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Background : Metachromatic leukodystrophy (MLD) is a rare autosomal recessive disorder that causes demyelination of both the central (CNS) and peripheral nervous systems (PNS). Objective : This study aims to report a unique MLD case presenting with cranial neuropathies and ataxia, initially without white matter changes on MRI, leading to diagnostic uncertainty. Results : A 20-month-old presented with bilateral abduction deficits, facial diplegia, and ataxia, raising the possibility of an acquired demyelinating condition. An MRI scan showed the enhancement of multiple cranial nerves, but normal white matter. A follow-up MRI showed new white matter changes that spared the U-fibers, suggesting a leukodystrophy. Biochemical assays were suggestive of metachromatic leukodystrophy, which was confirmed with genetic testing demonstrating a homozygous c.848+3A > G variant in ARSA . Conclusions : Our patient suggests that the initial presentation of MLD may mimic an acquired demyelinating condition and manifest with multiple cranial nerve palsies before more typical white matter changes evolve.

Background: In many parts of the world including India, the prevalence of autoimmune inflammatory diseases such as neuromyelitis optica spectrum disorders (NMOSD), myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD), and multiple sclerosis (MS) is rising. A diagnosis is often delayed due to insufficient diagnostic tools. Machine learning (ML) models have accurately differentiated eyes of patients with MS from those of healthy controls (HCs) using optical coherence tomography (OCT)-based retinal images. Examining OCT characteristics may allow for early differentiation of these conditions. The objective of this study was to determine feasibility of ML analyses to distinguish between patients with different autoimmune inflammatory diseases, other ocular diseases, and HCs based on OCT measurements of the peripapillary retinal nerve fiber layer (pRNFL), ganglion cell-inner plexiform layer (GCIPL), and inner nuclear layers (INLs)., Methods: Eyes of people with MS (n = 99 patients), NMOSD (n = 40), MOGAD (n = 74), other ocular diseases (OTHER, n = 16), and HCs (n = 54) from the Mangalore Demyelinating Disease Registry were included. Support vector machine (SVM) classification models incorporating age, pRNFL, GCIPL, and INL were performed. Data were split into training (70%) and testing (30%) data and accounted for within-patient correlations. Cross-validation was used in training to choose the best parameters for the SVM model. Accuracy and area under receiver operating characteristic curves (AUROCs) were used to assess model performance., Results: The SVM models distinguished between eyes of patients with each condition (i.e., MOGAD vs NMOSD, NMOSD vs HC, MS vs OTHER, etc) with strong discriminatory power demonstrated from the AUROCs for these comparisons ranging from 0.81 to 1.00. These models also performed with moderate to high accuracy, ranging from 0.66 to 0.81, with the exception of the MS vs NMOSD comparison, which had an accuracy of 0.53., Conclusions: ML models are useful for distinguishing between autoimmune inflammatory diseases and for distinguishing these from HCs and other ocular diseases based on OCT measures. This study lays the groundwork for future deep learning studies that use analyses of raw OCT images for identifying eyes of patients with such disorders and other etiologies of optic neuropathy., Competing Interests: F. Oertel receives fellowship support by the Hertie foundation for excellence in clinical neuroscience and research support by Novartis. In the past, she received fellowship support by the American Academy of Neurology (2021–2023) and the National Multiple Sclerosis Society (2021–2023). L. J. Balcer is a former Editor-in-Chief of the Journal of Neuro-Ophthalmology. The remaining authors report no conflicts of interest., (Copyright © 2025 by North American Neuro-Ophthalmology Society.)

