Figure 1. Importance of variables in the classifiers differentiating multiple sclerosis (MS), neuromyelitis optica (NMO), and healthy controls (HCs).

(A–C) The importance of each variable to classification inside random-forest algorithm. Importance is a relative measure, and is normalized to sum to 1 for each model, and should be used to compare the importance of variables inside each model (not among models). (D) The accuracy of models with different gray matter measures, including cortical volumes, cortical surface area, cortical thickness, thickness and surface area in combination, and subcortical volumes. The combination of surface area and thickness obtains a higher accuracy than volume. Each model has been trained and tested 1,000 times, after shuffling participants from 2 centers. DGM = deep gray matter.