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 DOI: 10.1177/1352458512442756

published online 11 April 2012Mult SclerStefan J Borgwardt and Kerstin Bendfeldt

Achim Gass, Till Sprenger, Thomas E Nichols, Hugo Vrenken, Frederik Barkhof, Chris Polman, Ernst-Wilhelm Radue, Lukas Filli, Louis Hofstetter, Pascal Kuster, Stefan Traud, Nicole Mueller-Lenke, Yvonne Naegelin, Ludwig Kappos,

multiple sclerosisremitting and secondary progressive−Spatiotemporal distribution of white matter lesions in relapsing

  

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Multiple Sclerosis Journal0(0) 1 –8© The Author(s) 2012Reprints and permissions: sagepub.co.uk/journalsPermissions.navDOI: 10.1177/1352458512442756msj.sagepub.com

MULTIPLESCLEROSIS MSJJOURNAL

Introduction

Multiple sclerosis (MS) is a chronic inflammatory disease of the central nervous system, characterized by pathologic changes, including the presence of multifocal demyelinated lesions, axonal loss and atrophy. An estimated 87% of patients with MS are initially diagnosed with relapsing–remitting MS (RRMS).1 During disease progression, the majority of RRMS patients will change into a secondary progressive MS course (SPMS), which is characterized by progressive disability with fewer or no relapses. As shown in long-term follow-up studies, 42% to 58% of RRMS patients develop SPMS within two decades.1,2

Spatiotemporal distribution of white matter lesions in relapsing–remitting and secondary progressive multiple sclerosis

Lukas Filli1, Louis Hofstetter1, Pascal Kuster1, Stefan Traud1, Nicole Mueller-Lenke1, Yvonne Naegelin2, Ludwig Kappos2, Achim Gass2, Till Sprenger2, Thomas E Nichols3,4, Hugo Vrenken5, Frederik Barkhof5, Chris Polman5, Ernst-Wilhelm Radue1, Stefan J Borgwardt1 and Kerstin Bendfeldt1

AbstractBackground: Multiple sclerosis (MS) is a chronic inflammatory disease of the central nervous system. MS lesions show a typical distribution pattern and primarily affect the white matter (WM) in the periventricular zone and in the centrum semiovale.Objective: To track lesion development during disease progression, we compared the spatiotemporal distribution patterns of lesions in relapsing–remitting MS (RRMS) and secondary progressive MS (SPMS).Methods: We used T1 and T2 weighted MR images of 209 RRMS and 62 SPMS patients acquired on two different 1.5 Tesla MR scanners in two clinical centers followed up for 25 (± 1.7) months. Both cross-sectional and longitudinal differences in lesion distribution between RRMS and SPMS patients were analyzed with lesion probability maps (LPMs) and permutation-based inference.Results: MS lesions clustered around the lateral ventricles and in the centrum semiovale. Cross-sectionally, compared to RRMS patients, the SPMS patients showed a significantly higher regional probability of T1 hypointense lesions (p≤0.03) in the callosal body, the corticospinal tract, and other tracts adjacent to the lateral ventricles, but not of T2 lesions (peak probabilities were RRMS: T1 9%, T2 18%; SPMS: T1 21%, T2 27%). No longitudinal changes of regional T1 and T2 lesion volumes between baseline and follow-up scan were found.Conclusion: The results suggest a particular vulnerability to neurodegeneration during disease progression in a number of WM tracts.

KeywordsMultiple sclerosis, MRI, lesion probability maps, white matter, relapsing–remitting, secondary progressive, T1 hypointense lesions, T2 hyperintense lesions

Date received: 28th December 2011; revised: 13th February 2012; accepted: 19th February 2012

1Medical Image Analysis Center, University Hospital Basel, Switzerland.2Department of Neurology, University Hospital Basel, Switzerland. 3University of Warwick, UK. 4Department of Statistics and Warwick Manufacturing Group, University of Warwick, UK. 5VU University Medical Center, The Netherlands.

