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...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-10-01
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| Series: | Frontiers in Physiology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fphys.2022.1015838/full |
| _version_ | 1798027941178769408 |
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| author | Yang Liu Chunsheng Li |
| author_facet | Yang Liu Chunsheng Li |
| author_sort | Yang Liu |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T19:00:55Z |
| format | Article |
| id | doaj.art-99e528fa43d74c7da1f1fa7d61477dd5 |
| institution | Directory Open Access Journal |
| issn | 1664-042X |
| language | English |
| last_indexed | 2024-04-11T19:00:55Z |
| publishDate | 2022-10-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Physiology |
| spelling | doaj.art-99e528fa43d74c7da1f1fa7d61477dd52022-12-22T04:08:03ZengFrontiers Media S.A.Frontiers in Physiology1664-042X2022-10-011310.3389/fphys.2022.10158381015838Localizing targets for neuromodulation in drug-resistant epilepsy using intracranial EEG and computational modelYang LiuChunsheng LiNeuromodulation 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.https://www.frontiersin.org/articles/10.3389/fphys.2022.1015838/fullneural computational modelneuromodulationdrug-resistant epilepsyintracranial EEGoptimal target |
| spellingShingle | Yang Liu Chunsheng Li Localizing targets for neuromodulation in drug-resistant epilepsy using intracranial EEG and computational model Frontiers in Physiology neural computational model neuromodulation drug-resistant epilepsy intracranial EEG optimal target |
| title | Localizing targets for neuromodulation in drug-resistant epilepsy using intracranial EEG and computational model |
| title_full | Localizing targets for neuromodulation in drug-resistant epilepsy using intracranial EEG and computational model |
| title_fullStr | Localizing targets for neuromodulation in drug-resistant epilepsy using intracranial EEG and computational model |
| title_full_unstemmed | Localizing targets for neuromodulation in drug-resistant epilepsy using intracranial EEG and computational model |
| title_short | Localizing targets for neuromodulation in drug-resistant epilepsy using intracranial EEG and computational model |
| title_sort | localizing targets for neuromodulation in drug resistant epilepsy using intracranial eeg and computational model |
| topic | neural computational model neuromodulation drug-resistant epilepsy intracranial EEG optimal target |
| url | https://www.frontiersin.org/articles/10.3389/fphys.2022.1015838/full |
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