FIG. 1.

Pipeline of the classification model. The whole dataset was split into a training set (80%) and a testing set (20%). Feature selection was performed on the training set (50% were selected randomly every time). To select the most predictive features, we repeated the feature selection process for ten rounds and retained those features with a high average weight (top 70%) among all the rounds. The final SVM model was built based on the selected FNC features. We ran the modeling process for a total number of 100 iterations to obtain a stable SVM model. FNC, functional network connectivity; SVM, support vector machine. Color images are available online.