Interplay between resource dynamics, network structure and spatial propagation of transient explosive synchronization in an adaptively coupled mouse brain network model

Abstract

Generalized epileptic attacks, which exhibit widespread disruption of brain activity, are characterized by recurrent, spontaneous and synchronized bursts of neural activity that self-initiate and self-terminate through critical transitions. Here we utilize the general framework of explosive synchronization (ES) from complex systems science to study the role of network structure and resource dynamics in the generation and propagation of seizures. We show that a combination of resource constraint and adaptive coupling in a Kuramoto network oscillator model can reliably generate seizure-like synchronization activity across different network topologies, including a biologically derived mesoscale mouse brain network. The model, coupled with a novel algorithm for tracking seizure propagation, provides mechanistic insight into the dynamics of transition to the synchronized state and its dependence on resources; and identifies key brain areas that may be involved in the initiation and spatial propagation of the seizure. The model, though minimal, efficiently recapitulates several experimental and theoretical predictions from more complex models, and makes novel experimentally testable predictions. Significance statement / Author SummaryUnderstanding seizure dynamics at the whole-brain level is crucial for controlling abnormal hypersynchronous activity. Currently, complete brain coverage recordings are lacking in both patients and animal models. We employ network science tools to investigate epileptic seizure-like synchronization in a mouse whole brain network, leveraging network structure and supported dynamics as the basis for seizure evolution. Our results align with experimental findings, suggesting that seizure activity initiates in the cortico-thalamic circuit. Importantly, our novel analysis identifies key nodes, primarily in the cortex, driving this hypersynchronous activity. Our findings highlight network structures role in shaping seizure dynamics and the techniques developed here could enhance our control of generalized seizures when combined with patient-specific data.