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Supplemental Digital Content is Available in the Text. Background: Spectral-domain (SD-) optical coherence tomography (OCT) can reliably measure axonal (peripapillary retinal nerve fiber layer [pRNFL]) and neuronal (macular ganglion cell + inner plexiform layer [GCIPL]) thinning in the retina. Measurements from 2 commonly used SD-OCT devices are often pooled together in multiple sclerosis (MS) studies and clinical trials despite software and segmentation algorithm differences; however, individual pRNFL and GCIPL thickness measurements are not interchangeable between devices. In some circumstances, such as in the absence of a consistent OCT segmentation algorithm across platforms, a conversion equation to transform measurements between devices may be useful to facilitate pooling of data. The availability of normative data for SD-OCT measurements is limited by the lack of a large representative world-wide sample across various ages and ethnicities. Larger international studies that evaluate the effects of age, sex, and race/ethnicity on SD-OCT measurements in healthy control participants are needed to provide normative values that reflect these demographic subgroups to provide comparisons to MS retinal degeneration. Methods: Participants were part of an 11-site collaboration within the International Multiple Sclerosis Visual System (IMSVISUAL) consortium. SD-OCT was performed by a trained technician for healthy control subjects using Spectralis or Cirrus SD-OCT devices. Peripapillary pRNFL and GCIPL thicknesses were measured on one or both devices. Automated segmentation protocols, in conjunction with manual inspection and correction of lines delineating retinal layers, were used. A conversion equation was developed using structural equation modeling, accounting for clustering, with healthy control data from one site where participants were scanned on both devices on the same day. Normative values were evaluated, with the entire cohort, for pRNFL and GCIPL thicknesses for each decade of age, by sex, and across racial groups using generalized estimating equation (GEE) models, accounting for clustering and adjusting for within-patient, intereye correlations. Change-point analyses were performed to determine at what age pRNFL and GCIPL thicknesses exhibit accelerated rates of decline. Results: The healthy control cohort (n = 546) was 54% male and had a wide distribution of ages, ranging from 18 to 87 years, with a mean (SD) age of 39.3 (14.6) years. Based on 346 control participants at a single site, the conversion equation for pRNFL was Cirrus = −5.0 + (1.0 × Spectralis global value). Based on 228 controls, the equation for GCIPL was Cirrus = −4.5 + (0.9 × Spectralis global value). Standard error was 0.02 for both equations. After the age of 40 years, there was a decline of −2.4 μm per decade in pRNFL thickness (P < 0.001, GEE models adjusting for sex, race, and country) and −1.4 μm per decade in GCIPL thickness (P < 0.001). There was a small difference in pRNFL thickness based on sex, with female participants having slightly higher thickness (2.6 μm, P = 0.003). There was no association between GCIPL thickness and sex. Likewise, there was no association between race/ethnicity and pRNFL or GCIPL thicknesses. Conclusions: A conversion factor may be required when using data that are derived between different SD-OCT platforms in clinical trials and observational studies; this is particularly true for smaller cross-sectional studies or when a consistent segmentation algorithm is not available. The above conversion equations can be used when pooling data from Spectralis and Cirrus SD-OCT devices for pRNFL and GCIPL thicknesses. A faster decline in retinal thickness may occur after the age of 40 years, even in the absence of significant differences across racial groups. [ABSTRACT FROM AUTHOR]

Background: The International Multiple Sclerosis Visual System Consortium (IMSVISUAL) was formed in November 2014 with the primary goal of improving research, care, and education regarding the role of the visual system in multiple sclerosis (MS) and related disorders. Methods: In this review, we describe the formation, goals, activities, and structure of IMSVISUAL, as well as the relationship of IMSVISUAL with the Americas Committee for Treatment and Research in MS (ACTRIMS). Finally, we provide an overview of the work IMSVISUAL has completed to date, as well as an outline of research projects ongoing under the auspices of IMSVISUAL. Results: IMSVISUAL has 140 members worldwide and continues to grow. Through IMSVISUAL-related research, optical coherence tomography (OCT)-derived peripapillary retinal nerve fiber layer (pRNFL) thinning has been established as a predictor of future disability in MS. IMSVISUAL has also developed guidelines for reporting OCT studies in MS. Moreover, a systematic review performed by IMSVISUAL found that not only are pRNFL and ganglion cell + inner plexiform layer (GCIPL) thicknesses reduced in patients with MS (particularly in eyes with prior optic neuritis [ON]), but that inner nuclear layer measures may be higher among MS ON eyes, relative to healthy control eyes. Currently, there are several ongoing IM- SVISUAL projects that will establish a role for visual outcomes in diagnosing MS and quantifying the effects of emerging therapies in clinical trials. Conclusions: The development of IMSVISUAL represents a major collaborative commitment to defining the role of visual outcomes in high-quality, large-scale studies that generate definitive and instructive findings in the field of MS. As a consortium, IMSVISUAL has completed several international collaborative projects, is actively engaged in numerous ongoing research studies, and is committed to expanding the role of vision research in MS and related disorders. [ABSTRACT FROM AUTHOR]