Background: The randomized, phase 2 RENEW trial (NCT01721161) evaluated efficacy/safety of opicinumab (anti-LINGO-1) versus placebo in patients with first-episode unilateral acute optic neuritis (AON). Although no significant differences in the latency recovery of visual evoked potential (VEP) were observed between opicinumab and placebo groups in the intention to treat (ITT) population, the prespecified per-protocol (PP) population showed better recovery with opicinumab than with placebo. RENEWED (NCT02657915) was a one-visit, follow-up study 2 years after the last RENEW study visit (Week 32) designed to assess the long-term electrophysiological and clinical outcomes for participants previously enrolled and having received study treatment in RENEW., Methods: In the original study (RENEW), participants (aged 18-55 years) with a first unilateral AON episode were enrolled ≤28 days from first symptom onset and after treatment with methylprednisolone 1 g/day intravenously for 3-5 days; these participants were randomized to receive opicinumab 100 mg/kg or placebo intravenously once every 4 weeks from baseline to Week 20, assessed up to Week 32. Participants who received ≥1 dose of opicinumab 100 mg/kg or placebo in RENEW were eligible for the RENEWED follow-up study. Participants enrolled in RENEWED at 2 years (with an additional up to 12-month window) after the last RENEW study visit (Week 32) in both ITT and PP populations. The primary endpoint was change in full-field VEP (FF-VEP) latency of the affected eye at RENEWED study visit versus baseline of the fellow eye in RENEW, comparing between participants who received opicinumab and placebo in RENEW. Clinical progression and severity of multiple sclerosis (MS) were assessed. A substudy evaluated latency recovery using multifocal VEP (mfVEP) as an exploratory endpoint., Results: Of 82 RENEW participants, 52 (63.4 %; opicinumab n = 28, placebo n = 24) enrolled in and completed RENEWED. The adjusted mean (95 % CI) difference in FF-VEP latency delay between opicinumab and placebo groups was -6.0 (-14.6, 2.6) msec (p = 0.165) for the PP population and -4.5 (-12.6, 3.7) msec (p = 0.274) for the ITT population at the RENEWED study visit. Nominally significant improvement on mfVEP latency in the opicinumab group versus placebo was observed in participants of the mfVEP substudy (p = 0.009). In participants from the PP population without clinically definite MS (CDMS) at RENEW baseline,12 (55 %) in the opicinumab group and 12 (67 %) in the placebo group developed CDMS from enrollment in the RENEW study up to RENEWED Day 1; the estimated proportion of participants with CDMS at 2 years after the last study visit assessment in RENEW was lower when treated with opicinumab (0.50) than when treated with placebo (0.61) (hazard ratio p-value = 0.23). No benefit on visual acuity or other neurological functions was observed in the opicinumab group vs placebo in RENEWED., Conclusion: The numerically increased VEP latency recovery with opicinumab treatment in RENEWED was consistent with those observed in the parent study RENEW. However, the VEP latency and clinical data in RENEWED should be interpreted with caution, given the nature of the follow-up study, the small sample size and the limitation in study design., Competing Interests: Declaration of competing interest OA: received grants, contracts, or consulting fees from Alexion, Almirall, Biogen, Celgene, Horizon, Merck, Novartis, Roche, Sanofi, and Teva. FZ: received grants and personal fees from Allergan/AbbVie, Alimera, Apellis, Bayer Healthcare, Biogen, Boehringer Ingelheim, Clearside, Kodiak, Novartis, Novo Nordisk, MSD Sharp & Dohme, Opthea, Oxurion, Regeneron, Roche/Genentech, Sandoz, Sanofi, and Stada. TZ: received grants or contracts from Alexion, Biogen, Celgene-BMS, Janssen-Cilag, Merck, Novartis, Roche, Sanofi, and Teva. AK is an inventor on two patents related to mfVEP technology, which are held by the University of Sydney. HB: received funding (through his institution) from Biogen, Roche, Merck, and Novartis for speaker engagements, study steering, and advisory committee service. GI: has nothing to disclose. LL: received honoraria for consulting services and/or speaking activities from Almirall, Biogen, Bristol-Myers Squibb, Excemed, Merck, Novartis, Janssen-Cilag, and Roche. LJB: has nothing to disclose. SLG: received consultation fees from Genentech. CC-V, DPB, MLN, SB, and NF are former employees and may be shareholders of Biogen. NF: equity holder of Nimbus Therapeutics and a current employee and officer of Nimbus Therapeutics, shareholder of OMass Therapeutics and serves on the Board of Director of OMass Therapeutics, consultant for Bain Capital, Atara Biotherapeutics. WC is a current employee and shareholder of Biogen., (Copyright © 2024. Published by Elsevier B.V.)

Background: Visual symptoms are common after concussion. Rapid automatized naming (RAN) tasks are simple performance measures that demonstrate worse time scores in the setting of acute or more remote injury., Methods: We evaluated the capacity for the Mobile Universal Lexicon Evaluation System (MULES) and Staggered Uneven Number (SUN) testing to be feasibly administered during preseason testing in a cohort of youth ice hockey athletes using a novel computerized app, the Mobile Integrated Cognitive Kit (MICK). Participants from a youth hockey league underwent preseason testing., Results: Among 60 participants, the median age was 13 years (range 6-17). The median best time for the MULES was 49.8 seconds (range = 34.2-141.0) and the median best time for the SUN was 70.1 (range = 36.6-200.0). As is characteristic of timed performance measures, there were learning effects between the first and second trials for both the MULES (median improvement = 10.6 seconds, range = -32.3 to 92.0, P < 0.001, Wilcoxon signed-rank test) and SUN (median improvement = 2.4 seconds, range= -8.0 to 15.1, P = 0.001, Wilcoxon signed-rank test). Age was a predictor of best baseline times, with longer (worse) times for younger participants for MULES ( P < 0.001, rs = -0.67) and SUN ( P < 0.001, rs = -0.54 Spearman rank correlation). Degrees of learning effect did not vary with age ( P > 0.05, rs = -0.2)., Conclusions: Vision-based RAN tasks, such as the MULES and SUN, can be feasibly administered using the MICK app during preseason baseline testing in youth sports teams. The results suggest that more frequent baseline tests are necessary for preadolescent athletes because of the relation of RAN task performance to age., Competing Interests: M. J. Balcer is the chief architect for Model Compilers. L. J. Balcer is a former editor-in-chief of the Journal of Neuro-Ophthalmology . The remaining authors report no conflicts of interest., (Copyright © 2024 by North American Neuro-Ophthalmology Society.)

