Moein Amin

The 2024 McDonald criteria allow diagnosis of multiple sclerosis (MS) in individuals presenting with symptoms not specific for MS or incidental imaging findings suggestive of demyelination when supported by biomarker evidence, reflecting a shift toward diagnostic definitions increasingly grounded in biological mechanisms of disease. The diagnostic yield of these criteria in such populations has not been evaluated in multicenter cohorts. We aimed to determine the proportion of individuals with nonspecific or incidental imaging presentations who meet the 2024 McDonald criteria and describe the contribution of central vein sign and cerebrospinal fluid (CSF) oligoclonal bands (OCBs) to diagnostic classification. This cross-sectional post-hoc analysis used data from the Central Vein Sign in Multiple Sclerosis (CAVS-MS) study, a multicenter observational cohort. Adults aged 18–65 years referred for diagnostic evaluation of possible MS were adjudicated by an expert panel (eFigure1). This analysis focused on participants with symptoms not specific for MS or incidental imaging findings suggestive of demyelination. Dissemination in space (DIS) and time (DIT) were assessed using 2017 MRI criteria. Fulfillment of the 2024 McDonald criteria at baseline—the primary outcome—was determined using the Select-6 central vein sign and CSF OCBs. Select-6 assessment was available for all participants, whereas OCB data were available for a subset based on prior clinical evaluation. Of 420 participants enrolled, 191 (45%) presented with either nonspecific symptoms (n=166) or incidental imaging findings (n=25). Mean age was 42 years, and 78% were female. Thirty-six (19%) met the 2024 McDonald criteria at baseline, including 28 (17%) in the nonspecific symptom cohort and 8 (32%) in the incidental imaging cohort. Among 51 participants meeting 2017 DIS, 22 (43%) were Select-6 positive, 17 (33%) had positive OCBs, and 4 (8%) met 2017 DIT. Nonspecific sensory symptoms, visual disturbances, and subacute cognitive decline were most associated with a diagnosis of MS. Application of the 2024 McDonald criteria identified nearly one-fifth of individuals without typical presentations as meeting diagnostic criteria for MS at baseline. Biomarker incorporation—particularly the central vein sign—accounted for a substantial proportion of diagnostic yield. Interpretation is limited by availability of CSF data and absence of longitudinal follow-up.

Background: Compared to the potent efficacy of autologous hematopoietic cell transplantation (AutoHCT) on multiple sclerosis (MS) lesion activity, benefit on disability progression is less clear. Paramagnetic rim lesions (PRLs) are imaging biomarkers associated with progression. Objectives: Evaluate effect of AutoHCT on PRLs using 7T magnetic resonance imaging (MRI). Methods: A single rater evaluated PRLs before/after AutoHCT on 7T MRI at an MS referral center. Results: Seven participants were included with mean age 38.9 years and median disease duration 9 years. Median PRL count pre-transplantation was 2. Post-AutoHCT (median 15 months in 5 participants) none of the PRLs resolved. Conclusion: These results provide low-level evidence that PRLs do not resolve following AutoHCT.

In this paper, we analyse the different advances in artificial intelligence (AI) approaches in multiple sclerosis (MS). AI applications in MS range across investigation of disease pathogenesis, diagnosis, treatment, and prognosis. A subset of AI, Machine learning (ML) models analyse various data sources, including magnetic resonance imaging (MRI), genetic, and clinical data, to distinguish MS from other conditions, predict disease progression, and personalize treatment strategies. Additionally, AI models have been extensively applied to lesion segmentation, identification of biomarkers, and prediction of outcomes, disease monitoring, and management. Despite the big promises of AI solutions, model interpretability and transparency remain critical for gaining clinician and patient trust in these methods. The future of AI in MS holds potential for open data initiatives that could feed ML models and increasing generalizability, the implementation of federated learning solutions for training the models addressing data sharing issues, and generative AI approaches to address challenges in model interpretability, and transparency. In conclusion, AI presents an opportunity to advance our understanding and management of MS. AI promises to aid clinicians in MS diagnosis and prognosis improving patient outcomes and quality of life, however ensuring the interpretability and transparency of AI-generated results is going to be key for facilitating the integration of AI into clinical practice.

