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Structural network disruption markers explain disability in multiple sclerosis

OBJECTIVE: To evaluate whether structural brain network metrics correlate better with clinical impairment and information processing speed in multiple sclerosis (MS) beyond atrophy measures and white matter lesions. METHODS: This cross-sectional study included 51 healthy controls and 122 patients co...

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Autores principales: Charalambous, Thalis, Tur, Carmen, Prados, Ferran, Kanber, Baris, Chard, Declan T, Ourselin, Sebastian, Clayden, Jonathan D, A M Gandini Wheeler-Kingshott, Claudia, Thompson, Alan J, Toosy, Ahmed T
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6518973/
https://www.ncbi.nlm.nih.gov/pubmed/30467210
http://dx.doi.org/10.1136/jnnp-2018-318440
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author Charalambous, Thalis
Tur, Carmen
Prados, Ferran
Kanber, Baris
Chard, Declan T
Ourselin, Sebastian
Clayden, Jonathan D
A M Gandini Wheeler-Kingshott, Claudia
Thompson, Alan J
Toosy, Ahmed T
author_facet Charalambous, Thalis
Tur, Carmen
Prados, Ferran
Kanber, Baris
Chard, Declan T
Ourselin, Sebastian
Clayden, Jonathan D
A M Gandini Wheeler-Kingshott, Claudia
Thompson, Alan J
Toosy, Ahmed T
author_sort Charalambous, Thalis
collection PubMed
description OBJECTIVE: To evaluate whether structural brain network metrics correlate better with clinical impairment and information processing speed in multiple sclerosis (MS) beyond atrophy measures and white matter lesions. METHODS: This cross-sectional study included 51 healthy controls and 122 patients comprising 58 relapsing–remitting, 28 primary progressive and 36 secondary progressive. Structural brain networks were reconstructed from diffusion-weighted MRIs and standard metrics reflecting network density, efficiency and clustering coefficient were derived and compared between subjects’ groups. Stepwise linear regression analyses were used to investigate the contribution of network measures that explain clinical disability (Expanded Disability Status Scale (EDSS)) and information processing speed (Symbol Digit Modalities Test (SDMT)) compared with conventional MRI metrics alone and to determine the best statistical model that explains better EDSS and SDMT. RESULTS: Compared with controls, network efficiency and clustering coefficient were reduced in MS while these measures were also reduced in secondary progressive relative to relapsing–remitting patients. Structural network metrics increase the variance explained by the statistical models for clinical and information processing dysfunction. The best model for EDSS showed that reduced network density and global efficiency and increased age were associated with increased clinical disability. The best model for SDMT showed that lower deep grey matter volume, reduced efficiency and male gender were associated with worse information processing speed. CONCLUSIONS: Structural topological changes exist between subjects’ groups. Network density and global efficiency explained disability above non-network measures, highlighting that network metrics can provide clinically relevant information about MS pathology.
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spelling pubmed-65189732019-06-05 Structural network disruption markers explain disability in multiple sclerosis Charalambous, Thalis Tur, Carmen Prados, Ferran Kanber, Baris Chard, Declan T Ourselin, Sebastian Clayden, Jonathan D A M Gandini Wheeler-Kingshott, Claudia Thompson, Alan J Toosy, Ahmed T J Neurol Neurosurg Psychiatry Multiple Sclerosis OBJECTIVE: To evaluate whether structural brain network metrics correlate better with clinical impairment and information processing speed in multiple sclerosis (MS) beyond atrophy measures and white matter lesions. METHODS: This cross-sectional study included 51 healthy controls and 122 patients comprising 58 relapsing–remitting, 28 primary progressive and 36 secondary progressive. Structural brain networks were reconstructed from diffusion-weighted MRIs and standard metrics reflecting network density, efficiency and clustering coefficient were derived and compared between subjects’ groups. Stepwise linear regression analyses were used to investigate the contribution of network measures that explain clinical disability (Expanded Disability Status Scale (EDSS)) and information processing speed (Symbol Digit Modalities Test (SDMT)) compared with conventional MRI metrics alone and to determine the best statistical model that explains better EDSS and SDMT. RESULTS: Compared with controls, network efficiency and clustering coefficient were reduced in MS while these measures were also reduced in secondary progressive relative to relapsing–remitting patients. Structural network metrics increase the variance explained by the statistical models for clinical and information processing dysfunction. The best model for EDSS showed that reduced network density and global efficiency and increased age were associated with increased clinical disability. The best model for SDMT showed that lower deep grey matter volume, reduced efficiency and male gender were associated with worse information processing speed. CONCLUSIONS: Structural topological changes exist between subjects’ groups. Network density and global efficiency explained disability above non-network measures, highlighting that network metrics can provide clinically relevant information about MS pathology. BMJ Publishing Group 2019-02 2018-11-22 /pmc/articles/PMC6518973/ /pubmed/30467210 http://dx.doi.org/10.1136/jnnp-2018-318440 Text en © Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY. Published by BMJ. This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See: http://creativecommons.org/licenses/by/4.0
spellingShingle Multiple Sclerosis
Charalambous, Thalis
Tur, Carmen
Prados, Ferran
Kanber, Baris
Chard, Declan T
Ourselin, Sebastian
Clayden, Jonathan D
A M Gandini Wheeler-Kingshott, Claudia
Thompson, Alan J
Toosy, Ahmed T
Structural network disruption markers explain disability in multiple sclerosis
title Structural network disruption markers explain disability in multiple sclerosis
title_full Structural network disruption markers explain disability in multiple sclerosis
title_fullStr Structural network disruption markers explain disability in multiple sclerosis
title_full_unstemmed Structural network disruption markers explain disability in multiple sclerosis
title_short Structural network disruption markers explain disability in multiple sclerosis
title_sort structural network disruption markers explain disability in multiple sclerosis
topic Multiple Sclerosis
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6518973/
https://www.ncbi.nlm.nih.gov/pubmed/30467210
http://dx.doi.org/10.1136/jnnp-2018-318440
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