Longitudinal network-based brain grey matter MRI measures are clinically relevant and sensitive to treatment effects in multiple sclerosis.

In multiple sclerosis clinical trials, MRI outcome measures are typically extracted at a whole-brain level, but pathology is not homogeneous across the brain and so whole-brain measures may overlook regional treatment effects. Data-driven methods, such as independent component analysis, have shown promise in identifying regional disease effects but can only be computed at a group level and cannot be applied prospectively. The aim of this work was to develop a technique to extract longitudinal independent component analysis network-based measures of co-varying grey matter volumes, derived from T1-weighted volumetric MRI, in individual study participants, and assess their association with disability progression and treatment effects in clinical trials. We used longitudinal MRI and clinical data from 5089 participants (22 045 visits) with multiple sclerosis from eight clinical trials. We included people with relapsing-remitting, primary and secondary progressive multiple sclerosis. We used data from five negative clinical trials (2764 participants, 13 222 visits) to extract the independent component analysis-based measures. We then trained and cross-validated a least absolute shrinkage and selection operator regression model (which can be applied prospectively to previously unseen data) to predict the independent component analysis measures from the same regional MRI volume measures and applied it to data from three positive clinical trials (2325 participants, 8823 visits). We used nested mixed-effect models to determine how networks differ across multiple sclerosis phenotypes are associated with disability progression and to test sensitivity to treatment effects. We found 17 consistent patterns of co-varying regional volumes. In the training cohort, volume loss was faster in four networks in people with secondary progressive compared with relapsing-remitting multiple sclerosis and three networks with primary progressive multiple sclerosis. Volume changes were faster in secondary compared with primary progressive multiple sclerosis in four networks. In the combined positive trials cohort, eight independent component analysis networks and whole-brain grey matter volume measures showed treatment effects, and the magnitude of treatment-placebo differences in the network-based measures was consistently greater than with whole-brain grey matter volume measures. Longitudinal network-based analysis of grey matter volume changes is feasible using clinical trial data, showing differences cross-sectionally and longitudinally between multiple sclerosis phenotypes, associated with disability progression, and treatment effects. Future work is required to understand the pathological mechanisms underlying these regional changes.

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.

An update on the role of magnetic resonance imaging in predicting and monitoring multiple sclerosis progression.

INTRODUCTION: While magnetic resonance imaging (MRI) is established in diagnosing and monitoring disease activity in multiple sclerosis (MS), its utility in predicting and monitoring disease progression is less clear. AREAS COVERED: The authors consider changing concepts in the phenotypic classification of MS, including progression independent of relapses; pathological processes underpinning progression; advances in MRI measures to assess them; how well MRI features explain and predict clinical outcomes, including models that assess disease effects on neural networks, and the potential role for machine learning. EXPERT OPINION: Relapsing-remitting and progressive MS have evolved from being viewed as mutually exclusive to having considerable overlap. Progression is likely the consequence of several pathological elements, each important in building more holistic prognostic models beyond conventional phenotypes. MRI is well placed to assess pathogenic processes underpinning progression, but we need to bridge the gap between MRI measures and clinical outcomes. Mapping pathological effects on specific neural networks may help and machine learning methods may be able to optimize predictive markers while identifying new, or previously overlooked, clinically relevant features. The ever-increasing ability to measure features on MRI raises the dilemma of what to measure and when, and the challenge of translating research methods into clinically useable tools.

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.

The relation of sarcopenia and disability in multiple sclerosis.