Competing Interests: F. C. Oertel was an employee of Nocturne GmbH and receives research support by the American Academy of Neurology and National Multiple Sclerosis Society (US), unrelated to this work and funding by the German Association of Neurology (Deutsche Gesellschaft für Neurologie) in context of this project. H. G. Zimmermann reports speaking honoraria and grants from Novartis and speaking honoraria from Bayer Healthcare, unrelated to this study. C. Chien has received speaking honoraria from Bayer and research funding from Novartis, unrelated to this study. R. Marignier serves on the scientific advisory board for MedImmune and has received funding for travel and honoraria from Biogen, Merck Serono, Novartis, Sanofi- Genzyme, Roche and Teva. A. Cobo-Calvo received funding from the Instituto de Salud Carlos III (Spain) JR19/00007 unrelated to this manuscript. L. Leocani received honoraria for consulting services from Merck, Roche, Biogen and for speaking activities from Teva; research support from Merck, Biogen, Novartis; travel support from Merck, Roche, Biogen, Almirall. M. Radaelli received speaker honoraria from Novartis, Bayer, Roche, Alexion and Ipsen and travel reimbursement from Bayer, Biogen, Merz, Genzyme, Teva, Roche and Merck, none related to this study. P. Villoslada hold stocks and has received consultancy fees from Accure Therapeutics, QMENTA, CLight, NeuroPrex, Attune Neurosciences, Spiral Therapeutics and Adhera Health. M. A. Lana-Peixoto has received funding for travel and speaker honoraria from Novartis, Sanofi- Genzyme, and Roche. O. Aktas has received honoraria for speaking/consultation and travel grants from Bayer Healthcare, Biogen Idec, Chugai, Novartis, Medimmune, Merck Serono, and Teva and research grants from Bayer Healthcare, Biogen Idec, Novartis, and Teva, not related to this study. M. Ringelstein received speaker honoraria from Novartis, Bayer, Roche, Alexion, and Ipsen, and travel reimbursement from Bayer, Biogen, Merz, Genzyme, Teva, Roche, and Merck, none related to this study. P. Albrecht reports grants, personal fees, and nonfinancial support from Allergan, Biogen, Ipsen, Merz Pharmaceuticals, Novartis, and Roche, personal fees and nonfinancial support from Bayer Healthcare, and Merck, and nonfinancial support from Sanofi-Aventis/Genzyme. M. R. Yeaman is the founder and a shareholder of NovaDigm Therapeutics, Inc.; he receives funding from the United States National Institutes of Health and United States Department of Defense; he holds U.S. and international patents on immunotherapeutic and anti-infective technologies, is a member of the Genentech-Roche Scientific Advisory Committee and adviser to The Guthy-Jackson Charitable Foundation. T. J. Smith was issued US patents covering the therapeutic targeting of IGF-I receptor in autoimmune diseases. He is a paid consultant for Horizon Thera and Immunovant and is a scientific advisor to the Guthy-Jackson Charitable Foundation. He receives research funding from the National Institutes of Health. F. Paul reports research grants and speaker honoraria from Bayer, Teva, Genzyme, Merck, Novartis, MedImmune and is member of the steering committee of the OCTIMS study (Novartis), all unrelated to this work. A. U. Brandt is cofounder and shareholder of Motognosis and Nocturne. He is named as inventor on several patent applications regarding MS serum biomarkers, OCT image analysis and perceptive visual computing. E. H. Martinez-Lapiscina, S. Motamedi, M. Pisa, SvS, C. Bereuter, and M. A. Fontenelle report no conflicts of interest.

