ORIGINAL ARTICLE
`
`Gray Matter Atrophy Is Related to Long-
`Term Disability in Multiple Sclerosis
`
`Leonora K. Fisniku, MRCP,1,2 Declan T. Chard, PhD,1,2 Jonathan S. Jackson, MSci,1,2
`Valerie M. Anderson, BSci,1,2 Daniel R. Altmann, PhD,1,3 Katherine A. Miszkiel, MRCP,4
`Alan J. Thompson, PhD,1,5 and David H. Miller, MD1,2
`
`Objective: To determine the relation of gray matter (GM) and white matter (WM) brain volumes, and WM lesion load, with
`clinical outcomes 20 years after first presentation with clinically isolated syndrome suggestive of multiple sclerosis (MS).
`Methods: Seventy-three patients were studied a mean of 20 years from first presentation with a clinically isolated syndrome (33
`of whom developed relapsing-remitting MS and 11 secondary-progressive MS, with the rest experiencing no further definite
`neurological events), together with 25 healthy control subjects. GM and WM volumetric measures were obtained from three-
`dimensional T1-weighted brain magnetic resonance images using Statistical Parametric Mapping 2.
`Results: Significant GM ( p ⬍ 0.001) and WM atrophy ( p ⫽ 0.001) was seen in MS patients compared with control subjects.
`There was significantly more GM, but not WM atrophy, in secondary-progressive MS versus relapsing-remitting MS ( p ⫽
`0.003), and relapsing-remitting MS versus clinically isolated syndrome ( p ⬍ 0.001). GM, but not WM, fraction correlated with
`⫽ 0.59; p ⬍ 0.001). WM
`⫽ ⫺0.48; p ⬍ 0.001) and MS Functional Composite scores (rs
`expanded disability status scale (rs
`⫽ ⫺0.63; p ⬍ 0.001), but not with WM fraction. Regression modeling indicated that the
`lesion load correlated with GM (rs
`GM fraction explained more of the variability in clinical measures than did WM lesion load.
`Interpretation: In MS patients with a relatively long and homogeneous disease duration, GM atrophy is more marked than
`WM atrophy, and reflects disease subtype and disability to a greater extent than WM atrophy or lesions.
`
`Magnetic resonance imaging (MRI)–detectable white
`matter (WM) lesions are usually seen early in relapse
`onset multiple sclerosis (MS), and in people who de-
`velop a clinically isolated syndrome (CIS) suggestive of
`MS, they are associated with conversion to clinically
`definite MS,1,2 although they predict subsequent dis-
`ability only to a limited degree.3,4
`Brain atrophy is also seen from clinical disease onset
`in MS5; it is prominent in the later stages of the disease,
`and is more marked in secondary progressive (SP) com-
`pared with relapsing-remitting (RR) phenotypes of
`MS,6,7 although the relative influence of disease pheno-
`type and disease duration on such atrophy is uncertain.
`From pathological studies extensive cortical damage
`has been observed predominantly in progressive forms
`of MS, suggesting that GM pathology may be an im-
`irreversible disability.8 Al-
`portant determinant of
`though whole-brain atrophy has been well explored,
`the advent of new MRI acquisition and analysis tools
`now makes it possible to determine the relative extent
`
`From the 1Nuclear Magnetic Resonance Research Unit; 2Depart-
`ment of Neuroinflammation, Institute of Neurology, University
`College London; 3Department of Neuroradiology, National Hospi-
`tal for Neurology and Neurosurgery; 4Department of Brain Repair
`and Rehabilitation, Institute of Neurology, University College Lon-
`don; and 5Medical Statistical Unit, London School of Hygiene and
`Tropical Medicine, London, United Kingdom.
`Received Mar 4, 2008, and in revised form Apr 9, 2008. Accepted
`for publication Apr 11, 2008.
`
`Ann Neurol 2008;64:247–254
`
`of both GM and WM atrophy. Recent work investi-
`gating the progression of tissue-specific atrophy, mea-
`sured using methods based on Statistical Parametric
`Mapping (SPM) segmentations, after first presenta-
`tion with a CIS showed significantly greater GM
`compared with WM atrophy in those patients who
`developed clinically definite MS within 3 years.9 Fur-
`thermore, in patients with early RRMS, GM atrophy
`over 2 years was more rapidly progressive than WM
`atrophy.10 These studies suggest that progressive GM
`atrophy occurs early in the clinical course of MS, and
`in the case of CIS, is of direct and immediate clinical
`relevance. Although some studies have detected pre-
`dominantly GM atrophy,9,11–14 not all have; indeed,
`some observed mostly WM atrophy,15 and it remains
`to be definitively determined which tissue is most af-
`fected at any given stage of the disease, particularly in
`the longer term.
`The relation between WM lesions and brain atrophy
`also remains unclear, with current evidence suggesting
`
`Published online in Wiley InterScience (www.interscience.wiley.com).
