NeuroImage 58 (2011) 157–167

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Arterial spin labeling for motor activation mapping at 3T with a 32-channel coil:Reproducibility and spatial accuracy in comparison with BOLD fMRI

Hélène Raoult a,b,c,d,⁎, Jan Petr b,c,d, Elise Bannier b,e, Aymeric Stamm b,c,d, Jean-Yves Gauvrit a,b,c,d,e,Christian Barillot b,c,d,e, Jean-Christophe Ferré a,b,c,d,e

a Department of Neuroradiology, Hôpital Pontchaillou, University Hospital Rennes, 2 rue Henri Le Guilloux, F-35000, Rennes, Franceb INRIA, VisAGeS Project-Team, F-35042 Rennes, Francec INSERM, U746, F-35042 Rennes, Franced University of Rennes I, CNRS, UMR 6074, IRISA, F-35042 Rennes, Francee Neurinfo Platform, University Hospital of Rennes, F-35043 Rennes, France

⁎ Corresponding author at: Department of NeuroraUniversity Hospital Rennes, 2 rue Henri Le Guilloux, F-352 99 28 43 64.

E-mail address: helene.raoult@chu-rennes.fr (H. Rao

1053-8119/$ – see front matter © 2011 Elsevier Inc. Aldoi:10.1016/j.neuroimage.2011.06.011

a b s t r a c t

a r t i c l e i n f o

Article history:Received 3 March 2011Revised 19 April 2011Accepted 6 June 2011Available online 14 June 2011

Functional arterial spin labeling (fASL) is an innovative biomarker of neuronal activation that allows directand absolute quantification of activation-related CBF and is less sensitive to venous contamination than BOLDfMRI. This study evaluated fASL for motor activation mapping in comparison with BOLD fMRI in terms ofinvolved anatomical area localization, intra-individual reproducibility of location, quantification of neuronalactivation, and spatial accuracy. Imaging was performed at 3T with a 32-channel coil and dedicated post-processing tools were used. Twelve healthy right-handed subjects underwent fASL and BOLD fMRI whileperforming a right hand motor activation task. Three sessions were performed 7 days apart in similarphysiological conditions. Our results showed an activation in the left primary hand motor area for all 36sessions in both fASL and BOLD fMRI. The individual functional maps for fASL demonstrated activation inipsilateral secondary motor areas more often than the BOLD fMRI maps. This finding was corroborated by thegroup maps. In terms of activation location, fASL reproducibility was comparable to BOLD fMRI, with adistance between activated volumes of 2.1 mm and an overlap ratio for activated volumes of 0.76, over the 3sessions. In terms of activation quantification, fASL reproducibility was higher, although not significantly, witha CVintra of 11.6% and an ICC value of 0.75. Functional ASL detected smaller activation volumes than BOLDfMRI but the areas had a high degree of co-localization. In terms of spatial accuracy in detecting activation inthe hand motor area, fASL had a higher specificity (43.5%) and a higher positive predictive value (69.8%) thanBOLD fMRI while maintaining high sensitivity (90.7%). The high intra-individual reproducibility and spatialaccuracy of fASL revealed in the present study will subsequently be applied to pathological subjects.

diology, Hôpital Pontchaillou,000, Rennes, France. Fax: +33

ult).

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© 2011 Elsevier Inc. All rights reserved.

Introduction

Functional MRI (fMRI) is the tool of choice for mappingvariations in neuronal activity during motor or cognitive tasks orduring sensitive or sensory stimulation. BOLD fMRI is considered tobe the gold standard for clinical activation fMRI studies. The BOLDsignal is based on capillary/venous hyperoxygenation in activatedbrain areas leading to a minor signal decrease on T2* images(Bandettini et al., 1992; Belliveau et al., 1991). The spatial accuracyof BOLD fMRI for motor activation mapping has shown goodagreement with perioperative electrical stimulation (Lehericyet al., 2000) and PET (Ramsey et al., 1996). However, BOLD fMRIpresents several limitations. Firstly, the BOLD signal results from

complex interactions between simultaneous variations in cerebralblood flow (CBF) (Stefanovic et al., 2006), cerebral blood volume(Stefanovic et al., 2006), and the cerebral metabolic rate of oxygen(Tuunanen and Kauppinen, 2006). Incomplete knowledge of therespective contributions to neuronal activity impairs the robustnessof BOLD fMRI and its reproducibility. Secondly, the sensitivity ofBOLD fMRI to signals originating from macroscopic veins (Mangiaet al., 2004) and to local modulation of vasoreactivity, impairs itsaccuracy. False-positive and false-negative activations were ob-served in areas such as neuro-oncology (Bartos et al., 2009) andischemic pathology (Krainik et al., 2005; Rossini et al., 2004), andwere attributed to vasoreactive modifications inherent to thesespecific pathological contexts. Functional arterial spin labeling(fASL) is an innovative biomarker of neuronal activity based ondirect measurement of the local perfusion variations and offers analternative to BOLD fMRI. Arterial spin labeling allows non-invasiveimaging and quantification of brain perfusion using magneticallylabeled arterial protons in the brain-feeding arteries as an