Background: Adult polyglucosan body disease (APBD) is caused by a deficiency in glycogen branching enzyme that leads to polyglucosan accumulation in multiple organs. It has a progressive clinical course with prominent neurologic manifestations. We aim to describe the neuro-ophthalmic manifestations of APBD., Methods: This is a case series of 3 individuals with genetically proven APBD. Written informed consent was provided by the brothers. We also performed a literature review on the current state of knowledge on APBD through PubMed., Results: Brother 1 developed gait imbalance and length-dependent polyneuropathy in his 40s followed by progressive urinary symptoms in his 50s. He reported diplopia and blurry vision in his 60s. Neuro-ophthalmic assessment revealed bilateral optic neuropathy, convergence insufficiency, and a right fourth nerve palsy. Genetic testing showed a homozygous pathogenic variant in GBE1 c.986A>C p.Tyr329Ser. Brother 2 developed progressive urinary symptoms in his 40s that were followed by cognitive deficits, length-dependent polyneuropathy, and lower extremity weakness in his 50s and 60s. He reported blurred vision, and neuro-ophthalmic evaluation revealed bilateral optic neuropathy. Genetic testing revealed the same variant as Brother 1, GBE1 c.986A>C p.Tyr329Ser. Brother 3 developed progressive urinary urgency and lower extremity weakness in his 50s followed by a length-dependent polyneuropathy in his 60s. He reported diplopia and blurry vision in his 70s. Neuro-ophthalmic assessment revealed bilateral optic neuropathy and convergence insufficiency. Genetic testing revealed the same variant as Brothers 1 and 2, GBE1 c.986A>C p.Tyr329Ser., Conclusions: There is an array of afferent and efferent neuro-ophthalmic manifestations in APBD. Neuro-ophthalmic evaluation is crucial in evaluating and treating patients with APBD, particularly in those with visual dysfunction., Competing Interests: The authors report no conflicts of interest., (Copyright © 2024 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the North American Neuro-Ophthalmology Society.)

Objectives: Hospital readmissions are associated with poor health outcomes including illness severity and medical complications. The objective of this study was to identify characteristics associated with 30-day post-stroke readmission in an academic urban hospital network., Materials and Methods: We collected data on patients admitted with stroke from 2017 through 2022 who were readmitted within 30 days of discharge and compared them to a subset of non-readmitted stroke patients. Chart review was used to collect demographics, characteristics of the stroke, co-morbid conditions, in-hospital complications, and post-discharge care. Univariate analyses followed by regression analysis were used to assess characteristics associated with post-stroke readmission., Results: We identified 4743 patients with stroke (18 % hemorrhagic, mean age 70.1 (standard deviation (SD) 17.2), 47.3 % female) discharged from the stroke services, of whom 282 (5.9 %) patients were readmitted within 30 days of index hospitalization. Univariate analyses identified 18 significantly different features between admitted and readmitted patients. Regression analysis revealed characteristics associated with readmission included private insurance (odds ratio (OR) 0.4, confidence interval (CI) 0.3-0.6, p < 0.001), comorbid peripheral vascular disease (PVD) (OR 2.7, CI 1.3-5.5, p = 0.009), malignancy (OR 1.6, CI 1.0-2.6, p = 0.04), seizure (OR 3.4, CI 1.4-8.2, p = 0.007), thrombolytic administration (OR 0.4, CI 0.2-0.7, p = 0.003), undergoing thrombectomy (OR 5.4, CI 2.9-10.1, p < 0.001), and higher discharge modified Rankin Scale score (OR 1.2, CI 1.0-1.3, p = 0.047)., Conclusions: Our data demonstrate that thrombectomy, high discharge Rankin score, comorbid malignancy, seizure or PVD, and lack of thrombolytic administration or private insurance predict readmission., Competing Interests: Declaration of competing interest The authors declare no competing interests., (Copyright © 2024. Published by Elsevier Inc.)

