Myelin oligodendrocyte glycoprotein (MOG) antibody-associated disease (MOGAD) is the most recently defined inflammatory demyelinating disease of the central nervous system (CNS). Over the last decade, several studies have helped delineate the characteristic clinical-MRI phenotypes of the disease, allowing distinction from aquaporin-4 (AQP4)-IgG-positive neuromyelitis optica spectrum disorder (AQP4-IgG+NMOSD) and multiple sclerosis (MS). The clinical manifestations of MOGAD are heterogeneous, ranging from isolated optic neuritis or myelitis to multifocal CNS demyelination often in the form of acute disseminated encephalomyelitis (ADEM), or cortical encephalitis. A relapsing course is observed in approximately 50% of patients. Characteristic MRI features have been described that increase the diagnostic suspicion (e.g., perineural optic nerve enhancement, spinal cord H-sign, T2-lesion resolution over time) and help discriminate from MS and AQP4+NMOSD, despite some overlap. The detection of MOG-IgG in the serum (and sometimes CSF) confirms the diagnosis in patients with compatible clinical-MRI phenotypes, but false positive results are occasionally encountered, especially with indiscriminate testing of large unselected populations. The type of cell-based assay used to evaluate for MOG-IgG (fixed vs. live) and antibody end-titer (low vs. high) can influence the likelihood of MOGAD diagnosis. International consensus diagnostic criteria for MOGAD are currently being compiled and will assist in clinical diagnosis and be useful for enrolment in clinical trials. Although randomized controlled trials are lacking, MOGAD acute attacks appear to be very responsive to high dose steroids and plasma exchange may be considered in refractory cases. Attack-prevention treatments also lack class-I data and empiric maintenance treatment is generally reserved for relapsing cases or patients with severe residual disability after the presenting attack. A variety of empiric steroid-sparing immunosuppressants can be considered and may be efficacious based on retrospective or prospective observational studies but prospective randomized placebo-controlled trials are needed to better guide treatment. In summary, this article will review our rapidly evolving understanding of MOGAD diagnosis and management.

Recent evidences showed the existence of a central nervous system ‘waste clearance’ system, defined as glymphatic system. Glymphatic abnormalities have been described in several neurodegenerative conditions, including Alzheimer’s and Parkinson’s disease. Glymphatic function has not been thoroughly explored in multiple sclerosis, where neurodegenerative processes are intermingled with inflammatory processes. We aimed to investigate glymphatic system function in multiple sclerosis and to evaluate its association with clinical disability, disease course, demyelination and neurodegeneration, quantified using different MRI techniques. In this retrospective study, we enrolled 71 multiple sclerosis patients (49 relapsing-remitting and 22 progressive multiple sclerosis) and 32 age- and sex- matched healthy controls. All subjects underwent neurological and MRI assessment including high-resolution T1, T2 and double inversion recovery sequences, diffusion- and susceptibility weighted imaging. We calculated the diffusion along perivascular space index, a proxy for glymphatic function, cortical and deep gray matter volume, white and cortical gray matter lesion volume and normal appearing white matter microstructural damage. Multiple sclerosis patients showed an overall lower diffusion along perivascular space index vs healthy controls (estimated mean difference: −0.09, P = 0.01). Both relapsing-remitting and progressive multiple sclerosis patients had lower diffusion along perivascular space index vs healthy controls (estimated mean difference: −0.06, P = 0.04 for relapsing-remitting and −0.19, P = 0.001 for progressive multiple sclerosis patients). Progressive multiple sclerosis patients showed lower diffusion along perivascular space index vs relapsing-remitting multiple sclerosis patients (estimated mean difference: −0.09, P = 0.03). In multiple sclerosis patients, lower diffusion along perivascular space index was associated with more severe clinical disability (r = −0.45, P = 0.001) and longer disease duration (r = −0.37, P = 0.002). Interestingly, we detected a negative association between diffusion along perivascular space index and disease duration in the first 4.13 years of the disease course (r = −0.38, P = 0.04) without any association thereafter (up to 34 years of disease duration). Lower diffusion along perivascular space index was associated with higher white (r = −0.36, P = 0.003) and cortical (r = −0.41, P = 0.001) lesion volume, more severe cortical (r = 0.30, P = 0.007) and deep (r = 0.42, P = 0.001) gray matter atrophy, reduced fractional anisotropy (r = 0.42, P = 0.001) and increased mean diffusivity (r = −0.45, P = 0.001) in the normal-appearing white matter. Our results suggest that the glymphatic system is impaired in multiple sclerosis, especially in progressive stages. Impaired glymphatic function was associated with measures of both demyelination and neurodegeneration and reflects a more severe clinical disability. These findings suggest that glymphatic impairment may be a pathological mechanism underpinning multiple sclerosis. The dynamic interplay with other pathological substrates of the disease deserves further investigation.