`DOI: 10.1002/ana.21423
`
`Address correspondence to Dr Fisniku, NMR Research Unit, Insti-
`tute of Neurology, Queen Square, London WC1N 3BG, United
`Kingdom. E-mail: l.fisniku@ion.ucl.ac.uk
`
`© 2008 American Neurological Association 247
`Published by Wiley-Liss, Inc., through Wiley Subscription Services
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`a partial discordance between these pathological mani-
`festations of MS16 both in cross-sectional and longitu-
`dinal studies.9,11,12,17–21 The suggestion that there is at
`least a partial discordance between T2 lesion load
`(T2LL) and atrophy measures during the evolution of
`MS is supported by the observation that, although
`disease-modifying therapies such as ␤-interferon are
`relatively effective in preventing new WM lesion for-
`mation, their effect in reducing atrophy has been mod-
`est,22,23 and in some studies, not evident at all.24,25
`With this context, the primary objective of this study
`was to estimate GM and WM volumes in a cohort of
`CIS patients followed-up 20 years from clinical disease
`onset, and to assess the relation between these measures
`of tissue-specific atrophy, clinical course, and disability,
`in particular, investigating the hypothesis that GM atro-
`phy will correlate better with clinical disease severity.
`Secondary objectives were as follows: (1) to evaluate the
`relation of GM and WM volumes with T2LL, and (2)
`to investigate the relative contributions of GM and WM
`volumes and T2LL to disability.
`
`Methods
`Subjects
`This report is based on 20-year follow-up data of a cohort
`who had clinical and MRI assessments at approximately
`5-yearly intervals after presenting with a CIS suggestive of
`MS.3,4 Clinical
`status was documented at
`the 20-year
`follow-up in 107 patients,4 of whom 75 had an MRI exam-
`ination, with data from two patients excluded (one who de-
`veloped cerebrovascular disease and one who did not com-
`plete the scanning protocol). The remaining 73 patients are
`the subject of this report.
`Clinically definite MS was diagnosed on clinical grounds
`alone.26 Disability was assessed using the expanded disabil-
`ity status scale (EDSS)27 and MS functional composite
`(MSFC) scores.28 The clinical course of MS (RRMS or
`SPMS) was defined by Lublin and Reingold criteria.29
`Those clinically definite MS patients with an EDSS ⱕ 3
`were defined as benign MS. Patients were studied a mean
`(standard deviation [SD]) of 20 [1.5] range, (18 –27) years
`after the CIS (49 women and 24 men; mean age, 51.4
`[7.2] years); 29 were still classified as CIS (mean disease
`duration, 20.4 [2.06] years; mean age, 51.5 [8.4] years), 33
`had developed RRMS (mean disease duration, 19.7 [1.1]
`years; mean age, 51 [6.1] years) and 11 SPMS (mean dis-
`ease duration, 19.8 [0.68] years; mean age, 52 [7.3] years).
`The median EDSS was 2.5 (range, 0 – 8) for all patients
`and 3.25 (range, 1– 8) for MS patients only. Three patients
`were receiving disease-modifying treatments. MRI was also
`performed in 25 healthy control subjects (14 women and
`11 men; mean age, 41.7 [7.7] years).
`The study was approved by the National Hospital for
`Neurology and Neurosurgery and Institute of Neurology
`Joint Research Ethics Committee. All study participants gave
`written informed consent.
`
`248 Annals of Neurology Vol 64 No 3
`
`September 2008
`
`Image Acquisitions and Processing
`Whole-brain MRI was performed on a 1.5-Tesla GE Signa
`scanner (General Electric, Milwaukee, WI) as follows: (1) two-
`dimensional, dual-echo proton density (TE, 17 milliseconds)
`and T2 (TE, 103 milliseconds)-weighted fast spin-echo (repe-
`tition time [TR], 2,000 milliseconds; 28 ⫻ 5mm slices; field
`of view, 24 ⫻ 18cm; in-plane resolution of 1.1 mm); and (2)
`three-dimensional, axial, T1-weighted, inversion-prepared, fast
`spoiled gradient recall (TR, 10.9 milliseconds; TE, 4.2 milli-
`seconds; inversion time, 450 milliseconds; 124 ⫻ 1.5mm slic-
`es; imaging matrix, 256 ⫻ 160, interpolated to a final in-plane
`resolution of 1.1mm). An experienced neuroradiologist
`(K.A.M.), blinded to clinical details, identified lesions on hard
`copies of the proton density–weighted images, with reference
`to the T2-weighted images. This was then used as a reference
`for contouring of the lesions on the proton density–weighted
`digital images, using a semiautomated local thresholding tech-
`nique implemented in the image display package DispImage
`(Plummer, Department of Medical Physics and Bioengineer-
`ing, University College London, London, United Kingdom).30
`Then a computer program summed all the individual lesion
`volumes (calculated as surface area of each lesion multiplied by
`slice thickness), and T2LLs were generated.
