Grey and white matter volume changes in early primary progressive multiple sclerosis: a longitudinal study

Abstract

We have recently reported brain atrophy in the early stages of primary progressive multiple sclerosis (PPMS), affecting both grey and white matter (GM and WM). However, to date no clinical or radiological predictors of GM and WM atrophy have been identified. The aim was to investigate short-term changes in GM and WM volumes and to assess the predictive value of demographic, clinical and radiological variables in order to gain a better understanding of the pathological substrate underlying these changes. Thirty-one subjects with PPMS within 5 years of symptom onset were studied at baseline and after 1 year. At baseline, patients underwent neurological examination and were scored on the Expanded Disability Status Scale (EDSS) and Multiple Sclerosis Functional Composite. They had 3D inversion-prepared fast spoiled gradient recalled (FSPGR), dual-echo and triple-dose post-contrast T1-weighted spin echo MRI scans. Proton density and enhancing lesion loads were determined. The 3DFSPGR sequence was repeated after 1 year and brain volume changes were calculated using two techniques, SPM99 (statistical parametric mapping) and SIENA (structural image evaluation, using normalization, of atrophy). Stepwise linear regression models were applied to baseline variables to identify independent predictors of atrophy development. Using SPM99, a decrease in brain parenchymal fraction (-1.03%; P < 0.001) and GM fraction (-1.49%; P < 0.001) was observed. The number of enhancing lesions independently predicted decrease in brain parenchymal fraction (P = 0.019) and decrease in WM fraction (P = 0.002). No independent predictors of GM fraction decrease were found. A mean brain volume change of -0.63% (range -4.27% to +1.18%; P = 0.002) was observed using SIENA, which was independently predicted by EDSS (P = 0.004). Global and GM atrophy can be detected over a 1-year period in early PPMS. The former may be predicted by the degree of inflammation, while the latter seems to be independent of it. SIENA and SPM-based methods appear to provide complementary information.