The decreases we observed in MWF suggest that changes in myelin integrity and loss of myelin may be occurring diffusely and over long time periods in the MS brain. The timescale of these changes indicates that chronic, progressive myelin damage is an evolving process occurring over many years.

Clinical NEDA predicted long-term disability outcome. By contrast, definitions of NEDA that included on-therapy changes in magnetic resonance imaging variables did not increase the predictive validity.

Background: Enhanced prediction of progression in secondary progressive multiple sclerosis (SPMS) could improve clinical trial design. Machine learning (ML) algorithms are methods for training predictive models with minimal human intervention. Objective: To evaluate individual and ensemble model performance built using decision tree (DT)based algorithms compared to logistic regression (LR) and support vector machines (SVMs) for predicting SPMS disability progression. Methods: SPMS participants (n ¼ 485) enrolled in a 2-year placebo-controlled (negative) trial assessing the efficacy of MBP8298 were classified as progressors if a 6-month sustained increase in Expanded Disability Status Scale (EDSS) (!1.0 or !0.5 for a baseline of 5.5 or !6.0 respectively) was observed. Variables included EDSS, Multiple Sclerosis Functional Composite component scores, T2 lesion volume, brain parenchymal fraction, disease duration, age, and sex. Area under the receiver operating characteristic curve (AUC) was the primary outcome for model evaluation. Results: Three DT-based models had greater AUCs (61.8%, 60.7%, and 60.2%) than independent and ensemble SVM (52.4% and 51.0%) and LR (49.5% and 51.1%). Conclusion: SPMS disability progression was best predicted by non-parametric ML. If confirmed, ML could select those with highest progression risk for inclusion in SPMS trial cohorts and reduce the number of low-risk individuals exposed to experimental therapies.

BACKGROUND AND PURPOSE Cognitive impairment is a core symptom in multiple sclerosis (MS). Damage to normal appearing white matter (NAWM) is likely involved. We sought to determine if greater myelin heterogeneity in NAWM is associated with decreased cognitive performance in MS. METHODS A total of 27 participants with MS and 13 controls matched for age, sex, and education underwent myelin water imaging (MWI) from which the myelin water fraction (MWF) was calculated. Corpus callosum, superior longitudinal fasciculus, and cingulum were chosen as regions of interest (ROIs) a priori based on their involvement in MS‐related cognitive impairment. Cognitive performance was assessed using the Symbol Digit Modalities Test (SDMT). Pearson ́s product moment correlations were performed to assess relationships between cognitive performance and myelin heterogeneity (variance of MWF within an ROI). RESULTS In MS, myelin heterogeneity in all three ROIs was significantly associated with performance on the SDMT. These correlations ranged from moderate (r = −.561) to moderately strong (r = −.654) and were highly significant (P values ranged from .001 to .0002). Conversely, myelin heterogeneity was not associated with SDMT performance in controls in any ROI (P > .108). CONCLUSION Increased myelin heterogeneity in NAWM is associated with decreased cognitive processing speed performance in MS.

Corticospinal pathway damage in individuals with NMO was evident by reduced recruitment curve slope and lower myelin water fraction. These specific measures of corticospinal function and structure may be used to obtain a better understanding and monitor brain injury caused by inflammatory central nervous system disorders.