doi:10.1093/brain/awh498 Brain (2005), 128, 1454–1460 Grey and white matter volume changes in early primary progressive multiple sclerosis: a longitudinal study

Jaume Sastre-Garriga, Gordon T. Ingle, Declan T. Chard, Mara Cercignani, Lluı´s Ramio´-Torrenta`, David H. Miller and Alan J. Thompson Downloaded from https://academic.oup.com/brain/article/128/6/1454/432021 by guest on 28 September 2021 Institute of Neurology, University College London, Correspondence to: Professor Alan Thompson, London, UK Institute of Neurology, UCL, 23 Queen Square, London WC1N 3BG, UK E-mail: [email protected]

Summary We have recently reported brain atrophy in the early were calculated using two techniques, SPM99 (statistical stages of primary progressive multiple sclerosis (PPMS), parametric mapping) and SIENA (structural image evalu- affecting both grey and white matter (GM and WM). ation, using normalization, of atrophy). Stepwise linear However, to date no clinical or radiological predictors of regression models were applied to baseline variables to GM and WM atrophy have been identified. The aim was to identify independent predictors of atrophy development. investigate short-term changes in GM and WM volumes Using SPM99, a decrease in brain parenchymal fraction and to assess the predictive value of demographic, clinical (À1.03%; P < 0.001) and GM fraction (À1.49%; P < 0.001) and radiological variables in order to gain a better under- was observed. The number of enhancing lesions independ- standing of the pathological substrate underlying these ently predicted decrease in brain parenchymal fraction changes. Thirty-one subjects with PPMS within 5 years (P = 0.019) and decrease in WM fraction (P = 0.002). No of symptom onset were studied at baseline and after 1 year. independent predictors of GM fraction decrease were At baseline, patients underwent neurological examination found. A mean brain volume change of À0.63% (range and were scored on the Expanded Disability Status Scale À4.27% to +1.18%; P = 0.002) was observed using (EDSS) and Multiple Sclerosis Functional Composite. They SIENA, which was independently predicted by EDSS had 3D inversion-prepared fast spoiled gradient recalled (P = 0.004). Global and GM atrophy can be detected over (FSPGR), dual-echo and triple-dose post-contrast T1- a 1-year period in early PPMS. The former may be weighted spin echo MRI scans. Proton density and enhanc- predicted by the degree of inflammation, while the latter ing lesion loads were determined. The 3DFSPGR sequence seems to be independent of it. SIENA and SPM-based was repeated after 1 year and brain volume changes methods appear to provide complementary information.

Keywords: primary progressive multiple sclerosis; early phase; white matter atrophy; grey matter atrophy; enhancing lesions

Abbreviations: EDSS = Expanded Disability Status Scale; FOV = field of view; GM = grey matter; MSFC = Multiple Sclerosis Functional Composite; NEX = number of excitations; PPMS = primary progressive multiple sclerosis; SPM = statistical parametric mapping; SIENA = structural image evaluation, using normalization, of atrophy; ST = slice thickness; TE = echo time; TIV = total intracranial volume; TR = repetition time; WM = white matter; 3DFSPGR = 3D inversion-prepared fast spoiled gradient recalled Received October 18, 2004. Revised January 7, 2005. Accepted March 11, 2005. Advance Access publication April 7, 2005

Introduction Atrophy is a relatively well-established marker of axonal loss Miller et al., 2002). However, it has been argued that the and disability in multiple sclerosis (Losseff et al., 1996; value of atrophy as a marker is limited by the fact that it Lycklama a` Nijeholt et al., 1998; Rovaris et al., 2001; represents the final step in the chain of pathological

