MRI multiparametric hemodynamic characterization of the normal brain

MRI multiparametric hemodynamic characterization of the normal brain

Neuroscience 240 (2013) 269–276 MRI MULTIPARAMETRIC HEMODYNAMIC CHARACTERIZATION OF THE NORMAL BRAIN M. ARTZI, a,b O. AIZENSTEIN, a R. ABRAMOVITCH c ...

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Neuroscience 240 (2013) 269–276

MRI MULTIPARAMETRIC HEMODYNAMIC CHARACTERIZATION OF THE NORMAL BRAIN M. ARTZI, a,b O. AIZENSTEIN, a R. ABRAMOVITCH c AND D. BEN BASHAT a,b*

hemodynamic parameters, obtained from healthy brains, and may be clinically important in the assessment of patients with various vascular pathologies. Ó 2013 IBRO. Published by Elsevier Ltd. All rights reserved.

a

The Functional Brain Center, The Wohl Institute for Advanced Imaging, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel

b

Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel

Key words: hemodynamic imaging, multiparametric segmentation, dynamic susceptibility contrast, hypercapnia, carbogen.

c

The Goldyne Savad Institute for Gene Therapy, Hadassah Hebrew University Medical Center, Jerusalem, Israel

Abstract—Characterization of the brain’s vascular system is of major clinical importance in the assessment of patients with cerebrovascular disease. The aim of this study was to characterize brain hemodynamics using multiparametric methods and to obtain reference values from the healthy brain. A multimodal magnetic resonance imaging (MRI) study was performed in twenty healthy subjects, including dynamic susceptibility contrast imaging and blood oxygen level dependence (BOLD) during hypercapnia and carbogen challenges. Brain tissues were defined using unsupervised cluster analysis based on these three methods, and several hemodynamic parameters were calculated for gray matter (GM), white matter (WM), blood vessels and dura (BVD); the three main vascular territories within the GM; and arteries and veins defined within the BVD cluster. The carbogen challenge produced a BOLD signal twice as high as the hypercapnia challenge, in all brain tissues. The three brain tissues differed significantly from each other in their hemodynamic characteristics, supporting the graded vascularity of the tissues, with BVD > GM > WM. Within the GM cluster, a significant delay of 1.2 s of the bolus arrival time was detected within the posterior cerebral artery territory relative to the middle and anterior cerebral artery territories. No differences were detected between right and left middle cerebral artery territories for all hemodynamic parameters. Within the BVD cluster, a significant delay of 1.9 s of the bolus arrival time was detected within the veins relative to the arteries. This parameter enabled to differentiate between the various blood vessels, including arteries, veins and choroid plexus. This study provides reference values for several

INTRODUCTION Characterization and quantification of brain hemodynamics are of major clinical importance in the assessment and follow up of patients with various brain pathologies. Several magnetic resonance imaging (MRI) methods are typically used to evaluate brain hemodynamics including dynamic susceptibility contrast (DSC) imaging and blood oxygen level dependent (BOLD) imaging during hypercapnia or hyperoxia challenges. Each of these methods has been previously used to study vascular properties in healthy subjects and in various brain pathologies. When combined, these techniques provide a large number of parameters, enabling a comprehensive characterization of the structure and function of the brain’s vascular system. DSC imaging is a commonly used method in MRI for the indirect measurement of brain perfusion. In this technique, a fast dynamic imaging sequence, usually echo-planar imaging (EPI), is acquired along the intravenous bolus injection of a contrast agent (Rosen et al., 1990; Ostergaard, 2005). Analysis of signal time curves enables the calculation of several hemodynamic parameters reflecting tissue microcirculation, such as mean transient time (MTT), time to peak (TTP), cerebral blood volume (CBV) and cerebral blood flow (CBF). The clinical applications of this method include diagnosis and assessment of patients with cerebrovascular diseases (Calamante et al., 1999; Leiva-Salinas et al., 2011), and the assessment and follow up of patients with brain tumors (Law, 2009). In stroke patients, DSC perfusion imaging is important to confirm diagnosis, to monitor the extent and severity of the ischemic lesion during the acute phase, and is considered to be an important outcome predictor (Farr and Wegener, 2010; Seevinck et al., 2010). Perfusion imaging was also used to study patients with carotid stenosis, to differentiate between asymptomatic and symptomatic patients (Soinne et al., 2003), to study patients with moderate stenosis (Trivedi et al., 2005), and for the assessment of cerebrovascular