`Segmentation of the axial, three-dimensional, T1-weighted
`images into WM, GM, and cerebrospinal fluid was performed
`using SPM2 (Statistical Parametric Mapping; Wellcome De-
`partment of Cognitive Neurology, Institute of Neurology,
`London), following a previously described method11 (software
`available free to the research community at www.nmrgroup.io-
`n.ucl.ac.uk/atrophy). The processing parameters for SPM2
`were set to 0.01 for the bias correction and 30 for the bias
`cutoff. WM and GM fraction volumes (GMF) relative to total
`intracranial volume were derived, corrected for lesion misclas-
`sification as GM.11 The tissue masks were inspected by an
`experienced operator, and no significant segmentation errors
`were detected.
`To assess the robustness of results obtained using SPM2,
`we reprocessed our data using SIENAX (Structural Image
`Evaluation, using Normalization, of Atrophy for cross-
`sectional measurement), a fully automated technique, to
`obtain the normalized GM and WM volumes.31 SIENAX
`methodology and results are provided in an Appendix.
`
`Statistical Analyses
`Group comparisons of the brain tissue volumes were per-
`formed using linear regression with group indicator and age
`and sex covariates. To assess the associations between the
`brain volume measurements, T2LL, and disability (EDSS
`and MSFC and its components), we used Spearman’s rank
`correlation.
`To assess the relative contribution of the WM and GM
`volume loss and T2LL to accrued disability, we used ordinal
`logistic regression (for EDSS) and linear regression (for
`MSFC). Both EDSS (categorized as follows: ⱕ1.5; ⬎1.5,
`and ⱕ3; ⬎3 and ⱕ6; ⬎6) and MSFC (as a continuous vari-
`able) were modeled as response variables, with tissue vol-
`umes, lesion load, age, and sex as covariate predictors. Lesion
`load was log-transformed to improve normality before inclu-
`sion in the regression models; where the T2LL was zero (10
`subjects), the log volume was given a value of 0.01 to in-
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`Table 1. Mean and Median (Standard Deviation) of
`Brain Volume Measurements
`
`Group (n)
`
`GMF Mean;
`Median (SD)
`
`WMF Mean;
`Median (SD)
`
`0.51; 0.52 (0.01)
`
`0.29; 0.29 (0.01)
`
`0.49; 0.49 (0.03)
`
`0.28; 0.28 (0.01)
`
`Control
`subjects
`(25)
`All patients
`(73)
`CIS (29)
`MS (44)
`RRMS (33)
`Benign MS
`(22)a
`Nonbenign
`MS (22)b
`0.27; 0.27 (0.01)
`0.45; 0.45 (0.03)
`SPMS (11)
`aBenign multiple sclerosis (MS) ⫽ expanded disability status
`scale (EDSS) ⱕ 3.
`bNonbenign MS ⫽ EDSS ⬎ 3.
`GMF ⫽ gray matter fraction; WMF ⫽ white matter fraction;
`CIS ⫽ clinically isolated syndrome; MS ⫽ multiple sclerosis
`(RRMS and SPMS); RRMS ⫽ relapsing-remitting MS;
`SPMS ⫽ secondary progressive MS.
`
`0.50; 0.50 (0.02)
`0.47; 0.48 (0.03)
`0.48; 0.49 (0.02)
`0.49; 0.49 (0.02)
`
`0.28; 0.28 (0.01)
`0.28; 0.28 (0.01)
`0.28; 0.28 (0.01)
`0.28; 0.28 (0.01)
`
`0.46; 0.46 (0.30)
`
`0.28; 0.27 (0.01)
`
`clude these subjects. Changing the EDSS category intervals,
`or the small value given for the log volume where the T2LL
`was zero, did not materially change the results.
`MRI covariates were entered together and removed singly
`by manual backward stepwise exclusion until all model predic-
`tors were significant at p ⬍ 0.1. Age and sex were added to
`the final models but omitted if the adjusted coefficients were
`both nonsignificant and not materially different from unad-
`justed coefficients. Models were applied to the whole cohort of
`patients and the MS subgroup separately.
`The data were analyzed using SPSS 11 (SPSS, Chicago,
`IL) and Stata 9.2 (Stata Corporation, College Station, TX).
`Statistical significance was taken at p ⬍ 0.05.
