The evolving contribution of MRI measures towards the prediction of secondary progressive multiple sclerosis.

BACKGROUND: In multiple sclerosis (MS), both lesion accrual and brain atrophy predict clinical outcomes. However, it is unclear whether these prognostic features are equally relevant throughout the course of MS. Among 103 participants recruited following a clinically isolated syndrome (CIS) and followed up over 30 years, we explored (1) whether white matter lesions were prognostically more relevant earlier and brain atrophy later in the disease course towards development of secondary progressive (SP) disease; (2) if so, when the balance in prognostic contribution shifts and (3) whether optimised prognostic models predicting SP disease should include different features dependent on disease duration. METHODS: Binary logistic regression models were built using age, gender, brain lesion counts and locations, and linear atrophy measures (third ventricular width and medullary width) at each time point up to 20 years, using either single time point data alone or adjusted for baseline measures. RESULTS: By 30 years, 27 participants remained CIS while 60 had MS (26 SPMS and 16 MS-related death). Lesions counts were prognostically significant from baseline and at all later time points while linear atrophy measure models reached significance from 5 years. When adjusted for baseline, in combined MRI models including lesion count and linear atrophy measures, only lesion counts were significant predictors. In combined models including relapse measures, Expanded Disability Status Scale scores and MRI measures, only infratentorial lesions were significant predictors throughout. CONCLUSIONS: While SPMS progression is associated with brain atrophy, in predictive models only infratentorial lesions were consistently prognostically significant.

Treatment reduces the incidence of newly appearing multiple sclerosis lesions evolving into chronic active, slowly expanding lesions: A retrospective analysis.

BACKGROUND AND PURPOSE: Newly appearing lesions in multiple sclerosis (MS) may evolve into chronically active, slowly expanding lesions (SELs), leading to sustained disability progression. The aim of this study was to evaluate the incidence of newly appearing lesions developing into SELs, and their correlation to clinical evolution and treatment. METHODS: A retrospective analysis of a fingolimod trial in primary progressive MS (PPMS; INFORMS, NCT00731692) was undertaken. Data were available from 324 patients with magnetic resonance imaging scans up to 3 years after screening. New lesions at year 1 were identified with convolutional neural networks, and SELs obtained through a deformation-based method. Clinical disability was assessed annually by Expanded Disability Status Scale (EDSS), Nine-Hole Peg Test, Timed 25-Foot Walk, and Paced Auditory Serial Addition Test. Linear, logistic, and mixed-effect models were used to assess the relationship between the Jacobian expansion in new lesions and SELs, disability scores, and treatment status. RESULTS: One hundred seventy patients had ≥1 new lesions at year 1 and had a higher lesion count at screening compared to patients with no new lesions (median = 27 vs. 22, p = 0.007). Among the new lesions (median = 2 per patient), 37% evolved into definite or possible SELs. Higher SEL volume and count were associated with EDSS worsening and confirmed disability progression. Treated patients had lower volume and count of definite SELs (ß = -0.04, 95% confidence interval [CI] = -0.07 to -0.01, p = 0.015; ß = -0.36, 95% CI = -0.67 to -0.06, p = 0.019, respectively). CONCLUSIONS: Incident chronic active lesions are common in PPMS, and fingolimod treatment can reduce their number.

Multicenter Evaluation of AI-generated DIR and PSIR for Cortical and Juxtacortical Multiple Sclerosis Lesion Detection.