*Correspondence to: D. Ben Bashat, The Functional Brain Center, The Wohl Institute for Advanced Imaging, 6 Weizmann Street, TelAviv 64239, Israel. Tel: +972-3-6973056, mobile: +972-524262515; fax: +972-3-6973080. E-mail address: [email protected] (D. Ben Bashat). Abbreviations: ACA, anterior cerebral artery; BOLD, blood oxygen level dependent; BVD, blood vessels and dura; CBF, cerebral blood flow; CBV, cerebral blood volume; DSC, dynamic susceptibility contrast; EPI, echo-planar imaging; GM, gray matter; MCA, middle cerebral artery; MRI, magnetic resonance imaging; MTT, mean transient time; PCA, posterior cerebral artery; SR, signal recovery; TCD, Transcranial Doppler; TTP, time to peak; VMR, vasomotor reactivity; VOI, volume of interest; WM, white matter.

0306-4522/13 $36.00 Ó 2013 IBRO. Published by Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.neuroscience.2013.03.004 269

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reserve capacity in patients with occlusive cerebrovascular disease (Guckel et al., 1996). Several advanced image analysis methods have been proposed for DSC data, including independent component analysis performed in healthy subjects (Kao et al., 2003) and in patients with carotid stenosis (Kao et al., 2008) and unsupervised k-mean analysis performed in healthy subjects (Artzi et al., 2011). Similarity mapping analysis was also used for white matter (WM) and gray matter (GM) segmentation in healthy subjects (Wiart et al., 2001), and factor analysis was used to extract the arterial and venous components and distinguish between areas of the brain with normal and abnormal perfusion in patients with acute stroke (Martel et al., 2001). BOLD imaging was originally proposed by Ogawa et al. (1990) for indirect imaging of blood oxygenation in the brain under normal physiological conditions. Changes in BOLD signal can occur due to endogenous stimuli such as neuronal activity or due to exogenous stimuli such as respiratory challenges (Ogawa et al., 1990). Hypercapnia and hyperoxia breathing challenges were previously shown to induce changes in cerebral hemodynamics (Haining et al., 1970). During hypercapnia challenge, induced by either breath-hold or inhalation of different concentrations of CO2, vasodilatation occurs, resulting in an increased BOLD signal, which can be detected using MRI (Lu et al., 2009). Hypercapnia has been used in several studies for the assessment of vasomotor reactivity (VMR) in healthy subjects (Yezhuvath et al., 2009), in patients with intracranial stenosis and other vasculopathy (Han et al., 2011; Mandell et al., 2011), and in animals and patients with brain tumors (Abramovitch et al., 1999, 2004; Rijpkema et al., 2002, 2004; Lu et al., 2009; Muller et al., 2010). During a hyperoxia challenge, an increased BOLD signal is detected due to an increase in the oxyhemoglobin concentration. When pure oxygen (100% O2) is used, reduced CBF and CBV is often detected (Watson et al., 2000; Lu et al., 2009), however while using a gas mixture of oxygen with different concentrations of CO2 (i.e. carbogen) an increase in CBF and CBV is detected (Taylor et al., 2001; Ashkanian et al., 2008). Oxygen inhalation, with or without various concentrations of CO2 was previously used for the evaluation of VMR in healthy subjects (Losert et al., 2002; Prisman et al., 2008), for the assessment of patients with brain tumors (Rostrup et al., 1994; Watson et al., 2000; Taylor et al., 2001; Rijpkema et al., 2002; Schuuring et al., 2002; Hsu et al., 2004, 2010), and the assessment of patients with severe carotid stenosis (Ziyeh et al., 2005). Combining hypercapnia and carbogen challenges has important clinical implications. Blood vessels with normal autoregulatory capacity, i.e. normal VMR will show increased BOLD signals during both hypercapnia and carbogen challenges due to vasodilatation occurring during both conditions and increased blood oxygenation during the carbogen challenge. Patients with impaired VMR (such as those with high-grade stenosis of the carotid artery) or with abnormal blood vessels (such as in

