AUTHOR=Roy Sayantika , Varillas Armelle , Pereira Emily A. , Myers Patrick , Kamali Golnoosh , Gunnarsdottir Kristin M. , Crone Nathan E. , Rouse Adam G. , Cheng Jennifer J. , Kinsman Michael J. , Landazuri Patrick , Uysal Utku , Ulloa Carol M. , Cameron Nathaniel , Inati Sara , Zaghloul Kareem A. , Boerwinkle Varina L. , Wyckoff Sarah , Barot Niravkumar , González-Martínez Jorge , Kang Joon Y. , Sarma Sridevi V. 

TITLE=Eigenvector biomarker for prediction of epileptogenic zones and surgical success from interictal data

JOURNAL=Frontiers in Network Physiology

VOLUME=Volume 5 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/network-physiology/articles/10.3389/fnetp.2025.1565882

DOI=10.3389/fnetp.2025.1565882

ISSN=2674-0109

ABSTRACT=Introduction:More than 50 million people worldwide suffer from epilepsy. Approximately 30% of epileptic patients suffer from medically refractory epilepsy (MRE), which means that over 15 million people must seek extensive treatment. One such treatment involves surgical removal of the epileptogenic zone (EZ) of the brain. However, because there is no clinically validated biomarker of the EZ, surgical success rates vary between 30%–70%. The current standard for EZ localization often requires invasive monitoring of patients for several weeks in the hospital during which intracranial EEG (iEEG) data is captured. This process is time-consuming as the clinical team must wait for seizures and visually interpret the iEEG during these events. Hence, an iEEG biomarker that does not rely on seizure observations is desirable to improve EZ localization and surgical success rates. Recently, the source-sink index (SSI) was proposed as an interictal (between seizure) biomarker of the EZ, which captures regional interactions in the brain and in particular identifies the EZ as regions being inhibited (“sinks”) by neighbors (“sources”) when patients are not seizing. The SSI only requires 5-min snapshots of interictal iEEG recordings. However, one limitation of the SSI is that it is computed heuristically from the parameters of dynamical network models (DNMs).Methods:In this work, we propose a formal method for detecting sink regions from DNMs, which has a strong foundation in linear systems theory. In particular, the steady-state solution of the DNM highlights the sinks and is characterized by the leading eigenvector of the state-transition matrix of the DNM. To test this, we build patient-specific DNMs from interictal iEEG data collected from 65 patients treated across 6 centers. From each DNM, we compute the average leading eigenvectors and evaluate their potential as a biomarker to accurately predict EZ and surgical success.Results:Our findings show the ability of the leading eigenvector to accurately predict EZ (average accuracy 66.81% ± 0.19%) and surgical success (average accuracy 71.9% ± 0.22%) with data from 65 patients across 6 centers from 5 min of data, which we show is comparable with the current method of localizing the EZ over several weeks.Discussion:This eigenvector biomarker has the potential to assist clinicians in localizing the EZ quickly and thus increase surgical success in patients with MRE, resulting in an improvement in patient care and quality of life.