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Figure 1.
Areas of gray matter volume loss in patients with primary progressive multiple sclerosis compared with control subjects (no cluster of voxels survived after correcting for multiple comparisons).

Areas of gray matter volume loss in patients with primary progressive multiple sclerosis compared with control subjects (no cluster of voxels survived after correcting for multiple comparisons).

Figure 2.
Areas of gray matter volume loss in patients with primary progressive multiple sclerosis after 1 year.

Areas of gray matter volume loss in patients with primary progressive multiple sclerosis after 1 year.

Table 1. 
Demographic and Clinical Data*
Demographic and Clinical Data*
Table 2. 
Clusters of Gray Matter Decrease During Follow-up in Patients With PPMS
Clusters of Gray Matter Decrease During Follow-up in Patients With PPMS
1.
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PubMedArticle
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PubMedArticle
3.
Cercignani  MBozzali  MIannucci  GComi  GFilippi  M Magnetisation transfer ratio and mean diffusivity of normal appearing white and grey matter from patients with multiple sclerosis. J Neurol Neurosurg Psychiatry 2001;70311- 317
PubMedArticle
4.
Chard  DTGriffin  CMMcLean  MA  et al.  Brain metabolite changes in cortical grey and normal-appearing white matter in clinically early relapsing-remitting multiple sclerosis. Brain 2002;1252342- 2352
PubMedArticle
5.
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PubMedArticle
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7.
Davies  GRRamio-Torrenta  LHadjiprocopis  A  et al.  Evidence for grey matter MTR abnormality in minimally disabled patients with early relapsing-remitting multiple sclerosis. J Neurol Neurosurg Psychiatry 2004;75998- 1002
PubMedArticle
8.
Losseff  NAWang  LLai  HM  et al.  Progressive cerebral atrophy in multiple sclerosis: a serial MRI study. Brain 1996;1192009- 2019
PubMedArticle
9.
Miller  DHBarkhof  FFrank  JAParker  GJThompson  AJ Measurement of atrophy in multiple sclerosis: pathological basis, methodological aspects and clinical relevance. Brain 2002;1251676- 1695
PubMedArticle
10.
Zivadinov  RBakshi  R Central nervous system atrophy and clinical status in multiple sclerosis. J Neuroimaging 2004;14 ((3)(suppl)) 27S- 35S
PubMedArticle
11.
Chard  DTGriffin  CMParker  GJKapoor  RThompson  AJMiller  DH Brain atrophy in clinically early relapsing-remitting multiple sclerosis. Brain 2002;125327- 337
PubMedArticle
12.
De Stefano  NMatthews  PMFilippi  M  et al.  Evidence of early cortical atrophy in MS: relevance to white matter changes and disability. Neurology 2003;601157- 1162
PubMedArticle
13.
Sastre-Garriga  JIngle  GTChard  DTRamio-Torrenta  LMiller  DHThompson  AJ Grey and white matter atrophy in early clinical stages of primary progressive multiple sclerosis. Neuroimage 2004;22353- 359
PubMedArticle
14.
Barkhof  F The clinico-radiological paradox in multiple sclerosis revisited. Curr Opin Neurol 2002;15239- 245
PubMedArticle
15.
Pagani  ERocca  MAGallo  A  et al.  Regional brain atrophy evolves differently in patients with multiple sclerosis according to clinical phenotype. AJNR Am J Neuroradiol 2005;26341- 346
PubMed
16.
Prinster  AQuarantelli  MOrefice  G  et al.  Grey matter loss in relapsing-remitting multiple sclerosis: a voxel-based morphometry study. Neuroimage 2006;29859- 867
PubMedArticle
17.
Rudick  RAFisher  ELee  JCSimon  JJacobs  LMultiple Sclerosis Collaborative Research Group, Use of the brain parenchymal fraction to measure whole brain atrophy in relapsing-remitting MS. Neurology 1999;531698- 1704
PubMedArticle
18.
Smith  SMZhang  YJenkinson  M  et al.  Accurate, robust, and automated longitudinal and cross-sectional brain change analysis. Neuroimage 2002;17479- 489
PubMedArticle
19.
Chard  DTParker  GJGriffin  CMThompson  AJMiller  DH The reproducibility and sensitivity of brain tissue volume measurements derived from an SPM-based segmentation methodology. J Magn Reson Imaging 2002;15259- 267
PubMedArticle
20.
Bermel  RASharma  JTjoa  CWPuli  SRBakshi  R A semiautomated measure of whole-brain atrophy in multiple sclerosis. J Neurol Sci 2003;20857- 65
PubMedArticle
21.
Ashburner  JFriston  KJ Voxel-based morphometry—the methods. Neuroimage 2000;11805- 821
PubMedArticle
22.
Good  CDJohnsrude  ISAshburner  JHenson  RNFriston  KJFrackowiak  RS A voxel-based morphometric study of ageing in 465 normal adult human brains. Neuroimage 2001;1421- 36
PubMedArticle
23.
Baron  JCChetelat  GDesgranges  B  et al.  In vivo mapping of gray matter loss with voxel-based morphometry in mild Alzheimer's disease. Neuroimage 2001;14298- 309
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24.
Bruno  SDBarker  GJCercignani  MSymms  MRon  MA A study of bipolar disorder using magnetization transfer imaging and voxel-based morphometry. Brain 2004;1272433- 2440
PubMedArticle
25.
Testa  CLaakso  MPSabattoli  F  et al.  A comparison between the accuracy of voxel-based morphometry and hippocampal volumetry in Alzheimer's disease. J Magn Reson Imaging 2004;19274- 282
PubMedArticle
26.
Grossman  MMcMillan  CMoore  P  et al.  What's in a name: voxel-based morphometric analyses of MRI and naming difficulty in Alzheimer's disease, frontotemporal dementia and corticobasal degeneration. Brain 2004;127628- 649
PubMedArticle
27.
Thompson  AJMontalban  XBarkhof  F  et al.  Diagnostic criteria for primary progressive multiple sclerosis: a position paper. Ann Neurol 2000;47831- 835
PubMedArticle
28.
Sastre-Garriga  JIngle  GTChard  DT  et al.  Grey and white matter volume changes in early primary progressive multiple sclerosis: a longitudinal study. Brain 2005;1281454- 1460
PubMedArticle
29.
Ashburner  JFriston  K Multimodal image coregistration and partitioning: a unified framework. Neuroimage 1997;6209- 217
PubMedArticle
30.
Cardenas  VADu  ATHardin  D  et al.  Comparison of methods for measuring longitudinal brain change in cognitive impairment and dementia. Neurobiol Aging 2003;24537- 544
PubMedArticle
31.
Bakshi  RBenedict  RHBermel  RAJacobs  L Regional brain atrophy is associated with physical disability in multiple sclerosis: semiquantitative magnetic resonance imaging and relationship to clinical findings. J Neuroimaging 2001;11129- 136
PubMedArticle
32.
Sailer  MFischl  BSalat  D  et al.  Focal thinning of the cerebral cortex in multiple sclerosis. Brain 2003;1261734- 1744
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33.
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Rashid  WParkes  LMIngle  GT  et al.  Abnormalities of cerebral perfusion in multiple sclerosis. J Neurol Neurosurg Psychiatry 2004;751288- 1293
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PubMedArticle
Original Contribution
August 2006

