Figure 1. Analysis of brain dynamics.

Example sliding window approach for computing dynamic functional network connectivity (dFNC). A) High-model order ICA creates functional parcellation of the brain, resulting in several independent components. B) Subject-specific timecourses are used to compute functional connectivity between pairwise components. Traditional static FNC analysis entails computing correlations across the entire duration of a scan per subject. Dynamic FNC analysis utilizes sliding windows (eg. 45 seconds in duration) to produce multiple correlation matrices for each subject (one per window). C) A concatenated data matrix is then subjected to k-means clustering, and the optimal k is identified using the elbow criterion (k=5 in this example). Each window is assigned to a dynamic state k regardless of subject assignment. Subject-specific medians are then back-reconstructed for each state k before they are averaged together to produce the final k dynamic states. Finally, group differences in dFNC can be computed.