The critical brain hypothesis suggests that efficient neural computation could be realized by critical brain dynamics hallmarked by scale-free avalanche activity. However, its further application requires not only accurately identifying the critical point but also depicting the phase transition in brains so that different cognitive states could be mapped on a spectrum. In this work, we mapped individuals’ brains onto an inverted-U curve between the mean synchronization and synchronization entropy of blood oxygenation level-dependent (BOLD) signals from resting-state fMRI scans. We found that the critical point lies at the tipping point (i.e., moderate synchrony and maximal variability in synchrony) of this curve, which is consistent with previous findings. We then verified that the complexity of functional connectivity, as well as the similarity between structural and functional networks, was maximized near the critical point, whereas reduction in complexity and structure-function decoupling were found both in the sub- and supercritical regimes. We then observed phase transitions in resting-state brain dynamics and found that the brains showed longer dwell times in the subcritical regime. These results provided strong evidence that the large-scale brain networks were hovering around the critical point. Finally, we found that critical dynamics were associated with high scores in fluid intelligence and working memory tests but not crystallized intelligence scores. Our results revealed the role that avalanche criticality plays in cognitive performance and provide a simple method to identify the critical point and map cortical states on a spectrum of neural dynamics, with a critical point in the domain.
bioRxiv Subject Collection: Neuroscience