Background Cortical multiple sclerosis lesions are clinically relevant but inconspicuous at conventional clinical MRI. Double inversion recovery (DIR) and phase-sensitive inversion recovery (PSIR) are more sensitive but often unavailable. In the past 2 years, artificial intelligence (AI) was used to generate DIR and PSIR from standard clinical sequences (eg, T1-weighted, T2-weighted, and fluid-attenuated inversion-recovery sequences), but multicenter validation is crucial for further implementation. Purpose To evaluate cortical and juxtacortical multiple sclerosis lesion detection for diagnostic and disease monitoring purposes on AI-generated DIR and PSIR images compared with MRI-acquired DIR and PSIR images in a multicenter setting. Materials and Methods Generative adversarial networks were used to generate AI-based DIR (n = 50) and PSIR (n = 43) images. The number of detected lesions between AI-generated images and MRI-acquired (reference) images was compared by randomized blinded scoring by seven readers (all with >10 years of experience in lesion assessment). Reliability was expressed as the intraclass correlation coefficient (ICC). Differences in lesion subtype were determined using Wilcoxon signed-rank tests. Results MRI scans of 202 patients with multiple sclerosis (mean age, 46 years ± 11 [SD]; 127 women) were retrospectively collected from seven centers (February 2020 to January 2021). In total, 1154 lesions were detected on AI-generated DIR images versus 855 on MRI-acquired DIR images (mean difference per reader, 35.0% ± 22.8; P < .001). On AI-generated PSIR images, 803 lesions were detected versus 814 on MRI-acquired PSIR images (98.9% ± 19.4; P = .87). Reliability was good for both DIR (ICC, 0.81) and PSIR (ICC, 0.75) across centers. Regionally, more juxtacortical lesions were detected on AI-generated DIR images than on MRI-acquired DIR images (495 [42.9%] vs 338 [39.5%]; P < .001). On AI-generated PSIR images, fewer juxtacortical lesions were detected than on MRI-acquired PSIR images (232 [28.9%] vs 282 [34.6%]; P = .02). Conclusion Artificial intelligence-generated double inversion-recovery and phase-sensitive inversion-recovery images performed well compared with their MRI-acquired counterparts and can be considered reliable in a multicenter setting, with good between-reader and between-center interpretative agreement. Published under a CC BY 4.0 license. Supplemental material is available for this article. See also the editorial by Zivadinov and Dwyer in this issue.

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.

Relationship between paramagnetic rim lesions and slowly expanding lesions in multiple sclerosis.

BACKGROUND: Magnetic resonance imaging (MRI) markers for chronic active lesions in MS include slowly expanding lesions (SELs) and paramagnetic rim lesions (PRLs). OBJECTIVES: To identify the relationship between SELs and PRLs in MS, and their association with disability. METHODS: 61 people with MS (pwMS) followed retrospectively with MRI including baseline susceptibility-weighted imaging, and longitudinal T1 and T2-weighted scans. SELs were computed using deformation field maps; PRLs were visually identified. Mixed-effects models assessed differences in Expanded Disability Status Scale (EDSS) score changes between the group defined by the presence of SELs and or PRLs. RESULTS: The median follow-up time was 3.2 years. At baseline, out of 1492 lesions, 616 were classified as SELs, and 80 as PRLs. 92% of patients had ⩾ 1 SEL, 56% had ⩾ 1 PRL, while both were found in 51%. SELs compared to non-SELs were more likely to also be PRLs (7% vs. 4%, p = 0.027). PRL counts positively correlated with SEL counts (ρ= 0.28, p = 0.03). SEL + PRL + patients had greater increases in EDSS over time (beta = 0.15/year, 95% confidence interval (0.04, 0.27), p = 0.009) than SEL+PRL-patients. CONCLUSION: SELs are more numerous than PRLs in pwMS. Compared with either SELs or PRLs found in isolation, their joint occurrence was associated with greater clinical progression.

In vivo imaging of chronic active lesions in multiple sclerosis.

New clinical activity in multiple sclerosis (MS) is often accompanied by acute inflammation which subsides. However, there is growing evidence that a substantial proportion of lesions remain active well beyond the acute phase. Chronic active lesions are most frequently found in progressive MS and are characterised by a border of inflammation associated with iron-enriched cells, leading to ongoing tissue injury. Identifying imaging markers for chronic active lesions in vivo are thus a major research goal. We reviewed the literature on imaging of chronic active lesion in MS, focussing on 'slowly expanding lesions' (SELs), detected by volumetric longitudinal magnetic resonance imaging (MRI) and 'rim-positive' lesions, identified by susceptibility iron-sensitive MRI. Both SELs and rim-positive lesions have been found to be prognostically relevant to future disability. Little is known about the co-occurrence of rims around SELs and their inter-relationship with other emerging techniques such as dynamic contrast enhancement (DCE) and positron emission tomography (PET).

