Title : Network-based biomarkers and state transitions in Epilepsy: Clinical and translational Implications
Abstract:
Epilepsy is increasingly recognized as a disorder of large-scale brain network dysfunction rather than a purely focal pathology. Advances in Network Neuroscience have revealed reproducible alterations in functional connectivity, graph topology, and network dynamics across epileptic syndromes.
This study presents a translational framework linking network alterations to clinically observable seizure phases. We propose a three-state model consisting of a preictal phase of network instability, an ictal phase of hypersynchronization, and a postictal phase of network suppression and fragmentation. These dynamic transitions are supported by EEG-based findings, including preictal increases in synchronization and postictal suppression patterns.
From a clinical perspective, network-based biomarkers such as functional connectivity measures, graph metrics, and EEG synchronization indices may improve seizure prediction and patient stratification. Furthermore, therapeutic strategies including neuromodulation (e.g., DBS, TMS, VNS) and surgical planning can be reinterpreted as interventions targeting pathological network hubs and restoring normal network dynamics.
By integrating theoretical and clinical insights, this approach provides a practical framework for developing personalized, network-guided treatment strategies in epilepsy.