angiogenesis occurring in patients with high-grade brain tumors) will show no changes in BOLD signals during the hypercapnia challenge and increased BOLD signals only during the hyperoxia challenge (Abramovitch et al., 1999; Rohrberg and Brodhun, 2001; Lu et al., 2009). A combined protocol of hypercapnia and hyperoxia/carbogen has been previously used in animal models to characterize cerebrovascular reactivity, for the study of therapeutic responses to anti-angiogenic therapy and to distinguish neural from non-neural contributions to fMRI signals (Abramovitch et al., 1999, 2004; Sicard and Duong, 2005; Lu et al., 2009). A combined protocol of hypercapnia and carbogen has also been applied in human studies for the assessment of patients with brain tumors (Ben Bashat et al., 2012). The combination of these various vascular methods can provide a comprehensive view of the structural and functional characteristics of the vascular system. However, as of yet, reference values of these parameters obtained from healthy subjects are limited. The aims of the current study were to characterize brain hemodynamics using multi modal parametric methods and to obtain reference values for different brain areas from healthy subjects.

EXPERIMENTAL PROCEDURES Subjects Twenty healthy subjects were included in this study (11 females, 36 ± 13 years old). Data were drawn from control groups of two studies performed in our lab, one on patients with brain tumors (Ben Bashat et al., 2012), and one on patients with cerebral vascular disease (unpublished data). Inclusion criteria: age 20– 50 years, normal kidney function (glomerular filtration rate > 60), no history of neurological or chronic diseases, not taking medication on a daily basis, and no abnormal findings in a conventional MRI scan. The study was approved by the hospital review board, and written informed consent was obtained from all subjects.

MRI protocol MRI scans were performed on a 3.0T MRI scanner (GE Signa EXCITE, Milwaukee, WI, USA). The hemodynamic imaging protocol included BOLD MRI challenges applied in two separated scans, each with a different block design paradigm, separated in time by at least 10 min during which anatomical images were acquired, similar to a previously reported study (Ben Bashat et al., 2012). Subjects inhaled 95%Air + 5%CO2 for the hypercapnia challenge and 95%O2 + 5%CO2 for the carbogen challenge, with room air at baseline, via a Hudson Oxygen mask at a rate of 8 pounds per square inch. A pulse oximeter was used to monitor changes in blood oxygenation. Data were acquired using a T2 gradient echo-EPI sequence (Field of View (FOV)/matrix = 240 mm/128  128, Time to Repeat/Time to Echo (TR/TE) = 5000/35 ms, slice thickness/ gap = 4/0 mm, and a total of 36 slices, covering the entailer brain). The DSC data were acquired using a 2D gradient echo EPI sequence during the injection of 0.4 cc/kg (double dose) of Gadolinium-Dotarem, using a power injector (MEDRAD, Spectris Solaris Indianola, Pennsylvania, United States), and followed by a flush of 20 cc of saline, both at a constant rate of 5 cc/s. Twelve subjects were scanned with FOV/ matrix = 240 mm/128  128; TR/TE = 1300/30 ms, 13 slices with 5-mm thickness with no gap and eight subjects were

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M. Artzi et al. / Neuroscience 240 (2013) 269–276 scanned with FOV/matrix = 220 mm/128  128; TR/TE = 1450/ 45 ms, 19 slices with 6.5-mm thickness with no gap. All other parameters were identical for the entire cohort. Slices were placed at the height of the fourth ventricle, tangent to the orbito-frontal cortex and above). The BOLD hypercapnia and carbogen challenges were performed before the contrast agent injection, based on a previous study in which we detected a reduced hemodynamic response in the BOLD imaging when performed after the contrast agent injection (Ben Bashat et al., 2012).