# The Author (2005). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: [email protected] Grey and white matter atrophy in PPMS 1455 changes leading to tissue loss, and, as a consequence, disability Z scores for the MSFC subtests were calculated using the published changes related to brain atrophy are likely to be irrecoverable methods with our own sample as a reference (Cutter et al., 1999). (Miller et al., 2002). However, the ability to predict atrophy One patient had a course of oral steroids 3 months before the second development is of clinical relevance, as it will help predict scan, and no patient was on disease-modifying drugs. The study irreversible disability. Until recently, atrophy measures were was approved by the joint ethics committee of the National Hospital for Neurology and Neurosurgery and the Institute of Neurology based on partial (Losseff et al., 1996), indirect (Lycklama a` (London) prior to study initiation. All subjects gave written Nijeholt et al., 1998) or global (Rudick et al., 1999) techniques informed consent before being included in the study. unable to focus on specific brain tissue types. The development of segmentation-based methods (Miller et al., 2002) has allowed white and grey matter volumes to be assessed MRI methods separately. Even though multiple sclerosis has long been Scan acquisition Downloaded from https://academic.oup.com/brain/article/128/6/1454/432021 by guest on 28 September 2021 considered a white matter condition, the involvement of All images were acquired using a 1.5 T Signa scanner (General grey matter in its pathology (Kidd et al., 1999; Peterson Electric, Milwaukee, WI, USA). No major hardware upgrades were et al., 2001) and its relevance to disability has recently been carried out on the scanner, and weekly quality assurance sessions reinforced (De Stefano et al., 2003), highlighting a potential confirmed the stability of measurements throughout the study. role for tissue-specific measures of atrophy. Three sets of images were acquired at baseline: a 3D inversion- Primary progressive multiple sclerosis (PPMS) is a useful prepared fast spoiled gradient recalled (3DFSPGR) sequence [124 model to investigate the evolution of atrophy and its predict- contiguous axial slices, echo time (TE) 4.2 ms, repetition time (TR) ors, as the potential confounders of inflammation and oedema 13.3ms,inversiontime450ms,numberofexcitations(NEX)1,fieldof play a lesser role (Thompson et al., 1991; Revesz et al., 1994). view (FOV) 300 3 225 mm over an image matrix of 256 3 160 However, findings in PPMS must be generalized with caution, (interpolated to a 256 3 256 reconstructed matrix for a final x–y in- as the pathological processes leading to atrophy might vary in plane resolution of 1.17 3 1.17 mm), slice thickness (ST) 1.5 mm]; a dual spin echo sequence (28 contiguous slices, TE 30/80 ms, TR different forms of the condition. The present study focuses on 1720 ms, NEX 0.75, ST 5 mm); and a T1-weighted spin echo sequence patients with early PPMS, as they are more likely to display a (28 contiguous axial slices, TE 20 ms, TR 540 ms, NEX 1, flip closer relationship between clinical and radiological variables angle 90, ST 5 mm), acquired before and 5 min after intravenous than those with more established disease (Cottrell et al., 1999; administration of triple-dose gadolinium (0.6 ml/kg of body weight; Sastre-Garriga et al., 2004). We used a method based on SPM Magnevist, Schering, Germany). For the sake of patient comfort, scan segmentation (statistical parametric mapping; Functional sessions were split into two, and T1 gadolinium-enhanced scans Imaging Laboratory, University College London) to obtain were acquired in a separate session; the mean separation between quantitative information on grey and white matter volume. sessions was 14.8 days (median 7; interquartile range 7–24). The Recognizing that different atrophy measurement methods 3DFSPGR sequence was acquired again 1 year later (mean time have different potential for bias, the MRI data have also between acquisitions 12.2 months; interquartile range 11.4–13.1). been processed using another fully automated and well tested methodology to assess the evolution of whole brain atrophy over time: SIENA (structural image evaluation, using nor- Image analysis malization, of atrophy; Centre for Functional Magnetic Lesion loads were estimated from proton density-weighted images Resonance Imaging of the Brain, Oxford) (Smith et al., 2002). and gadolinium-enhanced baseline scans using Dispimage Thus,this workset out tostudy the evolution oftissue-specific (D. L. Plummer, University College London, London, UK). brain atrophy in patients with clinically early PPMS. Its 3DFSPGR images from baseline and 1-year scans were segmented into white matter (WM), grey matter (GM) and cerebrospinal fluid applications are twofold: first, to generate a hypothesis on the (CSF) using SPM99 (available at http://www.fil.ion.ucl.ac.uk/spm/) pathological substrate underlining the changes observed, and following a previously described method (Chard et al., 2002a; secondly to identify factors which may predict these changes Sastre-Garriga et al., 2004). Using in-house software, lesion masks and to use these changes themselves to evaluate interventions. were obtained by contouring lesion on the 3DFSPGR scans; these masks were subtracted from WM, GM and CSF segmentations to obtain four mutually exclusive tissue masks with their volumes in millilitres. Total intracranial volume (TIV) was calculated as Methods WM + GM + CSF + lesion mask volumes. Brain parenchymal frac- Patients tion was calculated as (WM + GM + lesion mask volumes)/TIV, Thirty-one patients with PPMS within 5 years of disease onset were white matter fraction as (WM + lesion mask volumes)/TIV, and recruited at the National Hospital for Neurology and Neurosurgery, grey matter fraction as GM volume/TIV. Brain atrophy was also Queen Square, London. All patients fulfilled the PPMS diagnostic estimated on 3DFSPGR images from baseline and 1-year scans criteria (definite or probable) (Thompson et al., 2000). These patients using SIENA version 2.2 (available at http://www.fmrib.ox.ac.uk/ are part of a larger cohort of patients with clinically early PPMS from analysis/research/siena/). Briefly, SIENA is a fully automated which baseline results are already available (Sastre-Garriga et al., method of analysis which works on serially acquired images and 2004). All patients underwent conventional neurological assessment performs brain extraction on both baseline and follow-up images, and were scored on the Expanded Disability Status Scale (EDSS) followed by registration of both brain images to obtain an estimate and Multiple Sclerosis Functional Composite (MSFC) at baseline. of the percentage brain volume change (PBVC) between scans 1456 Jaume Sastre-Garriga et al.

