Extended Data Fig. 9: Replication with functional networks (inverse covariance) from functional MRI.
(a) Transition cost (energy) between each pair of 123 cognitive topographies (‘states’) from NeuroSynth, using functional connectomes obtained as inverse covariance between fMRI BOLD timeseries from n = 100 HCP participants. Rows indicate source states, columns indicate target states. (b) Transition cost between each pair of a subset of 25 out of 123 NeuroSynth states, shown for visualisation purposes. Matrices are sorted by increasing cost across both rows and columns. (c) Variability (standard deviation) of transition energy is greater along the column dimension (target states) than along the row dimension (source states), for each combination of n = 123 pairs of cognitive topographies. t(244) = 37.06, p < 0.001, Cohen’s d = 4.73. Box-plots: center line, median; box limits, upper and lower quartiles; whiskers, 1.5× interquartile range; ***, p < 0.001 from independent-samples t-tests (two-sided). (d) Overall transition energy (averaged across all transitions between possible pairs of 123 cognitive topographies) for each of n = 100 HCP participants, based on the individual inverse covariance network, and the corresponding degree- and cost-preserving network null models. Empirical vs geometry-preserving: t(99) = 53.15, p < 0.001, Cohen’s d = 6.55. Empirical vs degree-preserving: t(99) = 89.01, p < 0.001, Cohen’s d = 12.83. Box-plots: center line, median; box limits, upper and lower quartiles; whiskers, 1.5× interquartile range; ***, p < 0.001 from paired-samples t-tests (two-sided). Source data are provided as a Source Data file.