BACKGROUND: The relation of sarcopenia and disability in MS is unknown. OBJECTIVE: To investigate the relation of temporal muscle thickness (TMT) and disability. METHODS: A cohort of 132 people who presented with a clinically isolated syndrome (CIS) suggestive of MS at a mean age of 30.0 years, were prospectively followed clinically and with MRI over 30-years. TMT and expanded disability status scale (EDSS) were assessed at baseline, one- five- ten- fourteen- twenty- and thirty-year follow-up. RESULTS: At 30-years, 27 participants remained classified as having had a CIS, 34 converted to relapsing remitting MS, 26 to secondary progressive MS, and 16 had died due to MS. Using linear mixed effect models with subject nested in time, greater annualized TMT-thinning was seen in individuals who developed MS (-0.04 mm/a, 95%CI: -0.07 to -0.01, p = 0.023). In those who converted to MS, a thinner TMT was reached at 14- (p = 0.008), 20- (p = 0.002) and 30-years (p< 0.001). TMT was negatively correlated with EDSS at 20-years (R=-0.18, p = 0.032) and 30-years (R-0.244, p = 0.005). Longitudinally, TMT at earlier timepoints was not predictive for 30-year clinical outcomes. CONCLUSION: TMT thinning is accelerated in MS and correlated with disability in later disease stages, but is not predictive of future disability.

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.

Remyelination in humans due to a retinoid-X receptor agonist is age-dependent.

Remyelination efficiency declines with advancing age in animal models, but this has been harder to demonstrate in people with multiple sclerosis. We show that bexarotene, a putatively remyelinating retinoid-X receptor agonist, shortened the visual evoked potential latency in patients with chronic optic neuropathy aged under 42 years only (with the effect diminishing by 0.45 ms per year of age); and increased the magnetization transfer ratio of deep gray matter lesions in those under 43 years only. Addressing this age-related decline in human remyelination capacity will be an important step in the development of remyelinating therapies that work across the lifespan.

Association of Slowly Expanding Lesions on MRI With Disability in People With Secondary Progressive Multiple Sclerosis.

BACKGROUND AND OBJECTIVE: To explore the relationship between slowly expanding lesions (SELs) on MRI and disability in secondary progressive multiple sclerosis (SPMS). METHODS: We retrospectively studied 345 patients with SPMS enrolled in the MS-SMART trial. They underwent brain MRI at baseline and at 24 and 96 weeks. Definite SELs were defined as concentrically expanding T2 lesions, as assessed by nonlinear deformation of volumetric T1-weighted images. Associations of SEL volumes with other MRI metrics and disability were assessed through Pearson correlations and regression analyses. RESULTS: Averaged across patients, 29% of T2 lesions were classified as being definite SELs. A greater volume of definite SELs correlated with a higher total baseline T2 lesion volume (r = 0.55, p < 0.001) and percentage brain volume reduction (r = -0.26, p < 0.001), a higher number of new persisting T1 black holes (r = 0.19, p < 0.001), and, in a subset of 106 patients, with a greater reduction in magnetization transfer ratio (adjusted difference 0.52, p < 0.001). In regression analyses, a higher definite SEL volume was associated with increasing disability, as assessed by the Expanded Disability Status Scale (ß = 0.23, p = 0.020), z scores of the Multiple Sclerosis Functional Composite (ß = -0.47, p = 0.048), Timed 25-Foot Walk Test (ß = -2.10, p = 0.001), and Paced Auditory Serial Addition Task (ß = -0.27, p = 0.006), and increased risk of disability progression (odds ratio 1.92, p = 0.025). DISCUSSION: Definite SELs represent almost one-third of T2 lesions in SPMS. They are associated with neurodegenerative MRI markers and related to clinical worsening, suggesting that they may contribute to disease progression and be a new target for therapeutic interventions.

Spatial patterns of brain lesions assessed through covariance estimations of lesional voxels in multiple Sclerosis: The SPACE-MS technique.

