J Neural Transm DOI 10.1007/s00702-015-1406-4
NEUROLOGY AND PRECLINICAL NEUROLOGICAL STUDIES - ORIGINAL ARTICLE
Putaminal alteration in multiple sclerosis patients with spinal cord lesions Hilga Zimmermann1 • Hans O. Rolfsnes1 • Swantje Montag1 • Janine Wilting1 Amgad Droby1 • Eva Reuter1 • Joachim Gawehn2 • Frauke Zipp1 • Adriane Gro¨ger1
•
Received: 19 December 2014 / Accepted: 4 May 2015 Ó Springer-Verlag Wien 2015
Abstract Typical multiple sclerosis (MS) lesions occur in the brain as well as in the spinal cord. However, two extreme magnetic resonance imaging phenotypes appear occasionally: those with predominantly spinal cord lesions (MS ? SL) and those with cerebral lesions and no detectable spinal lesions (MS ? CL). We assessed whether morphological differences can be found between these two extreme phenotypes. We examined 19 patients with MS ? SL, 18 with MS ? CL and 20 controls. All subjects were examined using magnetic resonance imaging, including anatomical and diffusion tensor imaging sequences. Voxel-based morphologic and regions of interestbased analyses and tract-based spatial statistics were performed. Patients also underwent neuropsychological testing. Demographic, clinical and neuropsychological characteristics did not differ between MS ? SL and MS ? CL patients. Patients with MS ? SL showed significantly larger putamen volumes than those with MS ? CL which correlated negatively with disability. Compared to controls, only MS ? CL revealed clear cortical and deep gray matter atrophy, which correlated with
F. Zipp and A. Gro¨ger contributed equally to this work. The work presented in this paper was part of the doctoral thesis of Swantje Montag. & Adriane Gro¨ger [email protected] 1
Department of Neurology and Neuroimaging Center (NIC) of the Focus Program Translational Neuroscience (FTN), University Medical Center of the Johannes GutenbergUniversity Mainz, Langenbeckstraße 1, 55131 Mainz, Germany
2
Institute of Neuroradiology, University Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany
cerebral lesion volume. Additionally, extensive white matter microstructural damage was found only in MS ? CL compared to MS ? SL and controls in the tractbased spatial statistics. Higher putamen volumes in MS ? SL could suggest compensatory mechanisms in this area responsible for motor control. Widely reduced fractional anisotropy values in MS ? CL were caused by higher cerebral lesion volume and thus presumably stronger demyelination, which subsequently leads to higher global gray matter atrophy. Keywords
Multiple sclerosis VBM TBSS Putamen
Introduction Multiple sclerosis (MS) is a heterogeneous disease characterized by inflammation, demyelination, and subsequent axonal degeneration and neuronal loss, which occurs in both the brain and spinal cord and leads to a broad spectrum of symptoms. Whereas spinal cord lesions (SL) often lead to paralysis with sensory disturbances or incontinence, cerebral lesions (CL) may lead to hemiparesis, dysarthria and deficits in balance as well as cognitive impairments. In the 1990s, the spinal cord had already been attributed a distinct role in disability and the course of MS (Filippi et al. 1996; Losseff et al. 1996), and today, the spinal cord is one of the four relevant areas for diagnosing MS (Polman et al. 2011). The early occurrence of SL has been found to predict a worse prognosis of MS (Coret et al. 2010), e.g., it increases the risk of conversion from a preform of MS to clinically definite MS. In the beginning of the disease, most patients show relapses with clinical deficits that persist for days and up to weeks, usually followed by remission of symptoms; this is
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the relapsing–remitting MS subtype (RRMS) (Vukusic and Confavreux 2003). Within 15 years, more than half of these RRMS patients develop a progressive disease course referred to as secondary progressive MS (SPMS) (Weinshenker et al. 1989). Magnetic resonance imaging (MRI) is extensively used in the diagnosis and management of MS. MRI findings differ in lesion localization and development. In some MS patients, lesions occur mainly in the brain and not in the spinal cord (MS ? CL) and in other patients the spinal cord is primarily affected and only few cerebral lesions are found (MS ? SL). The clinical presentation of these patients may differ due to the affected region of the central nervous system. Voxel-based morphometry (VBM) is a neuroimaging analysis technique commonly used to investigate differences in brain anatomy through a statistical voxel-wise comparison. Alternatively, region-specific volumes of the brain can also be compared by drawing regions of interest (ROIs) on MRI scans and calculating the enclosed volume. Tract-based spatial statistics (TBSS) allow the voxel-wise comparison of neural fiber tracts across brains after defining these tracts by diffusion tensor modeling using data collected in diffusion tensor imaging (DTI). The goal of this study was to use VBM and ROI-based analyses as well as TBSS to assess whether morphological differences can be found between MS patients with these two extreme MRI phenotypes: MS ? SL and MS ? CL.
based on the presence of SL or CL. All patients were diagnosed according to the new McDonald criteria (Polman et al. 2011) and showed positive oligoclonal bands as a sign of intrathecal antibody production. They were assessed using the multiple sclerosis severity score (MSSS) (Roxburgh et al. 2005), a rating which takes both expanded disability status scale (EDSS) and disease duration into account. All patients underwent a standard cerebral as well as an additional spinal MRI examination with a mean time between scans of 2 months. Additionally, patients underwent neuropsychological testing, including measurements of information processing speed (SDMT), working memory (PASAT), memory (VLMT), alertness (TAP—subtest alertness) and word fluency (RWT), as well as fatigue (FSMC), and depression and anxiety (HADS). From this cohort, 17 RRMS and 2 SPMS patients showed predominantly SL with CL \1.0 ml (MS ? SL) and 17 RRMS and 1 SPMS patients showed no detectable SL but considerable CL (MS ? CL). All demographic and clinical data are summarized in Table 1. In addition, a third group of 20 healthy controls (HC) in the same age range was investigated using cerebral MRI. MR images obtained from each subject were visualized by a neuroradiologist in order to exclude other signal intensity or morphological changes. All subjects gave their written informed consent to the MRI and neuropsychological examinations before participating in this study, which was approved by the local ethics committee and adhered to institutional guidelines.
