Determinants of Deep Gray Matter Atrophy in Multiple Sclerosis: A Multimodal MRI Study.
Journal
AJNR. American journal of neuroradiology
ISSN: 1936-959X
Titre abrégé: AJNR Am J Neuroradiol
Pays: United States
ID NLM: 8003708
Informations de publication
Date de publication:
01 2019
01 2019
Historique:
received:
18
07
2018
accepted:
29
10
2018
pubmed:
24
12
2018
medline:
2
1
2020
entrez:
22
12
2018
Statut:
ppublish
Résumé
Deep gray matter involvement is a consistent feature in multiple sclerosis. The aim of this study was to evaluate the relationship between different deep gray matter alterations and the development of subcortical atrophy, as well as to investigate the possible different substrates of volume loss between phenotypes. Seventy-seven patients with MS (52 with relapsing-remitting and 25 with progressive MS) and 41 healthy controls were enrolled in this cross-sectional study. MR imaging investigation included volumetric, DTI, PWI and Quantitative Susceptibility Mapping analyses. Deep gray matter structures were automatically segmented to obtain volumes and mean values for each MR imaging metric in the thalamus, caudate, putamen, and globus pallidus. Between-group differences were probed by ANCOVA analyses, while the contribution of different MR imaging metrics to deep gray matter atrophy was investigated via hierarchic multiple linear regression models. Patients with MS showed a multifaceted involvement of the thalamus and basal ganglia, with significant atrophy of all deep gray matter structures ( Our study confirms the diffuse involvement of deep gray matter in MS, demonstrating a different behavior between MS phenotypes, with subcortical GM atrophy mainly determined by global WM lesion burden in patients with relapsing-remitting MS, while local microstructural damage and susceptibility changes mainly accounted for the development of deep gray matter volume loss in patients with progressive MS.
Sections du résumé
BACKGROUND AND PURPOSE
Deep gray matter involvement is a consistent feature in multiple sclerosis. The aim of this study was to evaluate the relationship between different deep gray matter alterations and the development of subcortical atrophy, as well as to investigate the possible different substrates of volume loss between phenotypes.
MATERIALS AND METHODS
Seventy-seven patients with MS (52 with relapsing-remitting and 25 with progressive MS) and 41 healthy controls were enrolled in this cross-sectional study. MR imaging investigation included volumetric, DTI, PWI and Quantitative Susceptibility Mapping analyses. Deep gray matter structures were automatically segmented to obtain volumes and mean values for each MR imaging metric in the thalamus, caudate, putamen, and globus pallidus. Between-group differences were probed by ANCOVA analyses, while the contribution of different MR imaging metrics to deep gray matter atrophy was investigated via hierarchic multiple linear regression models.
RESULTS
Patients with MS showed a multifaceted involvement of the thalamus and basal ganglia, with significant atrophy of all deep gray matter structures (
CONCLUSIONS
Our study confirms the diffuse involvement of deep gray matter in MS, demonstrating a different behavior between MS phenotypes, with subcortical GM atrophy mainly determined by global WM lesion burden in patients with relapsing-remitting MS, while local microstructural damage and susceptibility changes mainly accounted for the development of deep gray matter volume loss in patients with progressive MS.
Identifiants
pubmed: 30573464
pii: ajnr.A5915
doi: 10.3174/ajnr.A5915
pmc: PMC7048598
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
99-106Commentaires et corrections
Type : CommentIn
Informations de copyright
© 2019 by American Journal of Neuroradiology.
Références
Magn Reson Med. 2012 Dec;68(6):1932-42
pubmed: 22367604
AJNR Am J Neuroradiol. 2018 Jun;39(6):994-1000
pubmed: 29301779
Neurology. 2007 Feb 27;68(9):634-42
pubmed: 17325269
AJNR Am J Neuroradiol. 2012 Aug;33(7):1363-8
pubmed: 22383236
Eur Radiol. 2016 Dec;26(12):4577-4584
pubmed: 26905870
PLoS One. 2015 Aug 18;10(8):e0134963
pubmed: 26284778
J Cereb Blood Flow Metab. 2008 Jan;28(1):164-71
pubmed: 17473851
Br J Radiol. 2016 Aug;89(1064):20160321
pubmed: 27282838
PLoS One. 2015 Jun 01;10(6):e0126835
pubmed: 26030293
Radiology. 2018 Nov;289(2):487-496
pubmed: 30015589
Neuroimage. 2018 Feb 15;167:438-452
pubmed: 29097315
J Neuroradiol. 2017 Mar;44(2):158-164
pubmed: 27865557
AJNR Am J Neuroradiol. 2009 Aug;30(7):1380-6
pubmed: 19369608
PLoS One. 2014 Jul 21;9(7):e101199
pubmed: 25047083
Ann Neurol. 2011 Feb;69(2):292-302
pubmed: 21387374
J Neuropathol Exp Neurol. 2009 May;68(5):489-502
pubmed: 19525897
Psychiatry Res. 2015 Dec 30;234(3):352-61
pubmed: 26602610
Neurology. 2014 Jul 15;83(3):278-86
pubmed: 24871874
Neuroimage Clin. 2017 Apr 13;18:1007-1016
pubmed: 29868452
Brain. 2018 Jun 1;141(6):1665-1677
pubmed: 29741648
Radiology. 2013 Sep;268(3):831-41
pubmed: 23613615
Ann Neurol. 2013 Dec;74(6):848-61
pubmed: 23868451
AJNR Am J Neuroradiol. 2017 Jun;38(6):1079-1086
pubmed: 28450431
Lancet Neurol. 2015 Feb;14(2):183-93
pubmed: 25772897
J Neurol Neurosurg Psychiatry. 2014 May;85(5):544-51
pubmed: 24039024
Arch Neurol. 2007 Feb;64(2):196-202
pubmed: 17296835
J Neurol Neurosurg Psychiatry. 2014 Dec;85(12):1386-95
pubmed: 24899728
Mult Scler. 2013 Oct;19(11):1485-92
pubmed: 23462349
J Magn Reson Imaging. 2009 Jan;29(1):70-7
pubmed: 19097116
Neurology. 2015 Feb 24;84(8):776-83
pubmed: 25616483
Ann Neurol. 2018 Feb;83(2):210-222
pubmed: 29331092
Neurology. 2013 Jan 8;80(2):210-9
pubmed: 23296131