The topographical distribution of tissue injury in benign MS: A 3T multiparametric MRI study

www.elsevier.com/locate/ynimg NeuroImage 39 (2008) 1499 – 1509

The topographical distribution of tissue injury in benign MS: A 3T multiparametric MRI study Antonia Ceccarelli,a Maria A. Rocca,a,b,c Elisabetta Pagani,a Angelo Ghezzi,d Ruggero Capra,e Andrea Falini,b,f Giuseppe Scotti,b,f Giancarlo Comi,c and Massimo Filippia,b,c,⁎ a

Neuroimaging Research Unit, Scientific Institute and University Ospedale San Raffaele, Milan, Italy CERMAC, Scientific Institute and University Ospedale San Raffaele, Milan, Italy c Department of Neurology, Scientific Institute and University Ospedale San Raffaele, Milan, Italy d Multiple Sclerosis Center, Ospedale di Gallarate, Gallarate, Italy e Multiple Sclerosis Center, Ospedale Richiedei, Gussago, Brescia, Italy f Department of Neuroradiology, Scientific Institute and University Ospedale San Raffaele, Milan, Italy b

Received 27 September 2007; accepted 5 November 2007 Available online 13 November 2007

We compared the global and regional distribution of white matter (WM) and gray matter (GM) damage and T2-visible lesion between patients with benign (B) and relapsing remitting (RR) multiple sclerosis (MS). BMS and RRMS patients did not differ in terms of global volumes and diffusion tensor (DT) MRI metrics of the WM and GM. Compared to controls, BMS and RRMS patients had bilateral thalamic loss. Compared to controls, BMS and RRMS patients had lower WM fractional anisotropy (FA) in the corpus callosum (CC) and in several regions of temporal and occipital lobes. BMS also had a decreased WM FA in the parietal lobes. RRMS patients had also lower WM FA in several regions of the frontal lobes. Compared to BMS, RRMS patients had decreased WM FA in the frontal lobes, while the opposite comparison showed lower WM FA in the CC, the temporal lobes and the cuneus in BMS. Contrasted to controls, both MS groups showed several regions of increased MD in WM and GM, but no difference was found between MS sub-groups. T2-visible lesions were mainly located in the posterior regions of the brain in BMS patients, while they involved also regions in the frontal lobes, in RRMS patients. BMS and RRMS patients differ in terms of the topographical distribution of WM damage rather than in the overall extent of brain structural changes. The less prominent involvement of the frontal lobe WM and of the NAWM in general in BMS might be associated to their favorable clinical status. © 2007 Elsevier Inc. All rights reserved.

⁎ Corresponding author. Neuroimaging Research Unit Department of Neurology, Scientific Institute and University Ospedale San Raffaele, Via Olgettina, 60, 20132 Milan, Italy. Fax: +39 02 2643 3054. E-mail address: [email protected] (M. Filippi). Available online on ScienceDirect (www.sciencedirect.com). 1053-8119/$ - see front matter © 2007 Elsevier Inc. All rights reserved. doi:10.1016/j.neuroimage.2007.11.002

Introduction Multiple sclerosis (MS) is the most common chronic, inflammatory-demyelinating disease affecting the central nervous system (CNS) of young adults in Western countries, leading, in the majority of cases, to severe and irreversible clinical disability (Noseworthy et al., 2000). Nevertheless, MS is an extremely heterogeneous condition, not only in terms of clinical manifestations, but also in terms of genetic and immunological background as well as in the amount of tissue damage as detected by magnetic resonance imaging (MRI) (Noseworthy et al., 2000). The complexity of the disease is particularly evident when considering patients in the early phase of the disease (Miller et al., 2005) or those with “benign” (B) MS, where the clinical impact of the disease is mild compared to the amount of lesions seen on conventional MRI scans (Hawkins and Mc Donnell, 1999; Pittock et al., 2004; Ramsaransing et al., 2001). During the past decade, a significant effort has been spent in an attempt to define markers of the favorable disease evolution experienced by patients with BMS, who are typically defined as those patients with a low disability (EDSS ≤ 3) after a relatively long disease duration (≥ 15 years) (Hawkins and Mc Donnell, 1999; Lublin and Reingold, 1996; Pittock et al., 2004; Ramsaransing et al., 2001). Although several clinical predictors of a favorable clinical course have been identified in these patients (Ramsaransing et al., 2001), the assessment of the extent of focal (De Stefano et al., 2006; Droogan et al., 1999; Falini et al., 1998; Filippi et al., 1996, 1999, 2000; Horsfield et al., 1996; Traboulsee et al., 2003) and diffuse (Brass et al., 2004; Davie et al., 1999; De Stefano et al., 2006; Droogan et al., 1999; Filippi et al., 1996, 1999, 2000; Horsfield et al., 1996; Traboulsee et al., 2003) brain damage using MRI has yielded conflicting results. Among the

1500

A. Ceccarelli et al. / NeuroImage 39 (2008) 1499–1509

factors that might contribute to explain this disappointing situation, the relatively small numbers of subjects enrolled, the inconsistency in the definition of BMS, the variability of the disease control groups used, the different sensitivities of the MRI techniques applied and the use of low field scanners should all be considered. The introduction of high-field MR scanners and parallel imaging technology in the clinical arena is contributing to improve the sensitivity of MRI techniques for detecting CNS damage, thanks to an increased spatial resolution and signal-to-noise ratio (SNR) (Charil et al., 2006). This latter aspect is particularly important for diffusion tensor (DT) MRI since the application of diffusion gradients causes an attenuation of the signal, which in turn intrinsically limits the SNR available. Using 1.5 T scanners, several DT MRI studies have consistently shown increased diffusivity in the brain normal-appearing white matter (NAWM) and gray matter (GM) and reduced anisotropy in the NAWM from patients with MS when compared to the WM from healthy controls (Rovaris et al., 2005). The recent application of DT MRI at 3 T for the quantification of damage of the different brain compartments has confirmed the potential of such an approach in the assessment of patients with MS (Ceccarelli et al., 2007). Voxel-based approaches, including voxel-based morphometry (VBM) (Ashburner and Friston, 2000), hold substantial promise to gain additional insights into MS pathobiology since they allow to define reliably the presence and extent of damage in the WM and GM at a regional level. Although previous voxel-based studies in relapsing remitting (RR), secondary progressive (SP) and primary progressive (PP) MS suggested different patterns of topographical tissue damage in these patients (Audoin et al., 2006; 2007; Khaleeli et al., 2007; Morgen et al., 2006; Prinster et al., 2006; Sepulcre et al., 2006), a direct comparison of regional damage distribution between patients with different disease phenotypes has never been performed. In this study, we used a 3 T scanner to assess the global and regional distribution of WM and GM damage in patients with BMS. In order to gain additional insight into the possible mechanisms responsible for their favorable clinical status, we also compared findings in BMS with those obtained from a group of patients with RRMS and similar disability. Materials and methods Subjects From the MS population regularly followed at the MS clinics of the participating institutions, patients with either BMS or RRMS were selected. Patients were classified as having BMS when their Expanded Disability Status Scale (EDSS) score (Kurtzke, 1983) was V3.0 and their disease duration z 15 years. For the purposes of this study, RRMS patients should have had an EDSS score V 3 and a disease duration V 10 years. All patients also had to be relapseand steroid-free for at least 1 month before study entry. All patients were evaluated clinically by the same experienced neurologist, who was blinded to the MRI results, on the same day of MR examination. Twenty healthy volunteers (13 women and 7 men; mean age 36.8 years, SD ±6.8) with no history of neurological disorders and a normal neurological examination were also studied. The study was approved by the local ethics committee and a written informed consent was obtained from all subjects prior to study entry.