Objectives: To validate imaging features able to discriminate neuromyelitis optica spectrum disorders from multiple sclerosis with conventional magnetic resonance imaging (MRI). Methods: In this cross-sectional study, brain and spinal cord scans were evaluated from 116 neuromyelitis optica spectrum disorder patients (98 seropositive and 18 seronegative) in chronic disease phase and 65 age-, sex-, and disease duration-matched multiple sclerosis patients. To identify independent predictors of neuromyelitis optica diagnosis, after assessing the prevalence of typical/atypical findings, the original cohort was 2:1 randomized in a training sample (where a multivariate logistic regression analysis was run) and a validation sample (where the performance of the selected variables was tested and validated). Results: Typical brain lesions occurred in 50.9% of neuromyelitis optica patients (18.1% brainstem periventricular/periaqueductal, 32.7% periependymal along lateral ventricles, 3.4% large hemispheric, 6.0% diencephalic, 4.3% corticospinal tract), 72.2% had spinal cord lesions (46.3% long transverse myelitis, 36.1% short transverse myelitis), 37.1% satisfied 2010 McDonald criteria, and none had cortical lesions. Fulfillment of at least 2 of 5 of absence of juxtacortical/cortical lesions, absence of periventricular lesions, absence of Dawson fingers, presence of long transverse myelitis, and presence of periependymal lesions along lateral ventricles discriminated neuromyelitis optica patients in both training (sensitivity = 0.92, 95% confidence interval [CI] = 0.84-0.97; specificity = 0.91, 95% CI = 0.78-0.97) and validation samples (sensitivity = 0.82, 95% CI = 0.66-0.92; specificity = 0.91, 95% CI = 0.71-0.99). MRI findings and criteria performance were similar irrespective of serostatus. Interpretation: Although up to 50% of neuromyelitis optica patients have no typical lesions and a relatively high percentage of them satisfy multiple sclerosis criteria, several easily applicable imaging features can help to distinguish neuromyelitis optica from multiple sclerosis.Additional supporting information can be found in the online version of this article.

Spinal cord involvement can be observed in the course of immune-mediated disorders. Although multiple sclerosis (MS) represents the leading cause of inflammatory myelopathy, an increasing number of alternative etiologies must be now considered in the diagnostic work-up of patients presenting with myelitis. These include antibody-mediated disorders and cytotoxic T cell-mediated diseases targeting central nervous system (CNS) antigens, and systemic autoimmune conditions with secondary CNS involvement. Even though clinical features are helpful to orient the diagnostic suspicion (e.g., timing and severity of myelopathy symptoms), the differential diagnosis of inflammatory myelopathies is often challenging due to overlapping features. Moreover, noninflammatory etiologies can sometimes mimic an inflammatory process. In this setting, magnetic resonance imaging (MRI) is becoming a fundamental tool for the characterization of spinal cord damage, revealing a pictorial scenario which is wider than the clinical manifestations. The characterization of spinal cord lesions in terms of longitudinal extension, location on axial plane, involvement of the white matter and/or gray matter, and specific patterns of contrast enhancement, often allows a proper differentiation of these diseases. For instance, besides classical features, such as the presence of longitudinally extensive spinal cord lesions in patients with aquaporin-4-IgG positive neuromyelitis optica spectrum disorder (AQP4+NMOSD), novel radiological signs (e.g., H sign, trident sign) have been recently proposed and successfully applied for the differential diagnosis of inflammatory myelopathies. In this review article, we will discuss the radiological features of spinal cord involvement in autoimmune disorders such as MS, AQP4+NMOSD, myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD), and other recently characterized immune-mediated diseases. The identification of imaging pitfalls and mimics that can lead to misdiagnosis will also be examined. Since spinal cord damage is a major cause of irreversible clinical disability, the recognition of these radiological aspects will help clinicians achieve a correct and prompt diagnosis, treat early with disease-specific treatment and improve patient outcomes.

