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.

Department of Neuroinflammation Queen Square MS Centre UCL Queen Square Institute of Neurology Faculty of Brain Science University College of London WC1E 6BT London UK

Department of Neurology and Center of Clinical Neuroscience 1st Faculty of Medicine Charles University and General University Hospital 121 08 Prague Czech Republic

Department of Neurology Focus Program Translational Neuroscience University Medical Center of the Johannes Gutenberg University Mainz 55131 Mainz Germany

Department of Neurology Medical Faculty Heinrich Heine University 40225 Düsseldorf Germany

Department of Neurology Neuroimmunology Multiple Sclerosis Centre of Catalonia Hospital Universitari Vall d'Hebron 08035 Barcelona Spain

Department of Neurology Oslo University Hospital 0424 Oslo Norway

Department of Neuroradiology University Medical Center of the Johannes Gutenberg University Mainz 55131 Mainz Germany

Department of Neurosciences San Camillo Forlanini Hospital 00152 Rome Italy

Department of Neurosciences Sapienza University of Rome 00185 Rome Italy

Department of Radiology 1st Faculty of Medicine Charles University and General University Hospital 121 08 Prague Czech Republic

Department of Radiology and Nuclear Medicine Amsterdam UMC 1100 DD Amsterdam Netherlands

Division of Radiology and Nuclear Medicine Oslo University Hospital 0424 Oslo Norway

Institute of Clinical Medicine University of Oslo NO 0316 Oslo Norway

Institute of Neuroradiology St Josef Hospital Ruhr University Bochum 44791 Bochum Germany

Section of Neuroradiology Department of Radiology Hospital Universitari Vall d'Hebron Universitat Autònoma de Barcelona 08035 Barcelona Spain

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