Smoothness Harmonic: A Graph-Based Approach to Reveal Spatiotemporal Patterns of Cortical Dynamics in fMRI Data

Despite fMRI data being interpreted as time-varying graphs in graph analysis, there has been more emphasis on learning sophisticated node embeddings and complex structures rather than providing a macroscopic description of cortical dynamics. In this paper, I introduce the notion smoothness harmonics to capture slowly varying dynamics graph-based form spatiotemporal patterns. These are rooted eigendecomposition Laplacians, which reveal how low-frequency-dominated signals propagate across cortex through time. We showcase their usage real dataset differentiate children adults while also demonstrating empirical merit over static functional connectomes inter-subject between-group classification analyses.