Corresponding author:Kerstin Bendfeldt, Medical Image Analysis Center, University Hospital Basel, CH-4031 Basel, Switzerland. Email: kerstin.bendfeldt@unibas.ch

442756 MSJ0010.1177/1352458512442756Filli et al.Multiple Sclerosis Journal2012

Research Paper

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Over the past decades, neuroimaging techniques have been extensively employed to assess MS lesions. MS lesions of the white matter (WM) appear as T2 hyperin-tense regions when investigated with magnetic resonance imaging (MRI). Such hyperintensities are nonspecific regarding histopathology and can represent inflammation, edema, remyelination, gliosis, or axonal loss.3 Initially, up to 80% of the lesions are hypointense on T1-weighted images.4 Most of them become isointense after several months, whereas their persistence as hypointense lesions (20–30%) might indicate severe tissue damage5 and occurs more frequently in SPMS.6

MS lesions feature a typical distribution pattern in the WM of the brain and primarily affect the periventricular zone, the callosal body and the peripheral parts of the gyri.7,8 The high amount of veins in these areas and the possible dif-fusion of pathogenetic factors from the cerebrospinal fluid due to blood-brain barrier disturbances have been suggested as possible reasons for this.9 The total T1 lesion volume relates to some degree to disease duration and Expanded Disability Status Scale (EDSS) in both RRMS and SPMS patients.10 Due to longer disease duration, SPMS patients generally have a higher total T1 lesion load and T1/T2 ratio and suffer from greater clinical impairment compared to RRMS patients, e.g. regarding memory impairment.11–13 A recent study revealed associations of clinical and MRI parameters in RRMS and SPMS: 1) EDSS correlated with bilateral periventricular lesion location; 2) sensory and coor-dination deficits correlated with lesion accumulation in the thalamus; 3) decreased verbal fluency correlated with left parietal lesions comprising the putative location of the supe-rior longitudinal fascicle.14 However, clinically observed dis-ease progression generally correlates only poorly with MRI findings in individuals and potential reasons are underesti-mation of MRI invisible brain damage, lack of histopatho-logical specificity of MRI findings and compensatory functional and structural plasticity of the brain.15

There is little knowledge about the spatiotemporal lesion distribution during disease progression in MS. Previously, we showed that the probability of T2 lesions significantly increased during one year follow-up in a number of WM tracts, e.g. the corticospinal tract, callosal body, inferior lat-eral fascicle, in RRMS patients with progressive lesion vol-umes as compared to patients without progressive lesion volumes.16 In the present study, we compared the specific spatiotemporal lesion distribution patterns of cerebral T1 and T2 lesions in RRMS and SPMS patients both cross-sectionally and longitudinally. We aimed at clarifying which tracts or fascicles are significantly affected by MS lesions during disease progression. To characterize lesion distribution in vivo, we created lesion probability maps (LPMs) which have been used before in a number of MRI studies of MS.14,16–18

Based on the literature,10,14,16 we hypothesized that WM lesion distribution would display regional differences

between RRMS and SPMS patients and show higher lesion probability in SPMS. In particular, we hypothesized that the distribution patterns of T1 and T2 lesions would differ between time points and between the subtypes of disease. Furthermore, we expected that T2 lesions would be distrib-uted in a more widespread way than T1 lesions and that both T1 and T2 lesions would be more widespread in SPMS compared to RRMS. Longitudinally, we expected to find a significant increase of T2 lesion load (as a marker for inflammation) in RRMS compared to SPMS and a signifi-cant increase of T1 lesion load (as a marker for neurode-generation) in SPMS compared to RRMS.

Materials and methods

Study population

The design of the GeneMSA study (case-controlled study for genotype-phenotype associations in MS; Glaxo-SmithKline, UK) has been described in detail elsewhere.16,19 The present samples partly overlap with samples from previous studies by our group. We analyzed T1- and T2-weighted MR images from Caucasian RRMS and SPMS patients of the GeneMSA study recruited and scanned at two clinical centers participating in the GeneMSA consortium. All patients underwent MR scans twice (time between base-line and follow-up scan: 25 ± 1.7 months).

