Cognition emerges from phase dynamics of intrinsic coordination

Abstract

The brain generates diverse cognitive states while maintaining a stable functional architecture, a duality that remains difficult to reconcile. Prevailing views assume that flexible cognition necessitates correspondingly flexible architecture with different tasks demanding distinct reconfigurations, leaving the coexistence of stability and flexibility unexplained. Here we introduce the intrinsic network flow (INF) framework as a complementary view. This framework is built on temporally coordinated signal flows across brain networks that constitute a universal scaffold, stable across diverse cognitive states and common across individuals. We show that a wide range of task-evoked activation patterns can be reconstructed by modulating only the temporal phase alignment of these flows, whose fixed structure determines functional connectivity topology, gradients, and large-scale networks, thereby preserving these properties across task states and reconciling flexibility with stability. This situates resting-state and task-state dynamics within a unified framework and suggests a generative relationship from flow-like dynamics to the full landscape of resting-state and task-state phenomena. Crucially, phase information achieved 89.9% accuracy in classifying 23 cognitive tasks, outperforming amplitude- or activation-based markers. These findings reframe task-evoked activation and deactivation as interference among concurrent flows. Together, our work indicates that a key variable for cognition is when intrinsic dynamics align in time, not where or how much the brain activates.