MRI acquisition Brain MR scans were obtained using a 3.0 T scanner (Intera, Philips Medical Systems, Best, The Netherlands). During a single imaging session, the following sequences were obtained from all subjects: (1) dual-echo turbo spin echo (TSE) sequence # (TR = 3500, TE = 24/120 ms; echo train length = 5; flip angle = 150°, 44 contiguous, 3-mm-thick, axial slices with a matrix size = 256 × 256 and a field of view (FOV) = 240 × 240 mm2); (2) 3D T1-weighted fast field echo (FFE) sequence (TR = 25, TE = 4.6 ms, flip angle = 30°, 220 contiguous, axial slices with voxel size = 0.89 × 0.89 × 1 mm, matrix size = 256 × 256, FOV = 230 × 230 mm2); (3) pulsed-gradient spin-echo echo planar pulse sequence with SENSE (acceleration factor = 2.5, TR = 8283.2, TE = 80; 55, 2.5-mm-thick axial slices; acquisition matrix size = 96 × 96; FOV = 240 × 240 mm2; after SENSE reconstruction, the matrix dimension of each slice was 256 × 256, with an in-plane pixel size of 0.94 × 0.94 mm) and with diffusion gradients applied in 32 non-collinear directions, using a gradient scheme which is standard on this system (gradient over-plus) and optimized to reduce echo time as much as possible. Two optimized b factors were used for acquiring diffusion-weighted images (b1 = 0, b2 = 1000 s/mm2). Fat saturation was performed to avoid chemical shift artifacts. All slices were positioned to run parallel to a line that joins the most infero-anterior and infero-posterior parts of the corpus callosum (CC). Image analysis and postprocessing The structural MRI postprocessing was performed by a single experienced observer, unaware to whom the scans belonged. Lesion volumes (LV) were measured on T2-weighted images using a local thresholding segmentation technique (Rovaris et al., 1997). Global assessment of volumes and DT MRI changes of the NAWM and GM On 3D-FFE images, WM (WMV), GM (NGM) and intracranial (ICV) volumes were measured using the cross-sectional version of the fully automated Structural Imaging Evaluation of Normalized Atrophy (SIENAx) software (Smith et al., 2001). Diffusion gradient directions were corrected for scanner settings (i.e., slice angulation, slice orientation, etc.) before DT estimation (Farrell et al., 2007). From DT MR images, mean diffusivity (MD) and fractional anisotropy (FA) maps were derived (Pierpaoli and Basser, 1996) and average MD of the NAWM and GM and average FA of the NAWM were computed, as previously described (Ceccarelli et al., 2007). Average FA was derived only for the NAWM since no preferential direction of water molecular motion is expected to occur in the GM, due to the absence of a microstructural anisotropic organization of this tissue compartment. Assessment of topographical distribution of atrophy and DT MRI changes of the WM and GM Regional volumetry analysis was performed on 3D T1weighted FFE images using voxel-based morphometry (VBM) and statistical parametric mapping (SPM2) software (www.fil.ion. ucl.ac.uk/spm). An optimized VBM method was used as described by Good et al. (2001). Briefly, a customized T1 template, together with the corresponding probability maps of GM, WM and cerebrospinal fluid (CSF), was first created using 3D-FFE scans

A. Ceccarelli et al. / NeuroImage 39 (2008) 1499–1509

of both healthy controls and MS patients. Then, GM probability maps in the native space were normalized toward the new customized GM template and the computed transformation was used to modulate the normalized GM maps in order to incorporate the point-wise volume expansion/contraction induced by the transformation (Ashburner and Friston, 2000). To assess GM regional diffusivity changes, the non-linear transformation previously calculated was applied to the MD maps coregistered onto the 3D-FFE with a rigid transformation. To assess MD and FA regional changes in the WM, MD and FA maps were normalized into the standard SPM space. Using SPM2, a rigid transformation was calculated between the non-diffusion-weighted images and the T2-weighted images and between the T2-weighted images and the SPM T2-weighted atlas. The transformation was then applied to the FA maps. A customized FA atlas was obtained by averaging the transformed FA maps of both healthy controls and MS patients. Then, a non-linear transformation was calculated between the customized FA atlas and the FA maps and applied to the MD maps as well. GM modulated maps, WM FA and MD maps and GM MD maps were all smoothed with a 12 mm3 FWHM Gaussian kernel in the MNI space, before their use as input for the statistical analysis. To avoid MS lesion misclassification, lesions were masked out from the GM maps and reassigned to WM maps after each segmentation step of the described post-processing strategy. To this end, a lesion mask of the T2-visible lesions was created, coregistered to the 3D-FFE space and normalized to the MNI space. A probability map of the spatial distribution of T2-visible lesions was also created for both BMS and RRMS patients by averaging the normalized T2 lesion masks previously created. To exclude from the statistical analysis pixels assigned by the segmentation to GM/WM with low probability values and pixels with a low intersubject anatomical overlay after normalization, GM and WM masks were created by averaging GM and WM normalized maps from all subjects. These masks were eroded (erosion of the first-line outer voxels), thresholded at a value of 0.75 and then used as explicit mask during the statistical analysis. Statistical analysis An analysis of variance model adjusted for age was used to assess between-group differences in clinical and MRI measures of global NAWM and GM damage. Post hoc comparisons between healthy controls and MS patients and between patients with BMS and those with RRMS were performed only for those variables showing a significant heterogeneity across groups. An analysis of covariance (ANCOVA) was used to compare volumetry and diffusivity measurements between patients and controls, and between BMS and RRMS patients. Age, sex and ICV volume were included as nuisance covariates for the volumetry comparisons, whereas age and sex were used as nuisance covariates for diffusivity comparisons. Regional differences were considered significant only if they survived a correction for multiple comparisons (Family Wise Error, p V 0.05). In areas where significant differences were obtained by the comparison between each of the two groups of patients with healthy controls, the significance threshold was set at p b 0.001 (uncorrected for multiple comparisons). Since this threshold might have led to false positive results, in those areas which passed this threshold a small volume correction (SVC) for multiple comparisons was applied, setting the cutoff value for significance at p b 0.05 and using a 10 mm radius.