Materials and methods Data acquisition Subjects We retrospectively included all MS patients with one of the two extreme MRI phenotypes (MS ? SL, MS ? CL)
Cerebral MRI examinations were carried out on a 3 T MR scanner (Magnetom Tim Trio, Siemens Healthcare, Erlangen, Germany) with a 32-channel head coil. Imaging was
Table 1 Demographic and neuropsychological data MS ? SL (n = 19)
MS ? CL (n = 18)
HC (n = 20)
p
Women/men
12/7
14/4
12/8
0.471a
Median age (range) (years)
33 (18–56)
38.5 (20–66)
32 (24–54)
0.693b
Mean age of onset (SD) (years) Mean disease duration (SD) (years)
32 (9) 3.4 (3.6)
34 (11) 4.9 (4.2)
– –
0.831c 0.199c
Median EDSS (range)
2.0 (0–6.5)
2.0 (0–3.5)
–
0.198c
Mean MSSS (SD)
5.0 (2.9)
3.3 (2.7)
–
0.061c
Mean z score information processing speed (SD)
-0.28 (0.93)
-0.69 (0.94)
–
0.235c
Mean z score working memory (SD)
0.06 (0.89)
-0.48 (0.84)
–
0.132c
CL cerebral lesions, EDSS expanding disability status scale, MS multiple sclerosis, MSSS multiple sclerosis severity score, SD standard deviation, SL spinal cord lesions a
Chi-square test
b
Kruskal–Wallis test Mann–Whitney U test
c
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performed using sagittal 3D T1-weighted magnetization prepared rapid gradient echo (MP-RAGE) sequence (TE/TI/ TR = 2.52/900/1900 ms, flip angle = 9°, field of view (FOV) = 256 9 256 mm2, matrix size = 256 9 256, slab thickness = 192 mm, voxel size = 1 9 1 9 1 mm3), sagittal 3D T2-weighted fluid attenuated inversion recovery (FLAIR) sequence (TE/TI/TR = 388/1800/5000 ms, echo-train length = 848, field of view (FOV) = 256 9 256 mm2, matrix size = 256 9 256, slab thickness = 192 mm, voxel size = 1 9 1 9 1 mm3) and DTI sequence with single-shot echo-planar read-out (TE/TR = 102/ 9000 ms, b = 0/900 s/mm2, 30 directions, one average, field of view (FOV) = 256 9 256 mm2, matrix size = 128 9 128, 62 slices, slice thickness = 2.5 mm, voxel size = 2 9 2 9 2.5 mm3). Additionally, we acquired and analyzed conventional spinal cord MR images for each patient including sagittal and axial sections, based on which the presence of SL could be determined. However, spinal MRI examinations were performed on different 1.5 T MR scanners, thus precluding a quantitative spinal cord assessment such as the number and distribution of SL or spinal cord atrophy. VBM analysis For the VBM analysis, the statistical parameter mapping (SPM8) software (http://www.fil.ion.ucl.ac.uk/spm) with lesion segmentation toolbox (LST) (http://www.appliedstatistics.de/lst.html) and VBM5 toolbox (http://dbm.neuro. uni-jena.de/vbm/) was used. Lesion maps were drawn manually on T2-weighted 3D FLAIR images for all patients using the MRIcron software (http://www.mccaus landcenter.sc.edu/mricro/mricron/). First using LST, 3D FLAIR images were co-registered to 3D MP-RAGE images and bias corrected. After partial volume estimate (PVE) label estimation, lesion segmentation was performed with 20 different initial threshold values for the lesion growth algorithm (Schmidt et al. 2012). By comparing automatically and manually estimated lesion maps, the optimal threshold (j value) for lesion detection was determined for each patient and used for automatic lesion volume estimation and lesion filling in MP-RAGE images to avoid misclassification of hypointense MS lesions as GM during the segmentation step. Subsequently, using the VBM5 toolbox with default parameters, the filled MP-RAGE images for patients as well as the native MP-RAGE images for controls were segmented into tissue classes for gray matter (GM), white matter (WM), and cerebro-spinal fluid (CSF) using a priori information. Raw volumes for GM/WM/CSF were calculated by integrating all values that contribute to each tissue class. After normalizing to Montreal Neurological Institute (MNI) space, the GM, WM and CSF images were
smoothed using an 8-mm Gaussian kernel and tested for between-group differences using the two-sample t test. Resulting differences between the GM and WM probability maps were assessed using a family-wise error (FWE) correction at p \ 0.05 for multiple comparisons. ROI-based analysis To clarify possible differences in the VBM analysis, individual ROIs were identified on native MP-RAGE images and manually outlined on the same scans using the MRIcron software. The individual ROIs were used to mask the MP-RAGE images in SPM and then to calculate the ROI volumes for each subject in both native space and MNI space. Additionally, diffusion toolbox (http://sourceforge.net/ projects/spmtools) was used to calculate the fractional anisotropy (FA) and mean diffusivity (MD) maps for each subject. These maps were co-registered to the 3D MPRAGE images and also to the mask to calculate the mean FA and MD values within the ROIs. For the brain atrophy measurement, all results were assessed as fractions of total brain volume (TBV; sum GM, WM, and CSF volumes) (Rudick et al. 1999; Chard et al. 2002). The brain parenchymal fraction (BPF) was calculated as the sum of the GM and WM volumes divided by TBV, and similarly, the GM/WM/CSF fraction as the corresponding volume divided by TBV. TBSS A voxel-wise statistical analysis was performed using TBSS (Smith et al. 2006), part of the FSL software package. After distortion and eddy current corrections were applied, FA maps were calculated using a tensor fitting model (FSL; FDT toolbox). Then, all FA maps were aligned to a 1 9 1 9 1 mm3 standard space target image (FMRIB58 FA) via nonlinear registration. This target was then affine-aligned into MNI152 space, followed by calculation of the mean FA map as well as a skeletonized version of this mean map. A threshold of 0.2 was applied for the FA skeleton. The individual FA maps were projected onto the mean FA skeleton. Cross-subject voxelwise statistics for each skeleton voxel were calculated using FSL’s Randomize with 5000 permutations, and clusters of different FA and MD values were identified at p \ 0.05 using threshold-free cluster enhancement (TFCE) (Smith and Nichols 2009). Statistics Statistical analysis was performed using IBM SPSS Statistics 22.0 (SPSS, Chicago, IL, USA).
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Neuropsychological data were specified as z scores. Owing to the small sample size (\50), the Shapiro–Wilk normality test was used to verify the normal distribution of all parameters in the three groups. Afterwards, means and standard deviations (SD) as well as medians with ranges were calculated. The Kruskal–Wallis test, Mann–Whitney U test, and Chi-square test were used for testing betweengroup differences of demographic data, disease-specific parameters, neuropsychological data and lesion volumes. In a one-way ANOVA, the GM, WM and CSF fractions, BPF, putamen volumes and putamen volume/BPF ratios, as well as putamen FA and MD values were tested for between-group differences. Post hoc tests with a Tukey– Kramer correction for multiple comparisons were applied. The Spearman rank correlation test between CL volume, GM fraction, BPF, putamen fraction and disease duration as well as between EDSS, MSSS and normalized putamen volume was calculated. The significance level was set to p \ 0.05.