Data analysis Data analysis was performed using FMRIB Software Library (FSL, www.fmrib.ox.ac.uk/fsl) and MATLAB (MATLAB R2008a, The MathWorks Inc). First, the DSC, BOLD hypercapnia and carbogen raw images were realigned to each other using a FSL linear image registration tool (FLIRT) (Jenkinson et al., 2002). The first six volumes of each scan were removed to achieve signal equilibration. After removing the first volumes an analysis was performed on the next 60 volumes for the DSC data, 35 volumes for the hypercapnia data, and 30 volumes for the carbogen data. Next, signal recovery (SR) maps were calculated from the DSC data as the signal ratio between the mean signal value was detected before bolus arrival and the mean signal value of the volumes detected after the passage of the second bolus (Fig. 1A). In addition the maximum signal change maps (maximum DS) were calculated from the hypercapnia and carbogen BOLD data (Fig. 1B). Definition of volumes of interest (VOIs). Brain tissues: Unsupervised classification was performed for each subject based on the three hemodynamic parameters (SR, maximum DS-hypercapnia and maximum DS-carbogen) using the FSL automated segmentation tool. The number of clusters was set to three, with only the voxels with a probability of >50% belonging to any one of the three clusters included. The obtained three tissue clusters were identified as WM, GM, and blood vessels and dura (BVD). Vascular territories within the GM cluster: In each subject the identified GM cluster was divided into the anterior, left and right middle, and posterior cerebral artery territories (ACA, MCA-L, MCA-R, PCA, respectively) using a predefined vascular territories template (Artzi et al., 2011) (Fig. 3A). Arteries and veins within the BVD cluster: VOIs within the BVD cluster were manually defined in each subject at the deep cerebral veins and PCA/the posterior communicating arteries (Fig. 4A, B).

Calculation of the hemodynamic parameters. Several hemodynamic parameters were calculated based on the mean signal time curves of each VOI; five from the DSC, two from the hypercapnia, and two from the carbogen data (Fig. 1): Start time (s): bolus arrival time was calculated from the DSC data as the start of the first signal drop (first bolus). In order to overcome between-subject differences in the contrast agent injection time, the Start time of each VOI was calculated relative to the component with the lowest Start time in each subject. Transfer time (s): calculated from the DSC data, from the start to the end of the first bolus. TTP (s): calculated from the DSC data, as time from the start of the first bolus until maximum signal changes. AUC area under the curves (in arbitrary units): calculated from all three methods as the area from the start to the end of the signal change (defined automatically in the DSC and manually in the hypercapnia and carbogen data). The AUC obtained from the DSC data represents the tissue CBV. Maximum DS: maximum signal change (in %), calculated from all three methods as the percentage relative to baseline, representing the minimum (for the DSC data) and maximum (for the hypercapnia and carbogen data) signal changes. The Maximum DS values obtained from the hypercapnia and carbogen data represent the tissue VMR. SR: calculated from the DSC data as previously described.

Statistical analysis Paired samples t-test was used to study the effect of the different DSC acquisition parameters between the two subgroups and to study the differences between the MCA-L and MCA-R territories within the GM cluster. Multivariate analyses of variance (MANOVA) were performed using SPSS (SPSS 12.0, Chicago, IL, USA) to evaluate between-group differences in the hemodynamic parameters (separately for the brain tissue clusters; vascular territories; arteries and veins).

RESULTS Characterization of the different brain components based on the three MRI methods (DSC, hypercapnia and carbogen) was performed on all subjects. The breathing challenges were tolerated by all subjects. Differences between the calculated DSC parameters of the two subgroups, due to differences in the DSC acquisition parameters, were less

Fig. 1. The calculated hemodynamic indices obtained from the DSC (A) and BOLD carbogen (B) data sets. Start time = DSC bolus arrival time; Transfer time = contrast agent transit time; Maximum DS = maximum percent signal change; AUC = area under the curves (arbitrary units); SR = signal recovery.