Predicting disability in progressive multiple sclerosis (MS) is extremely challenging. Although there is some evidence that the spatial distribution of white matter (WM) lesions may play a role in disability accumulation, the lack of well-established quantitative metrics that characterise these aspects of MS pathology makes it difficult to assess their relevance for clinical progression. This study introduces a novel approach, called SPACE-MS, to quantitatively characterise spatial distributional features of brain MS lesions, so that these can be assessed as predictors of disability accumulation. In SPACE-MS, the covariance matrix of the spatial positions of each patient's lesional voxels is computed and its eigenvalues extracted. These are combined to derive rotationally-invariant metrics known to be common and robust descriptors of ellipsoid shape such as anisotropy, planarity and sphericity. Additionally, SPACE-MS metrics include a neuraxis caudality index, which we defined for the whole-brain lesion mask as well as for the most caudal brain lesion. These indicate how distant from the supplementary motor cortex (along the neuraxis) the whole-brain mask or the most caudal brain lesions are. We applied SPACE-MS to data from 515 patients involved in three studies: the MS-SMART (NCT01910259) and MS-STAT1 (NCT00647348) secondary progressive MS trials, and an observational study of primary and secondary progressive MS. Patients were assessed on motor and cognitive disability scales and underwent structural brain MRI (1.5/3.0 T), at baseline and after 2 years. The MRI protocol included 3DT1-weighted (1x1x1mm3) and 2DT2-weighted (1x1x3mm3) anatomical imaging. WM lesions were semiautomatically segmented on the T2-weighted scans, deriving whole-brain lesion masks. After co-registering the masks to the T1 images, SPACE-MS metrics were calculated and analysed through a series of multiple linear regression models, which were built to assess the ability of spatial distributional metrics to explain concurrent and future disability after adjusting for confounders. Patients whose WM lesions laid more caudally along the neuraxis or were more isotropically distributed in the brain (i.e. with whole-brain lesion masks displaying a high sphericity index) at baseline had greater motor and/or cognitive disability at baseline and over time, independently of brain lesion load and atrophy measures. In conclusion, here we introduced the SPACE-MS approach, which we showed is able to capture clinically relevant spatial distributional features of MS lesions independently of the sheer amount of lesions and brain tissue loss. Location of lesions in lower parts of the brain, where neurite density is particularly high, such as in the cerebellum and brainstem, and greater spatial spreading of lesions (i.e. more isotropic whole-brain lesion masks), possibly reflecting a higher number of WM tracts involved, are associated with clinical deterioration in progressive MS. The usefulness of the SPACE-MS approach, here demonstrated in MS, may be explored in other conditions also characterised by the presence of brain WM lesions.

Safety and efficacy of bexarotene in patients with relapsing-remitting multiple sclerosis (CCMR One): a randomised, double-blind, placebo-controlled, parallel-group, phase 2a study.

BACKGROUND: Progressive disability in multiple sclerosis occurs because CNS axons degenerate as a late consequence of demyelination. In animals, retinoic acid receptor RXR-gamma agonists promote remyelination. We aimed to assess the safety and efficacy of a non-selective retinoid X receptor agonist in promoting remyelination in people with multiple sclerosis. METHODS: This randomised, double-blind, placebo-controlled, parallel-group, phase 2a trial (CCMR One) recruited patients with relapsing-remitting multiple sclerosis from two centres in the UK. Eligible participants were aged 18-50 years and had been receiving dimethyl fumarate for at least 6 months. Via a web-based system run by an independent statistician, participants were randomly assigned (1:1), by probability-weighted minimisation using four binary factors, to receive 300 mg/m2 of body surface area per day of oral bexarotene or oral placebo for 6 months. Participants, investigators, and outcome assessors were masked to treatment allocation. MRI scans were done at baseline and at 6 months. The primary safety outcome was the number of adverse events and withdrawals attributable to bexarotene. The primary efficacy outcome was the patient-level change in mean lesional magnetisation transfer ratio between baseline and month 6 for lesions that had a baseline magnetisation transfer ratio less than the within-patient median. We analysed the primary safety outcome in the safety population, which comprised participants who received at least one dose of their allocated treatment. We analysed the primary efficacy outcome in the intention-to-treat population, which comprised all patients who completed the study. This study is registered in the ISRCTN Registry, 14265371, and has been completed. FINDINGS: Between Jan 17, 2017, and May 17, 2019, 52 participants were randomly assigned to receive either bexarotene (n=26) or placebo (n=26). Participants who received bexarotene had a higher mean number of adverse events (6·12 [SD 3·09]; 159 events in total) than did participants who received placebo (1·63 [SD 1·50]; 39 events in total). All bexarotene-treated participants had at least one adverse event, which included central hypothyroidism (n=26 vs none on placebo), hypertriglyceridaemia (n=24 vs none on placebo), rash (n=13 vs one on placebo), and neutropenia (n=10 vs none on placebo). Five (19%) participants on bexarotene and two (8%) on placebo discontinued the study drug due to adverse events. One episode of cholecystitis in a placebo-treated participant was the only serious adverse event. The change in mean lesional magnetisation transfer ratio was not different between the bexarotene group (0·25 percentage units [pu; SD 0·98]) and the placebo group (0·09 pu [0·84]; adjusted bexarotene-placebo difference 0·16 pu, 95% CI -0·39 to 0·71; p=0·55). INTERPRETATION: We do not recommend the use of bexarotene to treat patients with multiple sclerosis because of its poor tolerability and negative primary efficacy outcome. However, statistically significant effects were seen in some exploratory MRI and electrophysiological analyses, suggesting that other retinoid X receptor agonists might have small biological effects that could be investigated in further studies. FUNDING: Multiple Sclerosis Society of the United Kingdom.

