OUP user menu

Changes in cerebral perfusion precede plaque formation in multiple sclerosis: a longitudinal perfusion MRI study

Jens Wuerfel, Judith Bellmann‐Strobl, Peter Brunecker, Orhan Aktas, Henry McFarland, Arno Villringer, Frauke Zipp
DOI: http://dx.doi.org/10.1093/brain/awh007 111-119 First published online: 21 October 2003


New MRI techniques such as the analysis of magnetization transfer or diffusion have provided evidence for subtle progressive alterations in tissue integrity prior to focal leakage of the blood–brain barrier (BBB) as part of plaque formation in multiple sclerosis. Since inflammation is capable of modulating the microcirculation, we investigated the hypothesis that changes in the local perfusion might be one of the earliest signs of lesion development. 20 patients with definite relapsing–remitting multiple sclerosis were analysed with regard to cerebral blood volume, cerebral blood flow, mean transit time and apparent diffusion coefficient (ADC), as well as conventional MRI parameters, on monthly follow‐up scans. Among 89 gadolinium‐enhancing lesions, we selected 18 that developed during the study and met strict inclusion criteria. In these, changes of perfusion parameters were detectable not only prior to the BBB breakdown, but also prior to increases in the ADC. Our data indicate that inflammation is accompanied by altered local perfusion, which can be detected prior to permeability of the BBB.

  • diffusion; longitudinal; MRI; multiple sclerosis; perfusion
  • ADC = apparent diffusion coefficient; AIF = arterial input function; BBB = blood–brain barrier; CBF = cerebral blood flow; CBV = cerebral blood volume; EDSS = expanded disability status scale; GdDTPA = gadolinium diethyltriaminepentaacetic acid; MSFC = multiple sclerosis functional composite; MTR = magnetization transfer ratio; MTT = mean transit time; NAWM = normal‐appearing white matter; ROI = region of interest; RRMS = relapsing–remitting multiple sclerosis


Multiple sclerosis is the most common demyelinating disorder of the CNS. Despite increased insight into the mechanisms of disease (Noseworthy et al., 2000; Steinman et al., 2002), the precise sequence of events leading to plaque formation, the pathological hallmark of multiple sclerosis, is still not completely understood. The disruption of the blood–brain barrier (BBB) is well recognized as a crucial step in the evolution of the multiple sclerosis lesion (Harris et al., 1991; McFarland et al., 1992) and is hypothesized to be initiated by autoreactive CD4+ lymphocytes that migrate into the CNS and initiate an inflammatory response (Markovic‐Plese and McFarland, 2001). Upregulation of adhesion molecules on capillary endothelial cells, perivascular inflammation, and other factors that facilitate the invasion of leucocytes into the CNS have been studied extensively and are subject to current therapeutic concepts. The gold standard of lesion detection during the course of the disease is the focal enhancement in a T1‐weighted MRI after gadolinium diethyltriaminepenta acetic acid (GdDTPA) injection. However, there is more and more evidence indicating changes in the normal‐appearing white matter (NAWM) that precede the appearance of new contrast‐enhancing lesions. A decrease in magnetization transfer ratio (MTR) is described prior to enhancement, indicating a diminished ability for saturation exchange due to, for example, oedema and inflammation (Filippi et al., 1998; Silver et al., 1998). Changes in lipid spectra have been noted in magnetic resonance spectroscopy preceding lesions (Wolinsky and Narayana, 2002). Diffusion‐weighted imaging and analysis of the apparent diffusion coefficient (ADC) (Rocca et al., 2000; Werring et al., 2000) have provided evidence for subtle progressive alterations in tissue integrity several weeks before focal leakage of the BBB and plaque formation. Since an increased ADC reflects elevated levels of random water molecule motion, the observed changes may be due to local metabolic alterations in the inflammatory milieu, such as oedema, prior to tissue damage. However, a major consequence of inflammation, namely local changes in blood flow (Warren, 1994; Moller et al., 2002; Perretti and Ahluwalia, 2000) has so far been largely neglected. This prompted us to investigate changes in the perfusion of plaques and potential areas of plaque formation in combination with changes in the ADC in a longitudinal study. The overall aim was to further understand the process of lesion formation in vivo, which in patients can only be followed by imaging methods.

