Differential association of cortical,subcortical and spinal cord damage with multiple sclerosis disability milestones: A multiparametric MRI study

Abstract

Background:

In multiple sclerosis (MS), cortical, subcortical and infratentorial structural damage may have a differential contribution to clinical disability according to disease phases.

Purpose:

To determine the relative contributions of cortical, deep (D) grey matter (GM), cerebellar and cervical cord damage to MS disability milestones.

Methods:

Multi-centre 3T brain and cervical cord T₂- and three-dimensional (3D) T₁-weighted images were acquired from 198 MS patients (139 relapsing-remitting (RR) MS, 59 progressive (P) MS) and 67 healthy controls. Brain/cord lesion burden, cortical thickness (CTh), DGM and cerebellar volumetry and cord cross-sectional area (CSA) were quantified. Random forest analyses identified predictors of expanded disability status scale (EDSS) disability milestones (EDSS = 3.0, 4.0 and 6.0).

Results:

MS patients had widespread atrophy in all investigated compartments versus controls (p-range: ⩽0.001–0.05). Informative determinants of EDSS = 3.0 were cord CSA, brain lesion volume, frontal CTh and thalamic and cerebellar atrophy (out-of-bag (OOB) accuracy = 0.84, p-range: ⩽0.001–0.05). EDSS = 4.0 was mainly predicted by cerebellar and cord atrophy, frontal and sensorimotor CTh and cord lesion number (OOB accuracy = 0.84, p-range: ⩽0.001–0.04). Cervical cord CSA (p = 0.001) and cord lesion number (p = 0.003) predicted EDSS = 6.0 (OOB accuracy = 0.77).

Conclusion:

Brain lesion burden, cortical and thalamic atrophy were the main determinants of EDSS = 3.0 and 4.0, while cord damage played a major contribution to EDSS = 6.0.

Keywords

Disability volumetric MRI brain cord

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References

Filippi

Bar-Or

Piehl

, et al. Multiple sclerosis. Nat Rev Dis Primers 2018; 4: 43.

Geurts

Barkhof

Grey matter pathology in multiple sclerosis. Lancet Neurol 2008; 7: 841–851.

Kearney

Rocca

Valsasina

, et al. Magnetic resonance imaging correlates of physical disability in relapse onset multiple sclerosis of long disease duration. Mult Scler 2014; 20(1): 72–80.

Daams

Steenwijk

Wattjes

, et al. Unraveling the neuroimaging predictors for motor dysfunction in long-standing multiple sclerosis. Neurology 2015; 85: 248–255.

Rocca

Battaglini

Benedict

, et al. Brain MRI atrophy quantification in MS: From methods to clinical application. Neurology 2017; 88: 403–413.

Filippi

MRI measures of neurodegeneration in multiple sclerosis: Implications for disability, disease monitoring, and treatment. J Neurol 2015; 262(1): 1–6.

Cordani

Hidalgo de la Cruz

Meani

, et al. MRI correlates of clinical disability and hand-motor performance in multiple sclerosis phenotypes. Mult Scler. Epub ahead of print 14 September 2020. DOI: 10.1177/1352458520958356.

Hidalgo de la Cruz

Valsasina

Gobbi

, et al. Longitudinal cortical thinning progression differs across multiple sclerosis phenotypes and is clinically relevant: A multicentre study. Mult Scler 2021; 27: 827–840.

Ramasamy

Benedict

Cox

, et al. Extent of cerebellum, subcortical and cortical atrophy in patients with MS: A case-control study. J Neurol Sci 2009; 282: 47–54.

10.

Calabrese

Atzori

Bernardi

, et al. Cortical atrophy is relevant in multiple sclerosis at clinical onset. J Neurol 2007; 254(9): 1212–1220.

11.

Tur

Kanber

Eshaghi

, et al. Clinical relevance of cortical network dynamics in early primary progressive MS. Mult Scler 2020; 26: 442–456.

12.

Eshaghi

Prados

Brownlee

, et al. Deep grey matter volume loss drives disability worsening in multiple sclerosis. Ann Neurol 2018; 83: 210–222.

13.

