Figure 4.

Methodology for group-level analysis: (A) vectorized form of rs-FC matrix for each participant aggregated for T4, i.e., pre-therapy and T6, i.e., post-therapy time points. Each group had 20 participants with 27,730-dimesional features; (B) outliers (marked in yellow) at pre- and post-therapy were identified using MAD approach; (C) reduced rs-FC matrix after cumulative outliers were removed, i.e., each stage consisted of 20 participants and 17,614 features; (D) 679 features that were significantly different between pre- and post-therapy stages as identified by a paired t-test were retained and data across the two stages were combined together for a feature transformation step; (E) feature transformation using PCA was performed that resulted in data with 40 participants and 39 low-dimensional principal components features. Of them 25 features accounted for more than 85% variance and were used as final features for classification; (F) the selected features were fed to the binary SVM classifier that labels each test participant to either pre-therapy or post-therapy stage using LOOCV.