Surface-in pathology in multiple sclerosis: a new view on pathogenesis?

While multiple sclerosis can affect any part of the CNS, it does not do so evenly. In white matter it has long been recognized that lesions tend to occur around the ventricles, and grey matter lesions mainly accrue in the outermost (subpial) cortex. In cortical grey matter, neuronal loss is greater in the outermost layers. This cortical gradient has been replicated in vivo with magnetization transfer ratio and similar gradients in grey and white matter magnetization transfer ratio are seen around the ventricles, with the most severe abnormalities abutting the ventricular surface. The cause of these gradients remains uncertain, though soluble factors released from meningeal inflammation into the CSF has the most supporting evidence. In this Update, we review this 'surface-in' spatial distribution of multiple sclerosis abnormalities and consider the implications for understanding pathogenic mechanisms and treatments designed to slow or stop them.

Cortical involvement determines impairment 30 years after a clinically isolated syndrome.

Many studies report an overlap of MRI and clinical findings between patients with relapsing-remitting multiple sclerosis (RRMS) and secondary progressive multiple sclerosis (SPMS), which in part is reflective of inclusion of subjects with variable disease duration and short periods of follow-up. To overcome these limitations, we examined the differences between RRMS and SPMS and the relationship between MRI measures and clinical outcomes 30 years after first presentation with clinically isolated syndrome suggestive of multiple sclerosis. Sixty-three patients were studied 30 years after their initial presentation with a clinically isolated syndrome; only 14% received a disease modifying treatment at any time point. Twenty-seven patients developed RRMS, 15 SPMS and 21 experienced no further neurological events; these groups were comparable in terms of age and disease duration. Clinical assessment included the Expanded Disability Status Scale, 9-Hole Peg Test and Timed 25-Foot Walk and the Brief International Cognitive Assessment For Multiple Sclerosis. All subjects underwent a comprehensive MRI protocol at 3 T measuring brain white and grey matter (lesions, volumes and magnetization transfer ratio) and cervical cord involvement. Linear regression models were used to estimate age- and gender-adjusted group differences between clinical phenotypes after 30 years, and stepwise selection to determine associations between a large sets of MRI predictor variables and physical and cognitive outcome measures. At the 30-year follow-up, the greatest differences in MRI measures between SPMS and RRMS were the number of cortical lesions, which were higher in SPMS (the presence of cortical lesions had 100% sensitivity and 88% specificity), and grey matter volume, which was lower in SPMS. Across all subjects, cortical lesions, grey matter volume and cervical cord volume explained 60% of the variance of the Expanded Disability Status Scale; cortical lesions alone explained 43%. Grey matter volume, cortical lesions and gender explained 43% of the variance of Timed 25-Foot Walk. Reduced cortical magnetization transfer ratios emerged as the only significant explanatory variable for the symbol digit modality test and explained 52% of its variance. Cortical involvement, both in terms of lesions and atrophy, appears to be the main correlate of progressive disease and disability in a cohort of individuals with very long follow-up and homogeneous disease duration, indicating that this should be the target of therapeutic interventions.

A 30-Year Clinical and Magnetic Resonance Imaging Observational Study of Multiple Sclerosis and Clinically Isolated Syndromes.

OBJECTIVE: Clinical outcomes in multiple sclerosis (MS) are highly variable. We aim to determine the long-term clinical outcomes in MS, and to identify early prognostic features of these outcomes. METHODS: One hundred thirty-two people presenting with a clinically isolated syndrome were prospectively recruited between 1984 and 1987, and followed up clinically and radiologically 1, 5, 10, 14, 20, and now 30 years later. All available notes and magnetic resonance imaging scans were reviewed, and MS was defined according to the 2010 McDonald criteria. RESULTS: Clinical outcome data were obtained in 120 participants at 30 years. Eighty were known to have developed MS by 30 years. Expanded Disability Status Scale (EDSS) scores were available in 107 participants, of whom 77 had MS; 32 (42%) remained fully ambulatory (EDSS scores ≤3.5), all of whom had relapsing-remitting MS (RRMS), 3 (4%) had RRMS and EDSS scores >3.5, 26 (34%) had secondary progressive MS (all had EDSS scores >3.5), and MS contributed to death in 16 (20%). Of those with MS, 11 received disease-modifying therapy. The strongest early predictors (within 5 years of presentation) of secondary progressive MS at 30 years were presence of baseline infratentorial lesions and deep white matter lesions at 1 year. INTERPRETATION: Thirty years after onset, in a largely untreated cohort, there was a divergence of MS outcomes; some people accrued substantial disability early on, whereas others ran a more favorable long-term course. These outcomes could, in part, be predicted by radiological findings from within 1 year of first presentation. ANN NEUROL 2020;87:63-74.

