Background

Background: An improved understanding of the impact of demyelination on multiple sclerosis (MS) related cognitive impairment is crucial for targeting and testing therapies with the potential to slow cognitive decline. Demyelination can be assessed using myelin water imaging, a quantitative magnetic resonance imaging (MRI) technique that measures signal from water in the myelin bilayers, providing a specific measure of myelin (myelin water fraction, MWF).

Objectives

Objective: To determine if there is an anatomical-functional relationship between myelin content and location with cognitive performance.

Methods

Methods: 76 MS participants (mean (SD) age:50.4y(10.6y), 51F) underwent T2 relaxation imaging to calculate MWF maps and cognitive testing (Symbol Digit Modalities Test (SDMT); Selective Reminding Test (SRT); Controlled Oral Word Association Test (COWAT); Brief Visuospatial Memory Test-Revised (BVMT-R)). Nonparametric permutation testing with FSL Randomise was used to determine which white matter (WM) MWF voxels were associated with cognitive test performance for each test (p<0.01, after multiple comparisons correction), creating test-specific maps of associated WM areas. Pearson ́s correlations assessed relationships between mean MWF in the cognitive test-specific WM areas and respective test scores. MS patients were categorized into cognitively impaired, mildly impaired and cognitively preserved groups based on published norms. Kruskal Wallis ANOVA with post hoc pairwise comparisons investigated mean MWF differences between cognitive groups.

Results

Results: MWF in several WM areas was significantly associated with SDMT, SRT and BVMT-R scores but not the COWAT. All tests found voxels within the corona radiata, posterior thalamic radiation and parts of the corpus callosum significant. Unique WM areas were the inferior longitudinal fasciculus and anterior cingulum for SDMT and the retrolenticular part of the internal capsule for the BVMT-R. Mean MWF in the test-specific WM areas correlated significantly with performance on the SDMT (r=0.58, p= 4.11 x 10^-8), SRT (r=0.56, p= 4.14 x 10^-7) and BVMT-R (r=0.56, p= 1.0 x 10^-6). Mean MWF in the test-specific WM areas was significantly lower in the cognitively impaired group relative to the cognitively preserved group (p<0.01).

Conclusions

Conclusions: There is an anatomical-functional relationship between myelin damage and cognitive performance in MS with unique WM patterns for different cognitive domains.

Background

Susac’s Syndrome (SuS) is a rare autoimmune endotheliopathy of the brain, retina and cochlea that mimics multiple sclerosis (MS). Lesions presumed to be microinfarcts classically involve the corpus callosum (CC). While one brain biopsy reported demyelination, and MRI studies have shown reduced fractional anisotropy in the CC, the specific processes underlying SuS pathology are not yet clear. Myelin water imaging (MWI) and diffusion basis spectrum imaging (DBSI) can provide information about microstructural changes occurring in SuS. MWI provides a quantitative measurement of myelin, termed the myelin water fraction (MWF). DBSI yields various physiologically relevant metrics characterized by water diffusion: the apparent diffusion coefficient (ADC) which relates to overall tissue damage; fractional anisotropy (FA), which decreases with white matter (WM) damage; and radial diffusivity (RD) which increases with myelin loss.

Objectives

Determine in vivo WM microstructural changes in Susac Syndrome compared to MS and healthy controls (HC) using MWI and DBSI.

Methods

Participants included 7 SuS patients following the proposed European Susac Consortium diagnostic criteria (mean age 43.3y (30-78y), 6F), 10 MS patients (mean age 43.2y (26-70y), 9F) and 10 HC (MWI: 44y (27-64y), 9F, DBSI: 35.9y (22-47y), 5F). 3T MRI included MWI (48-echo 3D GRASE sequence), DBSI (9 b-values, 0-1500 s/mm², 99 directions) and a 3DT1 for anatomical reference. The CC and global WM (non-lesional tissue) were segmented and registered using FSL and the JHU atlas. One-way ANOVA with Tukey correction compared CC and global WM between groups.

Results

CC: SuS MWF (0.09±0.01) was lower than MS (0.11±0.02, p=0.03) and trending lower than HC (0.11±0.02, p=0.07). SuS ADC (0.84 ± 0.08 x 10^-3μm²/ms) was higher than MS (0.73±0.04 x 10^-3μm²/ms, p<0.001) and controls (0.71±0.04 x 10^-3μm²/ms, p<0.001). SuS FA (0.82±0.02) was lower than HC (0.86±0.02, p= 0.02). SuS RD was higher (0.27±0.03 x 10^-3μm²/ms) than HC (0.21±0.01 x 10^-3μm²/ms, p=0.004) and trending higher than MS (0.23±0.05 x 10^-3μm²/ms, p=0.05).