Material and methods


We studied 20 patients (18 female) meeting the newly introduced criteria for relapsing–remitting multiple sclerosis (RRMS) (McDonald et al., 2001) over a mean period of 11.4 months (range 5–23) with biweekly/monthly MRI examinations. Patients were enrolled at the Institute of Neuroimmunology, Outpatient Clinic, Department of Neurology, Charité University Hospital, Berlin, Germany and followed up regularly for clinical parameters, i.e. evaluation of expanded disability status scale (EDSS) and multiple sclerosis functional composite (MSFC) scores (Kurtzke, 1983; Fischer et al., 1999). Signed, informed consent was obtained from all participating subjects. Patients presented with mean disease duration of 30.9 months (range 1–187) and an EDSS score of 1.6 (range 0–4) (Table 1). During the study, three patients were started on disease‐modifying therapy (two patients with interferon‐β1a, one patient with glatiramer acetate).

View this table:
Table 1

Clinical data at onset of study

Patient IDSexAge (years)EDSSMSFCDuration of disease (months)


MRI measurements were performed on a scanner operating at 1.5 T (Siemens Vision; Siemens Medical Systems, Erlangen, Germany). The MRI protocol consisted of T2‐weighted imaging, T1‐weighted imaging before and 5 min after GdDPTA injection (Magnevist®; Schering AG, Berlin, Germany), diffusion‐weighted images and T2*‐weighted dynamic susceptibility contrast perfusion measurement. For T2‐weighted imaging a multi‐echo turbo‐spin‐echo sequence was used [repetition time (TR) 4060 ms, echo time (TE) 15/75/135 ms, matrix 256 × 256, acquisition time 345 s, field of view (FOV) 256 mm, slice thickness 5 mm, no gap, 28 slices], and for T1‐weighted imaging a spin‐echo sequence (TR 840 ms, TE 14 ms, matrix 256 × 256, acquisition time 164 s, FOV 256 mm, slice thickness 5 mm, no gap, 28 slices) was employed. Intravenous injection of 0.20 mmol/kg body weight GdDPTA was performed with a MRI compatible power injector (Spectris; MedRad, Pittsburgh, PA, USA) at an injection rate of 4 ml/s (5 s duration) followed by 20 ml saline. MRI data acquisition started at the beginning of the contrast agent injection with a temporal resolution of 1 s and was continued for 60 s.

Perfusion measurements were performed using a T2*‐weighted echo‐planar sequence (TR 800 ms, TE 54 ms, matrix 128 × 128, acquisition time 60 s, FOV 256 mm, slice thickness 5 mm, no gap) (Doege et al., 2001). In order to measure the bolus transit through the tissue at a reasonable temporal resolution, we selected only eight slices for the perfusion measurement. Spin‐echo diffusion echo‐planar imaging (TR 4000 ms, TE 118 ms, matrix 128 × 128, acquisition time 208 s, FOV 256 mm, slice thickness 5 mm, no gap) was performed using three different b values (0, 500, 1000 s/mm2). Diffusion gradients were applied in three orthogonal directions. Twenty‐eight axial slices were positioned in anterior commissure–posterior commissure orientation. Slices of T1‐, T2‐ and perfusion‐weighted images had the same orientation.