Patients with a clinical relapse or glucocorticosteroid treatment within the month before the baseline or follow-up scan were excluded, whereas the concomitant use of dis-ease-modifying therapies for MS was permitted. Our final study population consisted of 271 patients (center 1: n=106 RRMS, 31 SPMS; center 2: n=103 RRMS, 31 SPMS). One hundred and twenty patients (center 1: 60 RRMS, 17 SPMS; center 2: 36 RRMS, 7 SPMS) received immu-nomodulatory-immunosuppressive drugs (interferon-β-1a, interferon-β-1b, glatiramer acetate) during the entire study; no change of medication occurred between baseline and the follow-up scan at two years. The study was approved by the local ethics committee and written informed consent was obtained from each subject.

MR image acquisition and image processing

All MR images were acquired with one out of two 1.5 Tesla MR scanners (center 1: MAGNETOM Avanto; center 2: MAGNETOM Vision; Siemens, Erlangen, Germany) with similar protocols. Both centers used identical MR parame-ters (T2-weighted images: TE 14 ms, TR 3980 ms, flip angle 180°, turbo factor 7, matrix 192x256, field-of-view 18.75x25 cm2, voxel dimensions 0.98x0.98x3.0 mm3; T1-weighted images: echo time (TE) 8.1 ms, repetition time (TR) 400 ms, matrix 192x256, field-of-view 18.75x 25 cm2, voxel dimensions 0.98x0.98x3.0 mm3).

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Filli et al. 3

The MR images were processed with the SPM5 soft-ware (Statistical Parametric Mapping, version 958, Wellcome Department of Imaging Neurosciences, University College, London, www.fil.ion.ucl.ac.uk/spm), running under MATLAB 7.00.To create LPMs, lesions were outlined at voxel level by one of four independent observers by means of the AMIRA software (version 3.1.1, Mercury Computer Systems Inc., Massachusetts, USA) with a high inter-rater reliability (intraclass correlation coefficient=0.99, p=0.001). Each image was individually registered from native space to a template in standard space (Montreal Neurological Institute (MNI); MNI152_T1_1mm_brain, 1x1x1 mm3/voxel;) using a linear transfor-mation. For this purpose, we used the FMRIB Linear Image Registration Tool (FLIRT) function (settings: 12-parameter, correlation ratio, trilinear interpolation) which is imple-mented in FSL (version 4.1, FMRIB Software Library, University of Oxford, England) and has already been used in other studies to normalize brain volumes.20,21 Registration accuracy was assessed by displaying axial slices of each subject with edges from the MNI atlas image.

LPMs

All subjects’ scans were averaged and thresholded at 0.05 to create an analysis mask. Thus, a voxel was included in the analysis only if at least 5% of the patients had a lesion at that voxel. To account for the neuropathological features of MS with higher tissue damage in the center of lesions and anatomical variation unaccounted for by the linear inter-subject registration, we smoothed all images with an iso-tropic Gaussian filter (5 mm, full width at half maximum). To look for significant differences between LPMs while accounting for the non-normality of the positive-only data, we used ‘randomise’ (version 2.1) implemented in FSL to perform permutation-based inference.22 The number of per-mutations was set at 5,000 for all comparisons. To account for the neuropathological features of MS with higher tissue damage in the center of lesions, we smoothed all images with an isotropic Gaussian filter (5 mm, full width at half maximum). We used unpaired T-tests to compare LPMs of different patient groups cross-sectionally. For longitudinal comparisons, we used paired T-tests to compare the base-line and follow-up LPMs of each group.16 To look for sta-tistically significant differences, we used threshold-free cluster enhancement (TFCE) and a statistical significance level of α=0.05%.