1501

Results Clinical and conventional MRI findings Nineteen patients with BMS (15 women and 4 men, mean age [SD] = 41.5 [± 5.6] years, median EDSS score = 2.0, range = 1.0– 3.0, median disease duration = 20, range=15–30 years) and 15 RRMS (12 women and 3 men, mean age [SD] = 33.3 [± 7.8] years, median EDSS score = 1.5, range = 1.0–3.5, median disease duration = 6, range = 2–10 years) were enrolled in this study. As expected, BMS patients were older and had a longer disease duration than those with RRMS (p = 0.0001, for both comparisons). Nine BMS patients and all RRMS patients were treated with one of the available interferon-beta formulations at the time of the study. No abnormalities were seen on T2-weighted scans obtained from controls. Median T2LV was 12.2 ml (range = 1.0–37.9 ml) in BMS and 14.6 ml (range = 1.6–31.6 ml) in RRMS patients (p = n.s.). Volumes and diffusivity changes of overall NAWM and GM In Table 1, brain volumetry and DT MRI derived metrics from healthy volunteers and the two patients' groups are reported. A significant between-group heterogeneity was found for NGV, but not for WMV. NGV was significantly different between controls and BMS patients (p = 0.001). A significant between-group heterogeneity was also found for all the DT MRI derived metrics of the overall NAWM and GM. Average MD and FA were significantly different between BMS patients and controls (p values ranging from b 0.0001 to 0.003) and between RRMS patients and controls (p values ranging from b 0.0001 to 0.004). No difference was found for any NAWM and GM DT MRI derived metrics between patients with RRMS and BMS.

Table 1 Brain volumetric and DT MRI derived metrics of the normal-appearing white and gray matter from healthy volunteers and patients with MS

NGV (SD) (ml) WMV (SD) (ml) Average lesion FA (range) Average lesion MD (range) NAWM average FA (SD) NAWM average MD (SD) GM average MD (SD)

Healthy volunteers

BMS

RRMS

pa

762.4 (89.3) 900.5 (90.6) –

613.8 (94.9) 883.9 (94.2) 0.33 (0.3–0.4)

679.4 (91.2) 923.1 (117.1) 0.35 (0.3–0.4)

0.002 n.s. –

–

1.11 (0.9–1.4)

1.02 (0.8–1.2)

–

0.36 (0.2)

0.31 (0.2)

0.32 (0.3)

0.0001

0.69 (0.0)

0.72 (0.0)

0.73 (0.0)

0.001

0.82 (0.0)

0.90 (0.1)

0.88 (0.1)

0.0001

NAWM = normal-appearing white matter, GM = gray matter, SD = standard deviation, MD = mean diffusivity, FA = fractional anisotropy, GMV = normalized gray matter volume, WMV = normalized white matter volume. Note: average MD is expressed in units of mm2s− 1 × 10− 3, FA is a dimensionless index. a Test of heterogeneity (ANOVA model) between groups adjusted for age. See text for further details.

1502

A. Ceccarelli et al. / NeuroImage 39 (2008) 1499–1509

Fig. 1. Statistical parametric mapping (SPM) regions with decreased gray matter (GM) concentration in patients with benign multiple sclerosis (BMS) (A) and relapsing remitting (RR) MS (B) compared with control subjects (p b 0.05, corrected for multiple comparisons). The thalami, bilaterally, had a significantly reduced GM concentration. In C, the SPM regions with decreased GM concentration in BMS patients compared with RRMS patients are shown. The right thalamus of BMS had a significantly reduced GM concentration. Images are in neurological convention.

Regional distribution of atrophy and diffusivity changes of the WM and GM GM concentration changes Compared to controls, BMS and RRMS patients had significant clusters of locally reduced GM concentration in the right (SPM coordinates: 12, − 25, 12, t = 7.81 for BMS and SPM coordinates: 5, − 4, 11, t = 3.71 for RRMS patients) and left (SPM coordinates: − 7, −24, 12, t = 6.75, for BMS and SPM coordinates: − 9, − 15, 5, t = 3.63, for RRMS patients) thalamus (Fig. 1). GM loss in the right thalamus was more pronounced in BMS– than in RRMS patients (SPM coordinates: 12, −26, 9, t = 5.79) (Fig. 1). WM FA changes Compared to controls, BMS and RRMS patients had significant clusters of locally decreased WM FA in several regions of the parietal, temporal and occipital lobes, bilaterally, and in the splenium of the CC, bilaterally (Table 2). RRMS patients also showed reduced WM FA values in several regions of the frontal

lobes, bilaterally (Table 2, Fig. 2). Compared to BMS, RRMS patients had WM regions with significantly reduced FA in the frontal lobes, bilaterally (SPM coordinates: 14, 42, 28 and 32, 46, − 2 for the right, − 28, 48, −4, for the left) (Fig. 2). Conversely, compared to RRMS, BMS patients had WM regions with significantly reduced FA in the parietal lobe (SPM coordinates: 28, − 14, 26 and SPM coordinates: − 20, − 50, 44), the temporal lobes, bilaterally (SPM coordinates: 42, − 26, − 10; 36, − 38, 10 and − 42, − 50, 4) and in the splenium of the CC, bilaterally (SPM coordinates: 2, −34, 16, and − 8, − 38, 18) (Fig. 2). WM MD changes Compared to controls, BMS and RRMS patients had significant clusters of locally increased WM MD in the frontal lobes bilaterally, the right CC, the left temporal lobe and the right occipital lobe (Table 3). BMS patients also had regions with significantly increased WM MD in the parietal lobe, bilaterally, the right temporal lobe, the left occipital lobe, the right corticospinal tract (CST), and the left CC, while RRMS patients showed WM regions with significantly increased MD in the left cerebellar

Table 2 Regions of significantly decreased white matter fractional anisotropy in patients with BMS and RRMS compared to healthy controls Anatomical regions

BMS vs. controls

RRMS vs. controls

k

SPM coordinates

t

k

SPM coordinates

t

Right frontal fibers Left frontal fibers

– –

– –

– –

Right parietal fibers

815 815 76 15 815 448 195 195 195 815 34

24 − 24 28 26 − 20 26 − 24 − 22 26 − 18 − 54 30 36 − 38 10 − 42 − 52 2 16 − 46 20 − 16 − 50 22 − 10 − 44 16 30 − 74 2 − 30 − 68 − 2

4.98 4.82 4.43 4.23 6.11 5.42 5.11 5.00 4.91 5.53 4.74

498 22 15 17 – 15 – 359 566 15 31 – 214 20

14 28 40 −14 46 22 −22 −20 30 44 − 34 32 – −44 −30 28 – 44 − 36 − 10 −26 −22 − 4 16 − 44 24 −16 −52 24 – 28 − 78 6 −26 −74 14

4.60 3.61 3.37 3.49 – 3.83 – 4.54 4.62 3.29 3.63 – 5.32 3.34

Left parietal fibers Right temporal fibers Left temporal fibers Right corpus callosum Left corpus callosum Right occipital fibers Left occipital fibers See the text for further details. k = cluster size.

