Ibudilast reduces slowly enlarging lesions in progressive multiple sclerosis.

BACKGROUND: Ibudilast has shown beneficial effects on several imaging outcomes in progressive multiple sclerosis (MS). Slowly enlarging lesions are a proposed imaging biomarker of compartmentalized inflammation within chronic active lesions. OBJECTIVE: To assess the treatment effect of ibudilast on slowly enlarging lesion volumes over 96 weeks from a phase II clinical trial of ibudilast (Secondary and Primary Progressive Ibudilast NeuroNEXT Trial in Multiple Sclerosis [SPRINT-MS]). METHODS: In total, 255 participants with progressive MS from 28 sites were randomized to oral ibudilast or placebo. Participants with at least four analyzable magnetic resonance imaging (MRI) were included. Slowly enlarging lesions were quantified using Jacobian determinant maps. A linear model was used to assess the effect of ibudilast. Magnetization transfer ratio within slowly enlarging lesions was assessed to determine the effect of ibudilast on tissue integrity. RESULTS: In total, 195 participants were included in this analysis. Ibudilast significantly decreased slowly enlarging lesion volume (23%, p = 0.003). Ibudilast also reduced magnetization transfer ratio change in slowly enlarging lesions: 0.22%/year, p = 0.04. CONCLUSION: Ibudilast showed a significant effect on baseline volume of lesions that were slowly enlarging and magnetization transfer ratio in slowly enlarging lesions. The results support the use of slowly enlarging lesions for assessment of compartmentalized inflammation represented by chronic active lesions and provide further support for the neuroprotective effects of ibudilast in progressive MS.

Natalizumab reduces loss of gray matter and thalamic volume in patients with relapsing-remitting multiple sclerosis: A post hoc analysis from the randomized, placebo-controlled AFFIRM trial.

BACKGROUND: Loss of brain gray matter fractional volume predicts multiple sclerosis (MS) progression and is associated with worsening physical and cognitive symptoms. Within deep gray matter, thalamic damage is evident in early stages of MS and correlates with physical and cognitive impairment. Natalizumab is a highly effective treatment that reduces disease progression and the number of inflammatory lesions in patients with relapsing-remitting MS (RRMS). OBJECTIVE: To evaluate the effect of natalizumab on gray matter and thalamic atrophy. METHODS: A combination of deep learning-based image segmentation and data augmentation was applied to MRI data from the AFFIRM trial. RESULTS: This post hoc analysis identified a reduction of 64.3% (p = 0.0044) and 64.3% (p = 0.0030) in mean percentage gray matter volume loss from baseline at treatment years 1 and 2, respectively, in patients treated with natalizumab versus placebo. The reduction in thalamic fraction volume loss from baseline with natalizumab versus placebo was 57.0% at year 2 (p < 0.0001) and 41.2% at year 1 (p = 0.0147). Similar findings resulted from analyses of absolute gray matter and thalamic fraction volume loss. CONCLUSION: These analyses represent the first placebo-controlled evidence supporting a role for natalizumab treatment in mitigating gray matter and thalamic fraction atrophy among patients with RRMS. CLINICALTRIALS.GOV IDENTIFIER: NCT00027300URL: https://clinicaltrials.gov/ct2/show/NCT00027300.

Differentiating multiple sclerosis from non-specific white matter changes using a convolutional neural network image classification model.

BACKGROUND: The diagnosis of multiple sclerosis (MS) relies heavily on neuroimaging with magnetic resonance imaging (MRI) and exclusion of mimics. This can be a challenging task due to radiological overlap in several disorders and may require ancillary testing or longitudinal follow up. One of the most common radiological MS mimickers is non-specific white matter disease (NSWMD). We aimed to develop and evaluate models leveraging machine learning algorithms to help distinguish MS and NSWMD. METHODS: All adult patients who underwent MRI brain using a demyelinating protocol with available electronic medical records between 2015 and 2019 at Cleveland Clinic affiliated facilities were included. Diagnosis of MS and NSWMD were assessed from clinical documentation. Those with a diagnosis of MS and NSWMD were matched using total T2 lesion volume (T2LV) and used to train models with logistic regression and convolutional neural networks (CNN). Performance metrices were reported for each model. RESULTS: A total of 250 NSWMD MRI scans were identified, and 250 unique MS MRI scans were matched on T2LV. Cross validated logistic regression model was able to use 20 variables (including spinal cord area, regional volumes, and fractions) to predict MS compared to NSWMD with 68.0% accuracy while the CNN model was able to classify MS compared to NSWMD in two independent validation and testing cohorts with 77% and 78% accuracy on average. CONCLUSION: Automated methods can be used to differentiate MS compared to NSWMD. These methods can be used to supplement currently available diagnostic tools for patients being evaluated for MS.