Results Neither patient group differed in disease duration or age. In comparison to MS ? CL, MS ? SL showed slightly higher MSSS values, which includes both disability (EDSS) and disease duration to rate the disease severity. In neuropsychological testing, no differences could be found
between MS ? CL and MS ? SL (Table 1). A trend towards reduced BPF and increased CSF fractions, a measure of whole-brain atrophy, was found in both patient groups compared to the HC group (Table 2). The decrease in GM fraction was higher in MS ? CL than in MS ? SL compared to HC. Additionally, negative correlations were found between CL volume and GM fraction, CL volume and BPF, CL volume and putamen fraction, disease duration and GM fraction, and disease duration and BPF in MS ? CL as well as between EDSS and information processing speed in MS ? SL (Table 3). Differences between the two patient groups did not achieve the specified significance level. In the VBM analysis, differences in the regions of the bilateral putamen were found between the patient groups (Fig. 1) as well as between MS ? SL and HC. In MS ? SL, the probability of GM appearance was higher in the right and left putamen than in MS ? CL as well as in HC. These results were confirmed by testing differences for WM probability, which was lower in MS ? SL compared to MS ? CL. Additionally, MS ? CL showed clearly lower GM probabilities along the whole cortex and deep GM structures as well as higher CSF probabilities along the lateral ventricles than MS ? SL compared to HC. To corroborate these VBM findings, a selective ROIbased analysis of the putamen was performed for all subjects. Significantly larger putamen volumes were found in MS ? SL compared to MS ? CL and HC (Table 2). The
Table 2 MRI data (volumes, fractional anisotropy and mean diffusion values) MS ? SL (n = 19) Median CL volume (range) (ml)
0.16 (0–0.86)
MS ? CL (n = 18) 3.38 (1.08–29.89)
HC (n = 20)
p
–
\0.001a
GM fraction (SD)
0.417 (0.034)
0.402 (0.050)
0.435 (0.032)
0.045b,1
WM fraction (SD)
0.275 (0.022)
0.281 (0.028)
0.288 (0.024)
0.246b
CSF fraction (SD)
0.308 (0.047)
0.317 (0.073)
0.277 (0.041)
0.072b
BPF (SD)
0.692 (0.047)
0.683 (0.073)
0.723 (0.041)
0.072b
Native putamen volume (SD) (ml)
7.62 (1.05)
7.03 (1.05)
7.15 (0.48)
0.110b
Native putamen volume/BPF (SD)
11.15 (1.28)
10.28 (1.58)
9.93 (0.86)
0.014b,2
0.0046 (0.0007)
0.0042 (0.0006)
0.0044 (0.0004)
0.139b
Normalized putamen volume (SD) (ml)
10.26 (1.21)
9.35 (0.91)
9.59 (0.70)
0.018b,3
FA of putamen (SD)
0.246 (0.024)
0.247 (0.027)
0.256 (0.061)
0.724b
MD of putamen (SD) (9103 mm2/s)
0.729 (0.014)
0.754 (0.048)
0.728 (0.023)
0.021b,4
Putamen fraction (SD)
BPF brain parenchymal fraction, CL cerebral lesions, FA fractional anisotropy, GM gray matter, MD mean diffusivity, MS multiple sclerosis, SD standard deviation, SL spinal cord lesions, WM white matter a
Mann–Whitney U test
b
One-way ANOVA, p values derived from between-groups comparison
1
Post hoc comparison p = 0.035 for MS ? CL vs. controls
2
Post hoc comparison p = 0.012 for MS ? SL vs. controls
3
Post hoc comparison p = 0.019 for MS ? SL vs. MS ? CL
4
Post hoc comparison p = 0.036 for MS ? CL vs. controls and p = 0.043 for MS ? CL vs. MS ? SL
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Putaminal alteration in multiple sclerosis patients with spinal cord lesions Table 3 Correlations between demographic, neuropsychological and MRI data (Spearman correlation, two-tailed), separated for the two patient groups MS ? SL (n = 19)
EDSS
MSSS
Disease duration
Putamen fraction
r = -0.411, p = 0.090
r = -0.521, p = 0.027
r = 0.134, p = 0.596
Putamen volume/BPF
r = -0.186, p = 0.461
r = -0.371, p = 0.130
r = 0.484, p = 0.042
Information processing speed
r = -0.639, p = 0.019
r = -0.338, p = 0.259
r = -0.348, p = 0.244
MS ? CL (n = 18)
GM fraction
BPF
Putamen fraction
Putamen volume/BPF r = 0.253, p = 0.328
Disease duration
r = -0.599, p = 0.011
r = -0.422, p = 0.091
r = 0.030, p = 0.909
CL volume
r = -0.576, p = 0.016
r = -0.603, p = 0.010
r = -0.674, p = 0.003
r = -0.118, p = 0.653
Working memory
r = -0.321, p = 0.365
r = -0.224, p = 0.533
r = 0.079, p = 0.829
r = 0.721, p = 0.019
BPF brain parenchymal fraction, CL cerebral lesions, EDSS expanding disability status scale, GM gray matter, MS multiple sclerosis, MSSS multiple sclerosis severity score, SL spinal cord lesions
cannot be derived from an enlargement of intercellular spaces. In contrast, MS ? SL showed similar putamen MD values to HC. Comparing WM microstructures in all three groups, the TBSS analysis revealed no differences between MS ? SL and HC but showed significantly reduced FA values in MS ? CL compared to MS ? SL and HC (Fig. 2). This FA reduction in MS ? CL was not restricted to specific regions, but extended over the whole brain. Similar results were found for MD. MS ? CL showed a significant increase in MD compared to MS ? SL and HC, whereas MS ? SL and HC showed similar values.