Image analysis

Bulk white matter lesion load of T2weighted scans and number and volume of hypo‐ and hyperintense lesions on T1weighted scans were routinely measured using MedX© v. 3.42 software package (Sensor Systems Inc., Sterling, VA, USA). Diffusion‐weighted images were preprocessed immediately after acquisition as part of the sequence used. The ADC was calculated separately for each direction. Preprocessed data were transferred to a LINUX work station for further analysis. For the analysis of the perfusion‐weighted images, selected voxels of the baseline scan exhibiting high and early contrast influx were used for the measurement of the arterial input function (AIF). Identical voxels were used in all registered scans for subsequent AIF measurement. Perfusion‐weighted images were first corrected for BBB leakage artefacts, using a paradigm described by Haselhorst et al. (2000). An internal threshold enabled us to reliably correct only voxels that were affected by the leakage. Corrected data were then processed with MedX perfusion data analysis package. Pixel based calculations, with subsequent generation of relative cerebral blood volume (CBV), cerebral blood flow (CBF) and mean transit time (MTT), were performed as described by Ostergaard et al. (2000). We used relative perfusion values, since the determination of absolute perfusion values is still controversial based on magnetic resonance bolus‐tracking techniques currently in use.

In order to exactly compare different time points and magnetic resonance sequences, T1‐, T2‐, diffusion‐weighted and T2* images were spatially co‐registered using an automated six‐parameter rigid body image registration with trilinear interpolation [FMRIB’s Linear Image Registration Tool (FLIRT); FMRIB Analysis Group, University of Oxford, Oxford, UK] (Jenkinson and Smith, 2001). Registered T2* images were used for subsequent calculation of CBV, CBF and MTT maps. An example of the alignment of T1‐ and T2‐weighted images, ADC and CBV‐maps is shown in Fig. 1. Regions of interest (ROIs) were drawn on lesions visible on T1‐enhanced scans, copied to the other sets, respectively, and followed longitudinally. These ROIs were flipped to the contralateral hemisphere to create an unbiased, corresponding localization in NAWM without any GdDTPA enhancement throughout the longitudinal study and expressed as ratios (lesion/contralateral NAWM).

Fig. 1 Co‐registration of T1‐ and T2‐weighted images, and ADC and CBV maps. The development of a lesion is presented in a series of GdDTPA enhanced T1‐weighted scans and in co‐registered T2‐weighted images, and ADC and CBV maps at 2 weeks prior to enhancement, time of the first enhancement, and 2, 4 and 12 weeks thereafter.

For the analysis of the perfusion‐weighted images in regions of lesion formation, we applied the following inclusion criteria. To avoid partial volume effects, especially due to the registration process, we excluded all lesions that were adjacent to the CSF/ventricles. All lesions close to the grey–white matter boundary were also excluded, due to the fact that perfusion of grey matter and white matter differ significantly. Only lesions with a diameter of at least 7 mm were included, to assure sufficient reliability in a longitudinal study.

Three lesions developed a ring enhancement on T1‐weighted scans. In these lesions we measured the perfusion of the contrast‐enhancing ring and the hypointense interior seperately.

Statistical analysis

The scan at which GdDTPA enhancement was first noted defined the reference time point (time = 0) for the longitudinal measurements. To evaluate the evolution of lesions prior to appearance on GdDTPA‐enhanced T1‐weighted images, Wilcoxon signed rank tests were performed to compare CBV, CBF, MTT and ADC ratios (lesion/contralateral NAWM) at different intervals.


Clinical data and selection of lesions

Among 20 patients with RRMS (Table 1), 15 untreated patients developed gadolinium‐enhancing lesions during the course of the study. In total, 89 contrast‐enhancing lesions were detected; out of these 39 were detectable on the slices covered by the perfusion. A total of 18 lesions in seven patients met our inclusion criteria, among which three had ring enhancement (Table 2). We statistically analysed the data referring to nine lesions for which at least two baseline scans were available exceeding 6 weeks prior to GdDTPA enhancement.

View this table:
Table 2

Observed contrast‐enhancing lesions

Patient IDTotal number of lesionsNumber of lesions included

During the periods of lesion formation analysed, no significant changes of disability measured by EDSS and MSFC were detected (data not shown).