(Smith et al., 2002). Coefficients of variation for SPM are as follows: Table 1 Baseline clinical, demographic and radiological 0.2% for brain parenchymal fraction, 0.4% for grey matter fraction features of patients and 0.9% for white matter fraction (Chard et al., 2002a). For PBVC Patients (n = 31) the median absolute error is 0.15% (Smith et al., 2002). Sex ratio (male/female) 58%/42% Age at scanning* 46 (26–62) Statistical methods Disease duration (years)* 3.0 (2–5) Statistical analysis was performed using SPSS 11.5 (SPSS, Chicago, EDSS* 4.5 (3.5–7) IL, USA). A paired samples t-test was performed to detect statist- MSFC** À0.316 (1.44) Brain parenchymal fraction** 0.796 (0.035) ically significant differences between baseline and 1-year estimates White matter fraction** 0.266 (0.022) of atrophy (brain parenchymal, grey and white matter fractions) and Grey matter fraction** 0.529 (0.023) a one sample t-test for PBVC. Percentage changes in tissue fractions Number of gadolinium-positive lesions** 0.96 (1.44) Downloaded from https://academic.oup.com/brain/article/128/6/1454/432021 by guest on 28 September 2021 were obtained by subtracting the baseline from the 1-year follow-up Patients with gadolinium-positive lesions 48.4% estimates and dividing by the baseline values, then multiplying by Proton density lesion volume**,*** 26.01 (22.97) 100. Pearson correlation coefficients were used to assess the presence of linear associations between PBVC, the percentage change in tissue *Median (range); **mean (SD); ***Lesion volume in ml; fractions (brain parenchymal, grey and white matter fractions) and EDSS = Expanded Disability Status Scale; MSFC = Multiple clinical and radiological variables, except for EDSS, when Spearman Sclerosis Functional Composite. rank correlation coefficients were calculated. Significance values for correlation coefficients are reported without correction for multiple comparisons to avoid type II errors (Perneger, 1998). Stepwise linear Table 2 Values for brain parenchymal fraction, white regression was performed with PBVC and the per centage change matter fraction and grey matter fraction obtained with in tissue fractions (brain parenchymal, grey and white matter SPM-based methods and for PBVC obtained with fractions) as dependent variables against three sets of independent SIENAv2.2 variables: clinical (age, disease duration, EDSS, MSFC), radiological Baseline 12 months P (age, disease duration, number of gadolinium-enhancing lesions, proton density lesion volume) and combined (age, disease Brain parenchymal 0.796 (0.035) 0.787 (0.038) <0.001 duration, EDSS, MSFC, number of gadolinium-enhancing lesions, fraction* proton density lesion volume). White matter fraction* 0.266 (0.022) 0.266 (0.022) 0.749 Grey matter fraction* 0.529 (0.023) 0.521 (0.025) <0.001 PBVC*,** – À0.636 (1.05) 0.002 Results *Mean (standard deviation); **Values for PBVC represent percentage changes from baseline examination; Sample characteristics (Table 1) PVBC = percentage brain volume change. The 31 patients with clinically early PPMS (less than 5 years of disease duration) had a mean follow-up time of 12.2 months (interquartile range 11.4–13.1). There were 18 males and linear associations were found between enhancing lesion 13 females; median age at baseline was 46.0 years (range numbers at baseline and PVBC (r = À0.458, P = 0.010), 26–62) and median disease duration was 3.0 (range 2–5). brain parenchymal fraction percentage change (r = À0.419, Median EDSS at baseline was 4.5 (range 3.5 range: 7.0) P = 0.019) and white matter fraction percentage change and mean MSFC was À0.3162 (SD 1.44). Mean proton (r = À0.532, P = 0.002). Brain parenchymal fraction percent- density lesion load was 26.01 ml (SD 22.97) and the mean age change was also associated with baseline proton density number of gadolinium-enhancing lesions was 0.96 (SD 1.44). lesion load (r = À0.364, P = 0.044) and a significant linear association was found between EDSS at baseline and PVBC Evolution of brain atrophy (Table 2, Fig. 1) over the 12 months of the study (r = À0.509, P = 0.003). No significant associations for grey matter fraction change were Using the SPM-based segmentation method, significant found with any of the variables tested. decreases (P < 0.001) were observed in both brain paren- chymal fraction (mean À1.03%; SD 1.3%) and grey matter fraction (mean À1.50%; SD 1.6), whereas white matter Brain atrophy predictors: stepwise regression fraction did not change significantly (À0.07%; P = 0.749). models; dependent variables Using SIENAv2.2 a decrease of À0.63% (SD 1.05) was observed (P = 0.002). Brain parenchymal fraction percentage change over 12 months No independent predictor was found in the clinical model; Brain atrophy predictors: univariate analysis the number of enhancing lesions was the only independent (Table 3) predictor of brain parenchymal fraction percentage change No significant gender differences were found in the develop- in both the radiological and combined models (R2 = 0.17, ment of atrophy for any of the measures studied. Significant P = 0.019). Grey and white matter atrophy in PPMS 1457

