Graph Frequency Analysis of Brain Signals

TL;DR

This paper introduces graph spectral methods for analyzing brain signals and networks, revealing how different frequency components relate to brain activity and learning processes.

Contribution

It generalizes the concept of frequency to irregular brain graph domains and links spectral properties with functional connectivity and learning stages.

Findings

Brain signals vary across graph frequencies during skill learning.

Graph spectral features correlate with task exposure and familiarity.

Different frequencies show distinct levels of brain signal adaptability.

Abstract

This paper presents methods to analyze functional brain networks and signals from graph spectral perspectives. The notion of frequency and filters traditionally defined for signals supported on regular domains such as discrete time and image grids has been recently generalized to irregular graph domains, and defines brain graph frequencies associated with different levels of spatial smoothness across the brain regions. Brain network frequency also enables the decomposition of brain signals into pieces corresponding to smooth or rapid variations. We relate graph frequency with principal component analysis when the networks of interest denote functional connectivity. The methods are utilized to analyze brain networks and signals as subjects master a simple motor skill. We observe that brain signals corresponding to different graph frequencies exhibit different levels of adaptability throughout learning. Further, we notice a strong association between graph spectral properties of brain networks and the level of exposure to tasks performed, and recognize the most contributing and important frequency signatures at different task familiarity.