Using the GICA component maps as spatial predictors for each participant's 4-dimensional data, a linear regression generated a time course for each component. As a measure of cross-network coupling, we calculated correlation coefficients (CC) between component time courses derived from the SN, ECN, and the DMN (CCSN,ECN and CCSN,DMN). To assess the actions of the SN on DMN and ECN in the smoking and abstinence states with a single value, we defined a composite network association index as m = zSN,ECN − zSN,DMN = f(CCSN,ECN) − f(CCSN,DMN), where f(CC)=12ln(1+CC1−CC) and m refers to the RAI. The negative sign in front of the SNDMN correlation (zSN,DMN/CCSN,DMN) inverts the negative SN DMN correlation so that the SN-ECN and SN-DMN correlation strength is added up rather than cancelled out. Large RAI values are taken to reflect a high degree of synchronization of the SN with the ECN and/or DMN, with positive correlation between the SN and ECN and negative correlation between the SN and DMN.