Statistical analysis of demographic data

Statistical analysis of clinical data was performed with SPSS (Statistical Package for the Social Sciences, version 18, SPSS Inc., Chicago, USA). To compare demographic variables, we used one-way analysis of variance (ANOVA), Mann-Whitney-U, Wilcoxon, and Chi-squared

tests. For all comparisons, a significance level of p<0.05 was considered.

Results

Demographic data

The demographic, clinical and MRI characteristics of the dif-ferent patient groups are reported in Table 1. Cross-sectionally, within the subtypes of disease, subjects from both centers did not differ significantly with respect to age, disease duration, T1 and T2 lesion load neither at baseline nor at follow-up. Within each center, total T1 lesion load at the time of the baseline scan was significantly higher in SPMS patients as compared to RRMS patients (p<0.05), whereas total T2 lesion load was similar. The RRMS group of center 2 consisted of significantly more male patients than the RRMS group of center 1, the opposite was found for the two SPMS groups (p<0.05). RRMS and SPMS patients from center 2 both showed a significantly higher EDSS value compared to the related group from center 1 (p<0.05).

Cross-sectional comparison of lesion distribution in RRMS and SPMS

MS lesions predominantly occurred around the lateral ven-tricles and in the centrum semiovale. Both of the RRMS and the SPMS groups showed high probabilities for T2 lesions in the callosal body, the corticospinal tract, and other tracts adjacent to the occipital horn of the lateral ventricles.

Figure 1 (T1 lesions) and Figure 2 (T2 lesions) show representative axial slices of the different LPMs with MS lesions predominantly affecting the periventricular region. For illustrative purposes, we overlayed the LPMs on an MNI template derived from 152 individuals. A lower threshold was set at 0.05 for all LPMs so that only voxels with a lesion probability of at least 5% are shown.

Clusters showing a significant difference of T1 lesion probability between RRMS patients and SPMS patients are listed in Table 2. We only report clusters with at least 50 voxels to exclude potential artifacts. Most of the T1 lesion clusters indicated higher lesion probability in the SPMS group. The latter was true in particular for two larger clus-ters around the occipital horns of the lateral ventricles. We did not find, however, any significant regional differences in the spatial distribution of T2 lesions (best family-wise error (FWE)-corrected P-value=0.06).

Longitudinal comparison of lesion distribution in RRMS and SPMS

We did not find any significant longitudinal changes of regional T1 and T2 lesion volumes between baseline and follow-up using permutation-based analysis of the LPM

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data. The clusters with the lowest p-value regarding lesion probability in the different groups between baseline and follow-up scan were found in the callosal body (center 1: p=0.23, center 2: p=0.53), superior longitudinal fascicle (center 2: p= 0.23) in RRMS, and in the callosal body (center 1: p=0.29, center 2: p=0.53) in SPMS.

Comparison of study centers

Comparing the different LPMs, we did not find any signifi-cant differences in T1 and T2 lesion distribution within the RRMS and SPMS groups between the two study centers (best FWE-corrected P-value=0.23).

Discussion

Distribution of WM lesions in RRMS and SPMS

In order to improve our understanding of lesion develop-ment during MS progression, we studied the spatiotemporal

distribution pattern of WM lesions in RRMS patients and SPMS patients. In our relatively large study population, T1 hypointense lesions, which indicate severe tissue damage in MS,4 predominantly affected the WM around the occipital horns of the lateral ventricles, e.g. the callosal body, the cor-ticospinal tract and the forceps major. In particular, the periventricular clusters indicated a significantly higher T1 lesion probability in SPMS patients as compared to RRMS patients. These findings are driven by both study centers (see Table 2), and suggest that the periventricular zone is more susceptible to severe tissue damage and potentially axonal injury than other WM areas. The potential result of dysfunction of interhemispheric communication could partly explain evidence for poor recovery from motor and cognitive disability observed in MS patients.