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endogenous tracer (Detre et al., 1992). Recently proposed foractivation-fMRI, fASL eflects only the vascular component of thevascular coupling (Roy and Sherrington, 1890) and is potentially amore direct biomarker of neuronal activity than BOLD fMRI. fASLoffers two main advantages in comparison with BOLD fMRI. The firstadvantage is the direct and absolute quantification of activation-related CBF (Detre et al., 1992). This potentially yields higher intra-individual reproducibility (Leontiev and Buxton, 2007) and isessential for longitudinal studies. The second advantage is moreaccurate spatial localization of neuronal activity (Jin and Kim, 2008;Luh et al., 2000) notably due to lower sensitivity to venouscontamination (Aguirre et al., 2002; Luh et al., 2000). This isessential for presurgical planning because it conditions excisionmargins. fASL has shown high agreement with PET for CBFquantification and activation location during visual stimulation(Chen et al., 2008). Moreover, fASL has demonstrated a correlationbetween stimulation intensity and CBF increase not shown by PET(Chen et al., 2008). Yet fASL has a low signal-to-noise ratio (SNR)due to the minor difference between the label and control images(around 1% of label image magnitude) (Mildner et al., 2003). This ispartially compensated for by using long acquisition times andencourages imaging at higher field strength. Some experimentalstudies have already used fASL to investigate visual, motor andlanguage functions (Aguirre et al., 2002; Ances et al., 2008; Chenet al., 2008; Kemeny et al., 2005; Leontiev and Buxton, 2007; Tjandraet al., 2005; Wang et al., 2003) and indicated that, at 3T, fASLshowed some activations not seen at 1.5 T (for example in thesupplementary motor area (Yongbi et al., 2002)). High-field MRI(Golay and Petersen, 2006) can be associated with the use ofphased-array coils (Duyn et al., 2005; Wang et al., 2005) and parallelimaging. The increase of number of elements of phased-array coilsoffers higher SNR and higher perfusion contrast while parallelimaging reduces geometric distortion artifacts. Only a few studiesfocused on the intra-individual reproducibility of fASL. These weremostly conducted at 1.5 T (Aguirre et al., 2002; Hermes et al., 2007;Kemeny et al., 2005) or without comparison to BOLD fMRI(Borogovac et al., 2009). The only studies comparing fASL withBOLD fMRI at 3T included a limited number of subjects and/orseparate sessions (Leontiev and Buxton, 2007; Tjandra et al., 2005)and focused only on quantification of neuronal activation. To ourknowledge, no study has compared fASL and BOLD fMRI intra-individual reproducibility of neuronal activation location.

The aim of this study was to evaluate fASL for motor activationmapping in comparison with BOLD fMRI, at 3T with a 32-channel coiland dedicated post-processing tools. The evaluationwas performed interms of involved anatomical area location, intra-individual repro-ducibility of location and quantification of neuronal activation, andspatial accuracy.

Materials and methods

Subjects and paradigm

Twelve healthy subjects were included: 7 women and 5 men,mean age 28.6 (±2.7) years old who were strongly right-handed(92.5%, ±13.4% based on the Edinburgh Handedness Inventory).Exclusion criteria were psychiatric or central nervous systemdisorders, history of brain surgery or trauma and regular medication,and besides contraceptives. Written and informed consent wasobtained from all subjects. Three fASL sessions were performed7 days apart at the same time of day to minimize the effect of diurnalvariations in CBF and without caffeine ingestion in the 3 h precedingthe MR acquisition to avoid CBF overestimation (Chen and Parrish,2009). These conditions allowed narrowing physiological variationsin basal perfusion which may affect activation response (Cohen et al.,2002). A block design paradigmwas used with seven interleaved 30-s

periods of rest and maximum amplitude flexion-extension tasks ofthe dominant hand at 1 Hz frequency (Rao et al., 1996). This task issimple to perform and known to cause high cerebral perfusion activity(Ramsey et al., 1996) with robust somatotopy of activated areas(Lehericy et al., 2000), and a limited amount of artifacts (Hoeller et al.,2002). The paradigm was precisely explained to each subject andtested before the MR acquisition. The instructions for task and restperiods were given by an auditory spoken signal (pronouncing“action” or “rest”).

MRI acquisition

MR imaging was performed on a 3T Siemens Verio MR scanner(VB17 software release) with a 32-channel head coil. For each of thethree sessions, the imaging protocol consisted of isotropic milli-metric 3D T1 MPRAGE, fASL and BOLD fMRI sequences. Thefunctional images were acquired parallel to the AC–PC line, with a192×192 mm² FOV and a 64×64 acquisition matrix, with alignmentof the top of the superior slices of both the fASL and BOLDsequences. The acquisition parameters of the GRE-EPI PICOREQ2TIPS fASL sequence were: eight 7 mm slices with 3×3 mm² in-plane acquisition resolution, 0.7 mm slice gap, 60.9 cm coverage, TR/TE=3000/18 ms, Q2TIPS saturation pulse with 700 ms onset time(TI1), 800 ms duration (TI1s), 1700 ms PICORE labeling inversiontime (TI2), 4 cm/s crusher gradients, 7 min. 20 s. acquisition time,and 90° flip angle. The labeling slice was 10 cm thick and wasseparated from the acquisition volume by a 3 cm gap. The sequencebegan with a control/label acquisition for signal stabilization (Ishiujiet al., 2009) before the first rest period. Each period began with acontrol image acquisition to ensure the label image was acquiredduring neuronal activity and to limit synchronization problemsbetween the respective beginning of the acquisition, the task andthe perfusion curve increase (Detre and Wang, 2002; Lu et al.,2006). The acquisition parameters of BOLD fMRI were: TR/TE=3000/30 ms, thirty-six 3.5 mm slices with 3×3 mm² in-planeresolution, 0.35 mm slice gap and whole brain coverage.

Data processing

Pre-processingIndividual image pre-processing was performed with the

MATLAB SPM8 toolbox (MathWorks, Natick, MA). First, patientmovements during fASL and BOLD acquisition were corrected bysix-parameter 3D rigid registration. This involved alignment of eachimage of the fASL and BOLD sequences to the first volume of thecorresponding sequence. The T1-weighted image was co-registeredwith the aligned mean ASL control image and the mean BOLD imageusing the normalized mutual information cost function. The T1-weighted image was denoised using the NL-means algorithm (Petret al., 2010), inhomogeneity bias was corrected and the image wassegmented in the SPM8 toolbox using the ICBM-152 tissue templateas an a priori information. The brain mask extracted duringsegmentation was applied to the fASL and BOLD images in orderto limit the working area and processing time. T1 tissue segmen-tation was also used to calculate the partial volume of gray matter,white matter and cerebrospinal fluid in each voxel of the ASLsequence. Functional MRI statistical analysis was performed forpixels containing over 50% tissue. Because the fASL signal obtainedusing standard subtraction of successive label and control images iscontaminated by the BOLD signal (Lu et al., 2006), a specificadditional pre-processing step, namely surround subtraction, wasapplied to fASL images. Surround subtraction consists of temporallyinterpolating both the control and label images in order to obtain apair of synchronized images for each TR and to avoid BOLDcontamination of fASL images. Fig. 1 illustrates perfusion contrastresulting from pre-processing fASL images. Six-millimiter FWHM

Fig. 1. Perfusion fASL image resulting from pre-processing fASL images for one subject, axial planes. Pre-processing mainly included motion correction, co-registering and surroundsubtraction to avoid contamination of fASL images by the BOLD signal. The quality of perfusion contrast can be assessed on this image obtained before the last step of Gaussian spatialsmoothing. Right (R) and left (L) sides of the plane are indicated.

