Skip to main content
. 2018 Aug 8;13(8):851–862. doi: 10.1093/scan/nsy059

Fig. 3.

Fig. 3

Association between temporal metrics derived from subjects’ state vector and behavior score using different analysis parameters in dataset 1. (A) Heat map depicting the negatively correlated brain state when the window length was set as 20 s (i.e. state 1) with ICA components representing the five networks. Scatter plot depicting the negative association between the SWB score and the fraction of time spent in state 1 when the window length was set as 20 s. (B) Heat map depicting the negatively correlated brain state when the number of clusters was set as 5 (i.e. state 3) with ICA components representing the five networks. Scatter plot depicting the negative association between the SWB score and two temporal metrics (the fraction of time spent in state 3 and the mean dwell time in state 3) when the number of clusters was set as 5. (C) Heat map depicting the negatively correlated brain state when the number of clusters was set as 6 (i.e. state 4) with ICA components representing the five networks. Scatter plot depicting the negative association between the SWB score and two temporal metrics (the fraction of time spent in state 4 and the mean dwell time of state 4) when the number of clusters was set as 6. (D) Heat map depicting the negative correlation between the brain state when the window length was set as 44 s (i.e. state 3) and the ICA components representing the five networks. Scatter plot depicting the negative association between the SWB score and two temporal metrics (the fraction of time spent in state 3 and the mean dwell time in state 3) and positive correlation between the SWB score and the number of transitions when the window length was set as 44 s. The color bar represents the z value of FNC. The significance level for correction was set at P < 0.05. Multiple comparisons were performed using the FDR. aDMN, pDMN, SN, lFPN and rFPN.