Establishment of a leakage correction algorithm

A conspicuous artefact observed in perfusion maps of gadolinium‐enhancing lesions, which is due to the leakage of some contrast agent through the BBB, can be overcome by fitting a model function for the plasma concentration to the measured data, as described by Haselhorst et al. (2000). We calculated perfusion maps with data to which we had applied a leakage correction algorithm prior to calculation and compared these data with uncorrected images (Fig. 2). The concentration versus time curves obtained from acutely gadolinium‐enhancing plaques show a dramatic signal overshoot and a subsequent drop below the baseline in the non‐corrected image (Fig. 2A), due to a significant reduction in T1 relaxation time that is caused by the leakage of some contrast agent into the interstitial space. This leads to a signal increase, owing to the short repetition time of our sequence, but also to a significant underestimation of the integral taken as a measure of CBV. Figure 2B shows the same concentration versus time curve with prior application of the leakage correction algorithm. The integral of the corrected curve is up to 55% higher than the pre‐correction value. For non‐enhancing white matter areas, no significant difference was detected between any corrected and uncorrected perfusion maps (Fig. 2C and D).

Fig. 2 Impact of a leakage correction paradigm on signal intensity over time. Signal versus time curves were measured in a contrast‐enhancing lesion without (A) and with (B) application of a leakage correction paradigm described by Haselhorstet al. (2000). The non‐enhancing NAWM of the contralateral side does not show any significant changes before (C) and after (D) correction.

Perfusion and ADC measurements before and after GdDTPA enhancement

In the course of the selected lesions revealing gadolinium enhancement at time = 0, we were specifically interested in investigating CBV, CBF, MTT and ADC. A representative example of these measurements of lesion development is given in Fig. 3.

Fig. 3 A representative example of time courses for perfusion and diffusion parameters during plaque formation. CBV, CBF, MTT and ADC are shown. Time point 0 was defined as first GdDTPA enhancement visible on T1‐weighted images.

A rapid increase in ADC of lesion area compared with the corresponding contralateral NAWM was detected in all lesions studied at the time of initial enhancement. While this elevation was statistically significant, a smaller increase in ADC up to 3 weeks (range 2–4) prior to enhancement was seen only in three series of lesion development and was not statistically significant (P = 0.173; Wilcoxon signed rank; Table 3). The ADC remained slightly above baseline values in all lesions studied after the BBB leakage stopped until the end of the study period.

View this table:
Table 3

CBV, CBF and ADC ratios during lesion formation

Weeks before enhancementMean CBV ratio (SD), n = 9P valueMean CBF ratio (SD), n = 9P valueMean ADC ratio (SD), n = 9P value
Baseline (>6)1.0288 (0.182)1.0601 (0.226)1.0626 (0.107)
3 (±1)1.2154 (0.245)0.0081.2506 (0.259)0.0081.0489 (0.093)0.173
01.2603 (0.265)0.0081.3144 (0.268)0.0151.2845 (0.295)0.011

Mean values and SDs are shown. P‐values are given for changes of CBV, CBF and ADC in comparison with the baseline (Wilcoxon signed rank test).

The perfusion measurements analysed in those lesions showed alterations for CBV and CBF, while there were no statistically relevant changes in MTT. In each lesion studied prior to enhancement, we found a significant increase of CBV and CBF, not only at the time of initial GdDTPA enhancement in comparison with the baseline (CBV P = 0.008; CBF P = 0.015; Wilcoxon signed rank), but also in pre‐lesion ROI as early as 3 weeks (range 2–4) prior to BBB leakage (CBV P = 0.008; CBF P = 0.008; Wilcoxon signed rank; Table 3).

In fact, 3 weeks before GdDTPA enhancement CBV and CBF showed an increase from baseline of 18 and 17.9%, respectively. Comparing 3 weeks prior to enhancement with the time of BBB breakdown, no significant differences in CBV and CBF were observed. These results clearly emphasize early blood flow changes during the development of these multiple sclerosis lesions. CBV and CBF remained above baseline values for several weeks after the BBB breakdown had ceased, as shown in an overlay of all longitudinal data for the 18 lesions that fulfilled the inclusion criteria for the analysis of local perfusion changes (Fig. 4).

