;Kruger et al., 1998b). Further along this theme, Fox et al. studied theBOLD transients across a wider variety of tasks, and reported thatwhile the occurrence of undershoot is brain region-dependent, theamplitudes of the undershoots depends on the type of task, andhence on the underlying neuronal activity (Fox et al., 2005). It istherefore reasonable to postulate that the BOLD undershoot alsostems from stimulation-specific metabolic triggers, which are likelyto be reflected in the corresponding CBF response. Boden et al. foundthat changes in [Hb] lead those in [dHb] in the sensorimotor systembut not in the visual system, an effect shown to result from a systemicresponse accompanying sensorimotor activity, but otherwise foundno fundamental physiological difference between cortical hemody-namic regulation in motor and visual cortex (Boden et al., 2007). Thissystemic response was explored earlier by Vafaee and Gjedde, whoattributed the difference mainly to a difference in attentionrequirements (Vafaee and Gjedde, 2004). Unlike during visualstimulation, where the subject's attention is required both duringactivation and baseline, the sensorimotor system is required to be atrest between stimulation blocks. This motor task paradigm is likely toemphasize the subjects' level of anticipation, hence elevating base-line CBF in spite of the subjects being at rest. The ensuing elevation inbaseline oxygenation may give rise to a smaller BOLD undershoot.The BOLD post-stimulus undershoot is strongly influenced bybaseline hemodynamic and metabolic conditions. Cohen et al.observed that the post-stimulus undershoot resolved more quicklywith hypocapnia and appeared to be abolished with hypercapnia(Cohen et al., 2002), a behaviour that was reproduced by Behzadi etal. using the arterial compliance model (Behzadi and Liu, 2005). Thiswas further confirmed by Lindauer et al., who observed no under-shoot during hyperoxia, and more pronounced undershoots duringhypoxia (Lindauer et al., 2003).

The existence of a non-hemodynamic basis for the undershoot wassupported by simulations by Jones (1999), giving occasion to theories

inwhichmetabolic factors are the sole origin (Frahm et al., 2008; Lu etal., 2004). It has been postulated that the BOLD undershoot is incurredby elevated post-stimulus oxygen metabolism (CMRo2), which isneeded to restore the ion gradients depleted by action potentialgeneration (Lu et al., 2004). Derived from measurements of totalΔCBV, this CMRo2 elevation was further suggested to occur in theabsence of prolonged CBF or CBV elevation, resulting in a transientdissociation between CMRo2 and CBF. Notwithstanding the obviouslyslower response in post-stimulus ΔCBVv, which is one of our mainarguments in favour of a biomechanical contribution, as well as thepotential caveats of inferring CBV dynamics from a plasma-basedtechnique, our analysis of the BOLD undershoot sensitivities alsoargue against a post-stimulus ΔCMRo2 elevation being the main causeof the BOLD undershoot. Evidently, the level of oxygen consumption,which modulates dHb content, would depend on the type of stimulusand the level of activation elicited, as the latter contributes directly tothe degree of depletion experienced in the ion gradients, and hencethe level of metabolic deficit. Thus, should the sustained CMRo2 (theexistence of which was to eliminate this deficit) be the only cause ofthe BOLD undershoot, the amplitude and duration of the latter wouldbe expected to correlate with the intensity and/or duration of thestimulus. However, in this study, neither the BOLD post-stimulusundershoot amplitude nor duration was influenced by stimulationduration or intensity over the range studied by the above authors. Thisechoes our previous finding of BOLD undershoot independence onstimulus duration over awider range (Chen et al., 2007). It is thereforeevident that sustained metabolism cannot be the sole cause of theBOLD undershoots.

Finally, certain animal studies have reported small BOLD under-shoots (Silva et al., 2007) during motor stimulationwhile others showthe contrary (Huttunen et al., 2007; Mandeville et al., 1999a).However, the necessity of using anesthetic agents in these studiescould have substantially modified functional and hemodynamictransients. Urethane has been known to reduce baseline CBF(Osborne, 1997) and halve the rate of action potentials (Simons etal., 1992), while isofluorane has been shown to increase baselineperfusion and prolong activation due to its inhibitive effect onpotassium channels (Kuroda et al., 2000). Huttunen et al. found thaturethane-anesthetized rodents exhibited BOLD responses over amuch wider range of stimulus frequencies than alpha-chloralose-anesthetized rats, which only responded to low-frequency stimuli.Although tight neural–hemodynamic coupling was observed in bothcases (Huttunen et al., 2007), the varying baseline conditions areexpected to seriously influence the BOLD undershoot, and asdescribed earlier, baseline CBF elevation undermines BOLD under-shoots. Furthermore, Palmer et al. reported fluctuations in partialcarbon dioxide pressure and pH in alpha-chloralose anesthetized rats(Palmer et al., 1999). These parameters can affect the hemodynamicresponse to neuronal activation and could conceivably lead tovariation in one or more aspects of the response within and betweenanimals as physiological condition changes over time.

We have shown clear evidence for the contribution of prolongedvenous CBV elevation to the BOLD post-stimulus undershoot, asproposed by the Balloon and Windkessel models. However, thisbiomechanical factor is likely not the only contributor to theundershoot, the occurrence of which is dependent on the stimulus(Hoge et al., 1999a) and baseline (Fox et al., 2005). The BOLDundershoot has previously been suggested to result solely fromongoing metabolism and flow-metabolism uncoupling (Frahm et al.,2008; Lu et al., 2004), and was supported by a faster post-stimulusdrop-off in total ΔCBV. However, such observations may reflect apredominance of arterial CBV in their measurements, which is notthe main modulator of the BOLD signal. Our results also supportCBF undershoots as a significant contributor to the BOLD post-stimulus undershoot, whether engendered through biomechanicalor neuronal means.

567J.J. Chen, G.B. Pike / NeuroImage 46 (2009) 559–568

Acknowledgments

This research was supported by the Natural Sciences andEngineering Research Council of Canada (J. J. C.) and the CanadianInstitutes of Health Research (G. B. P.).

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