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.

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.

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.

Mind the gap: from neurons to networks to outcomes in multiple sclerosis.

MRI studies have provided valuable insights into the structure and function of neural networks, particularly in health and in classical neurodegenerative conditions such as Alzheimer disease. However, such work is also highly relevant in other diseases of the CNS, including multiple sclerosis (MS). In this Review, we consider the effects of MS pathology on brain networks, as assessed using MRI, and how these changes to brain networks translate into clinical impairments. We also discuss how this knowledge can inform the targeting of MS treatments and the potential future directions for research in this area. Studying MS is challenging as its pathology involves neurodegenerative and focal inflammatory elements, both of which could disrupt neural networks. The disruption of white matter tracts in MS is reflected in changes in network efficiency, an increasingly random grey matter network topology, relative cortical disconnection, and both increases and decreases in connectivity centred around hubs such as the thalamus and the default mode network. The results of initial longitudinal studies suggest that these changes evolve rather than simply increase over time and are linked with clinical features. Studies have also identified a potential role for treatments that functionally modify neural networks as opposed to altering their structure.

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.

Diagnosis of Progressive Multiple Sclerosis From the Imaging Perspective: A Review.

Importance: Although magnetic resonance imaging (MRI) is useful for monitoring disease dissemination in space and over time and excluding multiple sclerosis (MS) mimics, there has been less application of MRI to progressive MS, including diagnosing primary progressive (PP) MS and identifying patients with relapsing-remitting (RR) MS who are at risk of developing secondary progressive (SP) MS. This review addresses clinical application of MRI for both diagnosis and prognosis of progressive MS. Observations: Although nonspecific, some spinal cord imaging features (diffuse abnormalities and lesions involving gray matter [GM] and ≥2 white matter columns) are typical of PPMS. In patients with PPMS and those with relapse-onset MS, location of lesions in critical central nervous system regions (spinal cord, infratentorial regions, and GM) and MRI-detected high inflammatory activity in the first years after diagnosis are risk factors for long-term disability and future progressive disease course. These measures are evaluable in clinical practice. In patients with established MS, GM involvement and neurodegeneration are associated with accelerated clinical worsening. Subpial demyelination and slowly expanding lesions are novel indicators of progressive MS. Conclusions and Relevance: Diagnosis of PPMS is more challenging than diagnosis of RRMS. No qualitative clinical, immunological, histopathological, or neuroimaging features differentiate PPMS and SPMS; both are characterized by imaging findings reflecting neurodegeneration and are also impacted by aging and comorbidities. Unmet diagnostic needs include identification of MRI markers capable of distinguishing PPMS from RRMS and predicting the evolution of RRMS to SPMS. Integration of multiple parameters will likely be essential to achieve these aims.

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.