A. Ceccarelli et al. / NeuroImage 39 (2008) 1499–1509

1503

Fig. 2. SPM regions with decreased white matter (WM) fractional anisotropy (FA) in patients with BMS (A) and RRMS (B) compared with control subjects (p b 0.05, corrected for multiple comparisons). In C, the SPM regions with decreased WM FA in BMS patients compared with RRMS patients are shown. In D, the SPM regions with decreased WM FA in RRMS patients compared with BMS patients are shown. Images are in neurological convention. See text for further details.

peduncle and the left CST (Table 3, Fig. 3). No differences were found between RRMS and BMS patients. GM MD changes Compared to controls, BMS and RRMS patients had significant clusters of locally increased GM MD in the left inferior frontal gyrus (IFG), the left thalamus, the left red nucleus, the right parahippocampal gyrus, the insula, bilaterally, and the posterior lobe of cerebellum, bilaterally (Table 4, Fig. 4). BMS patients also had increased GM MD in the right thalamus, the left precuneus, the left lingual gyrus, the posterior cingulate gyrus, bilaterally, the fusiform gyrus, bilaterally, and in several regions of the temporal lobes, bilaterally (Table 4, Fig. 4). No differences were found between RRMS and BMS patients. Analysis of regional distribution of T2-weighted lesions The comparison of lesion distribution maps between the two groups of patients showed that T2 lesions were mainly located in the posterior regions of the brain in patients with BMS in comparison with those with RRMS (Fig. 5), while RRMS patients had a more diffuse pattern of lesion distribution (Fig. 5). To investigate the correspondence between areas of diffusivity changes and areas with an increased occurrence of lesions, lesion maps were superimposed on the MD and FA WM SPMt maps. This

analysis showed a relation between regional distribution of WM FA reductions and maps of lesion distribution in both groups of subjects (Fig. 5). In RRMS patients, areas of abnormal WM FA values were also found in regions not involved by T2-visible lesions. This was not the case in patients with BMS. Discussion In this study, we used very stringent criteria for the selection of patients with BMS, based on the accumulation of a low level of disability over a long period of time in order to increase the likelihood to detect MR changes possibly associated with their favorable clinical outcome. We also studied two control groups, one of which consisted of patients with RRMS, matched with BMS for disability. In addition to a global assessment of damage in the NAWM and GM of the brain, we also performed a voxel-based analysis of different imaging modalities to define the topographical distribution of brain damage in the two groups of patients. Assessment of volumes and diffusivity changes of overall NAWM and GM The analysis of volumes of the different brain compartments showed significant atrophy of the GM, but not of the WM in BMS

1504

A. Ceccarelli et al. / NeuroImage 39 (2008) 1499–1509

Table 3 Regions of significantly increased white matter mean diffusivity in patients with BMS and RRMS compared to healthy controls Anatomical regions

Right frontal fibers

Left frontal fibers Right parietal fibers Left parietal fibers

Right temporal fibers

Left temporal fibers

Right corpus callosum Left corpus callosum (splenium) Right occipital fibers Left occipital fibers Left cerebellar peduncle Right corticospinal tract Left corticospinal tract

BMS vs. controls

RRMS vs. controls

k

SPM coordinates

t

k

SPM coordinates

t

778 22 22 – 2117 – 5 49 2117 2117 5 69 21 5 21 15 7 12 145

28 8 38 16 42 22 16 54 6 – −12 −16 52 – 20 − 54 38 38 − 46 28 −38 −34 32 −30 −16 26 −18 −54 44 44 − 12 − 18 38 − 28 2 34 − 60 26 30 − 24 0 −44 −50 4 −40 −30 0 −26 −22 −2 2 − 34 16 12 42 24 −4 −38 14 −10 −44 20 36 − 74 − 4 −36 −78 0 – 16 − 12 − 2 –

5.64 4.30 4.27 – 5.33 – 4.41 4.27 4.59 4.57 4.02 4.83 4.51 4.41 4.17 4.69 4.40 4.31 5.32 4.69 5.08 4.87 5.44 4.71 – 4.31 –

117 18 15 50 14 20 – – – – – – – – – 15 – – 166 – – – 15 – 15 – 25

16 24 34 16 42 24 22 24 − 10 32 38 8 − 16 36 −14 − 28 34 6 – – – – – – – – – − 24 − 22 0 – – 12 34 − 12 – – – 32 −78 −4 – − 22 − 52 − 40 – − 18 − 16 4

3.68 3.37 3.39 3.49 3.41 3.43 – – – – – – – – – 3.28 – – 3.93 – – – 3.52 – 3.67 – 3.86

145 32 17 – 7 –

See the text for further details. k = cluster size.

when compared with healthy controls, while no difference was found between BMS and RRMS patients. This may be a consequence of the older age of patients with BMS since it has been shown that GM is more vulnerable than WM to aging (Benedetti et al., 2006). In agreement with previous studies performed with different quantitative MR techniques (Gass et al., 1994; De Stefano et al., 2006, Filippi et al., 1999, 2000), the analysis of MD and FA values of T2-visible lesions showed no differences between the two groups of patients studied. Similar results were obtained from the analysis of DT MRI metrics of the NAWM and GM compartments. Since we applied an accurate procedure to segment the brain tissue, the similar extent of NAWM and GM diffusivity changes in BMS and RRMS patients is likely not to be a consequence of partial volume effects from the CSF. In addition, since partial volume effect is expected to increase with the presence of atrophy and since we found reduced GM volumes in BMS patients, the absence of any difference of GM diffusivity in BMS vs. RRMS patients indicates, on the one hand, the robustness of our analysis and, on the other, that intrinsic GM damage in MS is actually similar to that of patients with RRMS. To our knowledge, only two seminal studies applied diffusionweighted MR technology for the assessment of disease-related damage in patients with BMS (Droogan et al., 1999; Horsfield et al., 1996). The first one compared apparent diffusion coefficient (ADC) of lesions and NAWM between patients with BMS and those with secondary progressive (SP) MS and healthy controls

