Prognostic value of single-subject grey matter networks in early multiple sclerosis

Abstract

Brain network measures; Graph theory; Relapsing-remitting multiple sclerosis

Medidas de la red cerebral; Teoría de grafos; Esclerosis múltiple recurrente-remitente

Mesures de la xarxa cerebral; Teoria de grafs; Esclerosi múltiple recurrent-remitent

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.

This work was supported by a grant from the German Research Council (Deutsche Forschungsgemeinschaft (D.F.G.); CRC-TR-128; V.F., S.B., F.Z., S.G.), by the National MS Society USA, grant RFA-220339314 (S.G.), by the DFG (Radiomics SPP 2177, S.G., G.E.G.), grants GR 4590/3-1 and GO 3493/1-1, by the German Federal Ministry for Education and Research, BMBF, German Competence Network Multiple Sclerosis (KKNMS), grants 01GI1601I and 01GI0914, and by the ‘Oppenheim-Förderpreis für Multiple Sklerose’ of Novartis Pharma GmbH (V.F.). In addition by the Research Council of Norway (Grant No. 240102, PI: H.F.H.), by the South-Eastern Regional Health Authorities of Norway (Grant No. 2011059 and ES563338/Biotek 2021, PI: H.F.H.) and by the Instituto de Salud Carlos III PI18/00823 (D.P.). The contribution of data from Prague (T.U. and M.V.) was supported by the Ministry of Health of the Czech Republic within the conceptual development of a research organization (00064165) at the General University Hospital in Prague, by the project National Institute for Neurological Research and by the European Union—Next Generation EU (Programme EXCELES, ID project No LX22NPO5107) and by Roche (NCT03706118).