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Fig. 2. The extracted brain tissue clusters: WM (blue), GM (green) and BVD (red) (A, representative data) and the mean and standard deviations of the signal time curves obtained from all subjects (N = 20) for the DSC (B), BOLD hypercapnia (C) and BOLD carbogen (D) data sets. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Fig. 3. Vascular territories within the GM cluster: representative data demonstrating the defined VOIs: anterior cerebral artery territory (ACA, green), left and right middle cerebral artery MCA-L territory (brown), MCA-R (orange) and posterior cerebral artery territory (PCA, blue) (A); and the DSC signal time curves (B) from the various vascular territories. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

than 10%. All classification results and VOI selection were approved by a senior neuro-radiologist (O.A). Brain tissue Multiparametric brain clustering based on three hemodynamic parameters (DSC-SR, maximum DSBOLD hypercapnia and maximum DS-BOLD crabogen)

extracted from the three methods was performed on all subjects. Fig. 2 demonstrates the obtained three tissue clusters: WM, GM and BVD (Fig. 2A), and the mean and standard deviation (SD) of the signal time curves obtained from the three tissue types: the DSC (Fig. 2B), the BOLD hypercapnia (5%CO2, Fig. 2C), and BOLD carbogen (Fig. 2D) data sets.

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Fig. 4. Veins and arteries within the BVD cluster: a representative data demonstrating the defined arteries (A) and veins (B) and the obtained DSC signal time curves (C).

Table 1. Hemodynamic values in the different brain tissues (n = 20) DSC

WM GM BVD

Max DS (%)

Trans time (s)

TTP (sec)

20.14 ± 2.47c 21.37 ± 2.50 23.19 ± 2.69b

7.75 ± 0.84c 9.09 ± 1.11c 11.89 ± 3.97a

53 ± 10a 76 ± 6a 83 ± 3a

BOLD hypercapnia

BOLD carbogen

AUC (a.u)

SR (%)

Max DS (%)

AUC (a.u)

Max DS (%)

AUC (a.u)

424 ± 80a 730 ± 98a 1016 ± 139a

71 ± 7a 50 ± 7a 33 ± 5a

0.55 ± 0.24a 2.76 ± 0.50a 12.82 ± 3.70a

6 ± 3a 40 ± 10a 186 ± 62a

1.07 ± 0.35a 5.26 ± 0.82a 22.68 ± 5.29a

10 ± 4a 59 ± 13a 259 ± 74a

DSC, dynamic susceptibility contrast; BOLD, blood oxygenation level dependence; WM, white matter; GM, gray matter; BVD, dura-blood vessels; TTP, time to peak; Max DS, maximum percent signal change; AUC, area under the curves (a.u., arbitrary units). a Significantly different from all other tissues. b Significantly different from WM tissue. c Significantly different from BVD tissue; all with p = 0.001.

Table 2. DSC Start time in the different brain areas DSC Start time (s) WM GM ACA MCA_LR PCA BVD Arteries Veins

2.29 ± 0.94b,c,d,e,g 0.87 ± 0.89a,f,h 0.59 ± 0.90a,f,h 0.59 ± 0.90a,f,h 1.81 ± 1.06b,c,d,e,g 0.27 ± 0.55a,f,h 0.13 ± 0.40a,f,h 2.04 ± 0.95b,c,d,e,g

DSC, dynamic susceptibility contrast; WM, white matter; GM, gray matter; BVD, dura-blood vessels; ACA, anterior cerebral artery; MCA_LR, left and right middle cerebral artery; PCA, posterior cerebral artery. The Start time is given in seconds, as the difference relative to the component with the lowest Start time. a Significantly different from the WM (p 6 0.001). b Significantly different from the GM (p 6 0.05). c Significantly different from the BVD (p 6 0.001). d Significantly different from the ACA (p 6 0.001). e Significantly different from the MCA_LR (p 6 0.001). f Significantly different from the PCA (p 6 0.05). g Significantly different from the Arteries (p 6 0.001). h Significantly different from the Veins (p 6 0.001).

