Statistical analyses were performed by using SPSS software (V.22; SPSS, Chicago, Illinois, USA), MATLAB Statistics and the Machine Learning Toolbox (MATLAB 2019). Categorical data are presented in percentages. Continuous data are presented by the mean and SD, and ranked data by the median and IQR. Categorical data between groups were analysed by the χ² test. Continuous and ranked data were analysed by one-way analysis of variance or Kruskal-Wallis tests followed by posthoc comparisons with Bonferroni correction. For MRI quantitative measures (except for the lesion volumes), total intracranial volume (TIV, only used for the volume measures), gender, age and scanner type were included as covariates. Partial correlation was performed to investigate the relationship between the MRI measures and the clinical variables, including the disease duration, EDSS and number of relapses with covariate adjustment including TIV (only used for volume measures), gender, age and scanner type. Logistic regression was conducted to identify factors distinguishing MOGAD from AQP4+ NMOSD and MS by using MRI measures and clinical variables (disease duration, EDSS and the number of relapses) with TIV, gender, age and scanner type as confounding variables. The classification accuracy, sensitivity, specificity and area under the curve (AUC) were calculated.

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