Network excitability of stimulation-induced spectral responses helps localize the seizure onset zone.

Hays, Mark A; Daraie, Amir H; Smith, Rachel J; Sarma, Sridevi V; Crone, Nathan E; Kang, Joon Y

Hays, Mark A; Daraie, Amir H; Smith, Rachel J; Sarma, Sridevi V; Crone, Nathan E; Kang, Joon Y.

Afiliação

Hays MA; Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA. Electronic address: mhays6@jhmi.edu.
Daraie AH; Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.
Smith RJ; Department of Electrical and Computer Engineering, University of Alabama at Birmingham, Birmingham, AL, USA; Department of Neuroengineering, University of Alabama at Birmingham, Birmingham, AL, USA.
Sarma SV; Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA; Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, USA.
Crone NE; Department of Neurology, Johns Hopkins University, Baltimore, MD, USA.
Kang JY; Department of Neurology, Johns Hopkins University, Baltimore, MD, USA.

Clin Neurophysiol ; 166: 43-55, 2024 Jul 24.

Article em En | MEDLINE | ID: mdl-39096821

ABSTRACT

ABSTRACT

OBJECTIVE:

While evoked potentials elicited by single pulse electrical stimulation (SPES) may assist seizure onset zone (SOZ) localization during intracranial EEG (iEEG) monitoring, induced high frequency activity has also shown promising utility. We aimed to predict SOZ sites using induced cortico-cortical spectral responses (CCSRs) as an index of excitability within epileptogenic networks.

METHODS:

SPES was conducted in 27 epilepsy patients undergoing iEEG monitoring and CCSRs were quantified by significant early (10-200 ms) increases in power from 10 to 250 Hz. Using response power as CCSR network connection strengths, graph centrality measures (metrics quantifying each site's influence within the network) were used to predict whether sites were within the SOZ.

RESULTS:

Across patients with successful surgical outcomes, greater CCSR centrality predicted SOZ sites and SOZ sites targeted for surgical treatment with median AUCs of 0.85 and 0.91, respectively. We found that the alignment between predicted and targeted SOZ sites predicted surgical outcome with an AUC of 0.79.

CONCLUSIONS:

These findings indicate that network analysis of CCSRs can be used to identify increased excitability of SOZ sites and discriminate important surgical targets within the SOZ.