Node Degree Volatility for Seizure Onset Zone Localization

Drug-resistant epilepsy, in which seizures cannot be controlled with medication, often requires invasive treatment interventions such as surgical resection, laser ablation, or neuromodulation targeting the seizure onset zone (SOZ). However, precise biomarkers for localizing the SOZ remain lacking, limiting treatment success. Here, we introduce a novel biomarker: node degree volatility, defined as the rate of change in a node’s functional connectivity over time. We analyze 82 seizures from 80 patients, reconstructing Granger-causal networks from intracranial EEG (iEEG) recordings, where nodes represent iEEG channels and edges denote directed causal influences between them. Nodes with the highest degree volatility at seizure onset predict the clinically identified SOZ in 75% of successful surgical cases, using a stringent 5% top-quantile threshold to designate SOZ nodes. On the analyzed dataset, this biomarker outperforms established iEEG-based methods, including neural fragility and conventional network metrics such as node degree and betweenness centrality, highlighting the value of capturing local temporal dynamics in functional brain networks. Unlike many machine learning methods that can be computationally intensive and often lack transparency, our approach is direct and interpretable, offering a clear and clinically actionable path to SOZ localization. Beyond epilepsy, node degree volatility may provide a framework for identifying dynamically critical nodes in evolving biological, social, and engineering networks.