Combined analysis of global and compartmental brain volume changes in early multiple sclerosis in clinical practice

Abstract

Background:

The extent and clinical significance of brain volume changes in different phases of multiple sclerosis (MS) is still under discussion.

Objective:

To determine the rate of global and compartmental brain volume changes in patients with a clinically-isolated syndrome (CIS) compared to patients with definite MS, by long-term follow-up and as a predictor of conversion to MS in a routine clinical setting.

Methods:

We investigated 120 patients (63 CIS and 57 MS) at baseline and after a mean follow-up period of 43 months, including detailed clinical examination and 3-Tesla magnetic resonance imaging (MRI). Our imaging analyses comprised the normalized brain volume (NBV), cortical grey matter (cGMV) and white matter (WMV) volumes using SIENA/X, the percentage of brain volume change (PBVC) using SIENA and the change in the volume of the thalami (TV) and basal ganglia (BGV). We also determined the amount and change of T2-lesion load (T2-LL).

Results:

At baseline, all the brain volume metrics, except cGMV, were significantly lower; and the T2-LL was significantly higher, in patients with MS rather than CIS. During the follow-up, only the PBVC was higher in MS (p = 0.008) and this difference was driven by converters from CIS to MS. Quartiles of PBVC did not allow us to predict conversion to MS, but were associated with the degree of disability.

Conclusions:

PBVC is the most sensitive marker of progressing atrophy and a higher PBVC was generally associated with more active disease; however, it did not serve to predict the course of MS on an individual basis, in this study.

Keywords

Brain volume clinically-isolated syndrome clinical outcome disability disease prediction disease progression magnetic resonance imaging multiple sclerosis

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References

Miller

Barkhof

Frank

. Measurement of atrophy in multiple sclerosis: Pathological basis, methodological aspects and clinical relevance. Brain 2002; 125: 1676–1695.

Filippi

Preziosa

Copetti

. Gray matter damage predicts the accumulation of disability 13 years later in MS. Neurology 2013; 81: 1759–1767.

Fisniku

Chard

Jackson

. Gray matter atrophy is related to long-term disability in multiple sclerosis. Ann Neurol 2008; 64: 247–254.

Sbardella

Petsas

Tona

. Assessing the correlation between grey and white matter damage with motor and cognitive impairment in multiple sclerosis patients. PLoS ONE 2013; 8: e63250.

Calabrese

Rinaldi

Mattisi

. The predictive value of gray matter atrophy in clinically-isolated syndromes. Neurology 2011; 77: 257–263.

Perez-Miralles

Sastre-Garriga

Tintore

. Clinical impact of early brain atrophy in clinically-isolated syndromes. Mult Scler J 2013; 19:1878–1886.

De Stefano

Giorgio

Battaglini

. Assessing brain atrophy rates in a large population of untreated multiple sclerosis subtypes. Neurology 2010; 74: 1868–1876.

Smith

Zhang

Jenkinson

. Accurate, robust, and automated longitudinal and cross-sectional brain change analysis. Neuroimage 2002; 17: 479–489.

Miller

Weinshenker

Filippi

. Differential diagnosis of suspected multiple sclerosis: A consensus approach. Mult Scler 2008; 14:1157–1174.

10.

Polman

Reingold

Edan

. Diagnostic criteria for multiple sclerosis: 2005 Revisions to the McDonald Criteria. Ann Neurol 2005; 58: 840–846.

11.

Kurtzke

. Rating neurologic impairment in multiple sclerosis: An Expanded Disability Status Scale (EDSS). Neurology 1983; 33: 1444–1452.

12.

Khalil

Langkammer

Ropele

. Determinants of brain iron in multiple sclerosis: A quantitative 3T MRI study. Neurology 2011; 77: 1691–1697.

13.

Smith

Jenkinson

Woolrich

. Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage 2004; 23: S208–219.

14.

Alexander

Crutcher

. Functional architecture of basal ganglia circuits: Neural substrates of parallel processing. Trends Neurosci 1990; 13: 266–271.

15.

Batista

Zivadinov

Hoogs

. Basal ganglia, thalamus and neocortical atrophy predicting slowed cognitive processing in multiple sclerosis. J Neurol 2012; 259: 139–146.

16.

Patenaude

Smith

Kennedy

. A Bayesian model of shape and appearance for subcortical brain segmentation. Neuroimage 2011; 56: 907–922.

17.

Enzinger

Ropele

Smith

. Accelerated evolution of brain atrophy and ‘black holes’ in MS patients with APOE-epsilon 4. Ann Neurol 2004; 55: 563–569.

18.

Calabresi

Radue

Goodin

. Safety and efficacy of fingolimod in patients with relapsing–remitting multiple sclerosis (FREEDOMS II): A double-blind, randomised, placebo-controlled, Phase 3 trial. Lancet Neurol 2014; 13: 545–556.

19.

Vollmer

Sorensen

Selmaj

. A randomized placebo-controlled Phase III trial of oral laquinimod for multiple sclerosis. J Neurol 2014; 261: 773–783.

20.

Khan

Bao

Shah

. Effect of disease-modifying therapies on brain volume in relapsing–remitting multiple sclerosis: Results of a five-year brain MRI study. J Neurolog Sci 2012; 312: 7–12.

21.

Paolillo

Piattella

Pantano

. The relationship between inflammation and atrophy in clinically-isolated syndromes suggestive of multiple sclerosis: A monthly MRI study after triple-dose gadolinium-DTPA. J Neurol 2004; 251: 432–439.

22.

Zivadinov

Bergsland

Dolezal

. Evolution of cortical and thalamus atrophy and disability progression in early relapsing–remitting MS during 5 years. Am J Neuroradiol 2013; 34: 1931–1939.

23.

Kalkers

Bergers

Castelijns

. Optimizing the association between disability and biological markers in MS. Neurology 2001; 57: 1253–1258.