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Background: The pathogenetic mechanisms sustaining neuroinflammatory disorders may originate from the cerebrospinal fluid. Objective: To evaluate white matter damage with diffusion tensor imaging and T1/T2-weighted ratio at progressive distances from the ventricular system in neuromyelitis optica spectrum disorders and multiple sclerosis. Methods: Fractional anisotropy, mean, axial, and radial diffusivity and T1/T2-weighted ratio maps were obtained from patients with seropositive neuromyelitis optica spectrum disorders, multiple sclerosis, and healthy controls ( n = 20 each group). White matter damage was assessed as function of ventricular distance within progressive concentric bands. Results: Compared to healthy controls, neuromyelitis optica spectrum disorders patients had similar fractional anisotropy, radial and axial diffusivity, increased mean diffusivity ( p = 0.009–0.013) and reduced T1/T2-weighted ratio ( p = 0.024–0.037) in all bands. In multiple sclerosis, gradient of percentage lesion volume and intra-lesional mean and axial diffusivity were higher in periventricular bands. Compared to healthy controls, multiple sclerosis patients had reduced fractional anisotropy ( p = 0.001–0.043) in periventricular bands, increased mean ( p < 0.001), radial ( p < 0.001–0.004), and axial diffusivity ( p = 0.002–0.008) and preserved T1/T2-weighted ratio in all bands. Conclusion: White matter damage is higher at periventricular level in multiple sclerosis and diffuse in neuromyelitis optica spectrum disorders. Fractional anisotropy preservation, associated with increased mean diffusivity and reduced T1/T2-weighted ratio may reflect astrocyte damage.

Inflammatory myelopathies can manifest with a combination of motor, sensory and autonomic dysfunction of variable severity. Depending on the underlying etiology, the episodes of myelitis can recur, often leading to irreversible spinal cord damage and major long-term disability. Three main demyelinating disorders of the central nervous system, namely multiple sclerosis (MS), aquaporin-4-IgG-positive neuromyelitis optica spectrum disorders (AQP4+NMOSD) and myelin oligodendrocyte glycoprotein-IgG associated disease (MOGAD), can induce spinal cord inflammation through different pathogenic mechanisms, resulting in a more or less profound disruption of spinal cord integrity. This ultimately translates into distinctive clinical-MRI features, as well as distinct patterns of disability accrual, with a step-wise worsening of neurological function in MOGAD and AQP4+NMOSD, and progressive disability accrual in MS. Early recognition of the specific etiologies of demyelinating myelitis and initiation of the appropriate treatment is crucial to improve outcome. In this review article we summarize and compare the clinical and imaging features of spinal cord involvement in these three demyelinating disorders, both during the acute phase and over time, and outline the current knowledge on the expected patterns of disability accrual and outcomes. We also discuss the potential implications of these observations for patient management and counseling.

Objectives The aims of this study were to present a deep learning approach for the automated classification of multiple sclerosis and its mimics and compare model performance with that of 2 expert neuroradiologists. Materials and Methods A total of 268 T2-weighted and T1-weighted brain magnetic resonance imagin scans were retrospectively collected from patients with migraine (n = 56), multiple sclerosis (n = 70), neuromyelitis optica spectrum disorders (n = 91), and central nervous system vasculitis (n = 51). The neural network architecture, trained on 178 scans, was based on a cascade of 4 three-dimensional convolutional layers, followed by a fully dense layer after feature extraction. The ability of the final algorithm to correctly classify the diseases in an independent test set of 90 scans was compared with that of the neuroradiologists. Results The interrater agreement was 84.9% (Cohen κ = 0.78, P < 0.001). In the test set, deep learning and expert raters reached the highest diagnostic accuracy in multiple sclerosis (98.8% vs 72.8%, P < 0.001, for rater 1; and 81.8%, P < 0.001, for rater 2) and the lowest in neuromyelitis optica spectrum disorders (88.6% vs 4.4%, P < 0.001, for both raters), whereas they achieved intermediate values for migraine (92.2% vs 53%, P = 0.03, for rater 1; and 64.8%, P = 0.01, for rater 2) and vasculitis (92.1% vs 54.6%, P = 0.3, for rater 1; and 45.5%, P = 0.2, for rater 2). The overall performance of the automated method exceeded that of expert raters, with the worst misdiagnosis when discriminating between neuromyelitis optica spectrum disorders and vasculitis or migraine. Conclusions A neural network performed better than expert raters in terms of accuracy in classifying white matter disorders from magnetic resonance imaging and may help in their diagnostic work-up.