Prinster

Quarantelli

Orefice

, et al. Grey matter loss in relapsing-remitting multiple sclerosis: A voxel-based morphometry study. NeuroImage 2006; 29: 859–867.

14.

Eshaghi

Marinescu

Young

, et al. Progression of regional grey matter atrophy in multiple sclerosis. Brain 2018; 141: 1665–1677.

15.

Parmar

Stadelmann

Rocca

, et al. The role of the cerebellum in multiple sclerosis-150 years after Charcot. Neurosci Biobehav Rev 2018; 89: 85–98.

16.

Moroso

Ruet

Lamargue-Hamel

, et al. Posterior lobules of the cerebellum and information processing speed at various stages of multiple sclerosis. J Neurol Neurosurg Psychiatry 2017; 88(2): 146–151.

17.

D’Ambrosio

Pagani

Riccitelli

, et al. Cerebellar contribution to motor and cognitive performance in multiple sclerosis: An MRI sub-regional volumetric analysis. Mult Scler 2017; 23(9): 1194–1203.

18.

Stoodley

Schmahmann

JD.

Functional topography of the human cerebellum. Handb Clin Neurol 2018; 154: 59–70.

19.

Gass

Rocca

Agosta

, et al. MRI monitoring of pathological changes in the spinal cord in patients with multiple sclerosis. Lancet Neurol 2015; 14(4): 443–454.

20.

Casserly

Seyman

Alcaide-Leon

, et al. Spinal cord atrophy in multiple sclerosis: A systematic review and meta-analysis. J Neuroimaging 2018; 28: 556–586.

21.

Horsfield

Sala

Neema

, et al. Rapid semi-automatic segmentation of the spinal cord from magnetic resonance images: Application in multiple sclerosis. NeuroImage 2010; 50: 446–455.

22.

Rocca

Valsasina

Damjanovic

, et al. Voxel-wise mapping of cervical cord damage in multiple sclerosis patients with different clinical phenotypes. J Neurol Neurosurg Psychiatry 2013; 84(1): 35–41.

23.

Rocca

Valsasina

Meani

, et al. Clinically relevant cranio-caudal patterns of cervical cord atrophy evolution in MS. Neurology 2019; 93: e1852–e1866.

24.

Kurtzke

JF.

Rating neurologic impairment in multiple sclerosis: An expanded disability status scale (EDSS). Neurology 1983; 33(11): 1444–1452.

25.

Desikan

Segonne

Fischl

, et al. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. NeuroImage 2006; 31: 968–980.

26.

Sailer

Fischl

Salat

, et al. Focal thinning of the cerebral cortex in multiple sclerosis. Brain 2003; 126(Pt 8): 1734–1744.

27.

Patenaude

Smith

Kennedy

, et al. A Bayesian model of shape and appearance for subcortical brain segmentation. NeuroImage 2011; 56: 907–922.

28.

Damjanovic

Valsasina

Rocca

, et al. Hippocampal and deep gray matter nuclei atrophy is relevant for explaining cognitive impairment in MS: A multicenter study. Am J Neuroradiol 2017; 38(1): 18–24.

29.

Schoonheim

Popescu

Rueda Lopes

, et al. Subcortical atrophy and cognition: Sex effects in multiple sclerosis. Neurology 2012; 79: 1754–1761.

30.

Louapre

Govindarajan

Gianni

, et al. Heterogeneous pathological processes account for thalamic degeneration in multiple sclerosis: Insights from 7 T imaging. Mult Scler 2018; 24(11): 1433–1444.

31.

Diedrichsen

A spatially unbiased atlas template of the human cerebellum. NeuroImage 2006; 33: 127–138.

32.

Valsasina

Rocca

Horsfield

, et al. A longitudinal MRI study of cervical cord atrophy in multiple sclerosis. J Neurol 2015; 262(7): 1622–1628.

33.

Kister

Chamot

Cutter

, et al. Increasing age at disability milestones among MS patients in the MSBase Registry. J Neurol Sci 2012; 318: 94–99.

34.

Altmann

Toloşi

Sander

, et al. Permutation importance: A corrected feature importance measure. Bioinformatics 2010; 26: 1340–1347.

35.

De Stefano

Giorgio

Battaglini

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

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