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The optic nerve is frequently involved in multiple sclerosis (MS). However, MRI of the optic nerve is considered optional in the differential diagnosis of optic neuropathy symptoms either at presentation or in established MS. In addition, unlike spinal cord imaging in comparable scenarios, no role is currently recommended for optic nerve MRI in patients presenting with optic neuritis for its confirmation, to plan therapeutic strategy, within the MS diagnostic framework, nor for the detection of subclinical activity in established MS. In this article, evidence related to these 3 aspects will be summarized and gaps in knowledge will be highlighted, including (1) the acquisition challenges and novel sequences that assess pathologic changes within the anterior visual pathways; (2) the clinical implications of quantitative magnetic resonance studies of the optic nerve, focusing on atrophy measures, magnetization transfer, and diffusion tensor imaging; and (3) the relevant clinical studies performed to date. Finally, an algorithm for the application of optic nerve MRI will be proposed to guide future studies aimed at addressing our knowledge gaps.

Progression independent of relapse activity (PIRA), a recent concept to formalize disability accrual in multiple sclerosis (MS) independent of relapses, has gained popularity as a potential clinical trial outcome. We discuss its shortcomings and appraise the challenges of implementing it in clinical settings, experimental trials, and research. The current definition of PIRA assumes that acute inflammation, which can manifest as a relapse, and neurodegeneration, manifesting as progressive disability accrual, can be disentangled by introducing specific time windows between the onset of relapses and the observed increase in disability. The term PIRMA (progression independent of relapse and MRI activity) was recently introduced to indicate disability accrual in the absence of both clinical relapses and new brain and spinal cord MRI lesions. Assessing PIRMA in clinical practice is highly challenging because it necessitates frequent clinical assessments and brain and spinal cord MRI scans. PIRA is commonly assessed using Expanded Disability Status Scale, a scale heavily weighted toward motor disability, whereas a more granular assessment of disability deterioration, including cognitive decline, using composite measures or other tools, such as digital tools, would possess greater utility. Similarly, using PIRA as an outcome measure in randomized clinical trials is also challenging and requires methodological considerations. The underpinning pathobiology of disability accumulation, that is not associated with relapses, may encompass chronic active lesions (slowly expanding lesions and paramagnetic rim lesions), cortical lesions, brain and spinal cord atrophy, particularly in the gray matter, diffuse and focal microglial activation, persistent leptomeningeal enhancement, and white matter tract damage. We propose to use PIRA to understand the main determinant of disability accrual in observational, cohort studies, where regular MRI scans are not included, and introduce the term of "advanced-PIRMA" to investigate the contributions to disability accrual of the abovementioned processes, using conventional and advanced imaging. This is supported by the knowledge that MRI reflects the MS pathogenic mechanisms better than purely clinical descriptors. Any residual disability accrual, which remains unexplained after considering all these mechanisms with imaging, will highlight future research priorities to help complete our understanding of MS pathogenesis.