Background and Objectives: We determined inter-modality (in-person vs telemedicine examination) and inter-rater agreement for telemedicine assessments (2 different examiners) using the Telemedicine Buffalo Concussion Physical Examination (Tele-BCPE), a standardized concussion examination designed for remote use., Methods: Patients referred for an initial evaluation for concussion were invited to participate. Participants had a brief initial assessment by the treating neurologist. After a patient granted informed consent to participate in the study, the treating neurologist obtained a concussion-related history before leaving the examination room. Using the Tele-BCPE, 2 virtual examinations in no specific sequence were then performed from nearby rooms by the treating neurologist and another neurologist. After the 2 telemedicine examinations, the treating physician returned to the examination room to perform the in-person examination. Intraclass correlation coefficients (ICC) determined inter-modality validity (in-person vs remote examination by the same examiner) and inter-rater reliability (between remote examinations done by 2 examiners) of overall scores of the Tele-BCPE within the comparison datasets. Cohen's kappa, κ, measured levels of agreement of dichotomous ratings (abnormality present vs absent) on individual components of the Tele-BCPE to determine inter-modality and inter-rater agreement., Results: For total scores of the Tele-BCPE, both inter-modality agreement (ICC = 0.95 [95% CI 0.86-0.98, p < 0.001]) and inter-rater agreement (ICC = 0.88 [95% CI 0.71-0.95, p < 0.001]) were reliable (ICC >0.70). There was at least substantial inter-modality agreement (κ ≥ 0.61) for 25 of 29 examination elements. For inter-rater agreement (2 telemedicine examinations), there was at least substantial agreement for 8 of 29 examination elements., Discussion: Our study demonstrates that the Tele-BCPE yielded consistent clinical results, whether conducted in-person or virtually by the same examiner, or when performed virtually by 2 different examiners. The Tele-BCPE is a valid indicator of neurologic examination findings as determined by an in-person concussion assessment. The Tele-BCPE may also be performed with excellent levels of reliability by neurologists with different training and backgrounds in the virtual setting. These findings suggest that a combination of in-person and telemedicine modalities, or involvement of 2 telemedicine examiners for the same patient, can provide consistent concussion assessments across the continuum of care., Competing Interests: M.N. Haider received personal compensation in the range of $5,000-$9,999 for serving on a Scientific Advisory or Data Safety Monitoring board for BlinkTBI and personal compensation in the range of $500-$4,999 for serving as a statistician with Oculogica; the institution of M.N. Haider received research support from NIH; J.J. Leddy received personal compensation in the range of $500-$4,999 for serving on a Scientific Advisory or Data Safety Monitoring board for Neuronasal, received personal compensation in the range of $500-$4,999 for serving on a Scientific Advisory or Data Safety Monitoring board for Quadrant Biosciences, and received stock or an ownership interest from Highmark Innovations, 360 Concussion Care, and Noggin Health; the institution of J.J. Leddy has received research support from NIH, research support from DoD, and research support from AMSSM; L.J. Balcer is Editor-in-Chief of the Journal of Neuro-Ophthalmology; N.A. Busis has received personal compensation for serving as an editorial advisory board member for Neurology Today and for serving as a speaker for the American Academy of Neurology and as the AAN's primary advisor to the American Medical Association's CPT Editorial Panel. The other authors report no disclosures relevant to the manuscript. Full disclosure form information provided by the authors is available with the full text of this article at Neurology.org/cp., (© 2024 American Academy of Neurology.)

Background: Neuro-ophthalmology frequently requires a complex and multi-faceted clinical assessment supported by sophisticated imaging techniques in order to assess disease status. The current approach to diagnosis requires substantial expertise and time. The emergence of AI has brought forth innovative solutions to streamline and enhance this diagnostic process, which is especially valuable given the shortage of neuro-ophthalmologists. Machine learning algorithms, in particular, have demonstrated significant potential in interpreting imaging data, identifying subtle patterns, and aiding clinicians in making more accurate and timely diagnosis while also supplementing nonspecialist evaluations of neuro-ophthalmic disease., Evidence Acquisition: Electronic searches of published literature were conducted using PubMed and Google Scholar. A comprehensive search of the following terms was conducted within the Journal of Neuro-Ophthalmology: AI, artificial intelligence, machine learning, deep learning, natural language processing, computer vision, large language models, and generative AI., Results: This review aims to provide a comprehensive overview of the evolving landscape of AI applications in neuro-ophthalmology. It will delve into the diverse applications of AI, optical coherence tomography (OCT), and fundus photography to the development of predictive models for disease progression. Additionally, the review will explore the integration of generative AI into neuro-ophthalmic education and clinical practice., Conclusions: We review the current state of AI in neuro-ophthalmology and its potentially transformative impact. The inclusion of AI in neuro-ophthalmic practice and research not only holds promise for improving diagnostic accuracy but also opens avenues for novel therapeutic interventions. We emphasize its potential to improve access to scarce subspecialty resources while examining the current challenges associated with the integration of AI into clinical practice and research., Competing Interests: The authors report no conflicts of interest., (Copyright © 2024 by North American Neuro-Ophthalmology Society.)