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

Featured Application Benefiting from its convenient derivation without resorting to complex learning algorithms, our smoothness harmonic approach can be deployed in numerous neuroscientific discoveries. This includes differentiating various types of neuropsychiatric disorders by utilizing the extracted smoothness harmonics and exploring the distinctive harmonic features between sleep and awake states of in vivo neuronal recordings. Despite fMRI data being interpreted as time-varying graphs in graph analysis, there has been more emphasis on learning sophisticated node embeddings and complex graph structures rather than providing a macroscopic description of cortical dynamics. In this paper, I introduce the notion of smoothness harmonics to capture the slowly varying cortical dynamics in graph-based fMRI data in the form of spatiotemporal smoothness patterns. These smoothness harmonics are rooted in the eigendecomposition of graph Laplacians, which reveal how low-frequency-dominated fMRI signals propagate across the cortex and through time. We showcase their usage in a real fMRI dataset to differentiate the cortical dynamics of children and adults while also demonstrating their empirical merit over the static functional connectomes in inter-subject and between-group classification analyses.

Automated summary

This study introduces the concept of smoothness harmonics to capture the slowly varying cortical dynamics in graph-based fMRI data, and showcases their application in differentiating the cortical dynamics of children and adults, as well as their empirical merit over static functional connectomes in classification analyses.