Association Between Disease-Modifying Therapy and Information Processing Speed in Multiple Sclerosis.

BACKGROUND: Cognitive impairment (CI) is common in multiple sclerosis (MS). Processing speed (PS) is often affected, making it an ideal target for monitoring CI. This study aims to evaluate the association between disease-modifying therapy (DMT) use and intensity and longitudinal changes in Processing Speed Test (PST) scores for individuals with MS. METHODS: A retrospective analysis of individual PST scores at a single MS center was conducted. Individuals with 2 or more PST assessments were included. Scores on the PST were compared longitudinally between those who had been on a DMT for 2 or more years and those who had been off a DMT for 2 or more years and between those on high-efficacy DMTs and those on low-/moderate-efficacy DMTs. A linear regression model was approximated to evaluate the rate of cognitive change over time. A propensity score adjustment was conducted using a multivariable logistic regression. RESULTS: The cohort was 642 individuals, 539 on DMT and 103 off DMT. Median age and disease duration was 49.7 (IQR 42.4-57.9) and 16.6 years (IQR 9.3-23.0) in the DMT group, and 58.9 (IQR 52.2-65.3) and 20.0 years (IQR 14.1-31.4) in the non-DMT group. Both cohorts were predominantly female (75% DMT, 79.6% non-DMT), with a mean of 4 assessments (IQR 3-5), and an average monitoring duration of 1.9 years (1.2-2.4) in the DMT group, and 1.8 years (1.4-2.4) in the non-DMT group. After adjusting for multiple factors, DMT status and intensity were not found to be significant predictors of longitudinal PST change. CONCLUSIONS: Neither DMT status nor intensity was a significant predictor of cognitive processing speed over a period of approximately 2 years. Future prospective studies are needed to further support these findings.

Comparing Cognitive Profiles in Older Adults With Multiple Sclerosis and Alzheimer Disease: More Similarities Than Differences.

Background and Objectives: Up to 65% of people with multiple sclerosis (MS) experience disease-related cognitive impairment, but even after decades of research, still very little is known about the cognitive issues among older adults with MS (EwMS; individuals aged 60+). To date, few studies have attempted to characterize cognitive impairment in this group or compare EwMS with those with other neurodegenerative diseases. Our goal was to address this knowledge gap by comparing EwMS with individuals experiencing cognitive impairment due to probable Alzheimer disease (AD) with biomarker confirmation. Methods: We conducted an observational study of individuals seen for routine clinical care at the Cleveland Clinic. After excluding for potential confounding factors, 6 groups were assembled based on the results of their clinical workup and neuropsychological examination: cognitively normal, cognitively normal with MS, mild neurocognitive disorder (due to MS or AD), and major neurocognitive disorder (due to MS or AD). These groups were compared in terms of cognitive test performance, percentage of the group impaired on specific cognitive skills, and rates of cognitive impairment. Results: The sample comprised 140 individuals (64 EwMS and 76 demographically matched individuals from a memory clinic). Among those with mild neurocognitive disorder, differences between MS and AD were marked. However, in those with major neurocognitive disorder, these differences largely disappeared, except persistent performance differences on a measure of rote verbal memory. EwMS outperformed those with AD on memory tests at each level of cognitive impairment. EwMS also exhibited both subcortical and cortical deficits, rather than solely subcortical deficits. Discussion: The overall characterization of the cognitive profile of MS may be different than once described, involving both classically cortical and subcortical functions. Clinically, our results suggest that distinguishing between the cognitive effects of MS and AD at more severe levels of cognitive impairment may be less reliable than once thought. Future work to replicate these findings in other samples and deepen the understanding of cognition in older individuals with MS is needed.

Longitudinal myelin content measures of slowly expanding lesions using 7T MRI in multiple sclerosis.