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Gaussian spatial smoothing was then applied to the fASL and BOLDfMRI images (Ye et al., 1997a, 1997b).

QuantificationAcquisition parameters were selected such that TI2≥TI1+Δt

(Wong et al., 1997) to overcome the problem of varying arterialtransit time between labeling and acquisition volumes.

The following formula was used for quantification (Wang et al.,2003; Cavusoglu et al., 2009):

λ:ΔΜ

2αΜ0:TI1:e−TI2.T1B

where ΔM is the control/label image difference,M0 is the equilibriumbrain tissue magnetization approximated from the mean signal of thefirst 10 control images (Wang et al., 2008), λ is the blood/tissue waterpartition coefficient (0.9 ml/g), T1B is the longitudinal relaxation timeof blood, estimated at 1664 ms at 3T (Wang et al., 2003), and α is theinversion efficiency, estimated at 1 for PASL (Cavusoglu et al., 2009).

Individual statistical analysisThe images of the second and third fASL sessions and of all three

BOLD fMRI sessions were rigidly co-registered with the first fASLsession. Statistical analysis was performed using the general linearmodel approach. Themodelwas specified using signal convolutionwiththe canonical hemodynamic response function. Activationmappingwasachieved by performing simultaneous univariate statistical tests with afamily-wise error rate of 5% corrected according to the SPM8 intrinsicmethod (Garraux et al., 2005) for BOLD fMRI and with a significancelevel of pb0.001 for fASL due to the low SNR (Mildner et al., 2003).Furthermore, a spatial thresholding with a 250 mm3 threshold oncluster sizewas used to prevent detection of false-positive. This cluster-size thresholding improvement relied on the assumption that areas oftrue neuronal activity tend to stimulate signal changes over contiguouspixels and so allowed to reject false-positive activations without loss ofpower to detect statistically significant activity (Forman et al., 1995).Activation and hypoactivation maps were generated for both fASL andBOLD fMRI sequences. For high-resolution functional map visualization,cubic interpolation was performed between the statistical map and thehigh-resolution anatomical 3D T1.

Group statistical analysisMaps of the three sessions were respectively averaged for

perfusion fASL and BOLD fMRI. Subsequently, the individual mapswere normalized to a common template using a non-rigid diffeo-morphic registration algorithm (DARTEL) (Ashburner, 2007), with 6iterations and 12 subjects, producing an anatomy-specific template.Lastly, statistical analysis was performed with random effects using a

one-sample t-test with p≤0.001 uncorrected thresholding. Activationand hypoactivation maps were generated for both fASL and BOLDfMRI sequences.

Functional map analysis

Visual analysisA neuroradiologist (N5 years' experience) evaluated the motor

activation and hypoactivation individual functional maps for eachsession, and group functional maps for both fASL and BOLD fMRI. Theevaluation focused on contralateral (left hemisphere) and ipsilateral(right hemisphere) areas: the hand primary motor area (PMA), thesupplementary motor area (SMA), the premotor area (preMA) andthe parietal association area (PAA-posterior parietal cortex). Theneuroradiologist sought to detect any hypoactivation.

Preliminary baseline perfusion and systematic control measuresPrior to the quantitative analysis of functional maps, baseline CBF

and temporal SNR measurements were performed to account forphysiological and systematic factors which may influence reproduc-ibility of activation response measurements (Cohen et al., 2002). Thebaseline CBF assessed the baseline perfusional physiology (Cohenet al., 2002) and was estimated from the first rest period and the last9 s of each subsequent rest period (Leontiev and Buxton, 2007). Thetemporal SNR assessed the quality of fASL and BOLD fMRI acquisitionsand was calculated in each pixel as the ratio of the mean signal to thetemporal standard deviation of signal over the whole time series ofcontrol images (Dietrich et al., 2007). The temporal SNR of the imagewas computed for pixels with gray matter partial volume over 10%,using only the control images of the resting phase.

Quantitative analysisTwo regions of interest (ROI) were defined on the individual high

resolution T1-weighted anatomical images for each subject. Each ROIwas manually outlined on the individual 3D T1 images by theneuroradiologist using the anatomical landmark (omega shape) in theaxial plane as defined by Yousry (Yousry et al., 1997). ROI1 outlinedthe primary motor area, corresponding to the posterior half of theprecentral gyrus. ROI2 outlined the primary sensorimotor area,corresponding to the postcentral gyrus associated with ROI1.

For each individual session and for both fASL and BOLD fMRI, thefollowing quantitative parameters were calculated (Fig. 2):

– the activated volume (Vact),– the coordinates of the barycenter of the activated volume (the

location was weighted with the signal intensities of the activatedvoxels in the individual ASL/BOLD image),

– the activation-related signal (Sact), and, for fASL only, theactivation-related CBF (CBFact) in the activated volume, using

Fig. 2. Quantitative parameters extracted from the region of interest defined in the leftprimary hand motor and primary sensorimotor areas. For each individual session andfor both fASL and BOLD fMRI, the following quantitative parameters were calculated:the activated volume (Vact), the three coordinates of the barycenter of the activatedvolume (x, y, z), the activation-related signal (Sact), and, for fASL only, the activation-related CBF (CBFact) in the activated volume.

