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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, Vincenz; Dineen, Robert A; Ballenberg, Barbara; Lukas, Carsten; Pareto, Deborah; Rovira, Alex; Sastre-Garriga, Jaume; Collorone, Sara; Prados, Ferran; Toosy, Ahmed; Ciccarelli, Olga; Saiz, Albert; Blanco, Yolanda; Llufriu, Sara

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Authors

Eloy Martinez-Heras

Elisabeth Solana

Francesc Vivó

Elisabet Lopez-Soley

Alberto Calvi

Salut Alba-Arbalat

Menno M Schoonheim

Eva M Strijbis

Hugo Vrenken

Frederik Barkhof

Maria A Rocca

Massimo Filippi

Elisabetta Pagani

Sergiu Groppa

Vincenz Fleischer

ROBERT DINEEN rob.dineen@nottingham.ac.uk
Professor of Neuroradiology

Barbara Ballenberg

Carsten Lukas

Deborah Pareto

Alex Rovira

Jaume Sastre-Garriga

Sara Collorone

Ferran Prados

Ahmed Toosy

Olga Ciccarelli

Albert Saiz

Yolanda Blanco

Sara Llufriu



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.

Citation

Martinez-Heras, E., Solana, E., Vivó, F., Lopez-Soley, E., Calvi, A., Alba-Arbalat, S., …Llufriu, S. (2023). Diffusion-based structural connectivity patterns of multiple sclerosis phenotypes. Journal of Neurology, Neurosurgery and Psychiatry, 94(11), 916-923. https://doi.org/10.1136/jnnp-2023-331531

Journal Article Type Article
Acceptance Date May 30, 2023
Online Publication Date Jun 15, 2023
Publication Date 2023-11
Deposit Date Jun 16, 2023
Publicly Available Date Jun 16, 2023
Journal Journal of Neurology, Neurosurgery and Psychiatry
Print ISSN 0022-3050
Electronic ISSN 1468-330X
Publisher BMJ Publishing Group
Peer Reviewed Peer Reviewed
Volume 94
Issue 11
Pages 916-923
DOI https://doi.org/10.1136/jnnp-2023-331531
Public URL https://nottingham-repository.worktribe.com/output/19010097
Publisher URL https://jnnp.bmj.com/content/94/11/916