`
`Results
`Tissue-Specific Volumes and Clinical Subgroups
`Tissue-specific volumes were significantly lower in MS
`patients and MS subgroups (RRMS and SPMS) versus
`control subjects (Tables 1 and 2). Significant GM and
`WM atrophy was seen in MS patients compared with
`control subjects. There was significantly more GM at-
`rophy, but not WM atrophy, in SPMS versus RRMS
`and RRMS versus CIS. There was significantly greater
`GM atrophy, but not WM atrophy, in those (nonbe-
`nign) MS patients with an EDSS ⬎ 3 (22 patients)
`compared with those (benign) MS patients with an
`EDSS ⱕ 3 (22 patients). There were no significant dif-
`ferences for any of the volume measurements between
`the control subjects and those remaining classified as a
`CIS after first presentation.
`
`Magnetic Resonance Imaging Measures and Disability
`GMF correlated significantly with EDSS and MSFC
`for all patients and for the MS subgroup alone (Table
`3). WM fraction volumes showed no such correlations.
`⫽ 0.49; p ⬍
`T2LL also correlated with EDSS (rs
`⫽ ⫺0.53; p ⬍ 0.001) for all
`0.001) and MSFC (rs
`⫽ 0.38,
`patients, as well as in the MS subgroup (rs
`⫽ ⫺0.42, p ⫽ 0.005, respectively).
`p ⫽ 0.009; and rs
`
`Correlations of Lesions with Gray and White
`Matter Volumes
`⫽ ⫺0.63;
`T2LL correlated significantly with GMF (rs
`⫽
`p ⬍ 0.001) but not with WM fraction volumes (rs
`⫺0.15; p ⫽ 0.19) for the whole cohort of patients and
`⫽ ⫺0.66, p ⬍ 0.001,
`for the MS subgroup only (rs
`⫽ ⫺0.18, p ⫽ 0.22, respectively).
`and rs
`
`Predicting Disability
`For the whole cohort of patients, only GMF and log-
`transformed T2LL independently predicted EDSS cat-
`egory, with GMF the stronger predictor: There was an
`estimated 64% ( p ⫽ 0.001) reduction in the odds of
`having greater disability per 1 SD greater GMF, and a
`52% odds reduction ( p ⫽ 0.05) per 1 SD greater log-
`transformed T2LL.
`Only GMF independently predicted disability as mea-
`sured by MSFC scores; there was an estimated 0.61 in-
`crease (p ⬍ 0.001) in MSFC per 1 SD greater GMF.
`Restricting regression models to the MS subgroup
`of patients, only GMF independently predicted dis-
`ability, whether EDSS or MSFC: There was a 59%
`( p ⫽ 0.007) reduction in the odds of being in more
`severe EDSS category per 1 SD greater GMF, and
`there was a 0.67 increase ( p ⫽ 0.001) in MSFC per
`1 SD greater GMF.
`The findings using SIENAX measured tissue vol-
`umes were similar to those obtained using SPM2 (see
`the Appendix for further details).
`
`Discussion
`This study builds on previous work,20,32,33 character-
`izing tissue-specific brain atrophy in a group of peo-
`ple with MS or CIS who have a uniquely long and
`homogeneous disease duration (approximately 20
`years). It has allowed an exploration of the associa-
`tions and role as predictors of MRI measures, tissue-
`specific (GM and WM) atrophy and WM lesion load,
`with clinical phenotype and disability, relatively free
`of confounding by variability in disease duration.
`In this cohort of patients, both GM and WM at-
`rophy was seen in MS patients compared with control
`subjects, and the extent of GM atrophy was greater
`than that of WM atrophy in keeping with some pre-
`vious studies.9,11–14 Furthermore, there was signifi-
`cantly more GM, but not WM, atrophy in SPMS
`versus RRMS, and RRMS versus those remaining CIS
`
`Fisniku et al: GM Atrophy and Disability in MS
`
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`Table 2. Age- and Sex-Adjusted Mean Difference between Patient Subgroups and Control Subjects
`
`Group Comparisons
`
`GMF
`
`WMF
`
`Adjusted Mean Difference (95% CI)
`
`Adjusted Mean Difference (95% CI)
`
`p
`
`p
`<0.001
`ⴚ0.009 (ⴚ0.017 to (ⴚ0.001))
`ⴚ0.027 (ⴚ0.041 to (ⴚ0.014))
`MS-control subjects
`<0.001
`⫺0.003 (⫺0.010 to 0.003)
`ⴚ0.028 (ⴚ0.039 to (ⴚ0.017))
`MS-CIS
`<0.001
`ⴚ0.013 (ⴚ0.024 to (ⴚ0.002))
`ⴚ0.046 (ⴚ0.063 to (ⴚ0.028))
`SPMS-control subjects
`ⴚ0.008 (ⴚ0.017 to (ⴚ0.001))
`ⴚ0.021 (ⴚ0.035 to (ⴚ0.008))
`0.002
`RRMS-control subjects
`⫺0.006 (⫺0.016 to 0.003)
`ⴚ0.046 (ⴚ0.062 to (ⴚ0.030))
`0.001
`SPMS-CIS
`<0.001
`⫺0.002 (⫺0.009 to 0.004)
`ⴚ0.022 (ⴚ0.033 to (ⴚ0.010))
`RRMS-CIS
`⫺0.004 (⫺0.014 to 0.005)
`ⴚ0.024 (ⴚ0.040 to (ⴚ0.008))
`0.003
`SPMS-RRMS
`0.004 (⫺0.004 to 0.013)
`0.01
`0.022 (0.004–0.040)
`Benign-nonbenign MSa
`⫺0.006 (⫺0.015 to 0.002)
`0.001 (⫺0.013 to 0.015)
`0.089
`CIS-control subjects
`Benign MS ⫽ expanded disability status scale (EDSS) ⱕ 3; nonbenign MS ⫽ EDSS ⬎ 3.
