Localizing targets for neuromodulation in drug-resistant epilepsy using intracranial EEG and computational model

Neuromodulation has emerged as a promising technique for the treatment of epilepsy. The target for neuromodulation is critical for the effectiveness of seizure control. About 30% of patients with drug-resistant epilepsy (DRE) fail to achieve seizure freedom after surgical intervention. It is difficu...

Full description

Saved in:
Bibliographic Details
Published inFrontiers in physiology Vol. 13; p. 1015838
Main Authors Liu, Yang, Li, Chunsheng
Format Journal Article
LanguageEnglish
Published Frontiers Media S.A 20.10.2022
Subjects
Online AccessGet full text

Cover

Loading…
More Information
Summary:Neuromodulation has emerged as a promising technique for the treatment of epilepsy. The target for neuromodulation is critical for the effectiveness of seizure control. About 30% of patients with drug-resistant epilepsy (DRE) fail to achieve seizure freedom after surgical intervention. It is difficult to find effective brain targets for neuromodulation in these patients because brain regions are damaged during surgery. In this study, we propose a novel approach for localizing neuromodulatory targets, which uses intracranial EEG and multi-unit computational models to simulate the dynamic behavior of epileptic networks through external stimulation. First, we validate our method on a multivariate autoregressive model and compare nine different methods of constructing brain networks. Our results show that the directed transfer function with surrogate analysis achieves the best performance. Intracranial EEGs of 11 DRE patients are further analyzed. These patients all underwent surgery. In three seizure-free patients, the localized targets are concordant with the resected regions. For the eight patients without seizure-free outcome, the localized targets in three of them are outside the resected regions. Finally, we provide candidate targets for neuromodulation in these patients without seizure-free outcome based on virtual resected epileptic network. We demonstrate the ability of our approach to locate optimal targets for neuromodulation. We hope that our approach can provide a new tool for localizing patient-specific targets for neuromodulation therapy in DRE.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
Edited by: Rong Liu, Dalian University of Technology, China
Reviewed by: Yangsong Zhang, Southwest University of Science and Technology, China
Paulo Ricardo Protachevicz, University of São Paulo, Brazil
This article was submitted to Computational Physiology and Medicine, a section of the journal Frontiers in Physiology
ISSN:1664-042X
1664-042X
DOI:10.3389/fphys.2022.1015838