Discussion
Fig. 1 Regions of increased GM probability in MS ? SL compared with MS ? CL (VBM analysis) at a threshold of p \ 0.01 (uncorrected), overlaid on the Montreal Neurological Institute average brain template
post hoc comparison revealed significant differences in putamen volume between MS ? SL and MS ? CL. Furthermore, negative correlations were found between MSSS and putamen fraction in MS ? SL and positive correlations of putamen volume/BPF with working memory in MS ? CL as well as with disease duration in MS ? SL (Table 3). In order to determine whether the increased putamen volume in the MS ? SL group was the result of inflammatory edematous or less tightly packed neuronal structures, FA and MD values of the putamen were calculated. MD values were significantly increased in the putamen of MS ? CL patients compared to MS ? SL and to HC, indicating that the increased putamen volume in MS ? SL
In this study, we divided our cohort of MS patients into two groups based on a predominance of either cerebral lesions (MS ? CL) or spinal cord lesions (MS ? SL). Interestingly, although well defined by MRI, the clinical and neuropsychological presentation of patients in the MS ? CL and MS ? SL groups did not markedly differ, likely due to the relatively short disease duration. Nonetheless, differences could be determined in several MRI parameters, such as the putamen volume, as well as correlations to clinical and neuropsychological measures. Observer-independent VBM and ROI-based analyses provided consistent evidence of higher putamen volumes in MS ? SL compared to MS ? CL and HC. Additionally, VBM revealed significantly stronger GM atrophy along the whole cortex and deep GM structures only in MS ? CL compared to HC, but not between MS ? SL and HC. Furthermore, the CL volumes were negatively correlated with putamen volumes in MS ? CL. These findings are in line with a study showing that deep GM atrophy is driven by WM lesions, most likely through axonal transection
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Fig. 2 Comparison of FA in the WM of the three groups (TBSS analysis). a MS ? CL showed significantly decreased FA values over the whole brain compared to HC (p \ 0.05, TFCE corrected). b No
differences were found between HC and MS ? SL. c Significant FA reduction in MS ? CL compared to MS ? SL over the whole brain
(Mu¨hlau et al. 2013). The putamen, part of the basal ganglia, is known to be involved in the execution of motor skills (Jaeger et al. 1995; Ueda and Kimura 2003) and modulation of motor responses (Draganski et al. 2008), and other studies have pointed to the left putamen playing a role in working memory (Shu et al. 2009) and learning processes (Cromwell et al. 2005). Normally, putamen volume decreases with age but in some diseases (e.g., Parkinson’s disease), an increase in the volume of distinct parts of the putamen has been detected and interpreted as a dynamic development which could be an active compensatory mechanism (Reetz et al. 2009). Furthermore, increased MD values in the putamen were found only for MS ? CL, whereas MS ? SL showed similar values to those of controls. Therefore, the increased putamen volumes in MS ? SL cannot be derived from enlargement of intercellular spaces or tissue damage. These results
indicate, probably, an early compensatory mechanism of motor deficits in MS ? SL. Regarding the neuropsychological testing results, mean z scores for information processing speed and working memory were higher in MS ? SL than in MS ? CL, which might indicate a stronger cognitive impairment in MS ? CL or further cognitive compensation effects in MS ? SL. However, the mean z scores were not found to be pathologic (z score \ -1) and did not significantly differ between the patient groups. Since the putamen plays an important role in working memory (Shu et al. 2009), better cognitive skills in MS ? SL might be attributable to the observed increase in putamen volume. The clearly higher CL volumes in MS ? CL were coupled with significant GM atrophy as well as extended damage to the WM microstructure over the whole brain. Furthermore, CL volumes as well as GM atrophy were correlated with disease duration in MS ? CL.
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Comi et al. (1995) showed that primary progressive MS patients with low CL but high SL loads have fewer neuropsychological deficits compared to SPMS patients with both CL and SL. Nevertheless, a correlation between hyperintense lesion load in T2-weighted images and cognitive deficits (Foong et al. 2000) or between GM atrophy and cognition level remains unconfirmed (Batista et al. 2012; Houtchens et al. 2007). In addition, there is evidence for a clear correlation between hyperintense lesion load in T2-weighted images and whole-brain atrophy. In our study, both patient groups showed decreased BPF compared to HC, indicating the presence of pronounced atrophy. Whereas atrophy was marked by a slight WM loss in MS ? SL, a clear loss of GM volume was observed in MS ? CL. Underlining these results, our data showed that the reduced FA values in the TBSS analysis, which correlate to damage in the WM microstructure, were found only in MS ? CL, whereas MS ? SL showed similar results to the controls. One assumption could be that inflammation leads to WM lesions affecting the fiber tracts, thus causing demyelination, axonal transection and subsequent brain atrophy with a reduction predominantly in the GM fraction. On the other hand, the MS ? SL group revealed slight WM atrophy compared to HC. However, MS ? SL do not exhibit extended damage to the WM microstructures, as seen in MS ? CL. This could be explained by the absence of gliosis due to considerable CL volumes, whereas the presence of SL could be the reason for higher disease severity, as seen in the increased MSSS value which reflects the shorter disease duration but similar levels of disability (EDSS). The existence of a correlation between hyperintense lesion load in T2-weighted images and disability level (EDSS) remains controversial (Barkhof 2002; Charil et al. 2003; Popescu et al. 2013; Kearney et al. 2014). Here, we found a higher disease severity (MSSS) in MS ? SL, even though the CL volume was significantly lower than in MS ? CL. This is not surprising in patients with spinal cord lesions as EDSS/MSSS assess in particular gait deficits. It should be noted that EDSS is not a comprehensive score of the patient’s symptoms and disease progression and should potentially be amended in the future to the MS functional composite (MSFC) score (Fischer et al. 1999). The role of damage to the spinal cord in predicting the accumulation of disability in MS patients has been highlighted in various cross-sectional (Lycklama a Nijeholt et al. 1998; Stevenson et al. 1998) and longitudinal MRI studies (Lin et al. 2003; Sastre-Garriga et al. 2005). Evangelou and colleagues (2005) suggest that axonal degeneration, possibly caused by the cumulative number of lesions in the brain and spinal cord, is responsible for spinal cord atrophy in MS, rather than tissue loss within
individual lesions. As found in pathological investigations, the extent of spinal cord atrophy has been demonstrated to be unrelated to spinal cord lesion load and independent of brain lesions and brain atrophy in primary progressive MS (Rovaris et al. 2008). Furthermore, spinal cord atrophy, not SL volume, has been found to correlate to disability (Cohen et al. 2012). However, investigations point to the existence of at least one SL having a predictive value for the conversion to clinically definite MS and a shorter time preceding this conversion (Sombekke et al. 2013). The negative correlation between EDSS/MSSS and normalized putamen volume in MS ? SL could be interpreted such that higher putamen volumes are associated with a better disability prognosis. A limitation of this study is the different method of data acquisition in spinal MRI so that further analysis such as correlation of the SL volume to EDSS/MSSS or to putamen volume would not yield reliable results. Furthermore, the presented results were derived from limited MS patient samples because of the low incidence of MS patients with both extreme MRI phenotypes. Therefore, in future studies, the statistical power should be increased to clarify whether the enlargement of the putamen in MS ? SL is a real compensatory effect. In conclusion, using VBM and ROI-based analyses as well as TBSS, differences in MRI findings between MS patients with predominantly SL (MS ? SL) and those with CL and no detectable SL (MS ? CL) could be identified. In MS ? SL, the putamen, which is known to be involved in motor and learning functions, was found to be enlarged, suggesting that these patients may be better able to adapt to the progression of disability. In contrast, extensively reduced FA values in MS ? CL were caused by higher CL volume and thus presumably stronger demyelination, which subsequently leads to higher global GM atrophy; the associated decrease in putamen volume is accompanied by deficits in working memory. This study contributes to the understanding of the pathomechanism and extreme phenotypes of MS. Acknowledgments This study was supported by the Ministry of Science and Education/German Competence Network for Multiple Sclerosis (BMBF/KKNMS, B7.3 to FZ).