`GMF ⫽ gray matter fraction; WMF ⫽ white matter fraction; CI ⫽ confidence interval; MS ⫽ multiple sclerosis (RRMS and SPMS);
`CIS ⫽ clinically isolated syndrome; SPMS ⫽ secondary progressive MS; RRMS ⫽ relapsing-remitting MS.
`
`0.017
`0.318
`0.018
`0.042
`0.179
`0.540
`0.361
`0.328
`0.142
`
`patients. It should be noted that GM atrophy has not
`been a universal finding in MS, and that a definitive
`consensus on the location and timing of brain atro-
`phy has yet to be reached; however, a significant
`number of recent studies suggest that GM atrophy is
`a consistent finding throughout the clinical course of
`MS, seemingly mirroring clinical status.9,10,32,34 The
`apparent discrepancy in some previous studies may
`represent a combination of cohort-related and techni-
`cal factors. Although there is no universally accepted
`gold standard method for measuring GM and WM
`volumes, the SPM-based approach has provided con-
`sistent findings in several previous studies,9 –11,32 and
`in this study, the robustness of the results obtained
`using SPM-based methods have been consolidated by
`similar findings with another widely used segmenta-
`tion method (SIENAX technique; see Appendix).
`Given the relatively homogeneous disease duration
`and age distribution of
`the clinical subgroups in-
`cluded in this work, the association of GM atrophy
`with clinical status is not explained by these factors;
`
`rather, the findings suggest a direct link between GM
`atrophy and clinical disease severity.
`Differential tissue-specific atrophy in MS may be par-
`tially explained by variable degrees of inflammatory ac-
`tivity in WM and GM,35,36 with relatively greater com-
`pensation of cell
`loss by inflammatory infiltrates and
`edema in WM compared with GM. Differential inflam-
`matory noise in the volumetric measures may also lead
`to greater attenuation of WM compared with GM asso-
`ciations with clinical parameters. However, it may be ex-
`pected that eventually atrophy, if progressive, would
`reach a magnitude where it would no longer be dis-
`guised by inflammatory interference; given this, our
`observations in MS patients with relatively long dis-
`ease duration suggest that WM atrophy is truly less
`progressive than that of GM, and not simply the re-
`sult of compensation by, and short-term fluctuations
`associated with, inflammation. In addition, although
`we detected no clear evidence of an association be-
`tween WM atrophy and disability, 50% of MS pa-
`tients in this cohort had a benign clinical course, and
`
`Table 3. Correlations of Brain Volume Measurements with Clinical Features
`
`rs (p)
`
`Z-WALK (n ⴝ 68)a
`(40b)
`
`Z-PASAT (n ⴝ 68)a
`(42b)
`
`GMFa
`GMFb
`WMFa
`WMFb
`aAll patients.
`bMultiple sclerosis (MS) subgroup only.
`rs ⫽ Spearman’s rank correlation coefficient; EDSS ⫽ expanded disability status scale; MSFC ⫽ multiple sclerosis functional composite
`score; GMF ⫽ gray matter fraction; WMF ⫽ white matter fraction.
`
`ⴚ0.40 (0.001)
`ⴚ0.49 (0.001)
`⫺0.11 (0.337)
`⫺0.09 (0.560)
`
`0.27 (0.026)
`0.32 (0.038)
`⫺0.07 (0.537)
`⫺0.04 (0.761)
`
`EDSS (n ⴝ
`73)a (44b)
`ⴚ0.48 (<0.001)
`ⴚ0.41 (0.005)
`⫺0.20 (0.086)
`⫺0.11 (0.443)
`
`MSFC (n ⴝ 67)a
`(41b)
`0.56 (<0.001)
`0.55 (<0.001)
`0.03 (0.784)
`0.10 (0.526)
`
`Z-PEG (n ⴝ 70)a
`(42b)
`0.59 (<0.001)
`0.44 (0.003)
`0.16 (0.176)
`0.28 (0.071)
`
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`it is conceivable that larger cohort with more severe
`disability (eg, EDSS score ⱖ 7) might exhibit more
`WM atrophy; further work is required to explore this
`possibility. Considered overall, our findings suggest
`that measures of GM atrophy will be more useful
`than WM volume in natural history studies or treat-
`ment trials, for example,
`in a study of potentially
`neuroprotective agents, although serial studies should
`further investigate the relation between longitudinal
`GM volume and clinical changes.