Fig. 3. Evaluation of spatial accuracy of individual functional maps considering the graymatter in ROI3 (left primary hand motor area) as the “ground truth” for hand motoractivation. ROI3 was divided into four areas: true positive (TP), false positive (FP), truenegative (TN) and false negative (FN) for motor activation detection. GM: gray matter;WM: white matter.

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the baseline signal and CBF estimated from the first rest period andthe last 9 s of each subsequent rest period (Leontiev and Buxton,2007). Using values expressed as percentage instead of absolutevalues allowed to overcome potential influence of basal perfusionvariations on neuronal activation measures (Cohen et al., 2002;Restom et al., 2007).

A third ROI (ROI3) exclusively considered the primary motor areafor the right hand using anatomical landmarks (Yousry et al., 1997) inthe axial (omega shape) and coronal (hook shape) planes. Graymatter in ROI3 was extracted from the initial segmentation resultusing the ICBM-152 tissue template available in SPM8 for as an a prioriinformation and was considered as the “ground truth” for handmotoractivation.

Statistical analysis

Repeatability of preliminary control measuresA two-way random effects of variance (ANOVA), with principal

factors “subject” and “session”, was performed on both the baselineCBF and the temporal SNR. Fisher's tests for significance of principalfactors were applied.

Reproducibility of activation location and size/co-localization betweenfASL and BOLD fMRI

An fMRI sequence is considered to be reproducible when the sameactivation location and size are obtained each time it is applied to a givensubject. Evaluation of intra-subject reproducibility was based on threeparameters considering the 3 sessions in pairs (Vlieger et al., 2003):

– the Euclidean distance between barycenters of activated volumes(D),

– the overlap ratio (Roverlap) for activated volumes defined asVoverlap/Vmean, where Voverlap is the common activated volumeand Vmean is the mean activated volume of the two comparedsessions,

– the size ratio (Rsize) for activated volumes, defined as Vmin/Vmean,where Vmin is the smallest activated volume.

The parameters D and Roverlap evaluated the reproducibility ofactivation location while Rsize evaluated the reproducibility ofactivation size. Rsize and Roverlap ranged from 0 (completelyunreproducible) to 1 (completely reproducible).

Co-localization between fASL and BOLD fMRI was evaluated usingthe same parameters as defined above, comparing fASL and BOLDactivated volumes session by session.

Reproducibility and reliability of activation quantificationReproducibility of activation quantification for both fASL and BOLD

was evaluatedwith the sample intra-individual coefficient of variation(CVintra) (Tjandra et al., 2005) for both Sact and CBFact. This coefficient

was defined as the sample standard deviation over the 3 sessionsnormalized to the sample mean value and was expressed as apercentage. Comparison between fASL and BOLD reproducibility wasonly performed on Sact since no CBFact value was available for BOLD.The coefficient CVintra was chosen firstly because this test allowsadjustment for differences in the mean Sact value between both fASLand BOLD fMRI (of 60% and 2% respectively), and secondly, because themean values of Sact and CBFact were always sufficiently positive toremedy the ill-conditioning problem. Reliability was evaluatedwith thesample intraclass correlation coefficient (ICC). The ICC was estimatedfrom a two-way random effects ANOVA based on the fact that thesample ICC is equal to (MSB−MSW)/(MSB+(k−1)·MSW) (Petersenet al., 2010), where MSB is the mean sum of squares between subjects,MSW is themean sum of squareswithin subjects and k is the number ofsessions. Reliability assesses the necessaryproperty of an fMRI sequencethat activation quantification should enable a medical diagnosis to bemade. In other words, variability of activation quantification shouldmainly be due to differences between subjects while intra-subjectvariability should be negligible in comparison to inter-subject variabil-ity. An ICC close to 1.00 indicates high reliability, an ICC below 0.5indicates randomness of the results.

Spatial accuracy evaluationThe right-handers' hand primary motor area ROI3 was used to

compare cortical and subcortical extent of the activation assumingcortical gray matter activation was the “ground truth” for hand motoractivation. To evaluate spatial accuracy, ROI3 was divided into four areas(Fig. 3): truepositive (TP), falsepositive (FP), truenegative (TN)and falsenegative (FN) respectively, corresponding to the activated graymatter inROI3, the activatedwhitematter in ROI3, the non-activatedwhitematterin ROI3 and the non-activated gray matter in ROI3 respectively. Thesmaller the FP and FN, the more accurate were the results. For eachsubject and session, the following parameters were computed:

– sensitivity: Se=TP/(TP+FN) being the probability that a trulyactivated region is found activated by fMRI,

– specificity: Sp=TN/(TN+FP) being the probability that a trulynon-activated region is found non-activated by fMRI,

– positive predictive value: PPV=TP/(TP+FP) being the probabil-ity that a region found activated by fMRI is truly activated; and,

– negative predictive value: NPV=TN/(TN+FN) being the proba-bility that a region found non-activated by fMRI is truly non-activated.

Comparison between fASL and BOLD fMRI: sequence reproducibility andspatial accuracy

Comparison between fASL and BOLD fMRI was performed usingtwo-tailed paired t-tests with a 5% significance level. To this end,

Fig. 4. Graph representing the prevalence of observed activation for 36 individual sessions (in %) according to the evaluated area for fASL and BOLD fMRI. Evaluated areas were on theleft contralateral (L) and right ipsilateral (R) side: primary motor area (PMA), premotor area (preMA), supplementary motor area (SMA) and parietal association area (PAA).Activation in the left primary hand motor area was observed for all 36 sessions with both fASL and BOLD fMRI. Activations in right preMA and PAA areas were more frequentlyobserved on fASL maps than on BOLD fMRI maps. One moderately right-handed subject showed activation in the ipsilateral PMA during each session.

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Gaussian distribution of data was verified with the Shapiro–Wilk testand a Box–Cox transformation to normality (Johnson andW.D., 2007)was applied to non-Gaussian data.

