Abstract
AbstractThe critical brain hypothesis suggests that efficient neural computation can be realized through dynamics of the brain characterized by scale-free avalanche activities. However, the relation between human cognitive performance and the avalanche criticality in large-scale brain networks remains unclear. In this study, we analyzed the mean synchronization and synchronization entropy of blood oxygenation level signals from resting-state fMRI. We found that the scale-free avalanche activity was associated with intermediate synchronization and maximal synchronization entropy. The complexity of functional connectivity, as well as structure-function coupling, is maximized by criticality, which is consistent with theoretical predictions. We observed order-disorder phase transitions in resting-state brain dynamics and found that there were longer times spent in the subcritical regime. These results support the hypothesis that large-scale brain networks lie in the vicinity of a critical point. Finally, we showed evidence that the neural dynamics of human participants with higher fluid intelligence and working memory scores are closer to criticality. We identified brain regions whose critical dynamics showed significant positive correlations with fluid intelligence performance, and found these regions were located in the prefrontal cortex and inferior parietal cortex, which are believed to be important nodes of brain networks underlying human intelligence. Our results reveal 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, ranging from subcriticality to supercriticality.
Publisher
Cold Spring Harbor Laboratory