Fig. 4 Mean ADC and CBV in all selected lesions (n = 18). Evolution of ADC and CBV ratios in 18 lesions. Time point 0 was defined as first GdDTPA enhancement visible on T1‐weighted images. Mean values (curve points) and SDs (vertical lines) are given for the number of corresponding intervals. The number of observations was lower at early and late time points compared with the number of observations at time point 0 (minimum number ≥5).

Plaques with ring enhancement

In three lesions that developed ring enhancement after contrast agent injection, patterns of CBV and CBF changes comparable to non‐ring‐enhancing lesions were seen only in the ‘ring tissue’. In line with our main finding of increased perfusion in the area of BBB breakdown, presumably due to inflammation, CBV and CBF exhibited higher levels in the region of the ring enhancement than the centre of these lesions, which appeared hypointense on T1‐weighted images. Thus, only the region of the enhanced ring was included in our overall perfusivity determination of vascular changes (see Table 3). Table 4 presents CBV, CBF and MTT values separately for the region of the gadolinium‐enhanced ring and the non‐enhancing inner part of the lesion, as defined by the T1‐weighted image.

View this table:
Table 4

Perfusion values (SD) of ring‐enhancing lesions

LesionCBV ringCBV insideCBF ringCBF insideMTT ringMTT inside
115.48 (8.6)10.26 (4.73)158 (96.26)93.84 (44.9)6.04 (1.37)6.7 (1.45)
29.25 (4.1)8.7 (3.83)88.38 (43.9) 70.25 (27.53)6.17 (1.67)7.4 (1.2)
321.17 (5.74)16.18 (1.21)211.41 (58.5)148.53 (20.4)6.04 (0.44)6.59 (0.64)

CBV and CBF are higher, MTT is faster in the area of the GdDTPA‐enhanced ring compared with the inside region that remains hypointense on T1‐weighted images in three ring‐enhancing lesions.

Development of T1‐hypointensity

In two lesions that became hypointense on T1‐enhanced scans (‘black holes’), several weeks after GdDTPA enhancement, CBV and CBF eventually dropped below baseline values (data not shown). This finding is in line with a previous report of reduced CBV in T1‐hypointense lesions in a cross‐sectional study (Haselhorst et al., 2000), and may well be the result of severe tissue destruction and gliosis, as suggested by magnetization transfer and spectroscopy studies (van Waesberghe et al., 1999; Li et al., 2003).


Perfusion‐weighted imaging has so far not become an element of the MRI techniques relevant for multiple sclerosis (Miller et al., 1998). This might be due to the fact that perfusion studies are technically challenging and at present still of lower resolution than other MRI techniques. To our knowledge, the present MRI study on perfusion measurements of multiple sclerosis lesion development is the first longitudinal investigation of its kind, and opens up new insights into the mechanisms of neuroinflammatory plaque formation. Our findings confirm the hypothesis that lesion formation actually begins several weeks before becoming evident on GdDTPA enhanced scans. In fact, a steep regional increase of CBV and CBF compared with the contralateral side could be detected up to 3 weeks prior to the breakdown of the BBB and subsequent contrast enhancement, indicating a dominant role of the vasculature preceding the inflammation of white matter tissue. The proximity of evolving plaques to venules is a well described feature in multiple sclerosis (Lucchinetti et al., 1998). In several models of neuroinflammation, various effects on the circulation by inflammation‐ and cytotoxicity‐mediating substances were reported. This might be due to different steps of the inflammatory processes being targeted by the different models. An increased permeability in the region of the BBB and higher CBVs were observed after intrathekal application of interleukin‐1β (Blamire et al., 2000). The reduction of the CBV observed in response to direct intrastriatal injection of tumour necrosis factor‐α (Sibson et al., 2002) might reflect a rather late stage of inflammation. In fact, other substances presumably originating during brain inflammation in multiple sclerosis, such as nitric oxide and substance P, are classical vasodilators (Kostyk et al., 1989; Hartung and Kieseier, 1996).

