Networks of microstructural damage predict disability in multiple sclerosis

Background: Network-based measures are emerging MRI markers in multiple sclerosis (MS). We aimed to identify networks of white (WM) and grey matter (GM) damage that predict disability progression and cognitive worsening using data-driven methods. // Methods: We analysed data from 1836 participants with different MS phenotypes (843 in a discovery cohort and 842 in a replication cohort). We calculated standardised T1-weighted/T2-weighted (sT1w/T2w) ratio maps in brain GM and WM, and applied spatial independent component analysis to identify networks of covarying microstructural damage. Clinical outcomes were Expanded Disability Status Scale worsening confirmed at 24 weeks (24-week confirmed disability progression (CDP)) and time to cognitive worsening assessed by the Symbol Digit Modalities Test (SDMT). We used Cox proportional hazard models to calculate predictive value of network measures. // Results: We identified 8 WM and 7 GM sT1w/T2w networks (of regional covariation in sT1w/T2w measures) in both cohorts. Network loading represents the degree of covariation in regional T1/T2 ratio within a given network. The loading factor in the anterior corona radiata and temporo-parieto-frontal components were associated with higher risks of developing CDP both in the discovery (HR=0.85, p<0.05 and HR=0.83, p<0.05, respectively) and replication cohorts (HR=0.84, p<0.05 and HR=0.80, p<0.005, respectively). The decreasing or increasing loading factor in the arcuate fasciculus, corpus callosum, deep GM, cortico-cerebellar patterns and lesion load were associated with a higher risk of developing SDMT worsening both in the discovery (HR=0.82, p<0.01; HR=0.87, p<0.05; HR=0.75, p<0.001; HR=0.86, p<0.05 and HR=1.27, p<0.0001) and replication cohorts (HR=0.82, p<0.005; HR=0.73, p<0.0001; HR=0.80, p<0.005; HR=0.85, p<0.01 and HR=1.26, p<0.0001). // Conclusions: GM and WM networks of microstructural changes predict disability and cognitive worsening in MS. Our approach may be used to identify patients at greater risk of disability worsening and stratify cohorts in treatment trials

Network-based magnetic resonance imaging measures for clinical trials in multiple sclerosis

My work, presented in this thesis, aimed to define MRI markers to be used in clinical trials for identifying participants most likely to worsen, monitoring disease progression, and assessing treatment effects. With my first study (Chapter 3), I identified from T1-weighted sequences data-driven patterns of grey matter covarying volumes that predicted physical and cognitive disability in a large cohort of participants with secondary progressive multiple sclerosis. Moreover, some of the identified components were better correlated with concurrent disability, and some better predicted disability progression than conventionally used MRI measures (i.e. regional and whole-brain volume). Therefore, with this study, I identified clinically relevant structural patterns that could be used in clinical trials to stratify participants that are most likely to progress. With my second study (Chapter 4), I expanded on the first project by investigating the involvement of microstructural WM and GM damage as prognostic markers of clinical disability and cognitive dysfunctions in multiple sclerosis. I found networks of microstructural changes predictive of clinical progression and cognitive worsening. Moreover, this was the first study to use standardised T1-weighted/T2-weighted measures of white and grey matter to identify patterns of covarying microstructural damage changes and use them to predict clinical and cognitive worsening in multiple sclerosis. Finally, because these measures were obtained from MRI sequences routinely acquired in clinical trials, they hold promises to be broadly used in future clinical trials. With the third and last study (Chapter 5), I have developed a new paradigm to obtain longitudinal individual-level network-based measures of grey matter regional volume changes by applying independent component analysis (ICA) and a self-supervised machine learning model. The identified networks were clinically relevant as they discriminated among multiple sclerosis phenotypes, explained clinical disability, and showed treatment effect. Moreover, while the ICA needs to be run on the whole cohort, the approach I developed allows retrieving network-based measures at the individual level without re-estimating model parameters on the whole population when applied to new data (e.g. participants and time-points). These measures could be used in future clinical trials to complement conventional MRI measures and open the possibility of estimating network measures prospectively and at the individual level

Treatment Effect on Brain Atrophy Correlates with Treatment Effect on Cognition in Multiple Sclerosis

Objective: The purpose of this study was to evaluate the extent to which treatment effect on magnetic resonance imaging (MRI)-derived measures of brain atrophy and focal lesions can mediate, at the trial level, the treatment effect on cognitive outcomes in multiple sclerosis (MS).Methods: We collected all published randomized clinical trials in MS lasting at least 2 years and including as end points: active MRI lesions (defined as new/enlarging T2 lesions), brain atrophy (defined as a change in brain volume between month 12 and month 24), and change in cognitive performance (assessed by the Paced Auditory Serial Addition Test [PASAT]). Relative reductions were used to quantify the treatment effect on MRI markers (lesions and atrophy), whereas the standardized mean difference (Hedges g) between baseline and follow-up cognitive assessment was used to quantify the treatment effects on cognition. A linear regression, weighted for trial size, was used to assess the relationship between the treatment effects on MRI markers and cognition.Results: Fourteen trials including more than 8,813 patients with MS were included in the meta-regression. Treatment effect on cognition was strongly associated with the treatment effect on brain atrophy (R-2 = 0.79, p &lt; 0.001), but was not correlated with the treatment effect on active MRI lesions (R-2 = 0.16, p = 0.14).Interpretation: Results reported here suggest that brain atrophy, a well-established MRI marker in MS clinical trials, can be used as a main outcome for clinical trials with drugs targeting cognitive impairment and neurodegeneration

Predicting disability progression and cognitive worsening in multiple sclerosis using patterns of grey matter volumes

Objective: In multiple sclerosis (MS), 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 patterns of covarying 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, Nine-Hole Peg Test (9HPT) and Symbol Digit Modalities Test (SDMT), from a clinical trial in 988 people with SPMS. We processed T1-weighted scans to obtain GM probability maps and applied spatial independent component analysis (ICA). We repeated ICA on 400 healthy controls. We used survival models to determine whether baseline patterns of covarying GM volume measures predict cognitive and motor worsening. Results: We identified 15 patterns of regionally covarying GM features. Compared with whole brain GM, deep GM and lesion volumes, some 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 to 1.52, p<0.005). Two ICA components were associated with 9HPT worsening (HR=1.30, 95% CI 1.06 to 1.60, p<0.01 and HR=1.21, 95% CI 1.01 to 1.45, p<0.05). ICA measures could better predict SDMT and 9HPT worsening (C-index=0.69-0.71) compared with models including only whole and regional MRI measures (C-index=0.65-0.69, p value for all comparison <0.05). Conclusions: The disability progression was better predicted by some of the covarying GM regions patterns, than by single regional or whole-brain measures. ICA, which may represent structural brain networks, can be applied to clinical trials and may play a role in stratifying participants who have the most potential to show a treatment effect