Predicting disability progression and cognitive worsening in multiple sclerosis using grey matter network measures

Abstract

Abstract Objective In multiple sclerosis (MS), magnetic resonance imaging (MRI) measures at the whole brain or regional level are only modestly associated with disability, while network-based measures are emerging as promising prognostic markers. We sought to demonstrate whether data-driven network-based measures of regional grey matter (GM) volumes predict future disability in secondary progressive MS (SPMS). Methods We used cross-sectional structural MRI, and baseline and longitudinal data of Expanded Disability Status Scale [EDSS], 9-Hole Peg Test [9HPT], and Symbol Digit Modalities Test [SDMT], from a clinical trial in 988 people with progressive MS. We processed T1-weighted scans to obtain GM probability maps and applied spatial independent component analysis (ICA) to identify co-varying patterns of GM volume change. We used survival models to determine whether baseline GM network measures predict cognitive and motor worsening. Results We identified 15 networks of regionally co-varying GM features. Compared with whole brain GM, deep GM, and lesion volumes, ICA-components correlated more closely with clinical outcomes. A mainly basal ganglia component had the highest correlations at baseline with the SDMT and was associated with cognitive worsening (HR= 1.29, 95% CI [1.09-1.52], p< 0.005). Two ICA-components were associated with 9HPT worsening (HR=1.30, 95% CI [1.06:1.60], p<0.01; and HR= 1.21, 95%CI [1.01:1.45], p<0.05). Post-hoc analyses revealed that for 9HPT and SDMT survival models including network-based measures reported a higher discrimination power (respectively, C-index= 0.69, se= 0.03; C-index= 0.71, se= 0.02) compared to models including only whole and regional MRI measures (respectively, C-index= 0.65, se= 0.03; C-index= 0.69, se= 0.02). Conclusions The disability progression was better predicted by networks of covarying GM regions, rather than by single regional or whole-brain measures. Network analysis can be applied in future clinical trials and may play a role in stratifying participants who have the most potential to show a treatment effect.