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NEUROLOGY AND PRECLINICAL NEUROLOGICAL STUDIES - ORIGINAL ARTICLE
Putaminal alteration in multiple sclerosis patients with spinal cord lesions Hilga Zimmermann1 • Hans O. Rolfsnes1 • Swantje Montag1 • Janine Wilting1 Amgad Droby1 • Eva Reuter1 • Joachim Gawehn2 • Frauke Zipp1 • Adriane Gro¨ger1
•
Received: 19 December 2014 / Accepted: 4 May 2015 Ó Springer-Verlag Wien 2015
Abstract Typical multiple sclerosis (MS) lesions occur in the brain as well as in the spinal cord. However, two extreme magnetic resonance imaging phenotypes appear occasionally: those with predominantly spinal cord lesions (MS ? SL) and those with cerebral lesions and no detectable spinal lesions (MS ? CL). We assessed whether morphological differences can be found between these two extreme phenotypes. We examined 19 patients with MS ? SL, 18 with MS ? CL and 20 controls. All subjects were examined using magnetic resonance imaging, including anatomical and diffusion tensor imaging sequences. Voxel-based morphologic and regions of interestbased analyses and tract-based spatial statistics were performed. Patients also underwent neuropsychological testing. Demographic, clinical and neuropsychological characteristics did not differ between MS ? SL and MS ? CL patients. Patients with MS ? SL showed significantly larger putamen volumes than those with MS ? CL which correlated negatively with disability. Compared to controls, only MS ? CL revealed clear cortical and deep gray matter atrophy, which correlated with
F. Zipp and A. Gro¨ger contributed equally to this work. The work presented in this paper was part of the doctoral thesis of Swantje Montag. & Adriane Gro¨ger [email protected] 1
Department of Neurology and Neuroimaging Center (NIC) of the Focus Program Translational Neuroscience (FTN), University Medical Center of the Johannes GutenbergUniversity Mainz, Langenbeckstraße 1, 55131 Mainz, Germany
2
Institute of Neuroradiology, University Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany
cerebral lesion volume. Additionally, extensive white matter microstructural damage was found only in MS ? CL compared to MS ? SL and controls in the tractbased spatial statistics. Higher putamen volumes in MS ? SL could suggest compensatory mechanisms in this area responsible for motor control. Widely reduced fractional anisotropy values in MS ? CL were caused by higher cerebral lesion volume and thus presumably stronger demyelination, which subsequently leads to higher global gray matter atrophy. Keywords
Multiple sclerosis VBM TBSS Putamen
Introduction Multiple sclerosis (MS) is a heterogeneous disease characterized by inflammation, demyelination, and subsequent axonal degeneration and neuronal loss, which occurs in both the brain and spinal cord and leads to a broad spectrum of symptoms. Whereas spinal cord lesions (SL) often lead to paralysis with sensory disturbances or incontinence, cerebral lesions (CL) may lead to hemiparesis, dysarthria and deficits in balance as well as cognitive impairments. In the 1990s, the spinal cord had already been attributed a distinct role in disability and the course of MS (Filippi et al. 1996; Losseff et al. 1996), and today, the spinal cord is one of the four relevant areas for diagnosing MS (Polman et al. 2011). The early occurrence of SL has been found to predict a worse prognosis of MS (Coret et al. 2010), e.g., it increases the risk of conversion from a preform of MS to clinically definite MS. In the beginning of the disease, most patients show relapses with clinical deficits that persist for days and up to weeks, usually followed by remission of symptoms; this is
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the relapsing–remitting MS subtype (RRMS) (Vukusic and Confavreux 2003). Within 15 years, more than half of these RRMS patients develop a progressive disease course referred to as secondary progressive MS (SPMS) (Weinshenker et al. 1989). Magnetic resonance imaging (MRI) is extensively used in the diagnosis and management of MS. MRI findings differ in lesion localization and development. In some MS patients, lesions occur mainly in the brain and not in the spinal cord (MS ? CL) and in other patients the spinal cord is primarily affected and only few cerebral lesions are found (MS ? SL). The clinical presentation of these patients may differ due to the affected region of the central nervous system. Voxel-based morphometry (VBM) is a neuroimaging analysis technique commonly used to investigate differences in brain anatomy through a statistical voxel-wise comparison. Alternatively, region-specific volumes of the brain can also be compared by drawing regions of interest (ROIs) on MRI scans and calculating the enclosed volume. Tract-based spatial statistics (TBSS) allow the voxel-wise comparison of neural fiber tracts across brains after defining these tracts by diffusion tensor modeling using data collected in diffusion tensor imaging (DTI). The goal of this study was to use VBM and ROI-based analyses as well as TBSS to assess whether morphological differences can be found between MS patients with these two extreme MRI phenotypes: MS ? SL and MS ? CL.
based on the presence of SL or CL. All patients were diagnosed according to the new McDonald criteria (Polman et al. 2011) and showed positive oligoclonal bands as a sign of intrathecal antibody production. They were assessed using the multiple sclerosis severity score (MSSS) (Roxburgh et al. 2005), a rating which takes both expanded disability status scale (EDSS) and disease duration into account. All patients underwent a standard cerebral as well as an additional spinal MRI examination with a mean time between scans of 2 months. Additionally, patients underwent neuropsychological testing, including measurements of information processing speed (SDMT), working memory (PASAT), memory (VLMT), alertness (TAP—subtest alertness) and word fluency (RWT), as well as fatigue (FSMC), and depression and anxiety (HADS). From this cohort, 17 RRMS and 2 SPMS patients showed predominantly SL with CL \1.0 ml (MS ? SL) and 17 RRMS and 1 SPMS patients showed no detectable SL but considerable CL (MS ? CL). All demographic and clinical data are summarized in Table 1. In addition, a third group of 20 healthy controls (HC) in the same age range was investigated using cerebral MRI. MR images obtained from each subject were visualized by a neuroradiologist in order to exclude other signal intensity or morphological changes. All subjects gave their written informed consent to the MRI and neuropsychological examinations before participating in this study, which was approved by the local ethics committee and adhered to institutional guidelines.
