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Interictal SEEG Resting‐State Connectivity Localizes the Seizure Onset Zone and Predicts Seizure Outcome
https://orcid.org/0000-0003-2944-8602
AbstractLocalization of epileptogenic zone currently requires prolonged intracranial recordings to capture seizure, which may take days to weeks. The authors developed a novel method to identify the seizure onset zone (SOZ) and predict seizure outcome using short‐time resting‐state stereotacticelectroencephalography (SEEG) data. In a cohort of 27 drug‐resistant epilepsy patients, the authors estimated the information flow via directional connectivity and inferred the excitation‐inhibition ratio from the 1/fpower slope. They hypothesized that the antagonism of information flow at multiple frequencies between SOZ and non‐SOZ underlying the relatively stable epilepsy resting state could be related to the disrupted excitation‐inhibition balance. They found flatter 1/fpower slope in non‐SOZ regions compared to the SOZ, with dominant information flow from non‐SOZ to SOZ regions. Greater differences in resting‐state information flow between SOZ and non‐SOZ regions are associated with favorable seizure outcome. By integrating a balanced random forest model with resting‐state connectivity, their method localized the SOZ with an accuracy of 88% and predicted the seizure outcome with an accuracy of 92% using clinically determined SOZ. Overall, this study suggests that brief resting‐state SEEG data can significantly facilitate the identification of SOZ and may eventually predict seizure outcomes without requiring long‐term ictal recordings.
Interictal SEEG Resting‐State Connectivity Localizes the Seizure Onset Zone and Predicts Seizure Outcome
https://orcid.org/0000-0003-2944-8602
AbstractLocalization of epileptogenic zone currently requires prolonged intracranial recordings to capture seizure, which may take days to weeks. The authors developed a novel method to identify the seizure onset zone (SOZ) and predict seizure outcome using short‐time resting‐state stereotacticelectroencephalography (SEEG) data. In a cohort of 27 drug‐resistant epilepsy patients, the authors estimated the information flow via directional connectivity and inferred the excitation‐inhibition ratio from the 1/fpower slope. They hypothesized that the antagonism of information flow at multiple frequencies between SOZ and non‐SOZ underlying the relatively stable epilepsy resting state could be related to the disrupted excitation‐inhibition balance. They found flatter 1/fpower slope in non‐SOZ regions compared to the SOZ, with dominant information flow from non‐SOZ to SOZ regions. Greater differences in resting‐state information flow between SOZ and non‐SOZ regions are associated with favorable seizure outcome. By integrating a balanced random forest model with resting‐state connectivity, their method localized the SOZ with an accuracy of 88% and predicted the seizure outcome with an accuracy of 92% using clinically determined SOZ. Overall, this study suggests that brief resting‐state SEEG data can significantly facilitate the identification of SOZ and may eventually predict seizure outcomes without requiring long‐term ictal recordings.
Interictal SEEG Resting‐State Connectivity Localizes the Seizure Onset Zone and Predicts Seizure Outcome
https://orcid.org/0000-0003-2944-8602
AbstractLocalization of epileptogenic zone currently requires prolonged intracranial recordings to capture seizure, which may take days to weeks. The authors developed a novel method to identify the seizure onset zone (SOZ) and predict seizure outcome using short‐time resting‐state stereotacticelectroencephalography (SEEG) data. In a cohort of 27 drug‐resistant epilepsy patients, the authors estimated the information flow via directional connectivity and inferred the excitation‐inhibition ratio from the 1/fpower slope. They hypothesized that the antagonism of information flow at multiple frequencies between SOZ and non‐SOZ underlying the relatively stable epilepsy resting state could be related to the disrupted excitation‐inhibition balance. They found flatter 1/fpower slope in non‐SOZ regions compared to the SOZ, with dominant information flow from non‐SOZ to SOZ regions. Greater differences in resting‐state information flow between SOZ and non‐SOZ regions are associated with favorable seizure outcome. By integrating a balanced random forest model with resting‐state connectivity, their method localized the SOZ with an accuracy of 88% and predicted the seizure outcome with an accuracy of 92% using clinically determined SOZ. Overall, this study suggests that brief resting‐state SEEG data can significantly facilitate the identification of SOZ and may eventually predict seizure outcomes without requiring long‐term ictal recordings.
Interictal SEEG Resting‐State Connectivity Localizes the Seizure Onset Zone and Predicts Seizure Outcome
Advanced Science
Jiang, Haiteng (author) / Kokkinos, Vasileios (author) / Ye, Shuai (author) / Urban, Alexandra (author) / Bagić, Anto (author) / Richardson, Mark (author) / He, Bin (author)
Advanced Science ; 9
2022-06-01
Article (Journal)
Electronic Resource
English
| Wiley | 2022
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