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

See more in PubMed

Filippi M, Bruck W, Chard D, et al. . Association between pathological and MRI findings in multiple sclerosis. Lancet Neurol. 2019;18:198–210. PubMed

Attfield KE, Jensen LT, Kaufmann M, Friese MA, Fugger L. The immunology of multiple sclerosis. Nat Rev Immunol. 2022;22:734–750. PubMed

Groppa S, Gonzalez-Escamilla G, Eshaghi A, Meuth SG, Ciccarelli O. Linking immune-mediated damage to neurodegeneration in multiple sclerosis: Could network-based MRI help? Brain Commun. 2021;3:fcab237. PubMed PMC

Filippi M, Preziosa P, Rocca MA. Magnetic resonance outcome measures in multiple sclerosis trials: time to rethink? Curr Opin Neurol. 2014;27:290–299. PubMed

Muthuraman M, Fleischer V, Kroth J, et al. . Covarying patterns of white matter lesions and cortical atrophy predict progression in early MS. Neurol Neuroimmunol Neuroinflamm. 2020;7:e681. PubMed PMC

Eshaghi A, Marinescu RV, Young AL, et al. . Progression of regional grey matter atrophy in multiple sclerosis. Brain. 2018;141:1665–1677. PubMed PMC

Fleischer V, Ciolac D, Gonzalez-Escamilla G, et al. . Subcortical volumes as early predictors of fatigue in multiple sclerosis. Ann Neurol. 2022;91:192–202. PubMed

De Stefano N, Stromillo ML, Giorgio A, et al. . Establishing pathological cut-offs of brain atrophy rates in multiple sclerosis. J Neurol Neurosurg Psychiatry. 2016;87:93–99. PubMed PMC

Kappos L, Wolinsky JS, Giovannoni G, et al. . Contribution of relapse-independent progression vs relapse-associated worsening to overall confirmed disability accumulation in typical relapsing multiple sclerosis in a pooled analysis of 2 randomized clinical trials. JAMA Neurol. 2020;77:1132–1140. PubMed PMC

Lublin FD, Haring DA, Ganjgahi H, et al. . How patients with multiple sclerosis acquire disability. Brain. 2022;145:3147–3161. PubMed PMC

Alexander-Bloch A, Giedd JN, Bullmore E. Imaging structural co-variance between human brain regions. Nat Rev Neurosci. 2013;14:322–336. PubMed PMC

Chard DT, Alahmadi AAS, Audoin B, et al. . Mind the gap: From neurons to networks to outcomes in multiple sclerosis. Nat Rev Neurol. 2021;17:173–184. PubMed

Tijms BM, Series P, Willshaw DJ, Lawrie SM. Similarity-based extraction of individual networks from gray matter MRI scans. Cerebral cortex. 2012;22:1530–1541. PubMed

Fleischer V, Radetz A, Ciolac D, et al. . Graph theoretical framework of brain networks in multiple sclerosis: A review of concepts. Neuroscience. 2019;403:35–53. PubMed

Zielinski BA, Gennatas ED, Zhou J, Seeley WW. Network-level structural covariance in the developing brain. Proc Natl Acad Sci U S A. 2010;107:18191–18196. PubMed PMC

Mechelli A, Friston KJ, Frackowiak RS, Price CJ. Structural covariance in the human cortex. J Neurosci. 2005;25:8303–8310. PubMed PMC

Bullmore E, Sporns O. The economy of brain network organization. Nat Rev Neurosci. 2012;13:336–349. PubMed

He Y, Dagher A, Chen Z, et al. . Impaired small-world efficiency in structural cortical networks in multiple sclerosis associated with white matter lesion load. Brain. 2009;132(12):3366–3379. PubMed PMC

Fleischer V, Koirala N, Droby A, et al. . Longitudinal cortical network reorganization in early relapsing-remitting multiple sclerosis. Ther Adv Neurol Disord. 2019;12:1756286419838673. PubMed PMC

Tur C, Eshaghi A, Altmann DR, et al. . Structural cortical network reorganization associated with early conversion to multiple sclerosis. Sci Rep. 2018;8:10715. PubMed PMC

Koubiyr I, Besson P, Deloire M, et al. . Dynamic modular-level alterations of structural-functional coupling in clinically isolated syndrome. Brain. 2019;142:3428–3439. PubMed

Muthuraman M, Fleischer V, Kolber P, Luessi F, Zipp F, Groppa S. Structural brain network characteristics can differentiate CIS from early RRMS. Front Neurosci. 2016;10:14. PubMed PMC

Tur C, Kanber B, Eshaghi A, et al. . Clinical relevance of cortical network dynamics in early primary progressive MS. Mult Scler. 2020;26:442–456. PubMed

Rimkus CM, Schoonheim MM, Steenwijk MD, et al. . Gray matter networks and cognitive impairment in multiple sclerosis. Mult Scler. 2019;25:382–391. PubMed PMC

Collorone S, Prados F, Hagens MH, et al. . Single-subject structural cortical networks in clinically isolated syndrome. Mult Scler. 2020;26:1392–1401. PubMed

Bevan CJ, Cree BA. Disease activity free status: A new end point for a new era in multiple sclerosis clinical research? JAMA Neurol. 2014;71:269–270. PubMed