A number of possible reasons for the periventricular emphasis of lesion distribution have been mentioned, such as the high amount of veins in these areas or possible diffu-sion of pathogenetic factors from the cerebrospinal fluid.9 In contrast to our results, a lack of spatial lesion distribution differences between RRMS patients and SPMS patients has

Table 1. Demographics and clinical characteristics of the different patient groups.

Group 1 (Center 1 RRMS)

Group 2 (Center 1 SPMS)

Group 3 (Center 2 RRMS)

Group 4 (Center 2 SPMS)

Groups with significant difference1

Number of patients n=106 n=31 n=103 n=31 Age at bs, mean (SD) years 43.1 (10.7) 53.4 (8.2) 43.6 (9.3) 52.1 (7.4) 1/2 (p=0.03)

3/4 (p=0.04)Male/female (ratio) 30/76 (1:2.5) 15/16 (1:1.1) 39/64 (1:1.64) 13/18 (1:1.4) 1/2 (p=0.01)

1/3 (p=0.01)3/4 (p=0.01)2/4 (p=0.01)

Disease duration since first symptoms at bs, median (IQR) years

10.0 (12.3) 19.0 (14.0) 8.0 (9.0) 19.0 (13.0) 1/2 (p=0.03)3/4 (p=0.02)

EDSS at bs, median (IQR) 2.3 (1.5) 5.0 (2.0) 3.0 (2.0) 6.0 (2.0) 1/2 (p=0.01)1/3 (p=0.03)3/4 (p=0.01)2/4 (p=0.04)

EDSS change (fu vs. bs), mean (p value) +0.1 (0.18) +0.4 (0.01) +0.3 (0.04) +0.5 (0.01) 1/2 (p=0.01)Drug treatment, % of patients 2 56.6% 54.8% 34.9% 22.6% 1/2 (p=0.05)

1/3 (p=0.01)3/4 (p=0.01)2/4 (p=0.01)

T1 lesion load at bs, median (IQR) cm3 0.88 (2.47) 1.95 (5.72) 0.53 (2.27) 1.39 (6.42) 1/2 (p=0.02)3/4 (p=0.03)

T1 lesion volume increase (fu>bs), p value 0.06 0.86 0.04 0.38 NoneT2 lesion load at bs, median (IQR) cm3 3.17 (6.51) 3.51(10.49) 2.97 (6.92) 4.79 (13.94) NoneT2 lesion volume increase (fu>bs), p value 0.02 0.25 0.02 0.68 NoneT1/T2 ratio at bs: mean (SD) 0.28 (0.16) 0.41 (0.15) 0.30 (0.41) 0.39 (0.22) 1/2 (p=0.02)

3/4 (p=0.04)

1All comparisons between two groups; no comparison was drawn between groups 1/4 and 2/3, because they differed in center as well as in disease subtype.2No changes in medication during follow-up; treatment with interferon-β-1a, interferon-β-1b and glatiramer acetate for at least 12 months.bs: baseline scan; EDSS: Expanded Disability Status Scale; fu: follow-up scan (2 years); IQR: inter-quartile range; RRMS: relapsing–remitting multiple sclerosis; SPMS: secondary progressive multiple sclerosis

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Figure 1. Lesion probability maps of T1 hypointense lesions of the different patient groups at baseline overlayed on an MNI template in standard space (no smoothing applied).MNI: Montreal Neurological Institute; RRMS: relapsing–remitting multiple sclerosis; SPMS: secondary progressive multiple sclerosis.

Figure 2. Lesion probability maps of T2 hyperintense lesions of the different patient groups at baseline overlayed on an MNI template in standard space (no smoothing applied).MNI: Montreal Neurological Institute; RRMS: relapsing–remitting multiple sclerosis; SPMS: secondary progressive multiple sclerosis.