Regional Gray Matter Atrophy in Early Primary Progressive Multiple SclerosisA Voxel-Based Morphometry Study

Author Affiliations

Author Affiliations: Institute of Neurology, University College of London, London, England.

Arch Neurol. 2006;63(8):1175-1180. doi:10.1001/archneur.63.8.1175
Abstract

Background  Gray matter (GM) atrophy has been reported in multiple sclerosis (MS). However, little is known about its regional distribution.

Objective  To investigate the regional distribution of GM atrophy in clinically early primary progressive MS (PPMS).

Design and Patients  Thirty-one patients with PPMS within 5 years of symptom onset (mean age, 43.2 years; median Expanded Disability Status Scale score, 4.5) and 15 healthy control subjects (mean age, 43.7 years) were studied. All subjects underwent a 3-dimensional inversion-recovery fast spoiled gradient-recalled echo sequence that was repeated after 1 year in patients only. Magnetic resonance images underwent an optimized voxel-based morphometric analysis that segments magnetic resonance data volumes in a normalized space and quantifies tissue atrophy on a voxel-by-voxel basis. A lesion mask was created for each patient and used in normalization and segmentation steps to minimize bias from lesions. A multisubject design was used in the cross-sectional study to compare patients with PPMS and controls. A 1-way analysis of variance (within-subjects) design was used in the longitudinal study.

