Fig. 5.

Example analysis of simulated changes in brain networks. Grey connections in the first column indicate nodes with a positive correlation (between 0.3 and 0.7) in the initial state, and red arrows point into nodes to indicate that additional signal was injected in the second state, producing an increase in variance of 20%. Here, a common stochastic process was added to nodes 1–3, uncorrelated with existing signal. The remaining columns represent connections that were detected as showing significant changes in correlation across states, FDR corrected (α = 0.2). Upper row Each circle plot represents connections determined to fall within a particular class of ASC change. Red connections indicate connections showing increases in correlation after the injection of signal, blue decreases. Node colors similarly represent change in variance. Note that, for clarity, connections falling within subclasses are not included in the more general ASC classes. The final (here, empty) column shows connections that cannot be explained by additive changes. Lower row The lower plots represent the distribution of absolute correlation changes for the shown connections. Note that it is not intended to be possible to identify specific connections. White dot - correlation in initial state. Blue/red dot - correlation in second state. Colour fill - range of correlation in second state that could be explained by the additive signal change class. The decreases in correlation between nodes are identified as potentially indicative of increases in uncorrelated signal, but could also be explained by common or other additive changes. The increased correlations between the three nodes receiving the introduced signal are identified as potentially indicative of increases in a common signal (but not a change in uncorrelated signal).