Emergent Spatiotemporal Dynamics in Large-Scale Brain Networks with Next Generation Neural Mass Models

TL;DR

This study uses advanced neural mass models to analyze large-scale brain networks, revealing complex spatiotemporal patterns and the importance of anatomical connectivity in generating diverse brain dynamics.

Contribution

It introduces a next-generation neural mass model framework that captures richer dynamics and elucidates the role of anatomical connectivity in large-scale brain activity.

Findings

Next-generation neural mass models expand the dynamical repertoire.

Anatomical connectivity influences cross-frequency coupling.

Models capture gamma oscillations modulated by slower rhythms.

Abstract

Understanding the dynamics of large-scale brain models remains a central challenge due to the inherent complexity of these systems. In this work, we explore the emergence of complex spatiotemporal patterns in a large scale-brain model composed of 90 interconnected brain regions coupled through empirically derived anatomical connectivity. An important aspect of our formulation is that the local dynamics of each brain region are described by a next-generation neural mass model, which explicitly captures the macroscopic gamma activity of coupled excitatory and inhibitory neural populations (PING mechanism). We first identify the system's homogeneous states-both resting and oscillatory-and analyze their stability under uniform perturbations. Then, we determine the stability against non-uniform perturbations by obtaining dispersion relations for the perturbation growth rate. This analysis enables us to link unstable directions of the homogeneous solutions to the emergence of rich spatiotemporal patterns, that we characterize by means of Lyapunov exponents and frequency spectrum analysis. Our results show that, compared to previous studies with classical neural mass models, next-generation neural mass models provide a broader dynamical repertoire, both within homogeneous states and in the heterogeneous regime. Additionally, we identify a key role for anatomical connectivity in cross-frequency coupling, allowing for the emergence of gamma oscillations with amplitude modulated by slower rhythms. These findings suggest that such models are not only more biophysically grounded but also particularly well-suited to capture the full complexity of large-scale brain dynamics. Overall, our study advances the analytical understanding of emerging spatiotemporal patterns in whole-brain models.