Fleischer, Vinzenz; Gonzalez-Escamilla, Gabriel; Pareto, Deborah; Rovira, Alex; Sastre-Garriga, Jaume; Sowa, Piotr; Høgestøl, Einar A; ... Groppa, Sergiu; + view all Fleischer, Vinzenz; Gonzalez-Escamilla, Gabriel; Pareto, Deborah; Rovira, Alex; Sastre-Garriga, Jaume; Sowa, Piotr; Høgestøl, Einar A; Harbo, Hanne F; Bellenberg, Barbara; Lukas, Carsten; Ruggieri, Serena; Gasperini, Claudio; Uher, Tomas; Vaneckova, Manuela; Bittner, Stefan; Othman, Ahmed E; Collorone, Sara; Toosy, Ahmed T; Meuth, Sven G; Zipp, Frauke; Barkhof, Frederik; Ciccarelli, Olga; Groppa, Sergiu; - view fewer (2024) Prognostic value of single-subject grey matter networks in early multiple sclerosis. Brain , 147 (1) pp. 135-146. 10.1093/brain/awad288.

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Abstract

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 five-year EDSS progression in patients with relapsing-remitting MS (RRMS). We hypothesized that network measures, derived from magnetic resonance imaging (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 five years (mean follow-up = 5.0 ± 0.6 years). Expanded Disability Status Scale (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 one year after baseline. Grey matter (GM) atrophy over one year and white matter (WM) lesion load were determined. A single-subject brain network was reconstructed from T1-weighted scans based on GM atrophy measures derived from a statistical parameter mapping (SPM)-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 GM atrophy, WM 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 five 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 GM atrophy. Network degree and global efficiency measurements demonstrated superiority of network measures in the ROC analyses over GM atrophy and WM lesion load in predicting EDSS worsening (all p-values < 0.05). Our findings provide evidence that GM network reorganization over one year discloses relevant information about subsequent clinical worsening in RRMS. Early GM restructuring towards lower network efficiency predicts disability accumulation and outperforms conventional MRI predictors.

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