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.

Diffusion-based structural connectivity patterns of multiple sclerosis phenotypes / Martinez-Heras, Eloy; Solana, Elisabeth; Vivó, Francesc; Lopez-Soley, Elisabet; Calvi, Alberto; Alba-Arbalat, Salut; Schoonheim, Menno M; Strijbis, Eva M; Vrenken, Hugo; Barkhof, Frederik; Rocca, Maria A; Filippi, Massimo; Pagani, Elisabetta; Groppa, Sergiu; Fleischer, Vinzenz; Dineen, Robert A; Bellenberg, Barbara; Lukas, Carsten; Pareto, Deborah; Rovira, Alex; Sastre-Garriga, Jaume; Collorone, Sara; Prados, Ferran; Toosy, Ahmed; Ciccarelli, Olga; Saiz, Albert; Blanco, Yolanda; Llufriu, Sara. - In: JOURNAL OF NEUROLOGY, NEUROSURGERY AND PSYCHIATRY. - ISSN 0022-3050. - (2023). [Epub ahead of print] [10.1136/jnnp-2023-331531]

Diffusion-based structural connectivity patterns of multiple sclerosis phenotypes

Rocca, Maria A;Filippi, Massimo;
2023-01-01

Abstract

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.
2023
MULTIPLE SCLEROSIS
NEUROIMMUNOLOGY
File in questo prodotto:
File Dimensione Formato  
jnnp-2023-331531.full.pdf

accesso aperto

Tipologia: PDF editoriale (versione pubblicata dall'editore)
Licenza: Altra licenza
Dimensione 2.45 MB
Formato Adobe PDF
2.45 MB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11768/144556
Citazioni
  • ???jsp.display-item.citation.pmc??? 0
  • Scopus 2
  • ???jsp.display-item.citation.isi??? 2
social impact