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been recently reported, suggesting that focal pathology affects similar regions in both subgroups.23 In that study however, patients with MS were significantly more likely, than those with clinically isolated syndrome (CIS) and an abnormal scan 20 years after onset, to have T1 and T2 lesions in the corona radiata, optic radiation, and splenium of the corpus callosum (periventricular) and T2 lesions in the right fronto-occipital fasciculus. Furthermore, MS patients with EDSS value>3, compared to those with EDSS value≤3, were more likely to have optic radiation and left internal capsule T2 lesions. The fact that, in our study, SPMS patients belong to a very disabled group, with a median disease duration of 19 years and showing a median EDSS value of 5–6, might explain the regional differences between RRMS patients and SPMS patients that we found and thus the differences between both studies.

The comparison of lesion types revealed that T2 hyper-intense lesions, which are thought to mainly represent inflammation and demyelination,3 were distributed in a more widespread fashion than T1 hypointense lesions. Nevertheless, the statistical comparison did not reveal any significant differences regarding T2 lesion distribution between the subtypes of disease, indicating that T2 lesions and their volumes are relatively stable once patients are at an advanced or progressive stage of disease. In the same lesion however, different pathological changes may occur simultaneously e.g. demyelination and remyelination. Conventional MRI measurements only represent weighted

averages of different biological and pathological factors24 and MRI signal changes reflect underlying neuropathology only indirectly and often non-specifically.25 Novel imaging techniques may help to disentangle this issue in the future: magnetization transfer imaging (MTI) is increasingly used to characterize the evolution of MS lesions and normal-appearing brain tissue. The evidence suggests that the dynamics of MTI changes correlate with the extent of demyelination and remyelination.26

Longitudinal comparison of WM lesion distribution

Using LPMs, regional T1 or T2 lesion probability did not change during follow-up both in the RRMS and SPMS groups. In the previous LPM-study, however, we found significant changes of WM lesion distribution in a number of tracts in an even smaller sample of RRMS patients (n=45) with progressive lesion volumes.16 In order to pro-vide for larger sample sizes and to improve statistical power in this study we gave up the division of the patient groups into progressive and non-progressive subgroups. Thus, statistical power in the current analysis of 106 (center 1) and 103 (center 2) RRMS patients should have been sufficient for the detection of lesion development. Although total T2 lesion volumes significantly increased between baseline and follow-up scans in the RRMS groups different pathological processes, such as demyelination

Table 2. Clusters with significant difference (family-wise error (FWE)-corrected p<0.05) of T1 lesion probability between patient groups at 'baseline' scan.

Compared groups1 Hemi-sphere Cluster size2 (mm3) Projection, commissural, and association fibres (Jülich Histological and JHU WM Tractography Atlas)

p value (FWE-corrected)

Center 1 only SPMS > RRMS right 517 callosal body, optic radiation 0.01SPMS > RRMS left 470 SLF, ILF 0.01SPMS < RRMS right 214 callosal body, corticospinal tract, SOF 0.04SPMS < RRMS left 114 callosal body, ILF, inferior fronto-occipital fascicle 0.04Center 2 only SPMS > RRMS right 3266 optic radiation, callosal body, ILF 0.01SPMS > RRMS left 921 callosal body, SLF, forceps major 0.01SPMS > RRMS left 420 corticospinal tract, callosal body, SOF 0.01Center 1 + 2 SPMS > RRMS left 2981 callosal body, forceps major, ILF 0.01SPMS > RRMS right 2879 callosal body, forceps major, ILF 0.01SPMS > RRMS right 327 callosal body, anterior thalamic radiation 0.03SPMS > RRMS right 213 callosal body, anterior thalamic radiation, forceps

minor0.03

SPMS > RRMS left 183 callosal body, SOF 0.02SPMS > RRMS left 127 corticospinal tract, callosal body, SOF 0.01

1RRMS > SPMS: higher probability in RRMS group; SPMS > RRMS: higher probability in SPMS group.2We only took account of clusters with at least50 voxels to exclude potential artifacts.ILF: inferior longitudinal fascicle; RRMS: relapsing–remitting multiple sclerosis; SLF: superior longitudinal fascicle; SOF: superior occipitofrontal fascicle; SPMS: secondary progressive multiple sclerosis