Longitudinal Assessment of Multiple Sclerosis with the Brain-Age Paradigm.

OBJECTIVE: During the natural course of multiple sclerosis (MS), the brain is exposed to aging as well as disease effects. Brain aging can be modeled statistically; the so-called "brain-age" paradigm. Here, we evaluated whether brain-predicted age difference (brain-PAD) was sensitive to the presence of MS, clinical progression, and future outcomes. METHODS: In a longitudinal, multicenter sample of 3,565 magnetic resonance imaging (MRI) scans, in 1,204 patients with MS and clinically isolated syndrome (CIS) and 150 healthy controls (mean follow-up time: patients 3.41 years, healthy controls 1.97 years), we measured "brain-predicted age" using T1-weighted MRI. We compared brain-PAD among patients with MS and patients with CIS and healthy controls, and between disease subtypes. Relationships between brain-PAD and Expanded Disability Status Scale (EDSS) were explored. RESULTS: Patients with MS had markedly higher brain-PAD than healthy controls (mean brain-PAD +10.3 years; 95% confidence interval [CI] = 8.5-12.1] versus 4.3 years; 95% CI = 2.1 to 6.4; p < 0.001). The highest brain-PADs were in secondary-progressive MS (+13.3 years; 95% CI = 11.3-15.3). Brain-PAD at study entry predicted time-to-disability progression (hazard ratio 1.02; 95% CI = 1.01-1.03; p < 0.001); although normalized brain volume was a stronger predictor. Greater annualized brain-PAD increases were associated with greater annualized EDSS score (r = 0.26; p < 0.001). INTERPRETATION: The brain-age paradigm is sensitive to MS-related atrophy and clinical progression. A higher brain-PAD at baseline was associated with more rapid disability progression and the rate of change in brain-PAD related to worsening disability. Potentially, "brain-age" could be used as a prognostic biomarker in early-stage MS, to track disease progression or stratify patients for clinical trial enrollment. ANN NEUROL 2020 ANN NEUROL 2020;88:93-105.

Linear brain atrophy measures in multiple sclerosis and clinically isolated syndromes: a 30-year follow-up.

OBJECTIVE: To determine 30-year brain atrophy rates following clinically isolated syndromes and the relationship of atrophy in the first 5 years and clinical outcomes 25 years later. METHODS: A cohort of 132 people who presented with a clinically isolated syndrome suggestive of multiple sclerosis (MS) were recruited between 1984-1987. Clinical and MRI data were collected prospectively over 30 years. Widths of the third ventricle and the medulla oblongata were used as linear atrophy measures. RESULTS: At 30 years, 27 participants remained classified as having had a clinically isolated syndrome, 34 converted to relapsing remitting MS, 26 to secondary progressive MS and 16 had died due to MS. The mean age at baseline was 31.7 years (SD 7.5) and the mean disease duration was 30.8 years (SD 0.9). Change in medullary and third ventricular width within the first 5 years, allowing for white matter lesion accrual and Expanded Disability Status Scale increases over the same period, predicted clinical outcome measures at 30 years. 1 mm of medullary atrophy within the first 5 years increased the risk for secondary progressive MS or MS related death by 30 years by 583% (OR 5.83, 95% CI 1.74 to 19.61, p<0.005), using logistic regression. CONCLUSIONS: Our findings show that brain regional atrophy within 5 years of a clinically isolated syndrome predicts progressive MS or a related death, and disability 25 years later.