Results  At baseline, patients with PPMS displayed bilateral thalamic atrophy compared with controls. In addition, a significant association between lesion load and decreased GM volume was found for the thalami. Loss of GM in the putamen, caudate, thalami, and cortical and infratentorial areas was observed in patients after 1 year of follow-up.

Conclusions  Atrophy is most obvious in deep GM in clinically early PPMS. This may reflect increased sensitivity of these regions to neurodegeneration. Cortical and infratentorial atrophy developed as the disease evolved.

Multiple sclerosis (MS) is described as a chronic demyelinating disease of the central nervous system, affecting white matter (WM). However, pathological1,2 and magnetic resonance (MR) imaging37 studies demonstrate that it also involves cortical and deep gray matter (GM). A reduction in brain and specifically GM volumes has been detected in MS,813 even in the early stages of the disease,1113 but it is not known whether some GM areas are more susceptible to volume loss than others. Knowledge of the potential differential susceptibility to GM loss may help explain the clinical presentation and improve our understanding of the clinicoradiological dissociation present in some patients with MS,14 especially in the subtype that is the focus of this study, primary progressive MS (PPMS). The assessment of regional atrophy could also be useful in monitoring disease progression, by identifying areas more sensitive to volume loss, and in the planning of clinical trials. A recent study has reported on the spatial evolution of brain atrophy in patients with MS according to their clinical phenotype,15 and another one has reported the regional GM loss in relapsing-remitting MS,16 but we are unaware of any such study in patients in the early stages of the disease.

Different computational methods have been used in MS to assess tissue-specific brain atrophy (GM, WM, and cerebrospinal fluid [CSF] volumes).1720 Voxel-based morphometric (VBM) analysis is an accurate method that includes segmentation of brain volumes into GM, WM, and CSF, normalization to a standard space, and quantification of GM atrophy on a voxel-by-voxel basis.2126 However, its application to MS has been limited owing to the bias introduced by the presence of WM lesions. To overcome this limitation, we have applied to high-resolution structural MR images an in-house modified version of the protocol, tailored to deal with MR images of patients with MS.

METHODS
PATIENTS AND CONTROL SUBJECTS

Thirty-one patients with PPMS (definite or probable)27 in the early clinical stages (within 5 years of symptom onset) and 15 control subjects were recruited at the National Hospital for Neurology and Neurosurgery, London, England (same cohort studied by Sastre-Garriga et al28). The control group matched the patient group for age and sex, and no controls had a history of neurological or psychiatric illness. One patient had undergone a course of oral corticosteroid therapy 3 months before the second scan, and no patient was receiving disease-modifying drugs. The ethics committee of the National Hospital for Neurology and Neurosurgery and the Institute of Neurology, London, approved the study, and all subjects gave their written informed consent according to the Helsinki declaration.

MR IMAGE ACQUISITION

We acquired MR images using a 1.5-T scanner (GE Signa; General Electric, Milwaukee, Wis). A 3-dimensional (3D) inversion-recovery fast spoiled gradient-recalled echo sequence (FSPGR) (124 contiguous axial slices; echo time, 4.2 milliseconds; repetition time, 13.3 milliseconds; inversion time, 450 milliseconds; number of excitations, 1; field of view, 300 × 225 mm over an image matrix of 256 × 160 [interpolated to a 256 × 256 reconstructed matrix for a final x-y in-plane resolution of 1.17 × 1.17 mm]; slice thickness, 1.5 mm) was acquired on all subjects at baseline and after 1 year of follow-up only in the patients with PPMS. A dual spin-echo sequence (28 contiguous slices; echo time, 30/80 milliseconds; repetition time, 1720 milliseconds; number of excitations, 0.75; slice thickness, 5 mm) was acquired on all patients at baseline.