(Horsfield et al., 1996). The second study (Droogan et al., 1999) also included a group of patients with RRMS. In line with our results, both these studies found increased ADC values in MS lesions in comparison with NAWM, without significant differences between the clinical phenotypes studied. The study of Horsfield et al. (1996) also showed a significant increase in ADC values in the NAWM of both SPMS and BMS patients, which reached statistical significance only for the latter group. Despite several factors that preclude a direct comparison between the results of our study and the previous ones, including the different clinical characteristics of the patients recruited, the criteria applied to define BMS, the control groups selected, the use of different MR sequences and of different MR field strengths and the methods of analysis (ROI-based in the previous studies vs. “overall” analysis in the present one), it seems that the extent of macroscopic and microscopic tissue damage in BMS, measured using diffusion-weighted MRI, is similar to that of patients with less favorable disease phenotypes. These results are in agreement with findings obtained with other quantitative MR-based techniques, including proton MR spectroscopy (Davie et al., 1999; Falini et al., 1998) and magnetization transfer (MT) MRI (Gass et al., 1994; De Stefano et al., 2006, Filippi et al., 1999, 2000). However, it is worth noting that a recent MT MRI study has demonstrated less pronounced tissue damage in BMS patients than in those with RRMS. In addition to the different clinical and conventional MRI characteristics between the patients recruited in our study and those by De Stefano et al. (2006), the fact that different MR techniques

A. Ceccarelli et al. / NeuroImage 39 (2008) 1499–1509

1505

Fig. 3. SPM regions with increased white matter (WM) mean diffusivity (MD) in patients with BMS (A–E) and RRMS (F–J) compared with control subjects (p b 0.05, corrected for multiple comparisons): (A) left frontal fibers and bilateral temporal and occipital fibers; (B) left frontal fibers and bilateral splenium of corpus callosum; (C) bilateral frontal and parietal fibers; (D) right frontal fibers and right corticospinal tract; (E) right parietal and temporal fibers; (F) left cerebellar peduncle; (G) bilateral frontal fibers; (H) right frontal and occipital fibers; (I) right rostrum of corpus callosum and frontal fibers; (J) left corticospinal tract and frontal fibers. Images are in neurological convention. See text for further details.

were used to quantify occult MS-related damage should be considered. Indeed, albeit demyelination and axonal loss/dysfunction are known to be strictly related in MS (van Waesberghe et al.,

1998; Schmierer et al., 2004), MT MRI changes are likely to be mostly affected by loss of myelin (Thorpe et al., 1995), which may not be necessarily associated to irreversible clinical disability,

Table 4 Regions of significantly increased gray matter mean diffusivity in patients with BMS and RRMS compared to healthy controls Anatomical regions

Left inferior frontal gyrus Right insula Left insula Right thalamus Left thalamus Right superior temporal gyrus Left middle temporal gyrus Left inferior temporal gyrus Right fusiform gyrus Left fusiform gyrus Right parahippocampal gyrus Right posterior cingulate gyrus Left posterior cingulate gyrus Left precuneus Left lingual gyrus Right posterior lobe of cerebellum

Left posterior lobe of cerebellum Left red nucleus See the text for further details. BA = Brodmann area, k = cluster size.

BMS vs. controls

RRMS vs. controls

k

BA

SPM coordinates

t

k

BA

SPM coordinates

t

42 424 – 220 – 71 148 5 7 6 6 9 199 44 13 522 522 85 323 201 – 1310 – 34

47 – – – – – – 41 37 20 37 20 36 31 24 23 7 17 – – – – – –

− 32 28 − 4 36 4 4 – − 32 − 24 8 – 12 − 26 − 2 − 8 − 28 − 2 46 − 28 − 4 − 46 − 62 − 4 − 34 − 42 − 22 44 − 64 − 16 − 44 − 26 − 24 28 −36 − 16 10 − 46 32 4 − 24 38 − 6 − 56 14 − 4 − 68 44 − 8 − 88 − 2 32 − 66 − 50 26 − 68 − 32 – − 26 − 60 − 26 – − 2 − 28 − 8

5.67 5.74 – 5.74 – 5.59 5.65 4.84 5.43 4.85 5.25 4.88 6.02 5.12 4.80 6.05 5.81 5.65 685 5.11 – 6.83 – 6.00

332 – 237 37 332 – 7 – – – – – 12 – – – – – 92 82 15 819 12 25

47 – 13 – – – – – – – – – 36 – – – – – – – – – – –

−28 22 − 16 – 28 20 − 12 −34 − 6 6 −30 14 0 – −10 − 28 − 4 – – – – – 28 − 36 − 18 – – – – – 34 − 68 − 48 30 − 60 − 28 16 − 78 − 26 −30 − 62 − 26 −8 −62 − 48 −2 −30 − 12

4.72 – 4.06 3.67 3.71 – 3.53 – – – – – 3.50 – – – – – 3.87 3.61 3.36 5.14 3.63 4.15

1506

A. Ceccarelli et al. / NeuroImage 39 (2008) 1499–1509

Fig. 4. SPM regions with increased gray matter (GM) mean diffusivity (MD) in patients with BMS compared with control subjects (p b 0.05, corrected for multiple comparisons): (A) left fusiform gyrus, left inferior temporal gyrus, bilateral posterior lobe of cerebellum; (B) left inferior frontal gyrus, left posterior lobe of the cerebellum, right parahippocampal gyrus, right insula and left red nucleus; (C) left lingual gyrus and bilateral thalamus and insula; (D) left precuneus and posterior cingulated gyrus bilaterally; (E) right superior temporal gyrus and right fusiform gyrus. Images are in neurological convention. See text for further details.

whereas MD and FA changes are thought to reflect axon integrity (Mottershead et al., 2003), which, if lost, may give rise to “fixed” neurological deficits. Assessment of topographical distribution of volume changes in the GM The VBM analysis of GM atrophy demonstrated selective thalamic damage, even if more pronounced in BMS, in both groups of patients. Thalamic involvement in MS has been reported by both pathologic (Brownell and Hughes, 1962; Ciccarelli et al., 2001; Cifelli et al., 2002) and imaging (Cifelli et al., 2002; Fabiano et al., 2003; Inglese et al., 2004, 2007; Ormerod et al., 1987; Wylezinska et al., 2003) studies. Such an involvement consists not only of macroscopic T2-visible lesions (Inglese et al., 2007; Ormerod et al., 1987), but also of microscopic damage (Ciccarelli et al., 2001, 2002; Fabiano et al., 2003; Inglese et al., 2004; Wylezinska et al., 2003), possibly

related to retrograde and transynaptic degeneration. A recent VBM study demonstrated that GM loss in the thalamus is an early feature of MS (Audoin et al., 2006). Therefore, the fact that BMS patients, despite the relatively long disease duration, show regional GM atrophy confined to the thalami prompts us to speculate that such a tendency to maintain a pattern of atrophy distribution similar to that observed in patients at the earliest clinical stages of the disease might be among the factors responsible for a favorable clinical outcome. In the VBM analysis, we applied several strategies to reduce possible technical biases on our results, including the masking of T2-visible lesions from the GM maps (to avoid lesion misclassification as GM) and the application of a GM mask during the statistical analysis (to exclude pixels with a low probability to belong to GM and those with a low intersubject anatomical overlay). In addition, we used a very stringent statistical threshold based on correction for multiple comparisons. Therefore, our results are unlikely to represent false positive differences.