Global WM: ADC and RD findings in the Global WM were similar to CC, i.e. ADC and RD were significantly higher in SuS compared to MS and HC (all p<=0.03). However, MWF and FA was insignificantly different between the groups.

Conclusions

We report the first use of MWI in SuS. Both CC and the global WM showed non-lesional myelin damage, which was more severe than MS.

Background

Deep grey matter (DGM) atrophy is a feature in all multiple sclerosis (MS) phenotypes. Studies have shown a strong relationship between DGM atrophy and clinical worsening but the utility of DGM volumes for predicting disease activity is largely unexplored, especially in early disease. Machine learning (ML) is a computational approach that can identify patterns that predict disease outcomes. In ML, the study dataset is divided into training and test subsets. The training set contains known outcomes, which the ML algorithm uses to form a prediction model, which is then evaluated on the test set.

Objectives

To develop an ML model for predicting new disease activity (clinical or MRI) within 2 years of a first clinical demyelinating event, using baseline DGM volumes. The motivation is to identify individuals at higher risk of new disease activity.

Methods

3D T1-weighted MRIs acquired within 90 days of a first clinical event in 140 subjects from a completed placebo-controlled trial of minocycline were used. Eighty subjects had new disease activity within 2 years, 28 were stable, and 32 withdrew early (unknown outcome). The stable and unknown groups were combined into 1 for ML training. Advanced Normalization Tools and FMRIB Software Library were used to segment the thalami, putamina, globi pallidi, and caudate nuclei. A random forest ML model was trained to predict new disease activity with feature vectors composed of individual DGM nuclei volumes and several other variables (e.g., minocycline vs. placebo, mono-focal vs. multi-focal CIS, normalized brain volume, and sex). Model performance was evaluated using 3-fold cross-validation, with 80% of the data used for training, and the rest for testing.

Results

Sequential elimination of variables ranked the least important by the trained model resulted in improved classification accuracy. Therefore, the less predictive variables were pruned from the feature vector. The best model used DGM volumes alone and achieved 82.1% accuracy, 87% precision, 81% recall and F1-score of 0.84 with area under the curve (AUC) of 0.76.

Conclusions

ML can learn patterns predictive of new disease activity within 2 years after a first clinical demyelinating event from baseline DGM volumes. This approach can potentially augment the many other clinical and demographic variables used in a typical MS clinical work up. Further investigation with larger data sets is warranted to determine generalizability of the approach.

Background

Neurological disability progression occurs across the spectrum of people living with multiple sclerosis (PwMS). Currently, no treatments exist that substantially modify the course of clinical progression in MS, one of the greatest unmet needs in clinical practice. Characterizing the determinants of clinical progression is essential for the development of novel therapeutic agents and treatment approaches that target progression in PwMS.

Objectives

The overarching aim of CanProCo is to evaluate a wide spectrum of factors associated with the onset and rate of disease progression in MS, and to describe how these factors interact with one another to influence progression.

Methods

CanProCo is a prospective, observational cohort study aiming to recruit 1000 individuals with radiologically-isolated syndrome (RIS), relapsing-remitting MS (RRMS), and primary-progressive MS (PPMS) within 10-15 years of disease onset, and 50 healthy controls (HCs) from five large academic MS centers in Canada. Participants undergo detailed clinical evaluations annually. A subset of participants enrolled within 5-10 years of disease onset (n=500) also have blood, cerebrospinal fluid, and MRIs collected facilitating study of biological measures (e.g. single-cell RNA-sequencing[scRNASeq]), MRI-based microstructural assessment, participant characteristics (self-reported, performance-based, clinician-assessed, health-system based), and environmental factors as determinants contributing to the differential progression in MS.

Results

Recruitment commenced in April/May 2019 and n=536 patients have been recruited to date (RRMS=457, PPMS=35, RIS=25, HC=19). Baseline age, sex distribution, and Expanded Disability Status Scale (EDSS) scores (median, range) of each subgroup are: RRMS=38 years, 73% female, EDSS=1.5 (0-6.0); PPMS=52 years, 40% female, EDSS=4.0 (1.5-6.5); RIS=41 years, 68% female, EDSS=0 (0-3.0); HC=37 years, 63% female. Recruitment has surpassed the 50% target but has been paused due to the COVID-19 pandemic. scRNASeq on frozen blood samples has been validated.

Conclusions

Halting the progression of MS is a fundamental clinical need to improve the lives of PwMS. Achieving this requires leveraging transdisciplinary approaches to better characterize mechanisms underlying clinical progression. CanProCo is the first prospective cohort study aiming to characterize these determinants to inform the development and implementation of efficacious and effective interventions.