Background and Objectives: Relapsing-remitting multiple sclerosis (RRMS), aquaporin-4 antibody-positive neuromyelitis optica spectrum disorder (AQP4-NMOSD), and myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD) may have overlapping clinical features. There is an unmet need for imaging markers that differentiate between them when serologic testing is unavailable or ambiguous. We assessed whether imaging characteristics typical of MS discriminate RRMS from AQP4-NMOSD and MOGAD, alone and in combination., Methods: Adult, nonacute patients with RRMS, APQ4-NMOSD, and MOGAD and healthy controls were prospectively recruited at the National Hospital for Neurology and Neurosurgery (London, United Kingdom) and the Walton Centre (Liverpool, United Kingdom) between 2014 and 2019. They underwent conventional and advanced brain, cord, and optic nerve MRI and optical coherence tomography (OCT)., Results: A total of 91 consecutive patients (31 RRMS, 30 APQ4-NMOSD, and 30 MOGAD) and 34 healthy controls were recruited. The most accurate measures differentiating RRMS from AQP4-NMOSD were the proportion of lesions with the central vein sign (CVS) (84% vs 33%, accuracy/specificity/sensitivity: 91/88/93%, p < 0.001), followed by cortical lesions (median: 2 [range: 1-14] vs 1 [0-1], accuracy/specificity/sensitivity: 84/90/77%, p = 0.002) and white matter lesions (mean: 39.07 [±25.8] vs 9.5 [±14], accuracy/specificity/sensitivity: 78/84/73%, p = 0.001). The combination of higher proportion of CVS, cortical lesions, and optic nerve magnetization transfer ratio reached the highest accuracy in distinguishing RRMS from AQP4-NMOSD (accuracy/specificity/sensitivity: 95/92/97%, p < 0.001). The most accurate measures favoring RRMS over MOGAD were white matter lesions (39.07 [±25.8] vs 1 [±2.3], accuracy/specificity/sensitivity: 94/94/93%, p = 0.006), followed by cortical lesions (2 [1-14] vs 1 [0-1], accuracy/specificity/sensitivity: 84/97/71%, p = 0.004), and retinal nerve fiber layer thickness (RNFL) (mean: 87.54 [±13.83] vs 75.54 [±20.33], accuracy/specificity/sensitivity: 80/79/81%, p = 0.009). Higher cortical lesion number combined with higher RNFL thickness best differentiated RRMS from MOGAD (accuracy/specificity/sensitivity: 84/92/77%, p < 0.001)., Discussion: Cortical lesions, CVS, and optic nerve markers achieve a high accuracy in distinguishing RRMS from APQ4-NMOSD and MOGAD. This information may be useful in clinical practice, especially outside the acute phase and when serologic testing is ambiguous or not promptly available., Classification of Evidence: This study provides Class II evidence that selected conventional and advanced brain, cord, and optic nerve MRI and OCT markers distinguish adult patients with RRMS from AQP4-NMOSD and MOGAD., (Copyright © 2022 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the American Academy of Neurology.)

Background and Objectives: In multiple sclerosis (MS), functional networks undergo continuous reconfiguration and topography changes over the disease course. In this study, we aimed to investigate functional network to pography abnormalities in MS and their association with disease phenotype, clinical and cognitive disability, and structural MRI damage., Methods: This is a multicenter cross-sectional study. Enrolled participants performed MRI and neurologic and neuropsychological assessment. Network topography was assessed on resting state fMRI data using degree centrality, which counted the number of functional connections of each gray matter voxel with the rest of the brain. SPM12 age-adjusted, sex-adjusted, scanner-adjusted, framewise displacement, and gray matter-volume adjusted analysis of variance and multivariable regressions were used ( p < 0.05, family-wise error [FWE] corrected)., Results: We enrolled 971 patients with MS (624 female patients; mean age = 43.1 ± 11.8 years; 47 clinically isolated syndrome [CIS], 704 relapsing-remitting MS [RRMS], 145 secondary progressive MS [SPMS], and 75 primary progressive MS [PPMS]) and 330 healthy controls (186 female patients; mean age = 41.2 ± 13.3 years). Patients with MS showed reduced centrality in the salience and sensorimotor networks as well as increased centrality in the default-mode network vs controls ( p < 0.05, FWE). Abnormal centrality was already found in CIS vs controls and in RRMS vs CIS ( p < 0.001, uncorrected); however, it became more severe in SPMS vs RRMS ( p < 0.05, FWE) and in PPMS vs controls ( p < 0.001, uncorrected). Cognitively impaired patients (39%) showed reduced centrality in the salience network and increased centrality in the default-mode network vs cognitively preserved patients ( p < 0.001, conjunction analysis). More severe disability correlated with increased centrality in the right precuneus ( r = 0.18, p < 0.05 FWE). Higher T2 lesion volume and brain/gray matter atrophy were associated with reduced centrality in the bilateral insula and cerebellum ( r = range -0.17/-0.15 and 0.26/0.28, respectively; p < 0.05, FWE). Higher brain/gray matter atrophy was also associated with increased centrality in the default-mode network ( r = range -0.31/-0.22, p < 0.05, FWE)., Discussion: Patients with MS presented with reduced centrality in the salience and primary sensorimotor networks and increased centrality in the default-mode network. Centrality abnormalities were specific for different disease phenotypes and associated with clinical and cognitive disability, hence suggesting that voxel-wise centrality analysis may reflect pathologic substrates underpinning disability accrual., (© 2022 American Academy of Neurology.)

