Uncovering dynamic human brain phase coherence networks

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 functional MRI 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 avenues for studying large-scale neural coordination.