Hierarchical whole-brain modeling of critical synchronization dynamics in the human brain

Abstract

The brain operates at the critical transition between order and disorder which supports optimal information processing. Whole-brain computational modeling is a powerful tool for uncovering the system-level mechanisms behind large-scale brain activity in both healthy and pathological states. However, most previous approaches have focused on either functional connectivity or criticality, making it difficult to capture both aspects simultaneously. Here, we introduce a method based on a Hierarchical Kuramoto model that incorporates two levels of hierarchy. In our model, each node contains a large number of coupled oscillators, which allows us to examine both local synchronization and long-distance interactions between brain regions. The model produces critical-like dynamics marked by emergent long-range temporal correlations (LRTCs) and both interareal phase synchronization and amplitude cross-correlations (CC) during the transition from asynchronous to synchronous states. Notably, structure–function coupling shows distinct patterns: correlations with structural connectivity peak at criticality for LRTCs and CC, but decay for local and interareal phase synchronization. Comparisons with human resting-state magnetoencephalography (MEG) data reveal that the model’s behavior most closely resembles MEG phase synchronization and multipeak power spectra on the subcritical side of an extended critical regime, supporting the hypothesis that the human brain operates in this state.