Kalincik T, Cutter G, Spelman T, et al. . Defining reliable disability outcomes in multiple sclerosis. Brain. 2015;138(11):3287–3298. PubMed

Schmidt P, Gaser C, Arsic M, et al. . An automated tool for detection of FLAIR-hyperintense white-matter lesions in multiple sclerosis. Neuroimage. 2012;59:3774–3783. PubMed

Tustison NJ, Avants BB, Cook PA, et al. . N4ITK: Improved N3 bias correction. IEEE Trans Med Imaging. 2010;29:1310–1320. PubMed PMC

Ashburner J, Ridgway GR. Symmetric diffeomorphic modeling of longitudinal structural MRI. Front Neurosci. 2012;6:197. PubMed PMC

Ashburner J. A fast diffeomorphic image registration algorithm. Neuroimage. 2007;38:95–113. PubMed

Ashburner J, Friston KJ. Unified segmentation. Neuroimage. 2005;26:839–851. PubMed

Lorio S, Fresard S, Adaszewski S, et al. . New tissue priors for improved automated classification of subcortical brain structures on MRI. Neuroimage. 2016;130:157–166. PubMed PMC

Gonzalez-Escamilla G, Ciolac D, De Santis S, et al. . Gray matter network reorganization in multiple sclerosis from 7-tesla and 3-tesla MRI data. Ann Clin Transl Neurol. 2020;7:543–553. PubMed PMC

Gonzalez-Escamilla G, Miederer I, Grothe MJ, et al. . Metabolic and amyloid PET network reorganization in Alzheimer's disease: Differential patterns and partial volume effects. Brain Imaging Behav. 2021;15:190–204. PubMed PMC

Rubinov M, Sporns O. Complex network measures of brain connectivity: uses and interpretations. Neuroimage. 2010;52:1059–1069. PubMed

Latora V, Marchiori M. Efficient behavior of small-world networks. Phys Rev Lett. 2001;87:198701. PubMed

Newman ME, Park J. Why social networks are different from other types of networks. Phys Rev E Stat Nonlin Soft Matter Phys. 2003;68(3 Pt 2):036122. PubMed

DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: A nonparametric approach. Biometrics. 1988;44:837–845. PubMed

Trapp BD, Peterson J, Ransohoff RM, Rudick R, Mork S, Bo L. Axonal transection in the lesions of multiple sclerosis. N Engl J Med. 1998;338:278–285. PubMed

De Stefano N, Giorgio A, Battaglini M, et al. . Assessing brain atrophy rates in a large population of untreated multiple sclerosis subtypes. Neurology. 2010;74:1868–1876. PubMed

Tur C, Carbonell-Mirabent P, Cobo-Calvo A, et al. . Association of early progression independent of relapse activity with long-term disability after a first demyelinating event in multiple sclerosis. JAMA Neurol. 2023;80:151–160. PubMed PMC

Schoonheim MM, Broeders TAA, Geurts JJG. The network collapse in multiple sclerosis: an overview of novel concepts to address disease dynamics. Neuroimage Clin. 2022;35:103108. PubMed PMC

Popescu V, Klaver R, Voorn P, et al. . What drives MRI-measured cortical atrophy in multiple sclerosis? Mult Scler. 2015;21:1280–1290. PubMed

van Olst L, Rodriguez-Mogeda C, Picon C, et al. . Meningeal inflammation in multiple sclerosis induces phenotypic changes in cortical microglia that differentially associate with neurodegeneration. Acta Neuropathol. 2021;141:881–899. PubMed PMC

Kiljan S, Meijer KA, Steenwijk MD, et al. . Structural network topology relates to tissue properties in multiple sclerosis. J Neurol. 2019;266:212–222. PubMed PMC

Fleischer V, Groger A, Koirala N, et al. . Increased structural white and grey matter network connectivity compensates for functional decline in early multiple sclerosis. Mult Scler. 2017;23:432–441. PubMed

Brownlee WJ, Altmann DR, Prados F, et al. . Early imaging predictors of long-term outcomes in relapse-onset multiple sclerosis. Brain. 2019;142:2276–2287. PubMed

Giorgio A, Stromillo ML, De Leucio A, et al. . Appraisal of brain connectivity in radiologically isolated syndrome by modeling imaging measures. J Neurosci. 2015;35:550–558. PubMed PMC

Gong G, He Y, Chen ZJ, Evans AC. Convergence and divergence of thickness correlations with diffusion connections across the human cerebral cortex. Neuroimage. 2012;59:1239–1248. PubMed

Alexander-Bloch A, Raznahan A, Bullmore E, Giedd J. The convergence of maturational change and structural covariance in human cortical networks. J Neurosci. 2013;33:2889–2899. PubMed PMC

Khundrakpam BS, Lewis JD, Jeon S, et al. . Exploring individual brain variability during development based on patterns of maturational coupling of cortical thickness: A longitudinal MRI study. Cerebral cortex. 2019;29:178–188. PubMed

Rocca MA, Valsasina P, Meani A, et al. . Network damage predicts clinical worsening in multiple sclerosis: A 6.4-year study. Neurol Neuroimmunol Neuroinflamm. 2021;8:e1006. PubMed PMC