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.

Increased functional sensorimotor network efficiency relates to disability in multiple sclerosis.

BACKGROUND: Network abnormalities could help explain physical disability in multiple sclerosis (MS), which remains poorly understood. OBJECTIVE: This study investigates functional network efficiency changes in the sensorimotor system. METHODS: We included 222 MS patients, divided into low disability (LD, Expanded Disability Status Scale (EDSS) ⩽3.5, n = 185) and high disability (HD, EDSS ⩾6, n = 37), and 82 healthy controls (HC). Functional connectivity was assessed between 23 sensorimotor regions. Measures of efficiency were computed and compared between groups using general linear models corrected for age and sex. Binary logistic regression models related disability status to local functional network efficiency (LE), brain volumes and demographics. Functional connectivity patterns of regions important for disability were explored. RESULTS: HD patients demonstrated significantly higher LE of the left primary somatosensory cortex (S1) and right pallidum compared to LD and HC, and left premotor cortex compared to HC only. The logistic regression model for disability (R2 = 0.38) included age, deep grey matter volume and left S1 LE. S1 functional connectivity was increased with prefrontal and secondary sensory areas in HD patients, compared to LD and HC. CONCLUSION: Clinical disability in MS associates with functional sensorimotor increases in efficiency and connectivity, centred around S1, independent of structural damage.

The role of pontine lesion location in differentiating multiple sclerosis from vascular risk factor-related small vessel disease.

BACKGROUND: Differentiating multiple sclerosis (MS) from vascular risk factor (VRF)-small vessel disease (SVD) can be challenging. OBJECTIVE AND METHODS: In order to determine whether or not pontine lesion location is a useful discriminator of MS and VRF-SVD, we classified pontine lesions on brain magnetic resonance imaging (MRI) as central or peripheral in 93 MS cases without VRF, 108 MS patients with VRF and 43 non-MS cases with VRF. RESULTS: MS without VRF were more likely to have peripheral pons lesions (31.2%, 29/93) than non-MS with VRF (0%, 0/43) (Exp(B) = 29.8; 95% confidence interval (CI) = (1.98, 448.3); p = 0.014) but there were no significant differences regarding central pons lesions between MS without VRF (5.4%, 5/93) and non-MS with VRF patients (16.3%, 7/43) (Exp(B) = 0.89; 95% CI = (0.2, 3.94); p = 0.87). The presence of peripheral pons lesions discriminated between MS and VRF-SVD with 100% (95% CI = (91.8, 100)) specificity. The proportion of peripheral pons lesions in MS with VRF (30.5%, 33/108) was similar to that seen in MS without VRF (31.2%, 29/93, p = 0.99). Central lesions occurred in similar frequency in MS with VRF (8.3%, 9/108) and non-MS with VRF (16.3%, 7/43, p = 0.15). CONCLUSION: Peripheral pons lesion location is a good discriminator of MS from vascular lesions.

Distinct influence of different vascular risk factors on white matter brain lesions in multiple sclerosis.

OBJECTIVE: To determine if vascular risk factor (VRF), that is, smoking, arterial hypertension (HT), dyslipidaemia and diabetes, have an effect on multiple sclerosis (MS) pathology as measured by MS typical brain lesions, we have compared brain MRIs from patients with MS with and without VRF age-matched and sex-matched. METHODS: Brain MRIs from five centres were scored for the presence of Dawson's fingers (DF) and juxtacortical lesions (JCL). A regression model was built to predict the effect of each individual VRF on DF and JCL, considering age and disease duration. RESULTS: 92 MS cases without VRF and 106 MS with one or more VRF (80 ever-smokers, 43 hypertensives, 25 dyslipidaemics and 10 diabetics) were included. Ever-smoking associated with a higher burden of DF (Exp(B)=1.29, 95% CI 1.10 to 1.51, p<0.01) and JCL (Exp(B)=1.38, 95% CI 1.21 to 1.57, p<0.01). No other VRF had an impact on DF. Dyslipidaemia associated with increased JCL (Exp(B)=1.30, 95% CI 1.10 to 1.56, p<0.01) but HT did not associate with any of the outcomes. CONCLUSIONS: Individual VRF appear to affect MS-specific lesions differently. An increase in MS lesions was mainly seen in smokers; however, this VRF is most likely to be present from onset of MS, and other VRF effects may be partly mitigated by treatment. Our findings support that treating VRF and cessation of smoking may be important in the management of MS.