The mean and SD values of the various calculated hemodynamic parameters obtained from each tissue

type are given in Tables 1 and 2. Multivariate analysis revealed significant differences (p < 0.001) between tissue types for Max DS, AUC, and SR, supporting the known graded pattern of vascularity with BVD > GM > WM. In addition: significant longer DSC Transfer times were detected in the WM relative to the BVD cluster (p < 0.001); significant prolonged TTP values were detected for the BVD cluster relative to the WM and GM clusters (p < 0.001); and significantly delayed DSC Start time was detected in the WM relative to the BVD and GM (p < 0.001) by 2.02 and 1.42 s, respectively. Note that the Start time values are given relative to the component with the earliest bolus arrival time (i.e the arteries). Vascular territories within the GM cluster Fig. 3 shows representative data of the defined VOIs: ACA (green), MCA-L (brown), MCA-R (orange) and PCA (blue) (Fig. 3A); and the DSC signal time curves of these VOIs (Fig. 3B). No significant differences were detected between the MCA-L and MCA-R for all parameters, therefore mean values were calculated (referred to as MCA_LR). A significantly delayed DSC Start time (p < 0.05) was detected in the PCA territory (1.81 s) as compared to the ACA and MCA territories (both 0.59 s) (see Table 2).

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Arteries and veins within the BVD cluster A significantly delayed DSC Start time (p < 0.001) was detected within the deep veins (2.04 s) as compared to the arteries (0.13 s). Note that these values are relative to the component with the lowest Start time in each subject. Representative data demonstrating the defined VOIs and the signal time curves are shown in Fig. 4. Based on this result, we further calculated DSC Start time maps within the BVD cluster for each subject. Fig. 5 shows a representative result illustrating a differentiation between the various BVD components with the contrast agent arriving first to the arteries (blue), followed by veins (red-orange), and finally arriving at the choroid plexus and dura (yellow) (approved by a senior neuro-radiologist).

DISCUSSION In this study, multimodal MRI methods were used to characterize various hemodynamic properties of the healthy brain. The identification of the various brain components (brain tissue, vascular territories and veins and arteries) was obtained by segmentation based on maps calculated from three vascular methods, based on a previously published methodology (Artzi et al., 2011). This method allows for a more accurate and replicable identification of various areas of interest, compared to manual VOI selection or segmentation based on anatomical/EPI images. Further, it enables accurate comparison between these methods. The quantitative assessment of hemodynamic parameters in healthy subjects provides a reference value for future research. The hemodynamic parameters in each VOI were calculated directly from the raw data of the DSC, carbogen and hypercapnia methods, without the need for additional models or input functions. The highest vascularity was detected for the BVD, followed by GM and WM clusters, apparent with all methods, consistent with the known graded pattern of vascularity of brain tissue. Differences between vascular

Fig. 5. DSC Start time map in a representative axial slice at the level of the lateral ventricles of one subject, demonstrating a good differentiation between arteries (blue), veins (red-orange) and the choroid plexus and dura (yellow) within the blood-vessels and dura (BVD) cluster. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