Fig. 5. Top row: Two representative axial slices showing lesion probability maps (gray scale) and areas with significant decreased fractional (FA) (SPMt color map) for BMS (A) and RRMS patients (B). Bottom row: areas of significant increased lesion occurrence in BMS compared to RRMS patients (C) and in RRMS compared to BMS patients (D), on the glass brain in the sagittal and axial slices view (p b 0.001, uncorrected for multiple comparisons).

A. Ceccarelli et al. / NeuroImage 39 (2008) 1499–1509

Assessment of regional distribution of FA changes in the WM The analysis of the WM FA changes showed that in BMS patients regional damage was mainly located in regions of the parietal, temporal and occipital lobes, bilaterally, while patients with RRMS also had an involvement of several regions of the frontal lobes. Although caution should be exerted when interpreting voxel-based results obtained from quantitative data (Jones et al., 2005; Smith et al., 2006), since suboptimal normalization and smoothing might influence accurate localizations of abnormal regions, we believe that our data are likely to reflect “true” abnormalities in the WM of patients with MS because the analysis of atrophy, performed using two different methods (i.e., VBM and SIENAx) did not show any significant WM atrophy in both groups of patients. In addition, similarly to the approach used by tractbased spatial statistics (TBSS) (Smith et al., 2006) method, individuals' FA maps were registered to an FA atlas and not to the T2-weighted images since WM fiber bundles are better visualized on FA maps. Finally, we applied a very conservative threshold to our WM masks (75%) to minimize the effect of partial volume from voxels containing CSF. Fiber bundles of the frontal lobes are important nodes for processing and integrating cognitive, sensorial and motor information, therefore the integrity of these pathways is likely to have an important role in limiting the accumulation of irreversible clinical deficits in MS. Assessment of regional distribution of MD changes in the WM and GM The voxel-based analysis of MD changes showed that, when contrasted to healthy controls, both groups of patients had widespread involvement of several regions in the GM and WM. Even if we found more regions with a significant difference between BMS patients and controls rather than between RRMS patients and controls, no differences were disclosed between the two groups of MS patients, even when lowering the statistical threshold applied at an uncorrected p value of 0.001. The partial mismatch between MD and FA findings of this study might be secondary to glial proliferation following axonal and myelin loss (Mottershead et al., 2003). Glial proliferation would indeed lead to a “pseudo-normalization” of MD values, but, since glial cells do not have the same anisotropic morphology as the tissue they replace, would contribute along with tissue damage to reduce FA. Assessment of regional distribution of T2-visible lesions In order to improve the understanding of the mechanisms responsible for the different patterns of regional distribution of WM damage between patients with BMS and RRMS, we investigated the location of T2-visible lesions in the two groups of patients separately and analyzed the correlation between the distribution of T2-visible lesions and that of FA WM damage. The first analysis showed that, despite the two groups of patients having similar T2LV, lesions were mainly located in the posterior regions of the brain in BMS patients, while they tended to have a more widespread distribution, involving also regions in the frontal lobes, in RRMS patients. These findings support previous results, which suggested that the location of the lesions in the brain may have a role in explaining the favorable course of BMS (Filippi et al., 1995). The second analysis showed that in patients with BMS there was a close correspondence between

1507

areas of abnormal WM FA values and the presence of T2 hyperintensities, while in patients with RRMS the involvement of normalappearing tissue was also evident. It is worth noting that, during the analysis of diffusivity changes in the WM, lesions were not removed from DT and FA maps because this would have inevitably caused problems with the normalization step. In addition, in case of lesion removal, the model estimation during the statistical analysis would have not been performed in those pixels where at least one subject had a value of zero due to the presence of a lesion, thus making the portion of analyzed brain influenced by intersubject variability of lesion distribution. Using an approach similar to the one we applied, Smith et al. (2006) found WM FA abnormalities well away from areas of lesions in a group of patients with MS. All of this suggests that, contrary to what is observed in patients with other disease phenotypes, tissue disorganization in patients with BMS is confined to areas corresponding to T2-visible lesions, with a relative sparing of NAWM outside focal MS abnormalities. This might yet be an additional factor associated to their favorable clinical status. Conclusions This study suggests that BMS and RRMS patients differ in terms of the topographical distribution of WM damage rather than in the overall extent of brain structural changes. The less prominent involvement of the frontal lobe WM and of the NAWM in general in BMS might be among the factors associated to their favorable clinical status. Clearly, our study is not without limitations. First, this is a cross-sectional study. However, longitudinal studies of long enough duration are difficult to be performed in BMS patients considering the major technology advances and scanner changes/ upgrades that might occur over a few years with an unavoidable impact on the comparability of data acquired at different time points. Second, we do not have histopathologic data to confirm our speculations about the nature of the substrates of the observed diffusivity changes. Nevertheless, a histopathologic assessment is unlikely to be ever possible in patients with BMS since such studies are typically performed on end-stage or in patients with large atypical lesions. Acknowledgments This study was partially supported by a grant from Fondazione Italiana Sclerosi Multipla (FISM) (2005/R/18). References Ashburner, J., Friston, K.J., 2000. Voxel-based morphometry—The methods. NeuroImage 11, 805–821 (Review). Audoin, B., Davies, G.R., Finisku, L., Chard, D.T., Thompson, A.J., Miller, D.H., 2006. Localization of grey matter atrophy in early RRMS: a longitudinal study. J. Neurol. 253, 1495–1501. Audoin, B., Davies, G., Rashid, W., Fisniku, L., Thompson, A.J., Miller, D.H., 2007. Voxel-based analysis of grey matter magnetization transfer ratio maps in early relapsing remitting multiple sclerosis. Mult. Scler. 13, 483–489. Benedetti, B., Charil, A., Rovaris, M., Judica, E., Valsasina, P., Sormani, M.P., Filippi, M., 2006. Influence of aging on brain gray and white matter changes assessed by conventional, MT, and DT MRI. Neurology 66, 535–539.