Currently, there are four monoclonal antibodies (mAbs) that target the cluster of differentiation (CD) 20 receptor available to treat multiple sclerosis (MS): rituximab, ocrelizumab, ofatumumab, and ublituximab. B-cell depletion therapy has changed the therapeutic landscape of MS through robust efficacy on clinical manifestations and MRI lesion activity, and the currently available anti-CD20 mAb therapies for use in MS are a cornerstone of highly effective disease-modifying treatment. Ocrelizumab is currently the only therapy with regulatory approval for primary progressive MS. There are currently few data regarding the relative efficacy of these therapies, though several clinical trials are ongoing. Safety concerns applicable to this class of therapeutics relate primarily to immunogenicity and mechanism of action, and include infusion-related or injection-related reactions, development of hypogammaglobulinemia (leading to increased infection and malignancy risk), and decreased vaccine response. Exploration of alternative dose/dosing schedules might be an effective strategy for mitigating these risks. Future development of biosimilar medications might make these therapies more readily available. Although anti-CD20 mAb therapies have led to significant improvements in disease outcomes, CNS-penetrant therapies are still needed to more effectively address the compartmentalized inflammation thought to play an important role in disability progression.

Background and Objectives Longitudinal outcome studies in leucine-rich glioma inactivated-1 (LGI-1) immunoglobulin G (IgG) autoimmune encephalitis (AE) are needed to inform clinical management and prognostication. This study aims to evaluate longitudinal predictors of disability and disease severity in LGI-1-IgG AE. Methods This retrospective observational study of patients with LGI-1-IgG AE was conducted between 2013-2022. Disability and disease severity were defined by scores on the modified Rankin Scale (mRS) and the clinical assessment scale in AE (CASE), respectively. Demographic variables, clinical/paraclinical data, brain MRI, and Montreal Cognitive Assessment (MOCA) scores were examined as predictors of mRS and CASE scores in logistic and linear regression models, respectively. Results Thirty patients (60% male, median age = 68.5; interquartile range (IQR) = 63.0–75.0) were included, with a median follow-up time of 19.1 months (IQR = 5.3–47.1) The majority developed seizures (29, [97%]) and/or cognitive impairment (30, [100%]) and received acute (27, [90%]) and maintenance (23 [77%]) immunotherapy. The median initial MOCA was 23/30 (IQR = 21.0–25.0). Baseline mRS (median = 2.0, IQR = 2.0–3.0) and CASE (mean = 4.3, SD = 3.7) correlated with one another (r = 0.58, p < 0.001) and with initial MOCA score (mRS r = –0.60, p = 0.012; CASE r = –0.56, p = 0.021) After 12 months from symptom onset, mRS (OR = 0.88, [95% CI = 0.82–0.94], p < 0.001) and CASE (β = −0.03, [SE = 0.01], p < 0.001) improved significantly. Lower initial MOCA score (OR = 0.68, 95% CI = 0.47–0.98, p = 0.041) and temporal lobe(s) T2 hyperintensity (OR = 16.50, 95% CI = 2.29–119.16, p = 0.006) were associated with higher mRS longitudinally. At last follow-up, most patients had persistent memory dysfunction (25, [83%]) while few had ongoing seizure activity (3, [10%]). Discussion Overall, there was a high degree of correlation between mRS and CASE scores in patients with LGI-1-IgG AE, with both scores improving significantly after 12 months. Memory dysfunction and psychiatric disturbance were the most prevalent longitudinal symptoms. Cognitive impairment and temporal lobe T2 hyperintensity at baseline were both associated with greater disability at long-term follow-up, underscoring these as important determinants of disability outcomes in LGI-1-IgG AE.

Background: The central vein sign (CVS) is a proposed magnetic resonance imaging (MRI) biomarker for multiple sclerosis (MS); the optimal method for abbreviated CVS scoring is not yet established. Objective: The aim of this study was to evaluate the performance of a simplified approach to CVS assessment in a multicenter study of patients being evaluated for suspected MS. Methods: Adults referred for possible MS to 10 sites were recruited. A post-Gd 3D T2*-weighted MRI sequence (FLAIR*) was obtained in each subject. Trained raters at each site identified up to six CVS-positive lesions per FLAIR* scan. Diagnostic performance of CVS was evaluated for a diagnosis of MS which had been confirmed using the 2017 McDonald criteria at thresholds including three positive lesions (Select-3*) and six positive lesions (Select-6*). Inter-rater reliability assessments were performed. Results: Overall, 78 participants were analyzed; 37 (47%) were diagnosed with MS, and 41 (53%) were not. The mean age of participants was 45 (range: 19–64) years, and most were female ( n = 55, 71%). The area under the receiver operating characteristic curve (AUROC) for the simplified counting method was 0.83 (95% CI: 0.73–0.93). Select-3* and Select-6* had sensitivity of 81% and 65% and specificity of 68% and 98%, respectively. Inter-rater agreement was 78% for Select-3* and 83% for Select-6*. Conclusion: A simplified method for CVS assessment in patients referred for suspected MS demonstrated good diagnostic performance and inter-rater agreement.