kuramoto-brain-network

Brain network phase synchronization is analyzed by extracting phase from fMRI signals using the Hilbert transform, then estimating coupling strength from phase differences to reveal synchronization patterns.

Modeling phase dynamics with Kuramoto oscillators provides a framework to simulate brain network synchronization, allowing you to compute phase coherence and detect functional communities via spectral clustering.

Yes, you can compare synchronization patterns under oxytocin versus placebo conditions by running end-to-end workflows that estimate coupling strength and analyze functional community structure across different cognitive science experiments.

Yes, you need Python libraries including numpy, scipy, networkx, and scikit-learn to perform phase extraction, coupling estimation, and spectral clustering for functional community detection in brain networks.

Spectral clustering identifies functional communities by grouping brain regions based on phase coherence and coupling strength metrics, revealing the underlying synchronization structure of the brain network.

Kuramoto oscillators simplify complex brain network phase dynamics into phase synchronization models, focusing on coupling estimation and community analysis rather than detailed neurophysiological simulations of individual neurons.