TY - JOUR
T1 - Diffusion-based structural connectivity patterns of multiple sclerosis phenotypes
AU - Martinez-Heras, Eloy
AU - Solana, Elisabeth
AU - Vivó, Francesc
AU - Lopez-Soley, Elisabet
AU - Calvi, Alberto
AU - Alba-Arbalat, Salut
AU - Schoonheim, Menno M.
AU - Strijbis, Eva M.
AU - Vrenken, Hugo
AU - Barkhof, Frederik
AU - Rocca, Maria A.
AU - Filippi, Massimo
AU - Pagani, Elisabetta
AU - Groppa, Sergiu
AU - Fleischer, Vinzenz
AU - Dineen, Robert A.
AU - Bellenberg, Barbara
AU - Lukas, Carsten
AU - Pareto, Deborah
AU - Rovira, Alex
AU - Sastre-Garriga, Jaume
AU - Collorone, Sara
AU - Prados, Ferran
AU - Toosy, Ahmed
AU - Ciccarelli, Olga
AU - Saiz, Albert
AU - Blanco, Yolanda
AU - Llufriu, Sara
N1 - Funding Information: This work was sponsored by the Instituto Carlos III (ISCIII) and co-funded by the European Union through the Plan Estatal de Investigación Científica y Técnica y de Innovación 2015-2024 (PI15/00587 to SL, and AS; PI18/01030 to SL and AS; PI21/01189 to SL and AS), by the Red Española de Esclerosis Múltiple (REEM-RD16/0015/0002, RD16/0015/0003). Part of this work was supported by the German Federal Ministry for Education and Research, BMBF, German Competence Network Multiple Sclerosis (KKNMS), grants 01GI1601I and 01GI0914. The authors are grateful to the IDIBAPS Magnetic resonance imaging platform for their support. This work was partially developed at the building Centro Esther Koplowitz, Barcelona, CERCA Programme/Generalitat de Catalunya. Publisher Copyright: © Author(s) (or their employer(s)) 2023. No commercial re-use. See rights and permissions. Published by BMJ.
PY - 2023/11/1
Y1 - 2023/11/1
N2 - Background: We aimed to describe the severity of the changes in brain diffusion-based connectivity as multiple sclerosis (MS) progresses and the microstructural characteristics of these networks that are associated with distinct MS phenotypes. Methods: Clinical information and brain MRIs were collected from 221 healthy individuals and 823 people with MS at 8 MAGNIMS centres. The patients were divided into four clinical phenotypes: clinically isolated syndrome, relapsing-remitting, secondary progressive and primary progressive. Advanced tractography methods were used to obtain connectivity matrices. Then, differences in whole-brain and nodal graph-derived measures, and in the fractional anisotropy of connections between groups were analysed. Support vector machine algorithms were used to classify groups. Results: Clinically isolated syndrome and relapsing-remitting patients shared similar network changes relative to controls. However, most global and local network properties differed in secondary progressive patients compared with the other groups, with lower fractional anisotropy in most connections. Primary progressive participants had fewer differences in global and local graph measures compared with clinically isolated syndrome and relapsing-remitting patients, and reductions in fractional anisotropy were only evident for a few connections. The accuracy of support vector machine to discriminate patients from healthy controls based on connection was 81%, and ranged between 64% and 74% in distinguishing among the clinical phenotypes. Conclusions: In conclusion, brain connectivity is disrupted in MS and has differential patterns according to the phenotype. Secondary progressive is associated with more widespread changes in connectivity. Additionally, classification tasks can distinguish between MS types, with subcortical connections being the most important factor.
AB - Background: We aimed to describe the severity of the changes in brain diffusion-based connectivity as multiple sclerosis (MS) progresses and the microstructural characteristics of these networks that are associated with distinct MS phenotypes. Methods: Clinical information and brain MRIs were collected from 221 healthy individuals and 823 people with MS at 8 MAGNIMS centres. The patients were divided into four clinical phenotypes: clinically isolated syndrome, relapsing-remitting, secondary progressive and primary progressive. Advanced tractography methods were used to obtain connectivity matrices. Then, differences in whole-brain and nodal graph-derived measures, and in the fractional anisotropy of connections between groups were analysed. Support vector machine algorithms were used to classify groups. Results: Clinically isolated syndrome and relapsing-remitting patients shared similar network changes relative to controls. However, most global and local network properties differed in secondary progressive patients compared with the other groups, with lower fractional anisotropy in most connections. Primary progressive participants had fewer differences in global and local graph measures compared with clinically isolated syndrome and relapsing-remitting patients, and reductions in fractional anisotropy were only evident for a few connections. The accuracy of support vector machine to discriminate patients from healthy controls based on connection was 81%, and ranged between 64% and 74% in distinguishing among the clinical phenotypes. Conclusions: In conclusion, brain connectivity is disrupted in MS and has differential patterns according to the phenotype. Secondary progressive is associated with more widespread changes in connectivity. Additionally, classification tasks can distinguish between MS types, with subcortical connections being the most important factor.
KW - MULTIPLE SCLEROSIS
KW - NEUROIMMUNOLOGY
UR - http://www.scopus.com/inward/record.url?scp=85164481874&partnerID=8YFLogxK
U2 - https://doi.org/10.1136/jnnp-2023-331531
DO - https://doi.org/10.1136/jnnp-2023-331531
M3 - Article
C2 - 37321841
SN - 0022-3050
VL - 94
SP - 916
EP - 923
JO - Journal of Neurology, Neurosurgery and Psychiatry
JF - Journal of Neurology, Neurosurgery and Psychiatry
IS - 11
M1 - jnnp-2023-331531
ER -