Fig. 5. The static Gaussian null model can explain the spatial patterns of BOLD activity and the cofluctuation patterns that characterise high-amplitude frames.

a The spatial mode underpinning high-amplitude cofluctuations is captured by the leading eigenvector of the nFC matrix. The 200 brain regions were partitioned into the same 16 networks used in ref. ¹⁶ (Fig. 2e) to facilitate a visual comparison. In addition to a scatter plot of the data, the violin plots show the probability densities and the box plots indicate the quartiles, with the maximum whisker length specified as 1.5 times the interquartile range. The sample sizes of the box plots are the sizes of the 16 networks, that is, n = [16, 15, 6, 14, 17, 6, 12, 10, 14, 16, 10, 19, 15, 6, 12, 12]. b Frames corresponding to large RSS values exhibit high similarity to the leading nFC eigenvectors. The similarity is measured as the Pearson correlation between the instantaneous FC estimate from a single frame and each of the estimates from the four leading nFC eigenvectors. c Higher principal component (PC1) coefficients are associated with large RSS events. In addition to a scatter plot of the data, the box plots indicate the quartiles and the whisker length is specified as 1.5 times the interquartile range (sample size n = 6000). Compare this figure with the results published in ref. ¹⁶ (Fig. 2c).