Background and Objectives: Recent studies have suggested that intereye differences (IEDs) in peripapillary retinal nerve fiber layer (pRNFL) or ganglion cell + inner plexiform (GCIPL) thickness by spectral domain optical coherence tomography (SD-OCT) may identify people with a history of unilateral optic neuritis (ON). However, this requires further validation. Machine learning classification may be useful for validating thresholds for OCT IEDs and for examining added utility for visual function tests, such as low-contrast letter acuity (LCLA), in the diagnosis of people with multiple sclerosis (PwMS) and for unilateral ON history., Methods: Participants were from 11 sites within the International Multiple Sclerosis Visual System consortium. pRNFL and GCIPL thicknesses were measured using SD-OCT. A composite score combining OCT and visual measures was compared individual measurements to determine the best model to distinguish PwMS from controls. These methods were also used to distinguish those with a history of ON among PwMS. Receiver operating characteristic (ROC) curve analysis was performed on a training data set (2/3 of cohort) and then applied to a testing data set (1/3 of cohort). Support vector machine (SVM) analysis was used to assess whether machine learning models improved diagnostic capability of OCT., Results: Among 1,568 PwMS and 552 controls, variable selection models identified GCIPL IED, average GCIPL thickness (both eyes), and binocular 2.5% LCLA as most important for classifying PwMS vs controls. This composite score performed best, with area under the curve (AUC) = 0.89 (95% CI 0.85-0.93), sensitivity = 81%, and specificity = 80%. The composite score ROC curve performed better than any of the individual measures from the model ( p < 0.0001). GCIPL IED remained the best single discriminator of unilateral ON history among PwMS (AUC = 0.77, 95% CI 0.71-0.83, sensitivity = 68%, specificity = 77%). SVM analysis performed comparably with standard logistic regression models., Discussion: A composite score combining visual structure and function improved the capacity of SD-OCT to distinguish PwMS from controls. GCIPL IED best distinguished those with a history of unilateral ON. SVM performed as well as standard statistical models for these classifications., Classification of Evidence: This study provides Class III evidence that SD-OCT accurately distinguishes multiple sclerosis from normal controls as compared with clinical criteria., (© 2022 American Academy of Neurology.)

Objective: To update the consensus recommendations for reporting of quantitative optical coherence tomography (OCT) study results, thus revising the previously published Advised Protocol for OCT Study Terminology and Elements (APOSTEL) recommendations., Methods: To identify studies reporting quantitative OCT results, we performed a PubMed search for the terms "quantitative" and "optical coherence tomography" from 2015 to 2017. Corresponding authors of the identified publications were invited to provide feedback on the initial APOSTEL recommendations via online surveys following the principle of a modified Delphi method. The results were evaluated and discussed by a panel of experts and changes to the initial recommendations were proposed. A final survey was recirculated among the corresponding authors to obtain a majority vote on the proposed changes., Results: A total of 116 authors participated in the surveys, resulting in 15 suggestions, of which 12 were finally accepted and incorporated into an updated 9-point checklist. We harmonized the nomenclature of the outer retinal layers, added the exact area of measurement to the description of volume scans, and suggested reporting device-specific features. We advised to address potential bias in manual segmentation or manual correction of segmentation errors. References to specific reporting guidelines and room light conditions were removed. The participants' consensus with the recommendations increased from 80% for the previous APOSTEL version to greater than 90%., Conclusions: The modified Delphi method resulted in an expert-led guideline (evidence Class III; Grading of Recommendations, Assessment, Development and Evaluations [GRADE] criteria) concerning study protocol, acquisition device, acquisition settings, scanning protocol, funduscopic imaging, postacquisition data selection, postacquisition analysis, nomenclature and abbreviations, and statistical approach. It will be essential to update these recommendations to new research and practices regularly., (Copyright © 2021 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the American Academy of Neurology.)

Purpose: To describe retinal lesion development in Susac syndrome during acute, postacute, and late phases of the disease., Methods: Cross-sectional study of four patients with Susac syndrome and longitudinal short-interval case study of one additional patient. Retinal changes were analyzed with high-resolution spectral domain optical coherence tomography and retinal fluorescein angiography., Results: Retinal Susac syndrome lesions comprise four different lesion sections, which can be distinguished in acute and postacute phases of the disease: a primary section at the site of branch retinal artery occlusion, which spans more layers than supplied by the affected vessel; hypoxic sections from superficial and deep capillary networks; and an axonal damage section with degenerating axons from perished ganglion cells in the main and hypoxic sections. In the later stages, main and hypoxic lesion sections can no longer be distinguished, and both show degeneration from outer plexiform to retinal nerve fiber layers., Conclusion: The dynamics of lesion development and morphologically distinct lesion sections suggest more complex mechanisms of lesion evolution beyond an isolated endothelial immune reaction and subsequent hypoxic tissue damage. The characteristic lesion morphology assists in differentiating the diagnosis of acute visual loss in neuroinflammatory disease. Specificity of the identified changes has to be determined in future studies also including patients with other retinal vascular diseases.