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Filli et al. 7

and remyelination, may have compensated for each other at the level of the individual lesion. Furthermore, medical treatment may also have limited the power of the RRMS analysis.27

In progressive MS, other mechanisms than lesion accu-mulation are thought to contribute to disease progression. Regional atrophy measures, in particular those of the grey matter, may more accurately reflect the neurodegenerative process28 than do T1 lesions. In line with this, in the present study, total WM lesion volumes did not change during fol-low-up in the SPMS groups indicating that some of the pro-gressive changes which play a role earlier in disease in the SPMS groups had already reached a plateau.

In general, it is known that clinically observed disease progression correlates poorly with conventional MRI find-ings. An underestimation of brain damage by conventional MRI and a lack of histopathological specificity of MRI findings15 have been discussed as possible reasons. Recent studies however, showed a relationship of the total T1 lesion volume to the EDSS score in both RRMS patients and SPMS patients10 and a correlation of EDSS score with periventricular lesion location.14,23 In the present study, the EDSS score increased significantly between baseline and the follow-up scan in three out of four patient groups (SPMS patients of both centers, RRMS patients of center 2), while two of the groups (SPMS patients of both centers) did not show any significant increase of total T1 and T2 lesion load. Conversely, T2 lesion load increased signifi-cantly in the fourth group (RRMS patients of center 1), while their EDSS value remained nearly stable. One possi-ble explanation is that the EDSS value does not reflect lesions in important WM fibres well, e.g. in the callosal body (cognitive functions) and in the occipital WM (e.g. color vision).

Methodological issues and limitations

In this study, we used an established voxel-wise statistical non-parametric mapping technique to compare regional WM lesion probability changes. A number of studies have used voxel-wise techniques to compare lesion distributions across populations before and have shown LPMs to be a powerful tool for studying lesion distribution in vivo.14,17,18,20,27,29–3

The demographics of patients from center 1 and center 2 showed significant differences (p < 0.05) only regarding the EDSS value (higher in center 2 for both RRMS and SPMS patients) and the male/female-ratio (RRMS: more male patients in center 1; SPMS: more female patients in center 2). There were no significant differences within the RRMS and SPMS groups between the LPMs of the study centers, probably due to similar scanner types, acquisition protocols and scan parameters, which suggests an excellent comparability of data derived from the two MR scanners, as shown previously.33

The following limitations may have influenced the results of our study. Disease progression coincides with ventricular enlargement due to brain atrophy34 which might eventually influence the brain normalization process (as this uses linear registration)and subsequently distort the distribution of periventricular lesions and increase scatter-ing of lesions. However, we carefully assessed the normali-zation process visually and could not find any extreme distortions. We did not account for differences in medica-tion between the different patient groups. Medication might have possibly influenced lesion load between groups.27,35 This is not very likely in our study because most patients in the groups underwent immunomodulatory treatment for a long time period and no change of medication occurred between baseline and follow-up.

Conclusion

Severe tissue damage predominantly affects the WM around the occipital horns of the lateral ventricles, which seems to be of increasing importance during disease pro-gression. Hence, the statistical evaluation of such periven-tricular lesions might improve prediction of individual MS progression. A complementary approach to the LPMs would be a spatial correlation analysis between brain vox-els in order to know the internal patterns of lesional voxel covariations.36,37 Future studies which investigate the local and distant patterns of voxels that are coupled together in MS pathology will bring further insights into MS spati-otemporal distribution patterns and help to disentangle why some brain regions are more susceptible to severe tissue damage than others.

Acknowledgements

The authors wish to thank the Head of GlaxoSmithKline Clinical Imaging Centre, Professor Paul Matthews, and the Swiss MS Society. They also acknowledge Peter Schaerli, Danilo Marzetti, Petra Huber and Markus Colussi for their expert technical assistance.

Funding

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

Conflict of interest statement

The authors declare that there is no conflict of interest.

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