3 3

2 2

1 1

0 0

-1 -1

-2 -2 Downloaded from https://academic.oup.com/brain/article/128/6/1454/432021 by guest on 28 September 2021

-3 -3

-4 -4

-5 -5 WMF percentage change PBVC

Cases Cases

3 3

2 2

1 1

0 0

-1 -1

-2 -2

-3 -3

-4 -4

-5 -5 BPF percentage change GMF percentage change

Cases Cases

Fig. 1 Bar graphs displaying percentage brain volume change (PBVC) obtained with SIENAv2.2 (top left) and percentage changes in brain parenchymal fraction (BPF; bottom left), white matter fraction (WMF; top right) and grey matter fraction (GMF; bottom right) obtained with SPM-based methods.

PBVC over 12 months the number of enhancing lesions as the only independent EDSS was found to be an independent predictor when applied predictor (R2 = 0.28; P = 0.002). to the clinical model (R2 = 0.25, P = 0.004); on application of the radiological model the number of enhancing lesions was Grey matter fraction percentage change over found to be the only independent predictor (R2 = 0.21, P = 12 months 0.010). Only EDSS at baseline was found to be independently associated (R2 = 0.25, P = 0.004) in the combined model. No significant predictors were found in any of the three models. White matter fraction percentage change over 12 months Discussion No independent predictor was found in the clinical model; the The findings of the present study may be summarized as: number of enhancing lesions was found to be the only inde- (i) changes in grey but not white matter volume can be detec- pendent predictor of white matter fraction percentage change ted in patients with clinically early PPMS over periods of time (R2 = 0.28; P = 0.002) in the radiological model; on applying as brief as 1 year; (ii) the number of enhancing lesions at the combined model a similar result was obtained, with baseline is predictive of white matter atrophy development; 1458 Jaume Sastre-Garriga et al.