Objective: In the work-up of patients presenting with a clinically isolated syndrome (CIS), 3T MRI might offer a higher lesion detection than 1.5T, but it remains unclear whether this affects the fulfilment of the diagnostic criteria for multiple sclerosis (MS)., Methods: We recruited 66 patients with CIS within 6 months from symptom onset and 26 healthy controls in 6 MS centers. All participants underwent 1.5T and 3T brain and spinal cord MRI at baseline according to local optimized protocols and the MAGNIMS guidelines. Patients who had not converted to MS during follow-up received repeat brain MRI at 3-6 months and 12-15 months. The number of lesions per anatomical region was scored by 3 raters in consensus. Criteria for dissemination in space (DIS) and dissemination in time (DIT) were determined according to the 2017 revisions of the McDonald criteria., Results: Three-Tesla MRI detected 15% more T2 brain lesions compared to 1.5T ( p < 0.001), which was driven by an increase in baseline detection of periventricular (12%, p = 0.015), (juxta)cortical (21%, p = 0.005), and deep white matter lesions (21%, p < 0.001). The detection rate of spinal cord lesions and gadolinium-enhancing lesions did not differ between field strengths. Three-Tesla MRI did not lead to a higher number of patients fulfilling the criteria for DIS or DIT, or subsequent diagnosis of MS, at any of the 3 time points., Conclusion: Scanning at 3T does not influence the diagnosis of MS according to McDonald diagnostic criteria., (© 2018 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the American Academy of Neurology.)

Objective: To assess the value of the central vein sign (CVS) on a clinical 3T scanner to distinguish between multiple sclerosis (MS) and neuromyelitis optica spectrum disorder (NMOSD)., Methods: Eighteen aquaporin-4-antibody-positive patients with NMOSD, 18 patients with relapsing-remitting MS, and 25 healthy controls underwent 3T MRI. The presence of a central vein in white matter lesions on susceptibility-weighted imaging, defined as a thin hypointense line or a small dot, was recorded., Results: The proportion of lesions with the CVS was higher in MS than NMOSD (80% vs 32%, p < 0.001). A greater proportion of lesions with the CVS predicted the diagnosis of MS, rather than NMOSD (odds ratio 1.10, 95% confidence interval [CI] 1.04 to 1.16, p = 0.001), suggesting that each percent unit increase in the proportion of lesions with the CVS in an individual patient was associated with a 10% increase in the risk of the same patient having MS. If more than 54% of the lesions on any given scan show the CVS, then the patient can be given a diagnosis of MS with an accuracy of 94% (95% CIs 81.34, 99.32, p < 0.001, sensitivity/specificity 90%/100%)., Conclusion: The clinical value of the CVS in the context of the differential diagnosis between MS and NMOSD, previously suggested using 7T scanners, is now extended to clinical 3T scanners, thereby making a step towards the use of CVS in clinical practice., Classification of Evidence: This study provides Class III evidence that the CVS on 3T MRI accurately distinguishes patients with MS from those with seropositive NMOSD., (© 2018 American Academy of Neurology.)