1508

A. Ceccarelli et al. / NeuroImage 39 (2008) 1499–1509

Brass, S.D., Narayanan, S., Antel, J.P., Lapierre, Y., Collins, L., Arnold, D.L., 2004. Axonal damage in multiple sclerosis patients with high versus low expanded disability status scale score. Can. J. Neurol. Sci. 31, 225–228. Brownell, B., Hughes, J., 1962. The distribution of plaques in the cerebrum in multiple sclerosis. J. Neurol. Neurosurg. Psychiatry 25, 315–320. Ceccarelli, A., Rocca, M.A., Falini, A., Tortorella, P., Pagani, E., Rodegher, M., Comi, G., Scotti, G., Filippi, M., 2007. Normal-appearing white and grey matter damage in MS: a volumetric and diffusion tensor MRI study at 3.0 Tesla. J. Neurol. 254, 513–518. Charil, A., Filippi, M., Falini, A., 2006. High-field strength MRI (3.0 T or more) in white matter diseases. In: Scarabino, T., Salvolini, U. (Eds.), High Field Brain MRI—Use in Clinical Practice. Springer, Berlin Heidelberg New York. Ciccarelli, O., Werring, D.J., Wheeler-Kingshott, C.A., Barker, G.J., Parker, G.J., Thompson, A.J., Miller, D.H., 2001. Investigation of MS normalappearing brain using diffusion tensor MRI with clinical correlations. Neurology 56, 926–933. Cifelli, A., Arridge, M., Jezzard, P., Esiri, M.M., Palace, J., Matthews, P.M., 2002. Thalamic neurodegeneration in multiple sclerosis. Ann. Neurol. 52, 650–653. Davie, C.A., Silver, N.C., Barker, G.J., Tofts, P.S., Thompson, A.J., McDonald, W.I., Miller, D.H., 1999. Does the extent of axonal loss and demyelination from chronic lesions in multiple sclerosis correlate with the clinical subgroup? J. Neurol. Neurosurg. Psychiatry 67, 710–715. De Stefano, N., Battaglini, M., Stromillo, M.L., Zipoli, V., Bartolozzi, M.L., Guidi, L., Siracusa, G., portaccio, E., Giorgio, A., Sorbi, S., Federico, A., Amato, M.P., 2006. Brain damage as detected by magnetization transfer imaging is less pronounced in benign than in early relapsing multiple sclerosis. Brain 129, 2008–2016. Droogan, A.G., Clark, C.A., Werring, D.J., Barker, G.J., McDonald, W.I., Miller, D.H., 1999. Comparison of multiple sclerosis clinical subgroups using navigated spin echo diffusion-weighted imaging. Magn. Reson. Imaging 17, 653–661. Fabiano, A.J., Sharma, J., Weinstock-Guttman, B., Munschauer, F.E. 3rd, Benedict, R.H., Zivadinov, R., Bakshi, R., 2003. Thalamic involvement in multiple sclerosis: a diffusion-weighted magnetic resonance imaging study. J. Neuroimaging 13, 307–314. Falini, A., Calabrese, G., Filippi, M., Origgi, D., Lipari, S., Colombo, B., Comi, G., Scotti, G., 1998. Benign versus secondary-progressive multiple sclerosis: the potential role of proton MR spectroscopy in defining the nature of disability. AJNR Am. J. Neuroradiol. 19, 223–229. Farrell, J.A., Landman, B.A., Jones, C.K.., Smith, S.A., Prince, J.L., van Zijl, P.C., Mori, S., 2007. Effects of SNR on the accuracy and reproducibility of DTI derived fractional anisotropy, mean diffusivity, and principal eigenvector measurements at 1.5T. J. Magn. Reson. Imaging 26, 756–767. Filippi, M., Campi, A., Mammi, S., Martinelli, V., Locatelli, T., Scotti, G., Amadio, S., Canal, N., Comi, G., 1995. Brain magnetic resonance imaging and multimodal evoked potentials in benign and secondary progressive multiple sclerosis. J. Neurol. Neurosurg. Psychiatry 58, 31–37. Filippi, M., Campi, A., Martinelli, V., Colombo, B., Scotti, G., Comi, G., 1996. Brain and spinal cord MR in benign multiple sclerosis: a follow-up study. J. Neurol. Sci. 143, 143–149. Filippi, M., Iannucci, G., Tortorella, C., Minicucci, L., Horsfield, M.A., Colombo, B., Soriani, M.P., Comi, G., 1999. Comparison of MS clinical phenotypes using conventional and magnetization transfer MRI. Neurology 52, 588–594. Filippi, M., Inglese, M., Rovaris, M., Sormani, M.P., Horsfield, M.A., Iannucci, G., Colombo, B., Comi, G., 2000. Magnetization transfer imaging to monitor the evolution of MS: a one-year follow up study. Neurology 55, 940–946. Gass, A., Barker, G.J., Kidd, D., Thorpe, J.W., MacManus, D., Brennan, A., Tofts, P.S., Thompson, A.J., McDonald, W.I., Millar, D.H., 1994.