Table 3 Correlations of PBVC, brain parenchymal the findings of this study would be that inflammation and fraction, white matter fraction and grey matter fraction gliosis may be obscuring white matter volume loss, whereas percentage changes after 12 months with demographic, grey matter tissue loss occurs without reactive gliosis or clinical and radiological measures at baseline inflammation that may mask volume loss (Peterson et al., rP2001). The present findings are also consistent with two longitudinal studies from our group, which have assessed PBVC the evolution of grey and white matter atrophy in patients Age 0.107 0.567 Disease duration 0.136 0.466 with clinically isolated syndromes (Dalton et al., 2004) and EDSS* À0.509 0.003 relapsing–remitting multiple sclerosis (Chard et al., 2004). MSFC 0.190 0.306 Both studies have found a significant decrease in grey

Proton density lesion load À0.202 0.275 matter fraction with no concomitant decrease in white Downloaded from https://academic.oup.com/brain/article/128/6/1454/432021 by guest on 28 September 2021 Enhancing lesion numbers À0.458 0.010 matter fraction, although white matter fraction was reduced Brain parenchymal fraction (% change) Age 0.286 0.119 at baseline in early relapsing–remitting and PPMS when Disease duration 0.288 0.117 compared with healthy control subjects, suggesting that it EDSS* À0.165 0.375 may be an early feature (Chard et al., 2002b; Sastre-Garriga MSFC 0.056 0.763 et al., 2004). As patients from both studies are in the early Proton density lesion load À0.364 0.044 clinical stages of the condition this might indicate that white Enhancing lesion numbers À0.419 0.019 White matter fraction (% change) matter loss has already taken place in a preclinical stage. Age 0.304 0.096 Disease duration 0.325 0.074 EDSS* À0.154 0.407 The number of enhancing lesions predicts the MSFC 0.160 0.390 development of white matter atrophy Proton density lesion load À0.177 0.342 Enhancing lesion numbers À0.532 0.002 Many studies have assessed the effect of enhancing lesions Grey matter fraction (% change) on subsequent brain volume changes in relapsing–remitting Age À0.174 0.349 multiple sclerosis (Rudick et al., 1999; Saindane et al., Disease duration 0.185 0.319 2000; Fisher et al., 2002; Hardmeier et al., 2003), clinically EDSS* À0.079 0.673 isolated syndromes suggestive of multiple sclerosis (Dalton MSFC À0.012 0.950 Proton density lesion load À0.341 0.060 et al., 2004; Paolillo et al., 2004) and secondary progressive Enhancing lesion numbers À0.237 0.199 multiple sclerosis (Inglese et al., 2004). Most have found that large volumes of enhancing lesions are predictive of or *All correlation coefficients reported are Pearson correlation associated with the development of atrophy. We are not coefficients except for EDSS (Spearman); Significant correlations appear in bold. PBVC = percentage brain volume change aware of any similar studies in patients with PPMS. obtained with SIENAv2.2. Resolution of inflammation-associated oedema is likely to be an important contributing factor in white matter volume changes, but the contribution of inflammation to subsequent (iii) grey matter volume loss cannot be predicted using con- white matter tissue loss cannot be ruled out. Ensuing ventional clinical and MRI parameters; and (iv) different gliosis, which might prevent volume loss, might complicate methodological approaches to the estimation of atrophy the picture even further. In this regard, Inglese and may provide complementary information which is relevant colleagues have shown, in a group of patients with rapidly to the clinical and radiological associations found. evolving secondary progressive multiple sclerosis and a percentage of annual global tissue loss of À1.88%, that even after suppression of inflammation through therapy with Volume changes over 1 year autologous haematopoietic stem cell transplantation, sub- Changes in atrophy have been detected in patients with estab- sequent brain atrophy development is still marginally lished PPMS over 1 year using non-normalized partial brain associated with the enhancing lesion load prior to treatment measures (Stevenson et al., 2000). This has also been shown, (Inglese et al., 2004). using longer follow-up times, for normalized global brain measures (Kalkers et al., 2002). The present study suggests that, over the short term, the development of atrophy in Failure to predict grey matter atrophy patients with early PPMS is due mainly to change in grey The baseline, cross-sectional analysis of this cohort has shown matter. It is also worth noting that the observed decrease in that grey matter fraction is not associated with white matter grey matter fraction seems to be a real finding and not a lesion loads (T2 and gadolinium-enhancing). This is in methodological artefact, as it occurs in spite of the bias in contrast with a previous study by our own group, in early SPM segmentation, whereby in subjects with increasing relapsing–remitting multiple sclerosis, in which correlations lesion loads SPM tends to overestimate grey matter were found between white matter lesion load and grey matter volumes (Chard et al., 2002a). A possible explanation for fraction that were thought to reflect dying back and Wallerian Grey and white matter atrophy in PPMS 1459 degeneration processes (Chard et al., 2002b). The phenotypic white matter volume changes are influenced by acute inflam- difference between the cohorts studied (early relapsing– mation even in PPMS. remitting multiple sclerosis versus early PPMS) may help explain this difference. Moreover, the possible relationship between lesions in white matter and grey matter atrophy may Acknowledgements be affected by the presence of grey matter lesions, which are We wish to thank the subjects who kindly agreed to take part difficult to observe using conventional MRI but are known to in this study. We acknowledge the support of the Wellcome be numerous (Bo¨ et al., 2003), although no data are available Trust (G.T.I.), the Spanish Ministry