Objective: We tested whether brain gray matter (GM) imaging measures can differentiate between multiple sclerosis (MS) and neuromyelitis optica (NMO) using random-forest classification., Methods: Ninety participants (25 patients with MS, 30 patients with NMO, and 35 healthy controls [HCs]) were studied in Tehran, Iran, and 54 (24 patients with MS, 20 patients with NMO, and 10 HCs) in Padua, Italy. Participants underwent brain T1 and T2/fluid-attenuated inversion recovery MRI. Volume, thickness, and surface of 50 cortical GM regions and volumes of the deep GM nuclei were calculated and used to construct 3 random-forest models to classify patients as either NMO or MS, and separate each patient group from HCs. Clinical diagnosis was the gold standard against which the accuracy was calculated., Results: The classifier distinguished patients with MS, who showed greater atrophy especially in deep GM, from those with NMO with an average accuracy of 74% (sensitivity/specificity: 77/72; p < 0.01). When we used thalamic volume (the most discriminating GM measure) together with the white matter lesion volume, the accuracy of the classification of MS vs NMO was 80%. The classifications of MS vs HCs and NMO vs HCs achieved higher accuracies (92% and 88%)., Conclusions: GM imaging biomarkers, automatically obtained from clinical scans, can be used to distinguish NMO from MS, even in a 2-center setting, and may facilitate the differential diagnosis in clinical practice., Classification of Evidence: This study provides Class II evidence that GM imaging biomarkers can distinguish patients with NMO from those with MS., (© 2016 American Academy of Neurology.)

Objective: To develop a composite MRI-based measure of motor network integrity, and determine if it explains disability better than conventional MRI measures in patients with multiple sclerosis (MS)., Methods: Tract density imaging and constrained spherical deconvolution tractography were used to identify motor network connections in 22 controls. Fractional anisotropy (FA), magnetization transfer ratio (MTR), and normalized volume were computed in each tract in 71 people with relapse onset MS. Principal component analysis was used to distill the FA, MTR, and tract volume data into a single metric for each tract, which in turn was used to compute a composite measure of motor network efficiency (composite NE) using graph theory. Associations were investigated between the Expanded Disability Status Scale (EDSS) and the following MRI measures: composite motor NE, NE calculated using FA alone, FA averaged in the combined motor network tracts, brain T2 lesion volume, brain parenchymal fraction, normal-appearing white matter MTR, and cervical cord cross-sectional area., Results: In univariable analysis, composite motor NE explained 58% of the variation in EDSS in the whole MS group, more than twice that of the other MRI measures investigated. In a multivariable regression model, only composite NE and disease duration were independently associated with EDSS., Conclusions: A composite MRI measure of motor NE was able to predict disability substantially better than conventional non-network-based MRI measures., (© 2015 American Academy of Neurology.)

Objectives: To investigate whether (1) there were differences between HLA-DRB1*15-positive and -negative patients at baseline, and (2) HLA-DRB1*15-positive patients showed a greater development of brain and spinal cord damage, as assessed by MRI, and greater progression of disability, during a 5-year follow-up, compared with HLA-DRB1*15-negative patients., Methods: HLA-DRB1*15 typing was performed in 41 patients with primary progressive multiple sclerosis (PPMS) who were recruited within 5 years of symptom onset. All patients and 18 healthy controls were studied clinically and with MRI at baseline, and every 6 months for 3 years, and then at 5 years. Magnetization transfer ratio parameters and volumes for brain gray matter and normal-appearing white matter, brain T2 lesion load, and spinal cord cross-sectional area were obtained. Patient disability was assessed at each visit using the Expanded Disability Status Scale and Multiple Sclerosis Functional Composite subscores., Results: There were no significant differences between HLA-DRB1*15-positive and -negative patients at baseline. HLA-DRB1*15-positive patients showed a greater decline in brain magnetization transfer ratio for gray matter and normal-appearing white matter (both p = 0.005) than HLA-DRB1*15-negative patients over 5 years, while the same parameters did not change over time in healthy controls. HLA-DRB1*15-positive patients also showed a trend toward a faster increase in brain T2 lesion load than HLA-DRB1*15-negative patients (0.29 [95% confidence interval 0.20-0.38] vs 0.21 [0.13-0.30] mL/mo, p = 0.085) and higher T2 lesion volumes at all time points (average difference [95% confidence interval]: 10.58 mL [7.09-14.07], p < 0.001) during the follow-up, after adjusting for disease duration., Conclusions: These findings suggest that HLA-DRB1*15 influences the progression of brain pathology in PPMS., (© 2014 American Academy of Neurology.)