Results

Visual evaluation of functional maps

Individual functional mapsActivation in the left primary handmotor area was observed for all

36 sessions with both fASL and BOLD fMRI. The prevalence ofactivation observed in primary and secondary motor areas isrepresented in the graph in Fig. 4. fASL individual functional mapsdemonstrated activation in ipsilateral secondary motor areas, i.e. inthe preMA, SMA and PAA, more often than BOLD fMRI maps, withanatomical depiction as shown in Fig. 5. Mainly, activation wasobserved in right preMA for 92% of sessions with fASL and for 80%with BOLD fMRI as well as in the right PAA for 58% of sessions withfASL and for 47% with BOLD fMRI. For the sole moderately right-handed subject (56% according to the Edinburgh HandednessInventory), a few large activations in the ipsilateral primary motorarea were observed in each session with both fMRI sequences. No

Fig. 5. Individual functional fASL map, overlapped with 3D T1 in the axial plane. Activation walso ipsilateral secondary motor areas. Right (R) and left (L) sides of the plane are indicate

significant hypoactivation was detected on any of the individual fASLor BOLD fMRI maps.

Group functional mapsThe fASL group functional map corroborated the results of the

individual maps, showing contralateral and ipsilateral activation in thepreMA, SMA and PAA (larger in inferior lobule). Moreover, bilateralhypoactivation was shown in the medial parietal areas (Fig. 6).

The BOLD group functional map also showed contralateralactivation in the preMA, SMA and PAA, but to a lesser extent thanfASL in the PAA, as well as ispilateral activation in the SMA. However,it did not show any activation in the ipsilateral preMA or PAA, or anysignificant hypoactivation (Fig. 7).

Preliminary baseline perfusion and systematic control measures

For all 36 sessions, mean baseline CBF value was 54.6 ml/100 g/min(±7.1) and mean temporal SNR values were 13.2 (±2.3) and 56.6(±1.3) in fASL images and BOLD fMRI images respectively. Fisher's testsshowed the repeatability of these control measures across the threesessions, with no statistically significant influence of the factor “session”on baseline CBF or on temporal SNR in fASL and in BOLD fMRI images

as observed in the contralateral primary hand motor area and in the contralateral andd, corresponding with ipsilateral and contralateral hemisphere respectively.

Fig. 6. Group functional fASL map overlapped with the DARTEL template. Axial plane (A): bilateral hypoactivation (blue) in the medial parietal areas and activation (yellow) in thesupplementary motor areas and parietal association areas. Parasagittal plane (B) and posterior coronal plane (C): hypoactivation (blue) in the medial parietal areas and activation(yellow) in the supplementary motor areas. Right (R) and left (L) sides of the plane correspond with ipsilateral and contralateral hemisphere respectively.

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(p 0.106, 0.592 and 0.977) while these measures significantly variedbetween subjects (p 0.0013 andb0.0001).

Intra-individual reproducibility of activation location, co-localizationand reproducibility of quantification for fASL and BOLD fMRI

The results for reproducibility and co-localization of fASL andBOLD fMRI for all 12 subjects are summarized in Table 1 andillustrated in Figs. 8 and 9.

Fig. 7. fASL (top row) and BOLD fMRI (bottom row) overlapped with the DARTEL template inand ipsilateral premotor area (preMA), which was not the case with BOLD fMRI. Activationlarger on the fASL map than on the BOLD fMRI map. The activation surface in the primary sen(L) sides are indicated correspond with ipsilateral and contralateral hemisphere respective

ROIs 1 and 2 had an average volume of 5659.2 and 15203.1 mm3,respectively, representing the primary motor area and the primarysensorimotor area. Vact was significantly lower with fASL than withBOLD fMRI corresponding to 68.1% of the BOLD Vact in ROI1 and 63.4%in ROI2 (t-test, pb0.001). BOLD Vact represented 93.2% of ROI1 and76.6% of ROI2.

In terms of activation location, in ROI1, fASL and BOLD fMRIreproducibility were comparable for D and Rsize. However, fASLreproducibility was significantly lower for Roverlap (pb0.0001). In

the axial plane. fASL showed activation in the ipsilateral parietal association area (PAA)surfaces in the ipsilateral supplementary motor area (SMA) and contralateral PAA aresorimotor area is larger on the BOLD fMRI map than on the fASL map. Right (R) and leftly.

Table 1Reproducibility of activation location, reproducibility and reliability of activationquantification, and co-localization between fASL and BOLD fMRI.

ROI 1 ROI 2

fASL BOLD fMRI fASL BOLD fMRI

CBFactMean,% (sd) 58.5 (7.0) na 57.1 (6.1) naCVintra,% (sd) 12.2 (3.8) na 10.7 (4.6) naICC 0.74 na 0.73 na

SactMean,% (sd) 59.0 (6.7) 2.4 (0.3) 57.3 (5.8) 2.04 (0.2)CVintra,% (sd) 11.6 (3.8) 11.9 (4.8) 10.2 (4.6) 11.5 (6.6)ICC 0.75 0.72 0.75 0.73

VactMean,mm3 (sd) 3591.3*

(411.4)5273.5(532.6)

7376.7*(862.0)

11639.0(948.6)

D, mm (sd) 2.1 (1.2) 1.6 (1.3) 2.6* (1.2) 1.7 (0.5)Roverlap (sd) 0.76* (0.12) 0.93 (0.03) 0.68* (0.12) 0.89 (0.05)Rsize (sd) 0.87 (0.12) 0.94 (0.07) 0.86 (0.12) 0.92 (0.06)

Co-localizationD, mm (sd) 2.4 (1.2) 3.1 (1.1)Roverlap (sd) 0.75 (0.11) 0.68 (0.09)Rsize (sd) 0.80 (0.12) 0.77 (0.11)

*: indicates a significant statistical difference; na: not applicable.sd: standard deviation; CVintra: intra-individual variation coefficient; ICC: intra-individual correlation coefficient.

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ROI2, fASL and BOLD fMRI reproducibility were comparable for Rsize,but fASL reproducibility was significantly lower for Roverlap(pb0.0001) and D (pb0.02).