territories and between arteries and veins were mainly significant for the temporal parameters (in the order of a few seconds) obtained from the DSC method. It should be noted that the sensitivity and accuracy of these results are limited by the temporal resolution of each method. The DSC data were acquired with a temporal resolution of 1.3–1.45 s, and indeed significant results were obtained in the order of this temporal resolution; whereas BOLD MRI imaging was performed with relatively low temporal resolution, with temporal resolution = 5 s, and was therefore not sensitive enough to detect such temporal differences. In order to study refined hemodynamic temporal differences of various brain components, additional studies with higher temporal resolution are required for both the DSC and BOLD imaging. The temporal parameters obtained from the DSC data differentiated between the three tissue clusters, the vascular territories within the GM cluster, and the arteries and veins within the BVD cluster. When all brain components were compared, the arteries showed the shortest Start time, followed by the GM, the veins, and lastly the WM. These results mirror a previous report in which the contrast agent arrival time was detected using independent component analysis of the DSC data (Kao et al., 2003). However, in the current study we provide further quantitative values for this parameter. Note that the Start time of the BVD given in Table 1 is an average value of both veins and arteries. Within the vascular territories, no significant differences in hemodynamic parameters were detected between the right and left MCA territories. The absence of vascular lateralization within the MCA territories is important for the study of patients with various brain pathologies, including carotid stenosis and stroke, among others. Within the PCA territory in the GM tissue, delayed Start time was detected relative to the ACA and MCA territories, consistent with previous studies (Ibaraki et al., 2007; Artzi et al., 2011). These results are noteworthy in light of several clinical findings reported on patients with stroke within the PCA territory regarding: lower incidence of ischemic events (Ng et al., 2007); lower risk of disability and mortality (Ntaios et al., 2011); and prolonged therapeutic window (Montavont et al., 2004). The DSC Start time within the veins was different from that of the arteries when manually defined. Furthermore, this parameter enabled segmentation between veins and arteries across the entire brain, as shown in Fig. 5. These results provide spatial differentiation and allow for the quantitative assessment of the vascular beds which can be used for the identification and assessment of arteries and veins in patients with various vascular pathologies. The current study aimed to provide hemodynamic reference values of various brain components in healthy subjects. Two subgroups of subjects were included in this study, in order to increase the statistical power. Although the two subgroups had slightly different DSC acquisition parameters, the differences between the calculated perfusion parameters were less than 10% between groups, as reflected by their SD, while

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differences in pathological populations are expected to be much larger (Guckel et al., 1996; Farr and Wegener, 2010; Christen et al., 2012). Quantitative assessment of VMR is important for several clinical applications and is often measured using Transcranial Doppler (TCD) ultrasound, under intravenous administration of acetazolamide, breath-hold or via inhalation of carbon dioxide (5–7% CO2). However, TCD-VMR is limited to the assessment of the main blood vessels, and in some cases cannot be performed due to a poor temporal bone window (Rohrberg and Brodhun, 2001; Wijnhoud et al., 2008). Therefore, several studies proposed the use of BOLD MRI with respiratory challenges for quantitative assessment of cerebral VMR (Bulte et al., 2012; Christen et al., 2012; Gauthier and Hoge, 2012) and demonstrated its applicability in several clinical applications, including risk assessment for cerebral ischemia in patients with high-grade stenosis or patients with occlusion of the internal carotid artery (Vernieri et al., 1999; Rohrberg and Brodhun, 2001; Ziyeh et al., 2005). Moreover, MR-VMR provides information on the entire brain, making this a superior method. In this study BOLD hypercapnia and carbogen were used in combination. During hypercapnia vasodilatation occurs while during inhalation of carbogen, increased blood oxygenation and vasodilatation occur, both resulting in an increased MR signal. However, a higher signal change percentage, almost twofold, was detected when using carbogen as compared to BOLD hypercapnia. These results suggest that carbogens may be preferable for VMR measurement, revealing a higher sensitivity. In patients with impaired VMR, the BOLD signal will be reduced due to impaired vasodilatation and reduced CBF (Watson et al., 2000) (similar to inhalation of pure oxygen); resulting in detection of much lower VMR values. This further extends the dynamic range of the measurement and thus increases the sensitivity of the method. Future research should assess differences in VMR using a comparison of hypercapnia challenges and carbogens in patients with impaired VMR.

CONCLUSIONS In this study a multimodal multiparametic characterization of brain components was performed, based on three hemodynamic methods, with values obtained from a group of 20 healthy subjects. The integration of the three methods, using model-free analysis, provides a structural and functional characterization of the brain’s vascular system in several areas. Findings from this study can be used as a reference for future studies to improve assessment and followup of patients with various vascular pathologies. Acknowledgments—To Vicki Myers for editorial assistance. This work was performed in partial fulfillment of the requirements for a Ph.D. degree of Artzi Moran, Sackler Faculty of Medicine, Tel Aviv University, Israel. This study was funded by The James S. McDonnell Foundation (Grant No. 220020176).

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(Accepted 5 March 2013) (Available online 13 March 2013)