Spreading dynamics on spatially constrained complex brain networks

TL;DR

This paper investigates how the physical structure of the rat brain's cortical surface influences neural activity spread, highlighting the importance of spatial embedding in modeling brain dynamics and epileptic seizures.

Contribution

It introduces a neuroimaging-based network model that accurately reflects cortical geometry, demonstrating its significant impact on activity propagation compared to standard models.

Findings

Cortical geometry affects propagation speed of neural activity.

Standard models may oversimplify dynamics by ignoring spatial structure.

Implications for modeling epileptic seizures are significant.

Abstract

The study of dynamical systems defined on complex networks provides a natural framework with which to investigate myriad features of neural dynamics, and has been widely undertaken. Typically, however, networks employed in theoretical studies bear little relation to the spatial embedding or connectivity of the neural networks that they attempt to replicate. Here, we employ detailed neuroimaging data to define a network whose spatial embedding represents accurately the folded structure of the cortical surface of a rat and investigate the propagation of activity over this network under simple spreading and connectivity rules. By comparison with standard network models with the same coarse statistics, we show that the cortical geometry influences profoundly the speed propagation of activation through the network. Our conclusions are of high relevance to the theoretical modelling of epileptic seizure events, and indicate that such studies which omit physiological network structure risk simplifying the dynamics in a potentially significant way.