Uncovering dynamic human brain phase coherence networks

Abstract Complex cognitive functions rely on coordinated communication between distributed brain regions, yet capturing these interactions as they evolve over time remains challenging. Traditional analyses of functional brain connectivity largely rely on correlations in signal amplitude, which are sensitive to noise and artifacts such as head motion. Here, we introduce a mixture modeling approach that focuses on the phase of brain signals, allowing dynamic patterns of large-scale synchronization in brain phase coherence networks to be studied directly and in their entirety. We lay the mathematical and conceptual groundwork for phase modeling and introduce the complex angular central Gaussian mixture model, providing a principled way to analyze phase-based interactions across the brain. Applied to fMRI data, the model identifies recurring states of brain-wide synchronized activity that reliably distinguish cognitive tasks and generalize across previously unseen individuals, without requiring any task labels during training. These results show that modeling signal phase offers a clean and informative view of brain synchronization dynamics, opening new avenues for studying large-scale neural coordination. Significance statement Understanding how the human brain coordinates activity across distant regions is central to explaining cognition and behavior. Most existing approaches study these interactions by tracking changes in signal strength, which can be strongly affected by non-neural artifacts. Here, we focus instead on the phase relationships between brain signals: How brain regions synchronize their oscillations forming dynamic phase coherence networks. We introduce a flexible and principled mixture modeling framework to capture these patterns directly and reveal recurring states of brain-wide synchronized activity consistent across individuals. This approach uncovers meaningful differences between diverse cognitive tasks, without requiring task labels during training. By emphasizing signal phase rather than amplitude, our method offers a complementary robust and interpretable way to study dynamic brain synchronization patterns.