Materials and methods Data acquisition Subjects We retrospectively included all MS patients with one of the two extreme MRI phenotypes (MS ? SL, MS ? CL)
Cerebral MRI examinations were carried out on a 3 T MR scanner (Magnetom Tim Trio, Siemens Healthcare, Erlangen, Germany) with a 32-channel head coil. Imaging was
Table 1 Demographic and neuropsychological data MS ? SL (n = 19)
MS ? CL (n = 18)
HC (n = 20)
p
Women/men
12/7
14/4
12/8
0.471a
Median age (range) (years)
33 (18–56)
38.5 (20–66)
32 (24–54)
0.693b
Mean age of onset (SD) (years) Mean disease duration (SD) (years)
32 (9) 3.4 (3.6)
34 (11) 4.9 (4.2)
– –
0.831c 0.199c
Median EDSS (range)
2.0 (0–6.5)
2.0 (0–3.5)
–
0.198c
Mean MSSS (SD)
5.0 (2.9)
3.3 (2.7)
–
0.061c
Mean z score information processing speed (SD)
-0.28 (0.93)
-0.69 (0.94)
–
0.235c
Mean z score working memory (SD)
0.06 (0.89)
-0.48 (0.84)
–
0.132c
CL cerebral lesions, EDSS expanding disability status scale, MS multiple sclerosis, MSSS multiple sclerosis severity score, SD standard deviation, SL spinal cord lesions a
Chi-square test
b
Kruskal–Wallis test Mann–Whitney U test
c
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performed using sagittal 3D T1-weighted magnetization prepared rapid gradient echo (MP-RAGE) sequence (TE/TI/ TR = 2.52/900/1900 ms, flip angle = 9°, field of view (FOV) = 256 9 256 mm2, matrix size = 256 9 256, slab thickness = 192 mm, voxel size = 1 9 1 9 1 mm3), sagittal 3D T2-weighted fluid attenuated inversion recovery (FLAIR) sequence (TE/TI/TR = 388/1800/5000 ms, echo-train length = 848, field of view (FOV) = 256 9 256 mm2, matrix size = 256 9 256, slab thickness = 192 mm, voxel size = 1 9 1 9 1 mm3) and DTI sequence with single-shot echo-planar read-out (TE/TR = 102/ 9000 ms, b = 0/900 s/mm2, 30 directions, one average, field of view (FOV) = 256 9 256 mm2, matrix size = 128 9 128, 62 slices, slice thickness = 2.5 mm, voxel size = 2 9 2 9 2.5 mm3). Additionally, we acquired and analyzed conventional spinal cord MR images for each patient including sagittal and axial sections, based on which the presence of SL could be determined. However, spinal MRI examinations were performed on different 1.5 T MR scanners, thus precluding a quantitative spinal cord assessment such as the number and distribution of SL or spinal cord atrophy. VBM analysis For the VBM analysis, the statistical parameter mapping (SPM8) software (http://www.fil.ion.ucl.ac.uk/spm) with lesion segmentation toolbox (LST) (http://www.appliedstatistics.de/lst.html) and VBM5 toolbox (http://dbm.neuro. uni-jena.de/vbm/) was used. Lesion maps were drawn manually on T2-weighted 3D FLAIR images for all patients using the MRIcron software (http://www.mccaus landcenter.sc.edu/mricro/mricron/). First using LST, 3D FLAIR images were co-registered to 3D MP-RAGE images and bias corrected. After partial volume estimate (PVE) label estimation, lesion segmentation was performed with 20 different initial threshold values for the lesion growth algorithm (Schmidt et al. 2012). By comparing automatically and manually estimated lesion maps, the optimal threshold (j value) for lesion detection was determined for each patient and used for automatic lesion volume estimation and lesion filling in MP-RAGE images to avoid misclassification of hypointense MS lesions as GM during the segmentation step. Subsequently, using the VBM5 toolbox with default parameters, the filled MP-RAGE images for patients as well as the native MP-RAGE images for controls were segmented into tissue classes for gray matter (GM), white matter (WM), and cerebro-spinal fluid (CSF) using a priori information. Raw volumes for GM/WM/CSF were calculated by integrating all values that contribute to each tissue class. After normalizing to Montreal Neurological Institute (MNI) space, the GM, WM and CSF images were
smoothed using an 8-mm Gaussian kernel and tested for between-group differences using the two-sample t test. Resulting differences between the GM and WM probability maps were assessed using a family-wise error (FWE) correction at p \ 0.05 for multiple comparisons. ROI-based analysis To clarify possible differences in the VBM analysis, individual ROIs were identified on native MP-RAGE images and manually outlined on the same scans using the MRIcron software. The individual ROIs were used to mask the MP-RAGE images in SPM and then to calculate the ROI volumes for each subject in both native space and MNI space. Additionally, diffusion toolbox (http://sourceforge.net/ projects/spmtools) was used to calculate the fractional anisotropy (FA) and mean diffusivity (MD) maps for each subject. These maps were co-registered to the 3D MPRAGE images and also to the mask to calculate the mean FA and MD values within the ROIs. For the brain atrophy measurement, all results were assessed as fractions of total brain volume (TBV; sum GM, WM, and CSF volumes) (Rudick et al. 1999; Chard et al. 2002). The brain parenchymal fraction (BPF) was calculated as the sum of the GM and WM volumes divided by TBV, and similarly, the GM/WM/CSF fraction as the corresponding volume divided by TBV. TBSS A voxel-wise statistical analysis was performed using TBSS (Smith et al. 2006), part of the FSL software package. After distortion and eddy current corrections were applied, FA maps were calculated using a tensor fitting model (FSL; FDT toolbox). Then, all FA maps were aligned to a 1 9 1 9 1 mm3 standard space target image (FMRIB58 FA) via nonlinear registration. This target was then affine-aligned into MNI152 space, followed by calculation of the mean FA map as well as a skeletonized version of this mean map. A threshold of 0.2 was applied for the FA skeleton. The individual FA maps were projected onto the mean FA skeleton. Cross-subject voxelwise statistics for each skeleton voxel were calculated using FSL’s Randomize with 5000 permutations, and clusters of different FA and MD values were identified at p \ 0.05 using threshold-free cluster enhancement (TFCE) (Smith and Nichols 2009). Statistics Statistical analysis was performed using IBM SPSS Statistics 22.0 (SPSS, Chicago, IL, USA).
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Neuropsychological data were specified as z scores. Owing to the small sample size (\50), the Shapiro–Wilk normality test was used to verify the normal distribution of all parameters in the three groups. Afterwards, means and standard deviations (SD) as well as medians with ranges were calculated. The Kruskal–Wallis test, Mann–Whitney U test, and Chi-square test were used for testing betweengroup differences of demographic data, disease-specific parameters, neuropsychological data and lesion volumes. In a one-way ANOVA, the GM, WM and CSF fractions, BPF, putamen volumes and putamen volume/BPF ratios, as well as putamen FA and MD values were tested for between-group differences. Post hoc tests with a Tukey– Kramer correction for multiple comparisons were applied. The Spearman rank correlation test between CL volume, GM fraction, BPF, putamen fraction and disease duration as well as between EDSS, MSSS and normalized putamen volume was calculated. The significance level was set to p \ 0.05.