In both ROIs, fASL and BOLD fMRI were strongly co-localized. Forexample in ROI1, Rsize was 0.80, Roverlap was 0.75 and D was2.4 mm.

In terms of activation quantification, fASL showed higher repro-ducibility than BOLD fMRI, regardless of the considered ROI, with aCVintra as low as 10.2% in ROI2, while CVintra for BOLD fMRI was11.5%. However, this difference was not statistically significant(pN0.05). fASL reliability was slightly higher than BOLD fMRI

Fig. 8. Intra-individual reproducibility of fASL and BOLD fMRI activation location. Axial planeInventory) during each of three consecutive sessions. Right (R) and left (L) sides correspon

reliability regardless of the considered ROI. For example in ROI1, theICC was 0.75 for fASL and 0.72 for BOLD fMRI.

Spatial accuracy of fASL and BOLD fMRI functional maps

The statistical results of spatial accuracy for both fMRI sequencesare represented in Table 2 and Figs. 10 and 11. Table 2 summarizes thequantitative results averaged over all 36 sessions along with the p-values of the statistical tests for comparison between fASL and BOLDfMRI.

fASL detected activation with higher Sp (43.5%) and PPV (69.8%)than BOLD fMRI at the expense of Se, which still remains above 90%.These results reflect the significant lower rate of false-positive voxelsobtained with fASL (white matter with significant activation in ROI3).In parallel, the rate of false-negative voxels remained low and fASLalso detected activation with a slightly higher NPV than BOLD fMRI.

Fig. 11 illustrates the results of spatial accuracy by comparing fASLand BOLD fMRI for the 36 sessions.

Discussion

fASL and BOLD functional motor maps were obtained for all 36sessions performed on 12 healthy right-handed subjects, all showingcontralateral activation in the primary sensorimotor, premotor andsupplementary motor areas. Individual and group functional ASLmaps showed ipsilateral activation in the premotor and parietalassociation areas that was not shown by BOLD fMRI. A high degree ofintra-individual reproducibility of fASL maps was observed, compa-rable to BOLD fMRI for activation quantification, and slightly butsignificantly lower than BOLD fMRI for activation location. Theactivated regions were highly co-localized. However, functional ASLdetected smaller activation volumes than BOLD fMRI. Spatial accuracywas higher for fASL than for BOLD fMRI according to the higherspecificity, and positive and negative predictive values for detectingmotor neuronal activation in the contralateral primary hand motorarea.

In terms of areas involved, fASL detected activation in thecontralateral PMA and preMA for all 36 sessions and the SMA for allbut one session. This activation in right-handed subjects during motor

s for one strongly right-handed subject (100.0% according to the Edinburgh Handednessd with ipsilateral and contralateral hemisphere respectively.

Fig. 9. Individual functional maps of fASL (yellow) and BOLD fMRI (blue) andoverlapping activation volume (gray). Axial (A), sagittal (B) and coronal (C) planes forone individual statistical map. Right (R) and left (L) sides correspond with ipsilateraland contralateral hemisphere respectively.

Fig. 10. Spatial accuracy of fASL and BOLD fMRI motor maps: Sensitivity (Se), specificity(Sp), positive (PPV) and negative predictive values (NPP) for detecting motoractivation. fASL detected activation with higher Sp and PPV than BOLD fMRI at theexpense of Se, which still remains above 90%. *: indicates a significant statisticaldifference.

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tasks has already been shownwith PET and BOLD fMRI (Babiloni et al.,2003; Catalan et al., 1998) and was greater for the right hand task incomparison with the left hand task. This is in agreement with theprevailing role of the left hemisphere regardless of laterality (Babiloniet al., 2003). The SMA and preMA are involved in planning andlearning motor movements (MJ., 2007). The SMA is more involved inplanning and complex tasks, whereas the preMA is more involved inthe integration of neurosensory information. Previous results suggestthat the SMA ismore involved in internally-guidedmovement and thepreMA in neurosensory signal-guided movement (MJ., 2007). Theauditory signal marking the beginning of a simple motor task in ourstudy might explain the constant contralateral preMA activation.Functional ASL also showed activation in the ipsilateral SMA andpreMA, which varied on the individual maps – 86% of sessions for SMAand 92% for preMA – but was clearly visible on the group functionalmap. However, the BOLD group functional map did not show anyactivation in the ipsilateral preMA, although it was detected on 80% ofindividual maps. Ipsilateral activation in the SMA was reported inseveral PET (Catalan et al., 1998), fASL and BOLD fMRI studies(Horenstein et al., 2009; Stefanovic et al., 2004). Ipsilateral activationin the preMA has already been reported in PET (Catalan et al., 1998)and BOLD fMRI studies (Horenstein et al., 2009; Verstynen et al.,2005) but was more anecdotal in fASL studies (Garraux et al.,2005). Although previous results showed that, in right-handedsubjects, ipsilateral activation was more pronounced during complex(Horenstein et al., 2009) or left-hand movements (Verstynen et al.,2005), our study reveals ipsilateral activation during a simple righthand task, thereby highlighting the frequent involvement of theipsilateral hemisphere, especially the preMA, for movement planningand execution. Moreover, fASL showed activation in the bilateral PAAon the group map, which was greater in the inferior lobule, and

Table 2Spatial accuracy of motor activation detection: comparison between fASL and BOLDfMRI.

fASL BOLD fMRI t-test (p)

Sensitivity, mean (sd) 90.7% (9.1) 96.5% (3.1) 0.0017Specificity, mean (sd) 43.5% (17.9) 12.7% (10.7) b0.0001Positive predictive value, mean (sd) 69.8% (7.6) 58.1% (4.9) b0.0001Negative predictive value, mean (sd) 77.5% (15.3) 71.4% (24.5) 0.1629True positive, mean (sd) 52.1% (6.1) 53.6% (5.3) 0.0602True negative, mean (sd) 18.7% (9.8) 5.8% (5.1) b0.0001False positive, mean (sd) 23.5% (9.0) 38.6% (5.8) b0.0001False negative, mean (sd) 5.7% (3.3) 2.0% (1.8) 0.0027

variable on individual maps but generally more frequent in the lefthemisphere. The BOLD fMRI group map did not reveal any activationin the PAA, which was also less frequently shown on individual BOLDfMRI than on fASL maps. The superior PAA receives auditory,somesthetic and visual information (Catalan et al., 1998; Jenkinset al., 1994) to organize spatio-temporal schemes and movementcoordination. The inferior PAA is involved in language understanding(part of Wernicke's area) which may explain its frequent activation inthis study because the task or rest periods were triggered by a spokensignal. Previous PET and BOLD fMRI studies (Catalan et al., 1998;Horenstein et al., 2009; Stefanovic et al., 2004) have also reported

Fig. 11. A and B. Spatial accuracy graphs: comparison between fASL and BOLD fMRI foreach of the 36 sessions. Se: sensitivity, Sp: specificity, PPV: positive predictive value,NPV: negative predictive value.