MR IMAGING DATA ANALYSIS AND VBM PREPROCESSING

We analyzed MR images for all subjects on a commercially available workstation (Sun Blade 150; Sun Microsystems Inc, Mountain View, Calif).

CREATION OF MS LESION MASKS

First, MS lesions were outlined on the FSPGR scans (to calculate 3D binary masks) and proton density scans (to calculate lesion loads) using a semiautomatic local thresholding contour technique (Dispimage; DL Plummer, University College London, London). Next, a 3D binary MS lesion mask for every patient was created by nulling the signal within the lesion outlines and setting the remaining to 1. The next stages of image processing were obtained using SPM2 software (Wellcome Department of Cognitive Neurology, University College of London [http://www.fil.ion.ucl.ac.uk/spm]) running under MATLAB, version 6.5 (Mathworks Inc, Natick, Mass). The images were processed according to a customized version of the optimized VBM protocol22 (http://dbm.neuro.uni-jena.de/vbm/), modified to reduce the influence of MS lesions in the process, which could alter the normalization and segmentation procedures.

CREATION OF CUSTOMIZED TEMPLATES

According to the VBM-optimized protocol, we first generated 2 sets of customized whole-brain T1-weighted templates and GM, WM, and CSF prior probability maps. The first set was obtained from the overall population (patients and controls) at baseline, whereas the second was obtained from patients with PPMS only (patients at baseline and after 1 year of follow-up). The creation of customized templates was done to reduce scanner-specific and population-specific biases.

This template creation has been described elsewhere22; however, because our protocol was slightly modified owing to the presence of WM lesions, we briefly summarize herein the steps involved. All of the FSPGR images were spatially normalized into the same stereotactic space using the Montreal Neurological Institute (MNI) template, and resliced to 1 × 1 × 1 isotropic voxels. The normalization was performed by first estimating the optimum 12-variable affine transformation for matching images and then optimizing the normalization using 16 nonlinear iterations.29 The patients' MR images were weighted by the corresponding 3D MS lesion masks. In our case, we assigned a weight of 0 to lesions and 1 to the rest of the brain, and therefore voxels within lesions were not considered during the normalization procedure. The images were then segmented into WM, GM, and CSF. Again, the 3D MS lesion masks, previously normalized using the same transformation variables as the FSPGR images, were later applied to GM and WM segments to remove any remaining lesion wrongly classified as either tissue. The normalized, whole-brain images were averaged, and the resulting mean was smoothed with an 8-mm full-width at half-maximum isotropic gaussian kernel to form a T1-weighted template.

The GM, WM, and CSF segments were also averaged and smoothed with an 8-mm gaussian kernel to create template images for the 3 classes of tissues. These 3 templates are used as prior images during image segmentation, and the GM template is also used as a target for image normalization.

NORMALIZATION AND SEGMENTATION FOR CROSS-SECTIONAL COMPARISON

The FSPGR original images were segmented in native space into GM, WM, and CSF images, using the customized WM, GM, and CSF prior maps. The extracted segmented GM images were normalized (using the same combination of affine and nonlinear functions as described in the “Creation of Customized Templates” subsection of the “Methods” section) to the customized GM template. As we mentioned, when dealing with patients' images, the 3D MS lesion masks were used to weight images during normalization and to remove any remaining lesion wrongly classified as either tissue. The optimized transformation variables obtained in this step were then applied to the original T1-weighted volumes in native space.

Finally, the optimally normalized T1-weighted images were segmented, again producing GM, WM, and CSF maps in MNI space. Again, normalized 3D MS lesion masks were used to remove MS lesions from these final maps. To preserve the total within-voxel volume, which may have been affected by the nonlinear transformation, every voxel signal intensity in the segmented WM and GM images was multiplied by the Jacobian determinants derived from the spatial normalization.22 The analysis of these modulated data tests was used for regional differences in absolute tissue volume. In this final step, all images were smoothed using a 12-mm full-width at half-maximum gaussian kernel.

NORMALIZATION AND SEGMENTATION FOR LONGITUDINAL COMPARISON

A procedure similar to that described in the preceding section was performed on baseline and follow-up MR images from patients only. In this case, however, we used an affine registration only in the normalization process, without nonlinear warping, because warping is known to reduce the sensitivity to subtle changes30 and use of only the affine registration was justified by assuming that the changes in brain shape during the follow-up period are expected to be subtle even in the presence of nonuniform atrophy.