of Health (J.S.G., BEFI comparing the extent and severity of grey matter lesions in 02/9115) and the Multiple Sclerosis Society of Great Britain PPMS and relapsing–remitting multiple sclerosis. Given that and Northern Ireland. previous studies have demonstrated a relationship between Downloaded from https://academic.oup.com/brain/article/128/6/1454/432021 by guest on 28 September 2021 cortical grey matter atrophy and functional activity and References clinical disability (De Stefano et al., 2003; Filippi and Bo¨ L, Vedeler CA, Nyland HI, Trapp BD, Mo¨rk SJ. Subpial demyelination in the cerebral cortex of multiple sclerosis patients. J Neuropathol Exp Neurol Rocca, 2003), the identification of predictors of grey matter 2003; 62: 723–32. loss or dysfunction is an important focus of future research. Chard DT, Parker GJM, Griffin CMB, Thompson AJ, Miller DH. The repro- Its independence from gross inflammation may make the ducibility and sensitivity of brain tissue volume measurements derived measurement of grey matter volume changes a useful surro- from an SPM-based segmentation methodology. J Magn Reson Imaging gate for the assessment of new neuroprotective strategies in 2002a; 15: 259–67. Chard DT, Griffin CM, Parker GJM, Kapoor R, Thompson AJ, Miller DH. multiple sclerosis. Brain atrophy in clinically early relapsing-remitting multiple sclerosis. Brain 2002b; 125: 327–37. Chard DT, Griffin CM, Rashid W, Davies GR, Altmann DR, Kapoor R, et al. Role of methodology Progressive grey matter atrophy in clinically early relapsing-remitting The estimates of percentage global tissue loss in this study, multiple sclerosis. Mult Scler 2004; 10: 387–91. Cottrell DA, Kremenchutzky M, Rice GPA, Koopman WJ, Hader W, À1.03% for SPM-based methodology and À0.63% for Baskerville J, et al. The natural history of multiple sclerosis: a geograph- SIENAv2.2, are within the range seen in different pheno- ically based study. 5. The clinical features and natural history of primary types of multiple sclerosis, which goes from À0.46 to progressive multiple sclerosis. Brain 1999; 122: 625–39. À1.9% (Inglese et al., 1999; Dalton et al., 2004), and uses Cutter GR, Baier ML, Rudick RA, Cookfair DL, Fischer JS, Petkau J, et al. a range of atrophy techniques. Although separate grey and Development of a multiple sclerosis functional composite as a clinical trial outcome measure. Brain 1999; 122: 871–82. white matter volumes cannot be obtained, SIENA is more Dalton CM, Chard DT, Davies GR, Myszkiel KA, Altmann DR, Fernando K, user-friendly as it needs no manual operator input and the et al. Early development of multiple sclerosis is associated with progressive software is freely available on the World-Wide Web in a grey matter atrophy in patients presenting with clinically isolated compact package. Conversely, SPM-derived methods need syndromes. Brain 2004; 127: 1101–7. considerable operator input (lesion contouring), and, De Stefano N, Matthews PM, Filippi M, Agosta F, De Luca M, Bartolozzi ML, et al. Evidence of early cortical atrophy in MS. Relevance to white matter although it can also be downloaded from the World-Wide changes and disability. Neurology 2003; 60: 1157–62. Web, in-house software is required to generate masks and Filippi M, Rocca MA. Disturbed function and plasticity in multiple sclerosis final volume outputs. Therefore, it could be suggested that as gleaned from functional magnetic resonance imaging. Curr Opin Neurol SPM-based methods be used for research purposes and 2003; 16: 275–82. SIENA v2.2 in clinical applications. However, even though Fisher E, Rudick RA, Simon JH, Cutter G, Baier M, Lee JC, et al. Eight-year follow-up study of brain atrophy in patients with MS. Neurology 2002; 59: the results of the present study are in keeping with other 1412–20. studies using SPM (Dalton et al., 2004) and SIENA Hardmeier M, Wagenpfeil S, Freitag P, Fisher E, Rudick RA, (Jasperse et al., 2004), it is important to note that markedly Kooijmans-Coutinho M, et al. Atrophy is detectable within a 3-month different associations between atrophy development and base- period in untreated patients with active relapsing remitting multiple line variables have been observed using the two methods. sclerosis. Arch Neurol 2003; 60: 1736–9. Horsfield MA, Rovaris M, Rocca MA, Rossi P, Benedict RH, Filippi M, et al. Estimates of atrophy are known to differ depending on the Whole-brain atrophy in multiple sclerosis measured by two segmentation segmentation procedure and pulse sequence used for their processes from various MRI sequences. J Neurol Sci 2003; 216: 169–77. estimation (Leigh et al., 2002; Horsfield et al., 2003). SPM Inglese M, Rovaris M, Giacomotti L, Mastronardo G, Comi G, Filippi M. segmentation is biased by the presence of lesions, although Quantitative brain volumetric analysis from patients with multiple this bias seems to have an effect on grey and white matter sclerosis: a follow-up study. J Neurol Sci 1999; 171: 8–10. Inglese M, Mancardi GL, Pagani E, Rocca MA, Murialdo A, Saccardi R, et al. only and not whole-brain estimates (Chard et al., 2002a). A Brain tissue loss occurs after suppression of enhancement in patients with trend for underestimation of higher atrophy has been multiple sclerosis treated with autologous haematopoietic stem cell reported with SIENA (Smith et al., 2002). More metho- transplantation. J Neurol Neurosurg Psychiatry 2004; 75: 643–4. dological studies are warranted before the present findings Jasperse MMS, Minneboo A, Kalkers NF, De Groot V, Karas GB, Uitdehaag can be fully explained. BMJ, et al. Brain volume measurement in early MS: clinical/radiological correlates and predictors. Mult Scler 2004, S2: S224. In summary, progressive grey matter atrophy can be detec- Kalkers NF, Ameziane N, Bot JCJ, Minneboo A, Polman CH, Barkhof F. ted in early PPMS over 1-year periods but cannot be pre- Longitudinal brain volume measurement in multiple sclerosis. Arch Neurol dicted by conventional clinical and MRI variables, while 2002; 59: 1572–6. 1460 Jaume Sastre-Garriga et al.