In the present study, an increase of perfusion was already found prior to the elevation of the diffusivity (ADC), indicating local blood flow changes early during the plaque formation process. Whereas changes in the perfusion measurements represent functional characteristics of a certain condition of the vasculature and its vicinity (Barbier et al., 2001), alterations in the apparent water diffusion rate reflect pathological changes in the brain tissue due to the diffusion characteristics of the intra‐ and extracellular water compartments (Gass et al., 2001). Accordingly, several stroke studies have shown a reduction of the ADC in areas of ischaemia and cytotoxic oedema (Moseley et al., 1990; Mintorovitch et al., 1994). An inverse relationship between diffusion and perfusion was revealed in some epilepsy studies, where a close correlation of reduced diffusion and signs of regional hyperperfusion after prolonged ictal activity on magnetic resonance angiography could be demonstrated (Wieshmann et al., 1997; Lansberg et al., 1999). A similar phenomenon has been described in functional MRI experiments. Here, an activation of certain areas after stimulation caused an increase in regional blood flow as measured by blood oxygen level‐dependent contrast (Bandettini et al., 1992) or contrast agent perfusion techniques (Belliveau et al., 1991), but interestingly it also lead to a transient decrease of the ADC in the same area (Darquie et al., 2001). In multiple sclerosis, vasogenic oedema and an increase of extracellular space in combination with myelin breakdown and tissue structure disruption were reported to result in increased ADC values in both, NAWM (Rocca et al., 2000; Werring et al., 2000; Cercignani et al., 2001; Caramia et al., 2002), and acute and chronic lesions (Tievsky et al., 1999; Filippi et al., 2000). Interestingly, a prominent perfusion increase was found prior to a significant increase in ADC in the present study. The peak of the CBV was followed by a gradual decline over 20 weeks, before it decreased more rapidly, and, in case of development of T1‐hypointensity (‘black hole’), remained below baseline. The initial elevation can presumably be explained by inflammation‐related vasodilation in the acute stage, whereas the decreased perfusion in later stages of the lesion might be due to the development of a (hypometabolic) gliotic scar, which is indicated by reduced N‐acetyl‐aspartate (NAA)/creatine ratios in magnetic resonance spectroscopy in T1‐hypointense lesions (van Walderveen et al., 1998; Li et al., 2003).

Three lesions developed a ring‐like appearance on GdDTPA‐enhanced scans. In such lesions the MTR, a measure of tissue damage (van Waesberghe et al., 1999), was found to be lowest inside the T1‐hypointense centre (Hiehle et al., 1995). These reports support our finding of reduced CBV and CBF inside those plaques. One may speculate that the reduced blood supply in such lesions accounts for a higher probability of permanent tissue destruction, which might be an explanation for the observation that ring‐enhancing lesions account for a more destructive disease course (Morgen et al., 2001).

Taken together, our data on cerebral blood perfusion measurements during lesion formation in multiple sclerosis patients with relapsing–remitting disease course indicate that elevation of perfusion is an early event in the development of a plaque. Improving the resolution of this technique might not only give new insight into the pathomechanisms in multiple sclerosis, but also lead to a more sensitive measurement of disease activity and treatment effects (Miller, 1996; McFarland et al., 2002).


We wish to thank Reta Haselhorst for providing a leakage correction paradigm, Andrea Rebmann and Jeff Quinlivan for introduction to the MedX perfusion module, Bianca Müller for assistance with scanning procedures, and Celia Forbes for carefully editing the manuscript for english. This work was supported by grants from the Bundesministerium für Bildung und Forschung (BMBF) and the Gemeinnützige Hertie Stiftung. J.W. was supported by the Boehringer Ingelheim Fonds (BIF).


View Abstract