Figure 1:

Analytical methods used in this study. (a) Principal component analysis (PCA) was performed on the dataset of individual subjects (T × V; the number of time points × number of voxels) along time direction. The reduced matrices (T′ × V) were combined across subjects to produce a concatenated matrix (KT′ × V), which was subjected to a second PCA followed by ICA. Through back-reconstruction, the subject-level independent components and associated time courses were obtained for further analysis. (b) For each subject in one of two paired groups (e.g., SN vs. PWH and baseline (TL1) vs. one month after training (TL2)), the reduced subject-level time courses T × N′ (N′ is the number of resting-state networks; group 1: K₁ subjects, group 2: K₂ subjects) were used to compute the subject-level FNC (N′ × N′). Specifically, Pearson’s correlation coefficient between pair-wise network time courses was calculated and transformed to z-score using Fisher’s z-transformation. To compare the differences of the FNC between two paired groups, for each location in the FNC matrix, a t-statistic was calculated by using the z-scores obtained above between the two groups, which resulted in a t-statistic matrix (N′ × N′). (c) Through thresholding and binarization, the subject-level FNC was transformed into an adjacency matrix (N′ × N′), from which a graph was created with nodes and edges.