# Macroscopic and Microscopic Spectral Properties of Multilayer Brain Networks during Local and Global Synchronization

## TL;DR

This paper presents a computationally efficient method to infer microscopic interactions in multilayer brain networks from macroscopic signals, demonstrated on rat EEG data during various brain states.

## Contribution

It introduces a novel technique linking macroscopic EEG signals to microscopic neural interactions, validated on a neurophysiological model and real rat data.

## Key findings

- Synchronization increases lead to spectral defragmentation of macroscopic signals.
- The method can estimate interaction degrees between brain regions during different activities.
- It provides insights into regional participation in consciousness levels.

## Abstract

We introduce a practical and computationally not demanding technique for inferring interactions at various microscopic levels between the units of a network from the measurements and the processing of macroscopic signals. Starting from a network model of Kuramoto phase oscillators which evolve adaptively according to homophilic and homeostatic adaptive principles, we give evidence that the increase of synchronization within groups of nodes (and the corresponding formation of synchronous clusters) causes also the defragmentation of the wavelet energy spectrum of the macroscopic signal. Our methodology is then applied for getting a glance to the microscopic interactions occurring in a neurophysiological system, namely, in the thalamo-cortical neural network of an epileptic brain of a rat, where the group electrical activity is registered by means of multichannel EEG. We demonstrate that it is possible to infer the degree of interaction between the interconnected regions of the brain during different types of brain activities, and to estimate the regions' participation in the generation of the different levels of consciousness.