STATISTICAL ANALYSIS

We used χ2 and unpaired t tests to assess and compare sex and age distribution in the 2 cohorts of subjects (controls and patients with PPMS). For these tests, a significance level of P<.05 was considered. Statistical analysis was performed with SPSS software, version 11 (SPSS Inc, Chicago, Ill).

Regional GM atrophy was assessed using SPM2 on a voxel-by-voxel basis. This analysis is based on the general linear model. For the cross-sectional analysis, a multisubject design with age and sex as nuisance variables was used to compare the modulated GM images of the patients with PPMS with those of the controls. For the longitudinal study, a 1-way analysis of variance (within-subjects) design was used to compare GM images of patients with PPMS at baseline and after 1 year of follow-up. We used P<.05, after correction for multiple comparisons with the familywise error method (to minimize type I error), for all statistical comparison. We retained only clusters with 100 or more contiguous voxels to obtain consistent results.26 We tested the association between the lesion load and the regional distribution of GM atrophy at baseline using a simple regression model with no covariates. Finally, the MNI coordinates corresponding to areas of significant differences were converted to Talairach coordinates and localized as a neuroanatomical area.

RESULTS

The demographic and clinical data of the patients with PPMS and the controls are shown in Table 1. There were no differences between the patients and controls in sex and age distribution.

CROSS-SECTIONAL ANALYSIS

In the cross-sectional analysis, compared with controls, patients with PPMS showed reduced GM volume bilaterally in several regions of the thalami (number of voxels, 1617; P<.001 [uncorrected]; z = 3.50; maximum coordinates [x, y, z], −6, −8, 1); particularly, the anterior, ventral anterior, medial dorsal, and ventral lateral left and right nuclei (Figure 1). However, no cluster of voxels survived after correcting for multiple comparisons. Moreover, a significant association between increased lesion load and reduced GM volume was found for the thalami, specifically in the anterior nuclei, and the association was still significant after multiple comparison correction (number of voxels, 893; P=.003 [corrected for multiple comparisons]; z = 4.95; maximum coordinates [x, y, z], 4, −9, 12).

LONGITUDINAL ANALYSIS

When considering the longitudinal changes (PPMS at baseline vs 1-year follow-up), we observed widespread significant reduction of GM volume at 1 year. This was seen in several deep GM areas (putamen, caudate, and thalami) and in some cortical and infratentorial areas (Figure 2 and Table 2).

COMMENT

The evaluation of the regional distribution of GM atrophy using MR techniques has the potential to improve our understanding of the neurodegenerative process in MS. Few studies have investigated regional GM changes15,3133 in MS, particularly in early PPMS. We used a VBM protocol specially designed to assess GM atrophy in patients with MS. This approach prevented voxel misclassification of MS lesions as GM, owing to similar signal intensities, in the segmentation process. In addition, given the dearth of information about local GM atrophy in patients with PPMS, the VBM method facilitated the search for GM volume changes without prior knowledge of its location. We report, in cross-sectional and longitudinal studies, a predominant involvement of deep GM in this clinical form of MS.

CROSS-SECTIONAL COMPARISON

In an initial approach with an uncorrected P value, we observed a reduction in the volume of the thalami in patients with PPMS in the early stage of the disease compared with controls. The change was most obvious in thalamic regions related with cognitive (anterior nuclei), motor (ventral anterior and ventral lateral nuclei), and sensory (medial dorsal nuclei) functions.34 Because the statistical significance was lost when correcting for multiple comparisons, the present findings should be considered with caution, and the possibility that they represent a false-positive result cannot be excluded. However, the extent of the area of abnormality and, more importantly, the fact that it is in agreement with previous diffusion, perfusion, and spectroscopy MR studies,3538 which have included patients with PPMS,38 suggest that our findings represent a real phenomenon. Abnormalities in the thalami of patients with clinically isolated syndromes suggestive of MS have also been detected by a voxel-based comparison of magnetization transfer ratios.33 In patients with relapsing-remitting MS, normalized thalamic volume has also been found to be reduced.36 The thalamus has extensive connections with other subcortical structures and the cerebral cortex, and it may be more sensitive to local inflammatory activity or to changes secondary to WM lesions.39 This would be supported by the significant (albeit moderate) association found in the present study between lesion load and thalamic atrophy. However, other mechanisms, such as secondary effects of damage in normal-appearing WM or a direct mechanism of injury to the thalamus, could also contribute to thalamic atrophy. Thus, we conclude that thalamic atrophy could represent an early stage in the development of GM atrophy in patients with PPMS.