Results Neither patient group differed in disease duration or age. In comparison to MS ? CL, MS ? SL showed slightly higher MSSS values, which includes both disability (EDSS) and disease duration to rate the disease severity. In neuropsychological testing, no differences could be found
between MS ? CL and MS ? SL (Table 1). A trend towards reduced BPF and increased CSF fractions, a measure of whole-brain atrophy, was found in both patient groups compared to the HC group (Table 2). The decrease in GM fraction was higher in MS ? CL than in MS ? SL compared to HC. Additionally, negative correlations were found between CL volume and GM fraction, CL volume and BPF, CL volume and putamen fraction, disease duration and GM fraction, and disease duration and BPF in MS ? CL as well as between EDSS and information processing speed in MS ? SL (Table 3). Differences between the two patient groups did not achieve the specified significance level. In the VBM analysis, differences in the regions of the bilateral putamen were found between the patient groups (Fig. 1) as well as between MS ? SL and HC. In MS ? SL, the probability of GM appearance was higher in the right and left putamen than in MS ? CL as well as in HC. These results were confirmed by testing differences for WM probability, which was lower in MS ? SL compared to MS ? CL. Additionally, MS ? CL showed clearly lower GM probabilities along the whole cortex and deep GM structures as well as higher CSF probabilities along the lateral ventricles than MS ? SL compared to HC. To corroborate these VBM findings, a selective ROIbased analysis of the putamen was performed for all subjects. Significantly larger putamen volumes were found in MS ? SL compared to MS ? CL and HC (Table 2). The
Table 2 MRI data (volumes, fractional anisotropy and mean diffusion values) MS ? SL (n = 19) Median CL volume (range) (ml)
0.16 (0–0.86)
MS ? CL (n = 18) 3.38 (1.08–29.89)
HC (n = 20)
p
–
\0.001a
GM fraction (SD)
0.417 (0.034)
0.402 (0.050)
0.435 (0.032)
0.045b,1
WM fraction (SD)
0.275 (0.022)
0.281 (0.028)
0.288 (0.024)
0.246b
CSF fraction (SD)
0.308 (0.047)
0.317 (0.073)
0.277 (0.041)
0.072b
BPF (SD)
0.692 (0.047)
0.683 (0.073)
0.723 (0.041)
0.072b
Native putamen volume (SD) (ml)
7.62 (1.05)
7.03 (1.05)
7.15 (0.48)
0.110b
Native putamen volume/BPF (SD)
11.15 (1.28)
10.28 (1.58)
9.93 (0.86)
0.014b,2
0.0046 (0.0007)
0.0042 (0.0006)
0.0044 (0.0004)
0.139b
Normalized putamen volume (SD) (ml)
10.26 (1.21)
9.35 (0.91)
9.59 (0.70)
0.018b,3
FA of putamen (SD)
0.246 (0.024)
0.247 (0.027)
0.256 (0.061)
0.724b
MD of putamen (SD) (9103 mm2/s)
0.729 (0.014)
0.754 (0.048)
0.728 (0.023)
0.021b,4
Putamen fraction (SD)
BPF brain parenchymal fraction, CL cerebral lesions, FA fractional anisotropy, GM gray matter, MD mean diffusivity, MS multiple sclerosis, SD standard deviation, SL spinal cord lesions, WM white matter a
Mann–Whitney U test
b
One-way ANOVA, p values derived from between-groups comparison
1
Post hoc comparison p = 0.035 for MS ? CL vs. controls
2
Post hoc comparison p = 0.012 for MS ? SL vs. controls
3
Post hoc comparison p = 0.019 for MS ? SL vs. MS ? CL
4
Post hoc comparison p = 0.036 for MS ? CL vs. controls and p = 0.043 for MS ? CL vs. MS ? SL
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Putaminal alteration in multiple sclerosis patients with spinal cord lesions Table 3 Correlations between demographic, neuropsychological and MRI data (Spearman correlation, two-tailed), separated for the two patient groups MS ? SL (n = 19)
EDSS
MSSS
Disease duration
Putamen fraction
r = -0.411, p = 0.090
r = -0.521, p = 0.027
r = 0.134, p = 0.596
Putamen volume/BPF
r = -0.186, p = 0.461
r = -0.371, p = 0.130
r = 0.484, p = 0.042
Information processing speed
r = -0.639, p = 0.019
r = -0.338, p = 0.259
r = -0.348, p = 0.244
MS ? CL (n = 18)
GM fraction
BPF
Putamen fraction
Putamen volume/BPF r = 0.253, p = 0.328
Disease duration
r = -0.599, p = 0.011
r = -0.422, p = 0.091
r = 0.030, p = 0.909
CL volume
r = -0.576, p = 0.016
r = -0.603, p = 0.010
r = -0.674, p = 0.003
r = -0.118, p = 0.653
Working memory
r = -0.321, p = 0.365
r = -0.224, p = 0.533
r = 0.079, p = 0.829
r = 0.721, p = 0.019
BPF brain parenchymal fraction, CL cerebral lesions, EDSS expanding disability status scale, GM gray matter, MS multiple sclerosis, MSSS multiple sclerosis severity score, SL spinal cord lesions
cannot be derived from an enlargement of intercellular spaces. In contrast, MS ? SL showed similar putamen MD values to HC. Comparing WM microstructures in all three groups, the TBSS analysis revealed no differences between MS ? SL and HC but showed significantly reduced FA values in MS ? CL compared to MS ? SL and HC (Fig. 2). This FA reduction in MS ? CL was not restricted to specific regions, but extended over the whole brain. Similar results were found for MD. MS ? CL showed a significant increase in MD compared to MS ? SL and HC, whereas MS ? SL and HC showed similar values.