Correlation of magnetization transfer ratio with clinical disability in multiple sclerosis. Ann. Neurol. 36, 62–67. Good, C.D., Johnsrude, I.S., Ashburner, J., Henson, R.N., Friston, K.J., Frackowiak, R.S., 2001. A voxel-based morphometric study of ageing in 465 normal adult human brains. NeuroImage 14, 21–36. Hawkins, S.A., Mc Donnell, G.V., 1999. Benign multiple sclerosis ? Clinical course, long term follow up, and assessment of prognostic factors. J. Neurol. Neurosurg. Psychiatry 67, 148–152. Horsfield, M.A., Lai, M., Webb, S.L., Barker, G.J., Tofts, P.S., Turner, R., Rudge, P., Miller, D.H., 1996. Apparent diffusion coefficients in benign and secondary progressive multiple sclerosis by nuclear magnetic resonance. Magn. Reson. Med. 36, 393–400. Inglese, M., Liu, S., Babb, J.S., Mannon, L.J., Grossman, R.I., Gonen, O., 2004. Three-dimensional proton spectroscopy of deep gray matter nuclei in relapsing-remitting MS. Neurology 63, 170–172. Inglese, M., Park, S.J., Johnson, G., Babb, J.S., Miles, L., Jaggi, H., Herbert, J., Grossman, R.I., 2007. Deep gray matter perfusion in multiple sclerosis: dynamic susceptibility contrast perfusion magnetic resonance imaging at 3T. Arch. Neurol. 64, 196–202. Jones, D.K., Symms, M.R., Cercignani, M., Howard, R.J., 2005. The effect of filter size on VBM analyses of DT-MRI data. NeuroImage 26, 546–554. Khaleeli, Z., Cercignani, M., Audoin, B., Ciccarelli, O., Miller, D.H., Thompson, A.J., 1999. Localized grey matter damage in early primary progressive multiple sclerosis contributes to disability. NeuroImage 37, 253–261. Kurtzke, J.F., 1983. Rating neurologic impairment in multiple sclerosis: an expanded disability status scale (EDSS). Neurology 33, 1444–1452. Lublin, F.D., Reingold, S.C., 1996. The National Multiple Sclerosis Society (USA) Advisory Committee on Clinical Trials of New Agents in Multiple Sclerosis. Defining the clinical course of multiple sclerosis: results of an international survey. Neurology 46, 907–911. Miller, D., Barkhof, F., Montalban, X., Thompson, A., Filippi, M., 2005. Clinically isolated syndromes suggestive of multiple sclerosis, part I: natural history, pathogenesis, diagnosis, and prognosis. Lancet Neurol. 4, 281–288 (Review). Morgen, K., Sammer, G., Courtney, S.M., Wolters, T., Melchior, H., Blecker, C.R., Oshmann, P., Kaps, M., Vaitl, D., 2006. Evidence for a direct association between cortical atrophy and cognitive impairment in relapsing-remitting MS. NeuroImage 15, 891–898. Mottershead, J.P., Schmierer, K., Clemence, M., Thornton, J.S., Scaravilli, F., Barker, G.J., Tofts, P.S., Newcombe, J., Cuzner, M.L., Ordidge, R.J., McDonald, W.I., Miller, D.H., 2003. High field MRI correlates of myelin content and axonal density in multiple sclerosis–a post-mortem study of the spinal cord. J. Neurol. 250, 1293–1301. Noseworthy, J.H., Lucchinetti, C., Rodriguez Weinshenker BG, M., 2000. Multiple sclerosis. N. Engl. J. Med. 343, 938–952 (Review). Ormerod, I.E., Miller, D.H., McDonald, W.I., du Boulay, E.P., Rudge, P., Kendall, B.E., Moseley, I.F., Johnson, G., Tofts, P.S., Halliday, A.M., et al., 1987. The role of NMR imaging in the assessment of multiple sclerosis and isolated neurological lesions. A quantitative study. Brain 110, 1579–1616. Pierpaoli, C., Basser, P.J., 1996. Toward a quantitative assessment of diffusion anisotropy. Magn. Reson. Med. 36, 893–906. Pittock, S.J., McClelland, R.L., Mayr, W.T., Jorgensen, N.W., Weinshenker, B.G., Noseworthy, J., Rodriguez, M., 2004. Clinical implications of benign multiple sclerosis: a 20-year population-based follow-up study. Ann. Neurol. 56, 303–306. Prinster, A., Quarantelli, M., Orefice, G., Lanzillo, R., Brunetti, A., Mollica, C., Salvatore, E., Morra, V.B., Coppola, G., Vacca, G., Alfano, B., Salvatore, M., 2006. Gray matter loss in relapsing-remitting multiple sclerosis: a voxel based morphometry study. NeuroImage 29, 859–867. Ramsaransing, G., Maurits, N., Zwanikken, C., De, K.J., 2001. Early prediction of a benign course of multiple sclerosis on clinical grounds: a systematic review. Mult. Scler. 7, 345–347. Rovaris, M., Filippi, M., Calori, G., Rodegher, M., Campi, A., Colombo, B., Comi, G., 1997. Intra-observer reproducibility in measuring new putative

A. Ceccarelli et al. / NeuroImage 39 (2008) 1499–1509 MR markers of demyelination and axonal loss in multiple sclerosis: a comparison with conventional T2-weighted images. J. Neurol. 244, 266–270. Rovaris, M., Gass, A., Bammer, R., Hickman, S.J., Ciccarelli, O., Miller, D.H., Filippi, M., 2005. Diffusion MRI in multiple sclerosis. Neurology 65, 1526–1532. Schmierer, K., Scaravilli, F., Altmann, D.R., Barker, G.J., Miller, D.H., 2004. Magnetization transfer ratio and myelin in postmortem multiple sclerosis brain. Ann. Neurol. 56, 407–415. Sepulcre, J., Sestre-Garriga, J., Cercignani, M., Ingle, G.T., Miller, D.H., Thomspon, A.J., 2006. Regional gray matter atrophy in early primary progressive multiple sclerosis: a voxel-based morphometry study. Arch. Neurol. 63, 1175–1180. Smith, S.M., De Stefano, N., Jenkinson, M., Matthews, P.M., 2001. Normalized accurate measurement of longitudinal brain change. J. Comput. Assist. Tomogr. 25, 466–475. Smith, S.M., Jenkinson, M., Johansen-Berg, H., Rueckert, D., Nichols, T.E., Mackay, C.E., Watkins, K.E., Ciccarelli, O., Cader, M.Z., Matthews, P.M., Behrens, T.E., 2006. Tract-based spatial statistics: voxelwise analysis of multi-subject diffusion data. NeuroImage 31, 1487–1505.

1509

Thorpe, J.W., Barker, G.J., Jones, S.J., Moseley, I., Losseff, N., MacManus, D.G., Webb, S., Mortimer, C., Plummer, D.L., Tofts, P.S., et al., 1995. Magnetisation transfer ratios and transverse magnetisation decay curves in optic neuritis: correlation with clinical findings and electrophysiology. J. Neurol. Neurosurg. Psychiatry 59, 487–492. Traboulsee, A., Dehmeshki, J., Peters, K.R., Griffin, C.M., Brex, P.A., Silver, N., Ciccarrelli, O., Chard, D.T., Barker, G.J., Thompson, A.J., Miller, D.H., 2003. Disability in multiple sclerosis is related to normal appearing brain tissue MTR histogram abnormalities. Mult. Scler. 9, 566–573. van Waesberghe, J.H., van Walderveen, M.A., Castelijns, J.A., Scheltens, P., Nijeholt, G.J., Polman, C.H., Barkhof, F., 1998. Patterns of lesion development in multiple sclerosis: longitudinal observations with T1weighted spin-echo and magnetization transfer MR. AJNR Am. J. Neuroradiol. 19, 675–683. Wylezinska, M., Cifelli, A., Jezzard, P., Palace, J., Alecci, M., Matthews, P.M., 2003. Thalamic neurodegeneration in relapsing-remitting multiple sclerosis. Neurology 60, 1949–1954.