Prognostic value of single-subject grey matter networks in early multiple sclerosis
Fleischer, Vinzenz (Johannes Gutenberg University Mainz (Alemanya))
González-Escamilla, Gabriel (University Medical Center of the Johannes Gutenberg University Mainz (Alemanya))
Pareto, Deborah (University Medical Center of the Johannes Gutenberg University Mainz (Alemanya))
Rovira, Alex (Hospital Universitari Vall d'Hebron)
Sastre-Garriga, Jaume (Hospital Universitari Vall d'Hebron)
Høgestøl, Einar A. (University of Oslo (Noruega))
Harbo, Hanne F. (University of Oslo)
Bellenberg, Barbara (Ruhr-Universität Bochum)
Ruggieri, Serena (Università degli Studi di Roma "La Sapienza")
Gasperini, Claudio (San Camillo-Forlanini Hospital (Roma, Itàlia))
Uher, Tomas (Charles University (Praga, República Txeca))
Vaneckova, Manuela (Charles University (Praga, República Txeca))
Bittner, Stefan (Johannes Gutenberg University Mainz (Alemanya))
Othman, Ahmed E. (University Medical Center of the Johannes Gutenberg University Mainz (Alemanya))
Collorone, Sara (University College of London)
Toosy, Ahmed T. (University College of London)
Meuth, Sven G. (Heinrich-Heine-University (Düsseldorf, Alemanya))
Zipp, Frauke (Johannes Gutenberg University Mainz (Alemanya))
Barkhof, Frederik (University College of London)
Ciccarelli, Olga (University College of London)
Groppa, Sergiu (Johannes Gutenberg University Mainz (Alemanya))
Universitat Autònoma de Barcelona

Data:	2023
Resum:	The identification of prognostic markers in early multiple sclerosis (MS) is challenging and requires reliable measures that robustly predict future disease trajectories. Ideally, such measures should make inferences at the individual level to inform clinical decisions. This study investigated the prognostic value of longitudinal structural networks to predict 5-year Expanded Disability Status Scale (EDSS) progression in patients with relapsing-remitting MS (RRMS). We hypothesized that network measures, derived from MRI, outperform conventional MRI measurements at identifying patients at risk of developing disability progression. This longitudinal, multicentre study within the Magnetic Resonance Imaging in MS (MAGNIMS) network included 406 patients with RRMS (mean age = 35. 7 ± 9. 1 years) followed up for 5 years (mean follow-up = 5. 0 ± 0. 6 years). EDSS was determined to track disability accumulation. A group of 153 healthy subjects (mean age = 35. 0 ± 10. 1 years) with longitudinal MRI served as controls. All subjects underwent MRI at baseline and again 1 year after baseline. Grey matter atrophy over 1 year and white matter lesion load were determined. A single-subject brain network was reconstructed from T1-weighted scans based on grey matter atrophy measures derived from a statistical parameter mapping-based segmentation pipeline. Key topological measures, including network degree, global efficiency and transitivity, were calculated at single-subject level to quantify network properties related to EDSS progression. Areas under receiver operator characteristic (ROC) curves were constructed for grey matter atrophy and white matter lesion load, and the network measures and comparisons between ROC curves were conducted. The applied network analyses differentiated patients with RRMS who experience EDSS progression over 5 years through lower values for network degree [H(2) = 30. 0, P < 0. 001] and global efficiency [H(2) = 31. 3, P < 0. 001] from healthy controls but also from patients without progression. For transitivity, the comparisons showed no difference between the groups [H(2) = 1. 5, P = 0. 474]. Most notably, changes in network degree and global efficiency were detected independent of disease activity in the first year. The described network reorganization in patients experiencing EDSS progression was evident in the absence of grey matter atrophy. Network degree and global efficiency measurements demonstrated superiority of network measures in the ROC analyses over grey matter atrophy and white matter lesion load in predicting EDSS worsening (all P -values < 0. 05). Our findings provide evidence that grey matter network reorganization over 1 year discloses relevant information about subsequent clinical worsening in RRMS. Early grey matter restructuring towards lower network efficiency predicts disability accumulation and outperforms conventional MRI predictors. Fleischer et al. examine the prognostic value of brain networks derived from MRI for predicting disability progression in patients with multiple sclerosis. They conclude that early brain network alterations identify clinical deterioration in multiple sclerosis and outperform classical MRI predictors.
Ajuts:	Instituto de Salud Carlos III PI19/00950 Instituto de Salud Carlos III PI18/00823
Drets:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original.
Llengua:	Anglès
Document:	Article ; recerca ; Versió publicada
Matèria:	Relapsing-remitting multiple sclerosis ; EDSS progression in MS ; Brain network measures ; Structural covariance ; Graph theory
Publicat a:	Brain, Vol. 147 (august 2023) , p. 135-146, ISSN 1460-2156

DOI: 10.1093/brain/awad288
PMID: 37642541

12 p, 883.5 KB

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