Harnessing functional segregation across brain rhythms as a means to detect EEG oscillatory multiplexing during music listening

TL;DR

This study investigates how different brain rhythms reorganize during music listening using EEG, revealing multiplexed network patterns and rhythm interactions that enhance understanding of neural processing of music.

Contribution

It introduces a novel graph-based methodology to analyze functional segregation and multiplexing of brain rhythms during music listening, including algorithms for pattern mining and node switching detection.

Findings

Identified rhythm-specific modular patterns during music listening.

Revealed dependence between delta and betaH network structures.

Demonstrated multiplexed reorganization of brain networks in response to music.

Abstract

Music, being a multifaceted stimulus evolving at multiple timescales, modulates brain function in a manifold way that encompasses not only the distinct stages of auditory perception but also higher cognitive processes like memory and appraisal. Network theory is apparently a promising approach to describe the functional reorganization of brain oscillatory dynamics during music listening. However, the music induced changes have so far been examined within the functional boundaries of isolated brain rhythms. Using naturalistic music, we detected the functional segregation patterns associated with different cortical rhythms, as these were reflected in the surface EEG measurements. The emerged structure was compared across frequency bands to quantify the interplay among rhythms. It was also contrasted against the structure from the rest and noise listening conditions to reveal the specific components stemming from music listening. Our methodology includes an efficient graph-partitioning algorithm, which is further utilized for mining prototypical modular patterns, and a novel algorithmic procedure for identifying switching nodes that consistently change module during music listening. Our results suggest the multiplex character of the music-induced functional reorganization and particularly indicate the dependence between the networks reconstructed from the {\delta} and {\beta}H rhythms. This dependence is further justified within the framework of nested neural oscillations and fits perfectly within the context of recently introduced cortical entrainment to music. Considering its computational efficiency, and in conjunction with the flexibility of in situ electroencephalography, it may lead to novel assistive tools for real-life applications.