Periventricular gradient of T1 tissue alterations in multiple sclerosis

Manuela Vaneckova, Gian Franco Piredda, Michaela Andelova, Jan Krasensky, Tomas Uher, Barbora Srpova, Eva Kubala Havrdova, Karolina Vodehnalova, Dana Horakova, Tom Hilbert, Bénédicte Maréchal, Mário João Fartaria, Veronica Ravano, Tobias Kober, Manuela Vaneckova, Gian Franco Piredda, Michaela Andelova, Jan Krasensky, Tomas Uher, Barbora Srpova, Eva Kubala Havrdova, Karolina Vodehnalova, Dana Horakova, Tom Hilbert, Bénédicte Maréchal, Mário João Fartaria, Veronica Ravano, Tobias Kober

Abstract

Objective: Pathology in multiple sclerosis is not homogenously distributed. Recently, it has been shown that structures adjacent to CSF are more severely affected. A gradient of brain tissue involvement was shown with more severe pathology in periventricular areas and in proximity to brain surfaces such as the subarachnoid spaces and ependyma, and hence termed the "surface-in" gradient. Here, we study whether (i) the surface-in gradient of periventricular tissue alteration measured by T1 relaxometry is already present in early multiple sclerosis patients, (ii) how it differs between early and progressive multiple sclerosis patients, and (iii) whether the gradient-derived metrics in normal-appearing white matter and lesions correlate better with physical disability than conventional MRI-based metrics.

Methods: Forty-seven patients with early multiple sclerosis, 52 with progressive multiple sclerosis, and 92 healthy controls were included in the study. Isotropic 3D T1 relaxometry maps were obtained using the Magnetization-Prepared 2 Rapid Acquisition Gradient Echoes sequence at 3 T. After spatially normalizing the T1 maps into a study-specific common space, T1 inter-subject variability within the healthy cohort was modelled voxel-wise, yielding a normative T1 atlas. Individual comparisons of each multiple sclerosis patient against the atlas were performed by computing z-scores. Equidistant bands of voxels were defined around the ventricles in the supratentorial white matter; the z-scores in these bands were analysed and compared between the early and progressive multiple sclerosis cohorts. Correlations between both conventional and z-score-gradient-derived MRI metrics and the Expanded Disability Status Scale were assessed.

Results: Patients with early and progressive multiple sclerosis demonstrated a periventricular gradient of T1 relaxation time z-scores. In progressive multiple sclerosis, z-score-derived metrics reflecting the gradient of tissue abnormality in normal-appearing white matter were more strongly correlated with disability (maximal rho = 0.374) than the conventional lesion volume and count (maximal rho = 0.189 and 0.21 respectively). In early multiple sclerosis, the gradient of normal-appearing white matter volume with z-scores > 2 at baseline correlated with clinical disability assessed at two years follow-up.

Conclusion: Our results suggest that the surface-in white matter gradient of tissue alteration is detectable with T1 relaxometry and is already present at clinical disease onset. The periventricular gradients correlate with clinical disability. The periventricular gradient in normal-appearing white matter may thus qualify as a promising biomarker for monitoring of disease activity from an early stage in all phenotypes of multiple sclerosis.

Trial registration: ClinicalTrials.gov NCT03706118.

Keywords: Atlas-based assessment; Gradient of tissue damage; MP2RAGE; Multiple sclerosis; T(1)-relaxometry.

Conflict of interest statement

M. Vaneckova received speaker honoraria, consultant fees, and travel expenses from Biogen Idec, Novartis, Roche, Genzyme, and Teva, as well as support for research activities from Biogen Idec. G.F. Piredda, T. Hilbert, B. Marechal and T. Kober are employees of Siemens Healthcare AG, Switzerland. M. Andelova received financial support for conference travel from Novartis, Genzyme, Merck Serono, Biogen Idec, and Roche. J. Krasensky received financial support for research activities from Biogen Idec. T. Uher received financial support for conference travel and honoraria from Biogen Idec, Novartis, Roche, Genzyme, and Merck Serono, as well as support for research activities from Biogen Idec and Sanofi.B. Srpova received financial support for conference travel from Novartis, Genzyme, Merck Serono, Biogen Idec, and Roche. E. Kubala Havrdova received speaker honoraria and consultant fees from Biogen Idec, Merck Serono, Novartis, Genzyme, Teva, Actelion, and Receptos, as well as support for research activities from Biogen Idec and Merck Serono. K. Vodehnalova received compensation for travel, conference fees, and consulting fees from Merck, Sanofi Genzyme, Biogen Idec, and Novartis. D. Horakova received compensation for travel, speaker honoraria and consultant fees from Biogen Idec, Novartis, Merck Serono, Bayer Shering, and Teva, as well as support for research activities from Biogen Idec.

Copyright © 2022 The Author(s). Published by Elsevier Inc. All rights reserved.

Figures

Graphical abstract
Graphical abstract
Fig. 1
Fig. 1
Computation of T1 z-score maps and periventricular gradient extraction. Representative slices of (A) the anatomical study-specific template, (B) the normative T1 atlas, and (C) the distance to ventricles of the supratentorial WM voxels. The intercept, sex, age, and age2 maps in (B) are intended as the β0, βsex, βage and βage2 coefficients of the following equation describing the expected reference T1 value in a given brain voxel: ET1=β0,T1+βsex,T1∗sex+βage,T1∗age+βage2,T1∗age2, with sex being a categorical variable and the age expressed in years and centred at the mean age of the healthy control cohort (37 y). The first band was excluded to minimise the partial volume effect.
Fig. 2
Fig. 2
Periventricular gradients in NAWM for mean absolute z-score (exceeding the prediction interval of the T1 normative linear model at a 95% level of confidence) and for volume with absolute (z-score) > 2. (A) mean absolute z-scores, (B) volume with absolute (|z-score|) > 2. Error bars indicate two standard errors.
Fig. 3
Fig. 3
Correlation between gradient of z-scores in NAWM and EDSS (change between bands for z-score thresholds of 0, 1, 2 and 3) in patients with progressive multiple sclerosis: (A) band 1:5; (B) band 1:10 (C) band 1:20. (* indicates statistically significant values).
Fig. 4
Fig. 4
Correlation between gradient of lesion volume exceeding a given z-score threshold at baseline and EDSS assessed 2 years after baseline (change between bands for z-score thresholds of 0, 1, 2 and 3) in patients with early multiple sclerosis: (A) band 1:5; (B) band 1:10; (C) band 1:20. (* indicates statistically significant values, * p 

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