Figure 1. G9a and P65 mice differ from controls and differ from each other.

A, B. Stationary analysis is shown using CC values of 6 datasets (control values at baseline, CTL-BSL, or under morphine, CTL-MOR; G9a values at baseline, G9a-BSL, or under morphine, G9a-MOR; P65 values at baseline, P65-BSL, or under morphine, P65-MOR). A. Hierarchical clustering for 7140 functional connections between 120 seeds covering the entire brain, with an enlarged view of the dendrogram tree (up right). B. Hierarchical clustering for 253 functional connections between 23 selected seeds, with an enlarged view of the dendrogram tree (bottom left). 1, 2 and 3 shows subdivisions from larger to smaller clusters. C, D. Analysis of dynamic FC patterns. A sliding window approach was used to assess FC modifications along image acquisition (915 time windows in total, see Suppl Info), and shows the mean temporal evolution of strength (C) and centrality (D) for each group. For each time window, the value of strength or centrality is represented at the middle timepoint of the time window. Left panels: mean values for the 23 seeds are superimposed and bold indicates time windows where a significant difference is found between G9a or P65 and CTL values (t-test, p<0.05). X-axis, acquisition time in min; Y-axis, strength values normalized to their baseline values for each seed. Right panels: quantification. Percent number of time windows where G9a or P65 values significantly differ from CTL over total number time windows, and averaged over the 23 seeds. Values are shown for strength (C) and centrality (D) for each G9a and P65 group either under baseline (G9s-BSL, P65-BSL) or under morphine (G9a-MOR, P65-MOR). The % number of time windows where strength or centrality are different from controls, calculated for each ROI, are detailed in (Suppl Table S3).