Structural network disruption markers explain disability in multiple sclerosis. Issue 2 (22nd November 2018)

• Record Type: Journal Article
• Authors: Charalambous, Thalis; Tur, Carmen; Prados, Ferran; Kanber, Baris; Chard, Declan T; Ourselin, Sebastian; Clayden, Jonathan D; A M Gandini Wheeler-Kingshott, Claudia; Thompson, Alan J; Toosy, Ahmed T
• Is Part Of: Journal of neurology, neurosurgery and psychiatry. Volume 90:Issue 2(2019)
• Page Start: 219
• Page End: 226
• Publication Date: 2018-11-22
• Subjects: multiple sclerosis -- mri -- network analysis -- EDSS -- SDMT; Neurology -- Periodicals; Nervous system -- Surgery -- Periodicals; Psychiatry -- Periodicals; 616.8
• DOI: 10.1136/jnnp-2018-318440
• Languages: English
• ISSNs: 0022-3050

Abstract

Abstract : Objective: To evaluate whether structural brain network metrics correlate better with clinical impairment and information processing speed in multiple sclerosis (MS) beyond atrophy measures and white matter lesions. Methods: This cross-sectional study included 51 healthy controls and 122 patients comprising 58 relapsing–remitting, 28 primary progressive and 36 secondary progressive. Structural brain networks were reconstructed from diffusion-weighted MRIs and standard metrics reflecting network density, efficiency and clustering coefficient were derived and compared between subjects' groups. Stepwise linear regression analyses were used to investigate the contribution of network measures that explain clinical disability (Expanded Disability Status Scale (EDSS)) and information processing speed (Symbol Digit Modalities Test (SDMT)) compared with conventional MRI metrics alone and to determine the best statistical model that explains better EDSS and SDMT. Results: Compared with controls, network efficiency and clustering coefficient were reduced in MS while these measures were also reduced in secondary progressive relative to relapsing–remitting patients. Structural network metrics increase the variance explained by the statistical models for clinical and information processing dysfunction. The best model for EDSS showed that reduced network density and global efficiency and increased age were associated with increased clinical disability. The best model for SDMT showed that lower deep grey matter volume, reduced efficiency and male gender were associated with worse information processing speed. Conclusions: Structural topological changes exist between subjects' groups. Network density and global efficiency explained disability above non-network measures, highlighting that network metrics can provide clinically relevant information about MS pathology.