Reduced Global Efficiency and Random Network Features in Patients with Relapsing-Remitting Multiple Sclerosis with Cognitive Impairment.
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:
03 2020
03 2020
Historique:
received:
29
03
2019
accepted:
11
01
2020
pubmed:
23
2
2020
medline:
21
10
2020
entrez:
22
2
2020
Statut:
ppublish
Résumé
Graph theory uses structural similarity to analyze cortical structural connectivity. We used a voxel-based definition of cortical covariance networks to quantify and assess the relationship of network characteristics to cognition in a cohort of patients with relapsing-remitting MS with and without cognitive impairment. We compared subject-specific structural gray matter network properties of 18 healthy controls, 25 patients with MS with cognitive impairment, and 55 patients with MS without cognitive impairment. Network parameters were compared, and predictive value for cognition was assessed, adjusting for confounders (sex, education, gray matter volume, network size and degree, and T1 and T2 lesion load). Backward stepwise multivariable regression quantified predictive factors for 5 neurocognitive domain test scores. Greater path length ( Patients with MS and cognitive impairment demonstrate more random network features and reduced global efficiency, impacting multiple cognitive domains. A model of normalized path length with normal-appearing white matter volume improved average cognitive
Sections du résumé
BACKGROUND AND PURPOSE
Graph theory uses structural similarity to analyze cortical structural connectivity. We used a voxel-based definition of cortical covariance networks to quantify and assess the relationship of network characteristics to cognition in a cohort of patients with relapsing-remitting MS with and without cognitive impairment.
MATERIALS AND METHODS
We compared subject-specific structural gray matter network properties of 18 healthy controls, 25 patients with MS with cognitive impairment, and 55 patients with MS without cognitive impairment. Network parameters were compared, and predictive value for cognition was assessed, adjusting for confounders (sex, education, gray matter volume, network size and degree, and T1 and T2 lesion load). Backward stepwise multivariable regression quantified predictive factors for 5 neurocognitive domain test scores.
RESULTS
Greater path length (
CONCLUSIONS
Patients with MS and cognitive impairment demonstrate more random network features and reduced global efficiency, impacting multiple cognitive domains. A model of normalized path length with normal-appearing white matter volume improved average cognitive
Identifiants
pubmed: 32079601
pii: ajnr.A6435
doi: 10.3174/ajnr.A6435
pmc: PMC7077890
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
449-455Informations de copyright
© 2020 by American Journal of Neuroradiology.
Références
Lancet Neurol. 2008 Sep;7(9):841-51
pubmed: 18703006
Neuroimage Clin. 2014 Aug 23;6:86-92
pubmed: 25379419
Lancet Neurol. 2018 Feb;17(2):162-173
pubmed: 29275977
Neurobiol Aging. 2013 Aug;34(8):2023-36
pubmed: 23541878
PLoS One. 2008 Apr 30;3(4):e0002051
pubmed: 18446219
Arch Neurol. 2007 Jan;64(1):76-80
pubmed: 17210812
Brain. 2002 Jun;125(Pt 6):1275-82
pubmed: 12023316
Nature. 1997 Jan 23;385(6614):313-8
pubmed: 9002514
Schizophr Res. 2015 Oct;168(1-2):1-8
pubmed: 26330380
AJNR Am J Neuroradiol. 2005 Feb;26(2):341-6
pubmed: 15709132
Cereb Cortex. 2007 Oct;17(10):2407-19
pubmed: 17204824
Brain. 2006 Aug;129(Pt 8):2177-88
pubmed: 16815874
Neuroimage. 2014 Jul 1;94:385-395
pubmed: 24361662
Nature. 1998 Jun 4;393(6684):440-2
pubmed: 9623998
Brain. 2009 Dec;132(Pt 12):3366-79
pubmed: 19439423
CNS Neurol Disord Drug Targets. 2012 Aug;11(5):506-17
pubmed: 22583433
Can J Neurol Sci. 2017 Sep;44(5):547-555
pubmed: 28683843
Hum Brain Mapp. 2014 Dec;35(12):5946-61
pubmed: 25053254
Mult Scler. 2017 Mar;23(3):432-441
pubmed: 27246143
Mult Scler. 2019 Mar;25(3):382-391
pubmed: 29320933
Neuroimage. 2010 Sep;52(3):1059-69
pubmed: 19819337
Arch Neurol. 1993 Aug;50(8):818-24
pubmed: 8352667
PLoS One. 2013 May 16;8(5):e63250
pubmed: 23696802
PLoS One. 2013;8(3):e58921
pubmed: 23536835
Hum Brain Mapp. 2014 Sep;35(9):4706-17
pubmed: 24687771
Neuroscientist. 2009 Aug;15(4):333-50
pubmed: 19458383
Neurobiol Aging. 2018 Jan;61:198-206
pubmed: 29111486
Brain. 2005 Sep;128(Pt 9):2034-41
pubmed: 15947059
Cereb Cortex. 2012 Jul;22(7):1530-41
pubmed: 21878484
Hum Brain Mapp. 2016 Mar;37(3):1194-208
pubmed: 26700243
J Neurosci. 2010 Dec 15;30(50):16876-85
pubmed: 21159959