The final classification accuracy was assessed for statistical significance compared to the chance-level accuracy, and adjusted for multiple comparisons using a permutation test with 2000 iterations. First, the MVPA procedure was executed in a consistent manner with the previously described method, utilizing a linear SVM and employing the same feature selection process based on F scores. Specifically, 10% of ReHo features were selected at intervals of 10% from 10% to 100%. However, during every cross-validation iteration, the class labels were randomly permuted to produce ten classification accuracies; subsequently, the highest accuracy was selected. Second, the initial step was iterated 2000× to produce 2000 maximum accuracies for all permutation steps. From these accuracies, a null distribution of chance-level accuracies was established. Third, the P-value for each accuracy was calculated by comparing the ten classification accuracies obtained from actual labels with the null distribution. The resultant P-values were adjusted for FWE P < 0.05.