`GM, but not WM volume measurements, corre-
`lated with clinical disability (EDSS, MSFC, and its
`components). Although T2LL correlated significantly
`with disability, GMF was a better predictor of disabil-
`ity when included in the regression models. Whereas
`noting the caveats about inflammatory noise discussed
`previously, these data suggest that GM atrophy has
`more clinical relevance in the long term than either
`lesion load or WM atrophy in people with MS, being
`more closely related to long-term disability and clin-
`ical course. This study’s findings consolidate and ex-
`tend the observation made in several previous studies
`that MRI markers of GM involvement correlate more
`strongly with measures of physical disability than
`WM lesion load.8,32,33,37
`The amount of tissue loss in MS probably represents
`a balance between several pathological processes: irre-
`versible neuronal and axonal loss, myelin loss, and re-
`versible neuroaxonal atrophy, on the one hand, with
`partial compensation by inflammation-associated cellu-
`lar infiltrates, and cellular (including axonal38) and in-
`terstitial edema on the other. With regard to the mech-
`anisms of brain atrophy, there may be: (1) antegrade
`and retrograde neuroaxonal tract degeneration associ-
`ated with focal WM inflammatory lesions,39 with a
`potentially significant delay between axonal demyelina-
`tion and subsequent neuroaxonal degeneration; and
`(2) a more widespread process directly targeting neu-
`rons, myelin (including cortical demyelination35,36),
`and glia.
`GM (but not WM) volume measurements correlated
`with WM lesion load, which is in keeping with other
`studies.9 –11,32 This correlation may reflect secondary de-
`generation from WM lesions to GM. That the degree of
`correlation is only moderate suggests that processes in-
`dependent of WM lesions are also contributing to GM
`atrophy in MS. One such explanation might be that
`GM demyelinating lesions, although not visible on con-
`ventional MRI, are commonly found at autopsy.35,36
`Our findings emphasize that further research to eluci-
`date pathogenic mechanisms in MS should focus on
`GM as well as WM pathology.
`When considering the significance of the findings ob-
`served in our study, it is important to take into account
`a few limitations. First, neither WM lesion volume nor
`tissue-specific brain atrophy measurement is pathologi-
`
`cally specific. WM lesions on T2-weighted MRI may
`contain variable amounts of inflammation, demyelina-
`tion, edema, and axonal loss. The brain volume mea-
`surements, although affected by the same factors, are
`thought to be more specifically weighted toward neuro-
`degeneration. Second, our brain volume measurement
`data are cross sectional and do not provide any direct
`information on the temporal evolution of atrophy, in-
`formation that can be gathered using only serial MRI
`data. We therefore cannot determine whether the atro-
`phy observed in this study occurred immediately before
`or many years before this study. Although the patients
`were scanned at earlier time points,3 there has been a
`major scanner hardware upgrade since then, rendering it
`difficult to directly compare measurements from earlier
`scanning with that obtained at 20 years. Third, some of
`the more disabled patients were not able to be scanned,
`so our data are relatively biased toward a less disabled
`subset of the patients previously studied.4 Fourth, spinal
`cord involvement makes an important contribution to
`locomotor disability in MS and was not included in this
`investigation. Finally, with the SPM-based methods,
`misclassification of lesions or nonbrain tissue as GM
`may lead to a relative underestimation of the apparent
`magnitude of GM disease effects; however, correction
`for lesion misclassification was performed, and quality
`assurance review of the scans found no additional signif-
`icant segmentation errors; thus, there should not have
`been significant misclassification effects.
`Notwithstanding these caveats,
`the study clearly
`found that in MS patients with a relatively long and
`homogeneous disease duration (approximately 20
`years), GM atrophy is greater than WM atrophy, and
`reflects disease subtype and disability. It also helps to
`understand why a limited relation between WM lesions
`and disability in MS has been evident in many previ-
`ous MRI clinical studies of both natural history and
`therapeutic intervention, and highlights a need to bet-
`ter understand and monitor GM pathology in MS.