BACKGROUND AND PURPOSE: Slowly expanding lesions (SELs) are thought to represent a subset of chronic active lesions and have been associated with clinical disability, severity, and disease progression. The purpose of this study was to characterize SELs using advanced magnetic resonance imaging (MRI) measures related to myelin and neurite density on 7 Tesla (T) MRI. METHODS: The study design was retrospective, longitudinal, observational cohort with multiple sclerosis (n = 15). Magnetom 7T scanner was used to acquire magnetization-prepared 2 rapid acquisition gradient echo and advanced MRI including visualization of short transverse relaxation time component (ViSTa) for myelin, quantitative magnetization transfer (qMT) for myelin, and neurite orientation dispersion density imaging (NODDI). SELs were defined as lesions showing ≥12% of growth over 12 months on serial MRI. Comparisons of quantitative measures in SELs and non-SELs were performed at baseline and over time. Statistical analyses included two-sample t-test, analysis of variance, and mixed-effects linear model for MRI metrics between lesion types. RESULTS: A total of 1075 lesions were evaluated. Two hundred twenty-four lesions (21%) were SELs, and 216 (96%) of the SELs were black holes. At baseline, compared to non-SELs, SELs showed significantly lower ViSTa (1.38 vs. 1.53, p < .001) and qMT (2.47 vs. 2.97, p < .001) but not in NODDI measures (p > .27). Longitudinally, only ViSTa showed a greater loss when comparing SEL and non-SEL (p = .03). CONCLUSIONS: SELs have a lower myelin content relative to non-SELs without a difference in neurite measures. SELs showed a longitudinal decrease in apparent myelin water fraction reflecting greater tissue injury.

High-resolution diffusion tensor imaging of the fornix predicts memory function in multiple sclerosis.

Background: Cognitive dysfunction is a known symptom of multiple sclerosis (MS), with memory recognized as a frequently impacted domain. Here, we used high-resolution MRI at 7 tesla to build on cross-sectional work by evaluating the longitudinal relationship of diffusion tensor imaging (DTI) measures of the fornix to episodic memory performance. Methods: A sample of 80 people with multiple sclerosis (mean age 51.9 ± 8.1 years; 24% male) underwent baseline clinical evaluation, neuropsychological assessment, and MRI. Sixty-four participants had follow-up neuropsychological testing after 1-2 years. Linear regression was used to assess the relationship of baseline imaging measures to follow-up episodic memory performance, measured using the Selective Reminding Test and Brief Visuospatial Memory Test. A reduced prediction model included cognitive function at baseline, age, sex, and disease course. Results: Radial (ß = -0.222, p < 0.026; likelihood ratio test (LRT) p < 0.018), axial (ß = -0.270, p < 0.005; LRT p < 0.003), and mean (ß = -0.242, p < 0.0139; LRT p < 0.009) diffusivity of the fornix significantly added to the model, with follow-up analysis indicating that a longer prediction interval may increase accuracy. Conclusion: These results suggest that fornix DTI has predictive value specific to memory function in MS and warrants additional investigation in the drive to develop predictors of disease progression.

Towards Automatic Cartilage Quantification in Clinical Trials - Continuing from the 2019 IWOAI Knee Segmentation Challenge.

Objective: To evaluate whether the deep learning (DL) segmentation methods from the six teams that participated in the IWOAI 2019 Knee Cartilage Segmentation Challenge are appropriate for quantifying cartilage loss in longitudinal clinical trials. Design: We included 556 subjects from the Osteoarthritis Initiative study with manually read cartilage volume scores for the baseline and 1-year visits. The teams used their methods originally trained for the IWOAI 2019 challenge to segment the 1130 knee MRIs. These scans were anonymized and the teams were blinded to any subject or visit identifiers. Two teams also submitted updated methods. The resulting 9,040 segmentations are available online.The segmentations included tibial, femoral, and patellar compartments. In post-processing, we extracted medial and lateral tibial compartments and geometrically defined central medial and lateral femoral sub-compartments. The primary study outcome was the sensitivity to measure cartilage loss as defined by the standardized response mean (SRM). Results: For the tibial compartments, several of the DL segmentation methods had SRMs similar to the gold standard manual method. The highest DL SRM was for the lateral tibial compartment at 0.38 (the gold standard had 0.34). For the femoral compartments, the gold standard had higher SRMs than the automatic methods at 0.31/0.30 for medial/lateral compartments. Conclusion: The lower SRMs for the DL methods in the femoral compartments at 0.2 were possibly due to the simple sub-compartment extraction done during post-processing. The study demonstrated that state-of-the-art DL segmentation methods may be used in standardized longitudinal single-scanner clinical trials for well-defined cartilage compartments.