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bilateral PAA activation. As we observed, these studies showed thatPAA activation was inconstant and predominant in the left hemi-sphere (Horenstein et al., 2009; Verstynen et al., 2005). Lastly, thefASL group map showed hypoactivation in bilateral medial parietalareas, which was detected neither on individual maps nor on anyBOLD fMRI individual or group maps. This hypoactivation has alreadybeen reported in PET (Raichle and Snyder, 2007) and fASL studies butno explanation was offered. Ipsilateral activation in the preMA andPAA along with bilateral hypoactivation in medial parietal areas wastherefore exclusively detected with fASL, reflecting the difference insensitivity between fASL and BOLD fRMI as biomarkers of neuronalactivity. This was also illustrated in some studies showing that fASLsignal variations were more pronounced in the PMA than in the SMAalthough BOLD signal variations were similar in both areas (Obataet al., 2004), or showing that fASL was more reliable than BOLD fMRIfor identifying Broca's area (Kemeny et al., 2005). Thus, despite thelow signal-to-noise ratio, the difference in the observed activatedareas between fASL and BOLD fMRI showed in this study serves toillustrate that fASL can offer higher accuracy than BOLD fMRI forquantifying neuronal activation variations at the capillary scale(Obata et al., 2004).

In terms of activation quantification, activation-related CBFquantified with fASL was almost 60% in the contralateral PMA,corroborating literature results that range from 40% to 100%(Garraux et al., 2005; Wang et al., 2003) which may firstly beexplained by measurements in anatomical ROIs and not insignificantly activated volumes, and secondly, by the larger spatialsmoothing (FWHM 8 mm (Garraux et al., 2005)) limiting the CBFincrease during the activation period (Ye et al., 1997b). Preliminarycontrol measures insured the repeatability of baseline CBF andtemporal SNR values across sessions so that these main physiolog-ical and systematic factors did not influence further activationreproducibility quantification. Moreover, the activation quantifica-tion was expressed as percentage notably reducing the influence ofbaseline perfusion value. Baseline CBF was 54.6 ml/100 g/min, inline with previous ASL studies (Hermes et al., 2007; Petersen et al.,2010). In terms of Sact and CBFact, the present study showed highintra-individual reproducibility and reliability of fASL, with CVintraranging from 10.2% to 12.2%, and ICC ranging from 0.72 to 0.75. Thisreproducibility level is similar to the levels reported in the twostudies performed at 3T with PICORE QUIPSSII fASL (Leontiev andBuxton, 2007; Tjandra et al., 2005) (Table 3). Our resultscorroborated the findings of the Tjandra et al. study (Tjandra et al.,2005) showing that fASL reproducibility tends to be higher thanBOLD fMRI for quantification of the motor activation-related signal,but without significant statistical difference. However, the repro-ducibility of BOLD fMRI was slightly higher in our study than inprevious studies (Leontiev and Buxton, 2007; Tjandra et al., 2005).

In terms of activation location, based on the activated volume inthe primary motor area, intra-individual reproducibility of fASL washigh with a distance between the barycenters of activated volumes

Table 3Intra-individual reproducibility of fASL and BOLD fMRI for quantifying the activation-relate

Present study: motor T

Subjects/sessions 12/3 5Sequence PICORE Q2TIPS 3T PCoil 32-channel 8

Sact ASLfMean% (±sd) 57.3 (±5.8)–59.0 (±6.7) 3CVintra% (±sd) 10.2 (±4.6)–11.6 (±3.8) 1

Sact BOLDmean (±sd) 2.04 (±0.2)–2.4 (±0.3) 1CVintra% (±sd) 11.5 (±6.6)–11.9 (±4.8) 1

Sact: activation-related signal variation, CVintra: intra-individual variation coefficient, sd: s

between the 3 sessions of 2.1 to 2.6 mm on average, thus lower thanthe in-plane resolution of the sequence (3×3 mm), and with anoverlap ratio of the activated volume of 0.76. A BOLD fMRI study(Vlieger et al., 2003) showed markedly lower reproducibility ofactivation location than we have observed both for BOLD fMRI andfASL, with Rsize, Roverlap and D of 0.72–0.75, 0.38–0.50 and 6.7–13.6 mm respectively, versus 0.87, 0.76 and 2.1 mm for fASL and0.94, 0.93 and 1.6 mm for BOLD fMRI in this study. These resultshighlight the particularly high reproducibility of BOLD fMRI in thepresent study in comparison with previous results. To ourknowledge, this is the first study comparing fASL and BOLD fMRIintra-individual reproducibility in terms of activation location. Ourresults showed that fASL reproducibility of the overlap ratio and, inthe sensorimotor area, of the barycenter difference, was signifi-cantly lower than for BOLD fMRI. An initial explanation may be thelower fASL spatial resolution, with 7 mm slice thickness versus3.5 mm for BOLD fMRI. Thus, a single voxel variation in fASLactivation volume could displace the barycenter by up to 7 mm onthe z-axis, while barycenter displacement was only 3.5 mm for thesame variation with BOLD fMRI. In the same way, a single voxelvariation in fASL activation volume could significantly reduce theoverlap ratio with an intersection volume decrease betweensessions of (7×3×3)mm3 with fASL, but only (3.5×3×3)mm3

with BOLD fMRI. A second explanation is the large BOLD fMRIactivation volume, which is almost 1.5 times larger than for fASL,occupying 76% to 93% of the anatomical ROI and sometimesexceeding its boundaries which significantly reduced locationvariations in BOLD activation volumes. Lastly, the present studyshowed that fASL was highly co-localized with BOLD fMRI,particularly in the primary motor area where the distance betweenthe barycenters of the activated volumes between both the fMRIsequences was 2.4 mm with an overlap ratio of 0.75, in agreementwith literature data (Luh et al., 2000; Tjandra et al., 2005).