`
`Appendix: Gray and White Matter Volumes
`Measured Using SIENAX and Their Relationship
`with Clinical Subgroups and Disability
`SIENAX Methodology
`SIENAX was used to obtain the normalized (per subject
`head size) GM and WM brain volumes (NGMV and
`NWMV). In brief, SIENAX first extracts brain and skull
`voxels from the input MR data, using the Brain Extrac-
`tion Tool (www.fmrib.ox.ac.uk/fsl). The brain image is
`then affine-registered to standard space brain and skull
`images, derived from the MNI152 standard space refer-
`ence set, with the skull registration used to determine
`the head size normalization factor. Next, tissue type seg-
`mentation, with partial volume estimation, is performed
`to calculate the total volume of brain tissues, including
`
`Fisniku et al: GM Atrophy and Disability in MS
`
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`Table 1. Normalized Brain Volume Measurements in Controls and Clinical Subgroups
`
`Groups (Number of
`Patients)
`
`NGMV, Mean (ml);
`Median (SD)
`
`NWMV Mean (ml);
`Median (SD)
`
`910; 916 (40.4)
`Controls (25)
`885; 888 (40.8)
`CIS (29)
`858; 870 (60.7)
`All patients (73)
`840; 853 (65.4)
`CDMS (44)
`856; 865 (60.4)
`RRMS (22)
`Benign MSa (22)
`874; 885 (61.8)
`806; 798 (50.3)
`Nonbenign MS (22)
`795; 793 (61.3)
`SPMS (11)
`aBenign MS ⫽ Expanded Disability Status Score (EDSS) ⱕ3; nonbenign MS ⫽ EDSS ⬎3.
`CDMS ⫽ clinically definite multiple sclerosis; CIS ⫽ clinically isolated syndrome; NGMV ⫽ normalized gray matter volume;
`NWMV ⫽ normalized white matter volume; RRMS ⫽ relapsing-remitting multiple sclerosis; SD ⫽ standard deviation; SPMS ⫽
`secondary progressive multiple sclerosis.
`
`663; 669 (33.0)
`658; 657 (36.0)
`653; 652 (35.4)
`650; 649 (35.0)
`649; 649 (33.7)
`659; 660 (31.0)
`640; 643 (36.8)
`652; 646 (40.6)
`
`separate estimates of volumes of GM, WM, and ventric-
`ular CSF. The estimated volumes for a subject then are
`multiplied by the normalization factor to obtain NGMV
`and NWMV (normalized CSF and whole brain volumes
`were also obtained; data not presented here).
`
`Relationship of SIENAX Tissue Volumes with
`Clinical Subgroups
`SIENAX yielded NGMV results consistent with the
`GMF obtained by SPM2. For the NWMV, there were
`no significant differences in any group comparisons
`(Appendix Tables 1 and 2).
`
`Relationship of SIENAX Tissue Volumes with
`Measures of Clinical Impairment and Disability
`NGMV correlated significantly with EDSS and MSFC
`for all patients, and the MS subgroup only (Table 3).
`
`NWMV correlated significantly only with EDSS and
`not with MSFC (Table 3).
`
`Correlations between SIENAX Tissue Volumes and
`Lesion Load
`⫽ ⫺0.57;
`T2LL correlated significantly with NGMV (rs
`⫽ ⫺0.14; p ⫽
`p ⬍ 0.001) but not with NWMV (rs
`0.22) for all patients and for the MS subgroup only:
`⫽ ⫺0.10; p ⫽ 0.50,
`⫽ ⫺0.67; p ⬍ 0.001 and rs
`rs
`respectively.
`
`Models to Predict Disability with SIENAX Tissue
`Volumes and Lesion Load
`On the regression models, only NGMV independently
`predicted disability whether EDSS or MSFC, for the
`whole cohort of patients and for the MS subgroup
`only. For the whole cohort of patients, there was a
`
`Table 2. Age- and Sex-Adjusted Mean Difference between Patient Subgroups and Controls
`
`Group
`Comparisons
`
`NGMV
`
`NWMV
`
`Adjusted Mean Difference
`(ml), 95% CI)
`
`p Value
`
`Adjusted Mean Difference
`(ml), (95% CI)
`
`p Value
`
`⫺14.11 (⫺33.99 to 5.76)
`⬍0.001
`⫺56.52 (⫺85.05 to ⫺27.99)
`MS-controls
`⫺8.62 (⫺25.37 to 8.11)
`⬍0.001
`⫺47.60 (⫺71.64 to ⫺23.57)
`MS-CIS
`⫺11.12 (⫺38.43 to 16.17)
`⬍0.001
`⫺96.63 (⫺133.91 to ⫺59.35)
`SPMS-controls
`⫺15.08 (35.95 to 5.78
`⫺43.49 (⫺71.98 to ⫺15.00)
`0.003
`RRMS-controls
`⫺5.68 (⫺30.55 to 19.18)
`⬍0.001
`⫺87.06 (⫺121.02 to ⫺53.11)
`SPMS-CIS
`⫺9.64 (27.63 to 8.33)
`⫺33.92 (⫺58.48 to ⫺9.37)
`0.007
`RRMS-CIS
`3.95 (⫺20.70 to 28.62)
`⫺53.14 (⫺86.08 to ⫺19.47)
`0.002
`SPMS-RRMS
`17.69 (⫺3.30 to 38.70)
`Benign-nonbenign MSa
`0.001
`65.25 (30.05 to 100.44)
`⫺5.48 (⫺26.80 to 15.82)
`⫺8.91 (⫺39.51 to 21.68)
`0.564
`CIS-controls
`aBenign MS ⫽ Expanded Disability Status Score (EDSS) ⱕ3; nonbenign MS ⫽ EDSS ⬎ 3
`CI ⫽ confidence interval; CIS ⫽ clinically isolated syndrome; MS ⫽ multiple sclerosis (all cases); NGMV ⫽ normalized gray matter
`volume; NWMV ⫽ normalized white matter volume; RRMS ⫽ relapsing-remitting multiple sclerosis; SPMS ⫽ secondary progressive
`multiple sclerosis.
