Interictal high-frequency oscillations, spikes, and connectivity profiles: A fingerprint of epileptogenic brain pathologies
| Authors | |
|---|---|
| Year of publication | 2023 |
| Type | Article in Periodical |
| Magazine / Source | Epilepsia |
| MU Faculty or unit | |
| Citation | |
| Web | https://onlinelibrary.wiley.com/doi/10.1111/epi.17749 |
| Doi | http://dx.doi.org/10.1111/epi.17749 |
| Keywords | connectivity; epileptogenicity; focal cortical dysplasia; gliosis; high-frequency oscillations; hippocampal sclerosis; spikes |
| Attached files | |
| Description | Objective: Focal cortical dysplasia (FCD), hippocampal sclerosis (HS), nonspecific gliosis (NG), and normal tissue (NT) comprise the majority of histopathological results of surgically treated drug-resistant epilepsy patients. Epileptic spikes, high-frequency oscillations (HFOs), and connectivity measures are valuable biomarkers of epileptogenicity. The question remains whether they could also be utilized for preresective differentiation of the underlying brain pathology. This study explored spikes and HFOs together with functional connectivity in various epileptogenic pathologies.Methods: Interictal awake stereoelectroencephalographic recordings of 33 patients with focal drug-resistant epilepsy with seizure-free postoperative outcomes were analyzed (15 FCD, 8 HS, 6 NT, and 4 NG). Interictal spikes and HFOs were automatically identified in the channels contained in the overlap of seizure onset zone and resected tissue. Functional connectivity measures (relative entropy, linear correlation, cross-correlation, and phase consistency) were computed for neighboring electrode pairs.Results: Statistically significant differences were found between the individual pathologies in HFO rates, spikes, and their characteristics, together with functional connectivity measures, with the highest values in the case of HS and NG/NT. A model to predict brain pathology based on all interictal measures achieved up to 84.0% prediction accuracy.Significance: The electrophysiological profile of the various epileptogenic lesions in epilepsy surgery patients was analyzed. Based on this profile, a predictive model was developed. This model offers excellent potential to identify the nature of the underlying lesion prior to resection. If validated, this model may be particularly valuable for counseling patients, as depending on the lesion type, different outcomes are achieved after epilepsy surgery. |
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