Kidd D, Barkhof F, McConnell R, Algra PR, Allen IV, Revesz T. Cortical Rovaris M, Comi G, Rocca MA, Wolinsky JS, Filippi M. Short-term lesions in multiple sclerosis. Brain 1999; 122: 17–26. brain volume changes in relapsing-remitting multiple sclerosis. Leigh R, Ostuni J, Pham D, Goldszal A, Lewis BK, Howard T, et al. Effect of glatiramer acetate and implications. Brain 2001; 124: Estimating cerebral atrophy in multiple sclerosis patients from various 1803–12. MR pulse sequences. Mult Scler 2002; 8: 420–9. Rudick RA, Fisher E, Lee JC, Simon J, Jacobs L. Use of the brain paren- Lycklama a` Nijeholt GJ, van Walderveen MA, Castelijns JA, van chymal fraction to measure whole brain atrophy in relapsing-remitting MS. Waesberghe JH, Polman C, Scheltens P, et al. Brain and spinal cord abnor- Neurology 1999; 53: 1698–704. malities in multiple sclerosis. Correlation between MRI parameters, clin- Saindane AM, Ge Y, Udupa JK, Babb JS, Mannon LJ, Grossman RI. The ical subtypes and symptoms. Brain 1998; 121: 687–97. effect of gadolinium-enhancing lesions on whole brain atrophy in Losseff NA, Wang L, Lai HM, Yoo DS, Gawne-Cain ML, McDonald WI, relapsing-remitting MS. Neurology 2000; 55: 61–5. et al. Progressive cerebral atrophy in multiple sclerosis. Brain 1996; 119: Sastre-Garriga J, Ingle GT, Chard DT, Ramio´-Torrenta` Ll, Miller DH, 2009–19. Thompson AJ. Grey and white matter atrophy in early clinical stages