`
`0.162
`0.309
`0.420
`0.155
`0.651
`0.290
`0.751
`0.096
`0.610
`
`252 Annals of Neurology Vol 64 No 3
`
`September 2008
`
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`Table 3. Correlations of Brain Volume Measurements with Clinical Features
`
`Normalized
`Brain
`Volumes
`
`aNGMV
`
`bNGMV
`
`aNWMV
`
`EDSS
`N ⴝ 73a
`(44b),
`
`MSFC
`N ⴝ 67a
`(41b)
`
`rs ( p )
`
`9HPT
`N ⴝ 70a
`(42b)
`
`Time-Walked
`N ⴝ 68a
`(40b)
`
`PASAT
`N ⴝ 68a
`(42b)
`
`⫺0.62 (⬍0.001)
`
`0.50 (⬍0.001)
`
`0.62 (⬍0.001)
`
`⫺0.37 (0.002)
`
`⫺0.53 (⬍0.001)
`
`0.41 (0.007)
`
`0.53 (⬍0.001)
`
`⫺0.45 (0.003)
`
`⫺0.31 (0.006)
`
`0.16 (0.179)
`
`0.13 (0.260)
`
`⫺0.30 (0.011)
`
`0.21
`(0.086)
`0.15
`(0.325)
`0.19
`(0.118)
`0.21 (0.75)
`
`⫺0.28 (0.058)
`
`0.16 (0.305)
`
`0.26 (0.097)
`
`⫺0.21 (0.183)
`
`bNWMV
`aAll patients.
`bCDMS patients only.
`CIS ⫽ clinically isolated syndrome; CDMS ⫽ clinically definite multiple sclerosis (all cases); RRMS ⫽ relapsing-remitting multiple
`sclerosis; SPMS ⫽ secondary progressive multiple sclerosis; NGMV ⫽ normalized gray matter volume; NWMV ⫽ normalized white
`matter volume; 9HPT ⫽ 9 hole peg test; PASAT ⫽ Paced Auditory Serial Addition Task Scores.
`
`79% ( p ⬍ 0.001) reduction in odds of being in a more
`severe EDSS category per 1 SD higher NGMV, and
`there was a 0.53 increase ( p ⬍ 0.001) in MSFC per 1
`SD higher NGMV.
`In the MS subgroup, there was a 69% ( p ⫽ 0.001)
`reduction in odds of being in a more severe EDSS cat-
`egory per 1 SD higher NGMV, and there was a 0.48
`increase ( p ⫽ 0.001) in MSFC per 1 SD higher
`NGMV.
`
`Summary
`In this study, SIENAX and SPM-based methods gen-
`erally provided similar results when investigating the
`relationship of GM and WM volumes with clinical
`course, clinical features, and T2LL. However, WM-
`segmentation obtained by SIENAX appeared to be
`less accurate compared with SPM2, because deep gray
`matter was not as clearly segmented, and misclassifi-
`cation of WM lesions was not adjusted for, perhaps
`explaining why there were no differences between the
`NWMV (derived from SIENAX) between MS pa-
`tients and controls, while differences were observed in
`WMF (derived from SPM2).
`
`The Nuclear Magnetic Resonance Research Unit, University College
`London is supported by the Multiple Sclerosis Society of Great Brit-
`ain and Northern Ireland. This work was undertaken at University
`College London Hospital/University College London, which re-
`ceived funding from the Department of Health’s National Institute
`for Health Research Biomedical Research Centers funding scheme.
`
`We thank Dr P. Brex for setting up the database, Dr D. Tozer for
`help with SIENAX analyses, C. Benton and R. Gordon for perform-
`ing the MRI scans, and the subjects who participated in this study.
`
`References
`1. Polman CH, Reingold SC, Edan G, et al. Diagnostic criteria
`for multiple sclerosis: 2005 revisions to the “McDonald Crite-
`ria.” Ann Neurol 2005;58:840 – 846.
`
`2. Swanton JK, Rovira A, Tintore M, et al. MRI criteria for mul-
`tiple sclerosis in patients presenting with clin