Citations

Background: The presence of co-existent neuronal antibodies (neuronal-IgG) in patients with myelin oligodendrocyte glycoprotein immunoglobulin G (MOG-IgG1) is not yet well understood. Objectives: The aim of this study was to investigate the co-existence of a broad range of neuronal-IgG in MOG-IgG1+ patients. Methods: MOG-IgG1+ patients were tested for 17 neuronal-IgGs in cerebrospinal fluid (CSF) and serum including NMDA-R-IgG, AMPA-R-IgG, GABAB-R-IgG, LGI1-IgG, CASPR2-IgG, GABAA-R-IgG, GAD65-IgG, mGLUR1-IgG, DPPX-IgG, CRMP5-IgG, amphiphysin-IgG, PCA1,2,Tr, and ANNA1,2,3. Clinical and radiological features of MOG-IgG1+ with NMDA-R-IgG in CSF were compared to a control cohort of MOG-IgG1+ patients without NMDA-R-IgG. Results: A total of 376 MOG-IgG1+ patients underwent testing for neuronal-IgGs. Serum testing for neuronal-IgGs (113 adults, 142 children) identified one child with NMDA-R-IgG (0.7%), one child with CASPR2-IgG (0.7%), one adult with LGI1-IgG (0.9%) and one adult with GABAA-R-IgG (0.9%). CSF testing for neuronal-IgGs (97 adults, 169 children) identified seven children (4%) and seven adults (7%) with NMDA-R-IgG, and one adult with GABAA-R-IgG (1%). The MOG-IgG1+/NMDA-R-IgG+ patients had a median age of 17 (range: 2–39) years. Features associated with MOG-IgG1+/NMDA-R-IgG+ included encephalopathy ( p = 0.001), seizures ( p = 0.045), and leptomeningeal enhancement ( p = 0.045). Conclusion: NMDA-R-IgG was the most frequently detected neuronal-IgG to co-exist with MOG-IgG1. MOG-IgG1+/NMDA-R-IgG+ patients most often presented with encephalopathy and seizures. Testing for MOG-IgG1 and NMDA-R-IgG may be warranted in patients with encephalopathy and inflammatory demyelinating syndromes.

Aquaporin-4 (AQP4) neuromyelitis optica spectrum disorder (NMOSD) has been demonstrated to be associated with non-organ and organ-specific autoantibodies (antinuclear antibody, extractable nuclear antibody, double-stranded DNA, muscle acetylcholine receptor antibody) and systemic autoimmune diseases. In this study, we evaluated whether a similar association with non-organ and organ-specifc autoantibodies occurs in patients with MOG-IgG1-associated disorders. We determined that MOG-IgG1 was not strongly associated with these organ and non-organ-specific autoantibodies. Systemic lupus erythematous (SLE) was significantly associated with AQP4-IgG+ NMOSD and not with MOGAD ( p = 0.037). These findings suggest differences in co-existing systemic and organ-specific autoimmunity between MOGAD and AQP4-IgG+ NMOSD.

ObjectiveTo compare the diagnostic performance of an artificial intelligence deep learning system with that of expert neuro‐ophthalmologists in classifying optic disc appearance.MethodsThe deep learning system was previously trained and validated on 14,341 ocular fundus photographs from 19 international centers. The performance of the system was evaluated on 800 new fundus photographs (400 normal optic discs, 201 papilledema [disc edema from elevated intracranial pressure], 199 other optic disc abnormalities) and compared with that of 2 expert neuro‐ophthalmologists who independently reviewed the same randomly presented images without clinical information. Area under the receiver operating characteristic curve, accuracy, sensitivity, and specificity were calculated.ResultsThe system correctly classified 678 of 800 (84.7%) photographs, compared with 675 of 800 (84.4%) for Expert 1 and 641 of 800 (80.1%) for Expert 2. The system yielded areas under the receiver operating characteristic curve of 0.97 (95% confidence interval [CI] = 0.96–0.98), 0.96 (95% CI = 0.94–0.97), and 0.89 (95% CI = 0.87–0.92) for the detection of normal discs, papilledema, and other disc abnormalities, respectively. The accuracy, sensitivity, and specificity of the system's classification of optic discs were similar to or better than the 2 experts. Intergrader agreement at the eye level was 0.71 (95% CI = 0.67–0.76) between Expert 1 and Expert 2, 0.72 (95% CI = 0.68–0.76) between the system and Expert 1, and 0.65 (95% CI = 0.61–0.70) between the system and Expert 2.InterpretationThe performance of this deep learning system at classifying optic disc abnormalities was at least as good as 2 expert neuro‐ophthalmologists. Future prospective studies are needed to validate this system as a diagnostic aid in relevant clinical settings. ANN NEUROL 2020;88:785–795

AbstractNeuromyelitis optica (NMO) is an antibody-mediated inflammatory disease of the central nervous system with a predilection for the optic nerves, spinal cord, and certain brain regions. While NMO was previously considered a variant of multiple sclerosis (MS), it is now known to have distinct clinical, pathological, and immunological features. The identification of AQP4-IgG, a pathogenic antibody against aquaporin-4 (AQP4), delineated NMO from MS and markedly advanced insights into the unique features of this disease. The specificity of this antibody has allowed an expanded view of the clinical presentations of NMO-spectrum disorders (NMOSD), without requiring all the clinical features that were previously essential to make a clinical diagnosis. Early, accurate diagnosis of patients with NMOSD permits treatment with appropriate acute and long-term immunosuppressive agents that are critical to mitigate the risk of disability associated with this disease. More recently, a subset of patients with the NMOSD phenotype have been found to have autoantibodies targeting myelin oligodendrocyte glycoprotein (MOG), which has a different pathogenesis and expected outcome. Better understanding of the distinct pathophysiology of these disorders has laid the foundation for targeted efforts to develop novel, disease-specific treatments. In this review, we discuss the revised diagnostic criteria for NMOSD, appraise the diagnostic significance of the AQP4-IgG and MOG-IgG tests, review evidence supporting the use of available treatments for acute episodes and long-term disease modification, and highlight key emerging immunotherapies.