Metastable Epileptic Networks: Investigating the State Transition in Synchronization Patterns
Abstract number :
1.184
Submission category :
3. Neurophysiology / 3G. Computational Analysis & Modeling of EEG
Year :
2021
Submission ID :
1826704
Source :
www.aesnet.org
Presentation date :
12/4/2021 12:00:00 PM
Published date :
Nov 22, 2021, 06:55 AM
Authors :
Miaolin Fan, PhD - Massachusetts General Hospital; Sydney Cash - Massachusetts General Hospital
Rationale: The synchronization phenomenon in epileptic networks has been historically a subject of controversy. Previous studies suggested a fractal, heterogeneous synchronization pattern with possible co-existence of opposite dynamics, for which we propose a metastable network model to capture the underlying state transitions.
Methods: We apply a recurrence plot-based technique to intracranial EEG data collected from epileptic patients in order to describe the system’s nonlinear dynamics by a network of evolving metastable states. By characterizing the local and global state transitions within and outside of seizure onset zone, we build a state space model for capturing the state transition in epileptic networks during seizure generation, propagation and termination.
Results: We found different state transition patterns for global and local network dynamics within and outside of seizure onset zones, which exhibits the existence of metastability in epileptic networks.
Conclusions: Metastable network models enable a novel perspective for understanding the heterogeneous nature of synchronization and the complex state transition patterns of epilepsy.
Funding: Please list any funding that was received in support of this abstract.: MGH postdoctoral fellowship.
Neurophysiology