Discussion
Fig. 1 Regions of increased GM probability in MS ? SL compared with MS ? CL (VBM analysis) at a threshold of p \ 0.01 (uncorrected), overlaid on the Montreal Neurological Institute average brain template
post hoc comparison revealed significant differences in putamen volume between MS ? SL and MS ? CL. Furthermore, negative correlations were found between MSSS and putamen fraction in MS ? SL and positive correlations of putamen volume/BPF with working memory in MS ? CL as well as with disease duration in MS ? SL (Table 3). In order to determine whether the increased putamen volume in the MS ? SL group was the result of inflammatory edematous or less tightly packed neuronal structures, FA and MD values of the putamen were calculated. MD values were significantly increased in the putamen of MS ? CL patients compared to MS ? SL and to HC, indicating that the increased putamen volume in MS ? SL
In this study, we divided our cohort of MS patients into two groups based on a predominance of either cerebral lesions (MS ? CL) or spinal cord lesions (MS ? SL). Interestingly, although well defined by MRI, the clinical and neuropsychological presentation of patients in the MS ? CL and MS ? SL groups did not markedly differ, likely due to the relatively short disease duration. Nonetheless, differences could be determined in several MRI parameters, such as the putamen volume, as well as correlations to clinical and neuropsychological measures. Observer-independent VBM and ROI-based analyses provided consistent evidence of higher putamen volumes in MS ? SL compared to MS ? CL and HC. Additionally, VBM revealed significantly stronger GM atrophy along the whole cortex and deep GM structures only in MS ? CL compared to HC, but not between MS ? SL and HC. Furthermore, the CL volumes were negatively correlated with putamen volumes in MS ? CL. These findings are in line with a study showing that deep GM atrophy is driven by WM lesions, most likely through axonal transection
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Fig. 2 Comparison of FA in the WM of the three groups (TBSS analysis). a MS ? CL showed significantly decreased FA values over the whole brain compared to HC (p \ 0.05, TFCE corrected). b No
differences were found between HC and MS ? SL. c Significant FA reduction in MS ? CL compared to MS ? SL over the whole brain
(Mu¨hlau et al. 2013). The putamen, part of the basal ganglia, is known to be involved in the execution of motor skills (Jaeger et al. 1995; Ueda and Kimura 2003) and modulation of motor responses (Draganski et al. 2008), and other studies have pointed to the left putamen playing a role in working memory (Shu et al. 2009) and learning processes (Cromwell et al. 2005). Normally, putamen volume decreases with age but in some diseases (e.g., Parkinson’s disease), an increase in the volume of distinct parts of the putamen has been detected and interpreted as a dynamic development which could be an active compensatory mechanism (Reetz et al. 2009). Furthermore, increased MD values in the putamen were found only for MS ? CL, whereas MS ? SL showed similar values to those of controls. Therefore, the increased putamen volumes in MS ? SL cannot be derived from enlargement of intercellular spaces or tissue damage. These results
indicate, probably, an early compensatory mechanism of motor deficits in MS ? SL. Regarding the neuropsychological testing results, mean z scores for information processing speed and working memory were higher in MS ? SL than in MS ? CL, which might indicate a stronger cognitive impairment in MS ? CL or further cognitive compensation effects in MS ? SL. However, the mean z scores were not found to be pathologic (z score \ -1) and did not significantly differ between the patient groups. Since the putamen plays an important role in working memory (Shu et al. 2009), better cognitive skills in MS ? SL might be attributable to the observed increase in putamen volume. The clearly higher CL volumes in MS ? CL were coupled with significant GM atrophy as well as extended damage to the WM microstructure over the whole brain. Furthermore, CL volumes as well as GM atrophy were correlated with disease duration in MS ? CL.
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Putaminal alteration in multiple sclerosis patients with spinal cord lesions
Comi et al. (1995) showed that primary progressive MS patients with low CL but high SL loads have fewer neuropsychological deficits compared to SPMS patients with both CL and SL. Nevertheless, a correlation between hyperintense lesion load in T2-weighted images and cognitive deficits (Foong et al. 2000) or between GM atrophy and cognition level remains unconfirmed (Batista et al. 2012; Houtchens et al. 2007). In addition, there is evidence for a clear correlation between hyperintense lesion load in T2-weighted images and whole-brain atrophy. In our study, both patient groups showed decreased BPF compared to HC, indicating the presence of pronounced atrophy. Whereas atrophy was marked by a slight WM loss in MS ? SL, a clear loss of GM volume was observed in MS ? CL. Underlining these results, our data showed that the reduced FA values in the TBSS analysis, which correlate to damage in the WM microstructure, were found only in MS ? CL, whereas MS ? SL showed similar results to the controls. One assumption could be that inflammation leads to WM lesions affecting the fiber tracts, thus causing demyelination, axonal transection and subsequent brain atrophy with a reduction predominantly in the GM fraction. On the other hand, the MS ? SL group revealed slight WM atrophy compared to HC. However, MS ? SL do not exhibit extended damage to the WM microstructures, as seen in MS ? CL. This could be explained by the absence of gliosis due to considerable CL volumes, whereas the presence of SL could be the reason for higher disease severity, as seen in the increased MSSS value which reflects the shorter disease duration but similar levels of disability (EDSS). The existence of a correlation between hyperintense lesion load in T2-weighted images and disability level (EDSS) remains controversial (Barkhof 2002; Charil et al. 2003; Popescu et al. 2013; Kearney et al. 2014). Here, we found a higher disease severity (MSSS) in MS ? SL, even though the CL volume was significantly lower than in MS ? CL. This is not surprising in patients with spinal cord lesions as EDSS/MSSS assess in particular gait deficits. It should be noted that EDSS is not a comprehensive score of the patient’s symptoms and disease progression and should potentially be amended in the future to the MS functional composite (MSFC) score (Fischer et al. 1999). The role of damage to the spinal cord in predicting the accumulation of disability in MS patients has been highlighted in various cross-sectional (Lycklama a Nijeholt et al. 1998; Stevenson et al. 1998) and longitudinal MRI studies (Lin et al. 2003; Sastre-Garriga et al. 2005). Evangelou and colleagues (2005) suggest that axonal degeneration, possibly caused by the cumulative number of lesions in the brain and spinal cord, is responsible for spinal cord atrophy in MS, rather than tissue loss within
individual lesions. As found in pathological investigations, the extent of spinal cord atrophy has been demonstrated to be unrelated to spinal cord lesion load and independent of brain lesions and brain atrophy in primary progressive MS (Rovaris et al. 2008). Furthermore, spinal cord atrophy, not SL volume, has been found to correlate to disability (Cohen et al. 2012). However, investigations point to the existence of at least one SL having a predictive value for the conversion to clinically definite MS and a shorter time preceding this conversion (Sombekke et al. 2013). The negative correlation between EDSS/MSSS and normalized putamen volume in MS ? SL could be interpreted such that higher putamen volumes are associated with a better disability prognosis. A limitation of this study is the different method of data acquisition in spinal MRI so that further analysis such as correlation of the SL volume to EDSS/MSSS or to putamen volume would not yield reliable results. Furthermore, the presented results were derived from limited MS patient samples because of the low incidence of MS patients with both extreme MRI phenotypes. Therefore, in future studies, the statistical power should be increased to clarify whether the enlargement of the putamen in MS ? SL is a real compensatory effect. In conclusion, using VBM and ROI-based analyses as well as TBSS, differences in MRI findings between MS patients with predominantly SL (MS ? SL) and those with CL and no detectable SL (MS ? CL) could be identified. In MS ? SL, the putamen, which is known to be involved in motor and learning functions, was found to be enlarged, suggesting that these patients may be better able to adapt to the progression of disability. In contrast, extensively reduced FA values in MS ? CL were caused by higher CL volume and thus presumably stronger demyelination, which subsequently leads to higher global GM atrophy; the associated decrease in putamen volume is accompanied by deficits in working memory. This study contributes to the understanding of the pathomechanism and extreme phenotypes of MS. Acknowledgments This study was supported by the Ministry of Science and Education/German Competence Network for Multiple Sclerosis (BMBF/KKNMS, B7.3 to FZ).
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