# Musicians’ brains at rest: multilayer network analysis of magnetoencephalography data

## TL;DR

This study shows that musicians' brains have distinct network patterns at rest, especially in specific frequency bands, compared to non-musicians.

## Contribution

The study introduces a multilayer network analysis method to reveal resting-state brain differences in musicians.

## Key findings

- Musicians show a distinct modular organization in visuo-motor and fronto-temporal areas.
- Differences between musicians and non-musicians are most prominent in theta, alpha1, and beta1 frequency bands.
- Multilayer analysis provides more insights than single-layer methods in resting-state brain data.

## Abstract

The ability to proficiently play a musical instrument requires a fine-grained synchronization between several sensorimotor and cognitive brain regions. Previous studies have demonstrated that the brain undergoes functional changes with musical training, identifiable also in resting-state data. These studies analyzed functional MRI or electrophysiological frequency-specific brain networks in isolation. While the analysis of such “mono-layer” networks has proven useful, it fails to capture the complexities of multiple interacting networks. To this end, we applied a multilayer network framework for analyzing publicly available data (Open MEG Archive) obtained with magnetoencephalography. We investigated resting-state differences between participants with musical training (n = 31) and those without (n = 31). While single-layer analysis did not demonstrate any group differences, multilayer analysis revealed that musicians show a modular organization that spans visuo-motor and fronto-temporal areas, known to be involved in musical performance execution, which is significantly different from non-musicians. Differences between the two groups are primarily observed in the theta (6.5 to 8 Hz), alpha1 (8.5 to 10 Hz), and beta1 (12.5 to 16 Hz) frequency bands. We demonstrate that the multilayer method provides additional information that single-layer analysis cannot. Overall, the multilayer network method provides a unique opportunity to explore the pan-spectral nature of oscillatory networks, with studies of brain plasticity as a potential future application.