Fig. 4: SFC variability represents a composite dimension of the emotion domain.
a The amount of covariance accounted for by each latent component. After conducting permutation testing with FDR correction, only the first latent component remained statistically significant (n = 5000, pFDR = 5.60 × 10−3, one-sided), which explained 19.42% of SFC variability-behavior covariance. b Significant association between behavioral and SFC variability composite scores in the first latent component (Pearson’s r(989) = 0.14, two-sided, p = 1.61 × 10−5). c Associations between behavioral composite scores and the corresponding behavioral traits. Error bars denote bootstrapped standard deviation with 1000 bootstrap estimations (n = 1000). The correlation coefficients were divided by their estimated standard deviation to calculate the Z-scores, which were further transformed into p-values and adjusted for multiple comparisons using the FDR correction (pFDR < 0.05). d Significant associations between SFC variability composite scores and the corresponding SFC variability distribution. The correlation coefficients were divided by their estimated standard deviation to calculate the Z-scores, which were further transformed into p-values and adjusted for multiple comparisons using the Bonferroni correction (p < 0.001). e Associations between SFC variability composite scores and the SFC variability distribution that averaged within Yeo 7 functional subnetworks. Source data are provided as a Source Data file.