Magnetisation transfer ratio abnormalities in primary and secondary progressive multiple sclerosis.

BACKGROUND: In relapse-onset multiple sclerosis (MS), tissue abnormality - as assessed with magnetisation transfer ratio (MTR) imaging - is greater in the outer cortical and inner periventricular layers. The cause of this remains unknown but meningeal inflammation has been implicated, particularly lymphoid follicles, which are seen in secondary progressive (SP) but not primary progressive (PP) MS. Cortical and periventricular MTR gradients might, therefore, differ in PPMS and SPMS if these follicles are responsible. OBJECTIVE: We assessed cortical and periventricular MTR gradients in PPMS, and compared gradients between people with PPMS and SPMS. METHODS: Using an optimised processing pipeline, periventricular normal-appearing white matter and cortical grey-matter MTR gradients were compared between 51 healthy controls and 63 people with progressive MS (28 PPMS, 35 SPMS). RESULTS: The periventricular gradient was significantly shallower in healthy controls (0.122 percentage units (pu)/band) compared to PPMS (0.952 pu/band, p < 0.0001) and SPMS (1.360 pu/band, p < 0.0001). The cortical gradient was also significantly shallower in healthy controls (-2.860 pu/band) compared to PPMS (-3.214 pu/band, p = 0.038) and SPMS (-3.328 pu/band, p = 0.016). CONCLUSION: Abnormal periventricular and cortical MTR gradients occur in both PPMS and SPMS, suggesting comparable underlying pathological processes.

Periventricular magnetisation transfer ratio abnormalities in multiple sclerosis improve after alemtuzumab.

BACKGROUND: In multiple sclerosis (MS), disease effects on magnetisation transfer ratio (MTR) increase towards the ventricles. This periventricular gradient is evident shortly after first symptoms and is independent of white matter lesions. OBJECTIVE: To explore if alemtuzumab, a peripherally acting disease-modifying treatment, modifies the gradient's evolution, and whether baseline gradients predict on-treatment relapses. METHODS: Thirty-four people with relapsing-remitting MS underwent annual magnetic resonance imaging (MRI) scanning (19 receiving alemtuzumab (four scans each), 15 untreated (three scans each)). The normal-appearing white matter was segmented into concentric bands. Gradients were measured over the three bands nearest the ventricles. Mixed-effects models adjusted for age, gender, relapse rate, lesion number and brain parenchymal fraction compared the groups' baseline gradients and evolution. RESULTS: Untreated, the mean MTR gradient increased (+0.030 pu/band/year) but decreased following alemtuzumab (-0.045 pu/band/year, p = 0.037). Within the alemtuzumab group, there were no significant differences in baseline lesion number (p = 0.568) nor brain parenchymal fraction (p = 0.187) between those who relapsed within 4 years (n = 4) and those who did not (n = 15). However, the baseline gradient was significantly different (p = 0.020). CONCLUSION: Untreated, abnormal periventricular gradients worsen with time, but appear reversible with peripheral immunotherapy. Baseline gradients - but not lesion loads or brain volumes - may predict on-treatment relapses. Larger confirmatory studies are required.

Deep gray matter volume loss drives disability worsening in multiple sclerosis.