In this study, we have not investigated the association between thalamic changes and specific clinical symptoms. Future studies that focus on patients with defined clinical presentations (pain, fatigue, cognitive impairment, or motor-sensory symptoms) should help to establish associations between GM regional atrophy patterns and clinical findings.

The lack of significant differences for any cortical region might be because atrophy of those areas is patchier and less consistent between patients, thus may not show up in group comparisons.

LONGITUDINAL COMPARISON

In 1 year, patients with PPMS showed a decrease in volume in several GM structures, particularly in the basal ganglia. Loss of GM volume was observed not only in thalamic or deep GM areas but also in many other regions such as cortical and infratentorial regions, suggesting ongoing widespread GM damage. The new areas were mainly related to motor control and cognitive function. This temporal evolution might indicate that, after an initial stage of focal deep GM atrophy, extensive GM damage develops as disease evolves. Although it is important to highlight that the effect of normal aging cannot be ruled out, because no longitudinal control data can be used for comparison, a previous study22 using similar methodology in 465 healthy adults showed that the thalamic areas, amygdala, hippocampus, and entorhinal cortex develop less GM atrophy with aging than other cortical and cerebellar regions. These data would strengthen the value of deep GM findings. However, the lack of longitudinal data in our control group does not allow for a direct comparison between GM loss in the PPMS and control populations. This is especially relevant in the case of cortical GM loss. Furthermore, the lack of control data at follow-up makes it impossible to account for any possible scanner drift over time. However, the scanner used for this study undergoes regular quality assurance tests, ensuring its stability during the follow-up time.

Because the mechanisms of axonal damage in MS are not well established, it is difficult to determine why the deep GM seems to be particularly at risk of developing atrophy. Deep GM enclosed by WM could be a more direct target of local, dying-back, or transsynaptic degeneration evoked by demyelination, at least in this form of the condition. Methodological aspects must also be considered because deep GM changes might be easy to detect and/or GM changes in the cortex might be more difficult to find owing to a patchy and inconsistent distribution between patients but widespread distribution of cortical atrophy.

In conclusion, our findings demonstrate predominant volume loss in the deep GM of patients with PPMS in the early stages of the disease and widespread GM atrophy during follow-up. Controlled longitudinal studies are required to confirm these findings, especially using different methodology and including patients with different disability patterns and other early forms of the condition over longer periods.

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Article Information

Correspondence: Alan J. Thompson, FRCP, Institute of Neurology, University College of London, 23 Queen Square, London WC1N 3BG, England (a.thompson@ion.ucl.ac.uk).

Accepted for Publication: March 10, 2006.

Author Contributions:Study concept and design: Sepulcre, Sastre-Garriga, Ingle, and Thompson. Acquisition of data: Sastre-Garriga and Ingle. Analysis and interpretation of data: Sepulcre, Sastre-Garriga, Cercignani, Miller, and Thompson. Drafting of the manuscript: Sepulcre and Sastre-Garriga. Critical revision of the manuscript for important intellectual content: Sepulcre, Sastre-Garriga, Cercignani, Ingle, Miller, and Thompson. Statistical analysis: Sepulcre and Sastre-Garriga. Obtained funding: Miller. Administrative, technical, and material support: Ingle. Study supervision: Sastre-Garriga, Miller, and Thompson.

Funding/Support: This study was supported by the Wellcome Trust (Dr Ingle), grant BEFI 02/9115 from the Spanish Ministry of Health (Dr Sastre-Garriga), and the MS Society of Great Britain and Northern Ireland.

Acknowledgment: We thank the subjects who kindly agreed to take part in this study and Ros Gordon and Christopher Benton, who performed MR imaging.

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