Fig. 1.

Schematic representation of group-level independent component analysis (ICA) and static and dynamic resting-state functional connectivity (rsFC) estimation. a) Preprocessed and de-noised resting-state datasets (N = 55) were submitted to a group-level ICA to identify 12 spatially-independent and temporally synchronous components (networks). Five of these components were identified as neurocognitive networks of interest (i.e., DMN, SEN, CEN; see Fig. 2). Components were then back reconstructed into individual participant space to produce single-participant network time courses and spatial maps; b) Conventional static rsFC was computed as Fisher's Z-transformed Pearson correlations between network components of interest, averaged across the entire resting-state scan. Dynamic rsFC was computed using a sliding windows analysis and k-means clustering of Fisher's Z-transformed Pearson correlations. Here, five dynamic rsFC states were identified that re-occurred across the scan and across participants.