OBJECTIVE: Gray matter (GM) atrophy occurs in all multiple sclerosis (MS) phenotypes. We investigated whether there is a spatiotemporal pattern of GM atrophy that is associated with faster disability accumulation in MS. METHODS: We analyzed 3,604 brain high-resolution T1-weighted magnetic resonance imaging scans from 1,417 participants: 1,214 MS patients (253 clinically isolated syndrome [CIS], 708 relapsing-remitting [RRMS], 128 secondary-progressive [SPMS], and 125 primary-progressive [PPMS]), over an average follow-up of 2.41 years (standard deviation [SD] = 1.97), and 203 healthy controls (HCs; average follow-up = 1.83 year; SD = 1.77), attending seven European centers. Disability was assessed with the Expanded Disability Status Scale (EDSS). We obtained volumes of the deep GM (DGM), temporal, frontal, parietal, occipital and cerebellar GM, brainstem, and cerebral white matter. Hierarchical mixed models assessed annual percentage rate of regional tissue loss and identified regional volumes associated with time-to-EDSS progression. RESULTS: SPMS showed the lowest baseline volumes of cortical GM and DGM. Of all baseline regional volumes, only that of the DGM predicted time-to-EDSS progression (hazard ratio = 0.73; 95% confidence interval, 0.65, 0.82; p < 0.001): for every standard deviation decrease in baseline DGM volume, the risk of presenting a shorter time to EDSS worsening during follow-up increased by 27%. Of all longitudinal measures, DGM showed the fastest annual rate of atrophy, which was faster in SPMS (-1.45%), PPMS (-1.66%), and RRMS (-1.34%) than CIS (-0.88%) and HCs (-0.94%; p < 0.01). The rate of temporal GM atrophy in SPMS (-1.21%) was significantly faster than RRMS (-0.76%), CIS (-0.75%), and HCs (-0.51%). Similarly, the rate of parietal GM atrophy in SPMS (-1.24-%) was faster than CIS (-0.63%) and HCs (-0.23%; all p values <0.05). Only the atrophy rate in DGM in patients was significantly associated with disability accumulation (beta = 0.04; p < 0.001). INTERPRETATION: This large, multicenter and longitudinal study shows that DGM volume loss drives disability accumulation in MS, and that temporal cortical GM shows accelerated atrophy in SPMS than RRMS. The difference in regional GM atrophy development between phenotypes needs to be taken into account when evaluating treatment effect of therapeutic interventions. Ann Neurol 2018;83:210-222.

Structural network disruption markers explain disability in multiple sclerosis.

OBJECTIVE: To evaluate whether structural brain network metrics correlate better with clinical impairment and information processing speed in multiple sclerosis (MS) beyond atrophy measures and white matter lesions. METHODS: This cross-sectional study included 51 healthy controls and 122 patients comprising 58 relapsing-remitting, 28 primary progressive and 36 secondary progressive. Structural brain networks were reconstructed from diffusion-weighted MRIs and standard metrics reflecting network density, efficiency and clustering coefficient were derived and compared between subjects' groups. Stepwise linear regression analyses were used to investigate the contribution of network measures that explain clinical disability (Expanded Disability Status Scale (EDSS)) and information processing speed (Symbol Digit Modalities Test (SDMT)) compared with conventional MRI metrics alone and to determine the best statistical model that explains better EDSS and SDMT. RESULTS: Compared with controls, network efficiency and clustering coefficient were reduced in MS while these measures were also reduced in secondary progressive relative to relapsing-remitting patients. Structural network metrics increase the variance explained by the statistical models for clinical and information processing dysfunction. The best model for EDSS showed that reduced network density and global efficiency and increased age were associated with increased clinical disability. The best model for SDMT showed that lower deep grey matter volume, reduced efficiency and male gender were associated with worse information processing speed. CONCLUSIONS: Structural topological changes exist between subjects' groups. Network density and global efficiency explained disability above non-network measures, highlighting that network metrics can provide clinically relevant information about MS pathology.

Commentary on Al Hussona et al. 'New-onset seizures as a sole clinical presentation of multiple sclerosis'.

Despite the now significant contribution of magnetic resonance imaging, the accurate and timely diagnosis of multiple sclerosis (MS) is still clinically challenging. Al Hussona et al., with their case series, highlight the complexities of attributing paroxysmal, and in particular cortical, symptoms such as epileptic seizures to inflammatory demyelinating lesions, and establishing a diagnosis of MS based on them. In such circumstances an MS diagnosis is likely to be more tentative than for more typical MS presentations, and treatment choices should be weighed accordingly.