A hierarchical network organization helps to retain comparable oscillation patterns in rats and human-sized brains

TL;DR

This study demonstrates that hierarchical modular organization of large-scale neuronal networks supports multiple neural oscillation patterns similar across rat and human brains, highlighting a scalable structural basis for brain rhythms.

Contribution

The paper reveals how hierarchical network topology influences neural oscillations, providing a scalable structural framework for brain rhythms across species.

Findings

Hierarchical networks support species-specific spectral patterns.

Multiple peak frequencies with non-integer ratios occur in models.

Longer connections are balanced by fewer long-distance links.

Abstract

Activity in coupled systems is often oscillatory, for example, the firing pattern of neuronal populations. Whereas these oscillations have been studied predominantly in local circuits, here we show how the topology of large-scale networks, leading to large feedback loops, influences oscillations in the resting state. We find that the hierarchical modular organization of neuronal networks supports distinct spectral patterns of neural rhythms similar to those observed experimentally in different species such as rat and human. For individual neurons, multiple peak frequencies with non-integer ratios between subsequent peaks occurred. These ratios occurred both for models with the spatial size of the rat as well as for the human brain. We argue that the potential influence of longer connections, and thus longer delays, are balanced by a reduced number of long-distance connections in larger brain networks. In conclusion, we show that a hierarchical neuronal network provides a scalable backbone for multiple brain rhythms. This structural backbone could complement well-studied regional and cellular mechanisms for the generation or prevention of rhythms.