Multiple coexisting pathways to synchronization shape seizure dynamics in a mesoscale mouse brain model

Abstract

The computational study of epileptic seizure dynamics has primarily focused on the identification of seizure onset zones and propagation pathways. Here, we present a network dynamical model implemented on the empirically measured mesoscale mouse brain network that reveals previously unresolved organizational principles underlying seizure propagation. Rather than the conventional assumption of a single dominant pathway to synchronization, the model reveals multiple competing pathways to synchronization with distinct dynamical properties including transition propensity, recruitment speed, spatial coverage, synchronization stability and transition kinetics. Consequently, node ablation does not uniformly suppress synchronization across pathways, but instead selectively alters pathway occupancy, producing non-trivial alterations to seizure dynamics with potential implications for resection and network-targeted intervention strategies. Biologically, the olfactory and limbic sub-networks emerge as key mesoscale regulators of synchronization dynamics, with olfactory recruitment preferentially constraining global synchronization while limbic-driven pathways preferentially support seizure generalization. More broadly, these findings extend transient explosive synchronization theory by demonstrating that synchronization in biologically constrained networks may emerge through competing mesoscale recruitment programs rather than a single transition process. Together, these findings introduce a new conceptual framework for seizure propagation, suggesting that pathological synchronization emerges not through a single dominant route, but through competing mesoscale dynamical pathways whose accessibility depends on both network architecture and ongoing network state. Significance statementEpileptic seizure propagation is conventionally understood as progressing through a dominant pathway that recruits increasingly larger portions of the brain into pathological synchronization. Using a network dynamical model implemented on the empirical mesoscale mouse connectome, we show that seizure-like synchronization instead emerges through multiple competing pathways with distinct spatial and temporal characteristics. These pathways differ in their propensity for generalization, synchronization stability and sensitivity to node perturbation, such that network interventions selectively reshape pathway accessibility rather than uniformly suppressing seizure dynamics. Our findings introduce a new framework for understanding seizure propagation, identify mesoscale mechanisms linking network architecture to synchronization dynamics and suggest that competing synchronization pathways may represent an important organizing principle in complex brain networks. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=97 SRC="FIGDIR/small/730931v1_ufig1.gif" ALT="Figure 1"> View larger version (36K): org.highwire.dtl.DTLVardef@7c4076org.highwire.dtl.DTLVardef@16c0961org.highwire.dtl.DTLVardef@1dbd8a0org.highwire.dtl.DTLVardef@6b06de_HPS_FORMAT_FIGEXP M_FIG C_FIG