Miller DH, Barkhof F, Frank JA, Parker GJM, Thompson AJ. Measurement of of primary progressive multiple sclerosis. Neuroimage 2004; 22: Downloaded from https://academic.oup.com/brain/article/128/6/1454/432021 by guest on 28 September 2021 atrophy in multiple sclerosis: pathological basis, methodological aspects 353–9. and clinical relevance. Brain 2002; 125: 1676–95. Smith SM, Zhang Y, Jenkinson M, Chen J, Matthews PM, Federico A, et al. Paolillo A, Piattella MC, Pantano P, Di Legge S, Caramia F, Russo P, et al. Accurate, robust, and automated longitudinal and cross-sectional brain The relationship between inflammation and atrophy in clinically isolated change analysis. Neuroimage 2002; 17: 479–89. syndromes suggestive of multiple sclerosis. A monthly MRI study after Stevenson VL, Miller DH, Leary SM, Rovaris M, Barkhof F, Brochet B, et al. triple-dose gadolinium-DTPA. J Neurol 2004; 251: 432–9. One year follow up study of primary and transitional progressive multiple Perneger TV. What’s wrong with Bonferroni adjustments. BMJ 1998; 316: sclerosis. J Neurol Neurosurg Psychiatry 2000; 68: 713–8. 1236–8. Thompson AJ, Kermode AG, Wicks D, MacManus DG, Kendall BE, Peterson JW, Bo¨ L, Mo¨rk S, Chang A, Trapp BD. Transected neurites, Kingsley DPE, et al. Major differences in the dynamics of primary apoptotic neurons, and reduced inflammation in cortical multiple sclerosis and secondary progressive multiple sclerosis. Ann Neurol 1991; 29: lesions. Ann Neurol 2001; 50: 389–400. 53–62. Revesz T, Kidd D, Thompson AJ, Barnard RO, McDonald WI. A comparison Thompson AJ, Montalban X, Barkhof F, Brochet B, Filippi M, Miller DH, of the pathology of primary and secondary progressive multiple sclerosis. et al. Diagnostic criteria for primary progressive multiple sclerosis: a Brain 1994; 117: 759–65. position paper. Ann Neurol 2000; 47: 831–5.