The corollary of the difference between fASL and BOLD fMRIactivation volume size was higher spatial accuracy for fASL indetecting neuronal activation. Using gray matter in the left primaryhand motor area as a “ground truth” (Yousry et al., 1997) we showedthat specificity and positive predictive values of fASL were signifi-cantly higher than for BOLD fMRI values, reflecting fewer false-positive activations. Moreover, this advantage of fASL was associatedwith an above 90% sensitivity and a trend toward higher negativepredictive values in agreement with a low rate of false-negativeactivations of about 5%. Few publications have studied fASL in terms ofspatial accuracy. A study performed at 7T in healthy subjects (Luhet al., 2000) suggested higher accuracy of fASL, showing that the fASLactivation volume was essentially located in areas with a T1 valueclose to graymatter whereas the BOLD fMRI activation volume largelyincluded voxels with a T1 value close to cerebrospinal fluid or blood.In healthy subjects at 3T, Dieckhoff et al. (Diekhoff et al., 2011) alsoshowed that maximum fASL motor activation intensity on the t-valuemap was deeper in the cortex than with BOLD fMRI activation. Lastly,

d signal.

jandra et al. (2005): motor Leontiev and Buxton, 2007: visual

/3 10/2ICORE QUIPSSII 3T PICORE QUIPSSII 3T-channel Not available

9.5 (±6.9)–42.9 (±10.5) 72.9 (±28.8) to 81.5 (±2.,8)3 (±5)–16 (±5) 9.1–21.2

.2 (±0.6)–1.4 (±0.9) 1.8 (±0.3) to 2.2 (±0.5)7 (±14)–24 (±19) 11.7–20.2

tandard deviation.

166 H. Raoult et al. / NeuroImage 58 (2011) 157–167

a study performed at 9.4T in small animals (Jin and Kim, 2008)showed that maximum activation-signal intensity was located at themiddle cortical layer with fASL but located at the cortical surface withBOLD fMRI due to BOLD sensitivity to pial veins.

This study was performed at 3T with a 32-channel coil, offeringhigher SNR and a PICORE Q2TIPS sequence to avoid the effects ofvariations in arterial transit time (Luh et al., 1999) and thus providemore accurate CBF quantification. Up until now, the few fASL studiesat 3T used up to 8 channel phased-array coils (Ances et al., 2008;Leontiev and Buxton, 2007; Owen et al., 2008). Post-processing toolsfor fASL statistical mapping were implemented and includeddedicated processing steps such as pre-processing with surroundsubtraction of label and control images (Lu et al., 2006) to avoidcontamination of the static signal by the BOLD signal, and CBFquantification performed before any spatial normalization to avoidsignal variations due to image interpolation (Wang et al., 2008). Ourstudy has several limitations. Firstly, fASL spatial resolution was low,with 7 mm slice thickness, as compared to motor cortex width (3.5–4 mm) (Truex, 1959), which implies that our results need to bemoderated in terms of the spatial accuracy of fASL. Slice thicknessmay also reduce fASL reproducibility of activation location, asalready suggested. In addition, fASL coverage was limited to 6 cm,thereby precluding studying activation in the cerebellum and basalganglia (Horenstein et al., 2009; Stefanovic et al., 2004). Secondly,the homogeneous young adults population might be a bias becauseof lower percent increase in neuronal activity in comparison witholder adults (Restom et al., 2007) which may influence activationreproducibility measurements. Lastly, a major methodologicallimitation of our study is the lack of fASL validation as perioperativecortical stimulation used as the “ground truth” (Lehericy et al.,2000) is only available in pathological subjects. We therefore choseBOLD fMRI as the standard, and macroscopic sulcogyral landmarksto identify the ROI although these did not systematically correspondto cytoarchitectonic limits of eloquent areas (Geyer et al., 1996).

Further studies will focus on fASL application in a pathologicalcontext, including arteriovenous malformations or tumors where useof BOLD fMRI can be limited. False negatives were observed foractivation with BOLD fMRI in neuro-oncology (Bartos et al., 2009) andischemic pathology (Krainik et al., 2005; Rossini et al., 2004) andattributed to vasoreactivity modifications. For example, BOLD fMRIfailed to detect part of the primary motor area in peritumoral areas in17% of cases in comparison with cortical stimulation (Bartos et al.,2009). Also, BOLD fMRI may fail to detect activated areas near thenidus of arteriovenous malformations (Mangia et al., 2004), due tointerference with severe flow abnormalities. fASL validation studiesneed to be performed in comparison with cortical stimulation andmagnetoencephalography.

Conclusion

Functional ASL is an innovative biomarker of neuronal activationproviding motor activation mapping with high intra-individualreproducibility. Functional ASL seems to detect neuronal activationcloser to the involved neurons, with high spatial accuracy, and scoreshigher positive predictive values than BOLD fMRI. The performanceand advantages of fASL shown in this study need to be confirmed inpathological subjects. The growing availability of high-field MRI andphased-array coils along with post-processing strategies should helpto implement fASL in clinical protocols. Thus, in pathological settings,both fASL and BOLD fMRI sequences could be used in a presurgicalplanning protocol to secure the surgical procedure by determining theeloquent areas near tumoral or vascular lesions more accurately or, inthe investigation of psychiatric disorders or neuronal plasticitymechanisms, to elucidate location and relative activation of investi-gated areas.

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