Interictal high‐frequency oscillations, spikes, and connectivity profiles: A fingerprint of epileptogenic brain pathologies

Author:

Sklenarova Barbora12ORCID,Zatloukalova Eva12ORCID,Cimbalnik Jan2ORCID,Klimes Petr23ORCID,Dolezalova Irena12ORCID,Pail Martin123ORCID,Kocvarova Jitka1ORCID,Hendrych Michal4ORCID,Hermanova Marketa4ORCID,Gotman Jean5ORCID,Dubeau François5,Hall Jeffery5,Pana Raluca5ORCID,Frauscher Birgit5ORCID,Brazdil Milan16ORCID

Affiliation:

1. Brno Epilepsy Center, Department of Neurology St. Anne's University Hospital and Medical Faculty of Masaryk University Brno Czech Republic

2. International Clinical Research Center St. Anne's University Hospital and Medical Faculty of Masaryk University Brno Czech Republic

3. Institute of Scientific Instruments of the Czech Academy of Sciences Brno Czech Republic

4. First Department of Pathology St. Anne's University Hospital and Faculty of Medicine, Masaryk University Brno Czech Republic

5. Montreal Neurological Institute and Hospital, McGill University Montreal Quebec Canada

6. Behavioral and Social Neuroscience Research Group Central European Institute of Technology, Masaryk University Brno Czech Republic

Abstract

AbstractObjectiveFocal 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.MethodsInterictal 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.ResultsStatistically 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.SignificanceThe 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.

Funder

Agentura Pro Zdravotnický Výzkum České Republiky

Canadian Institutes of Health Research

George E. Hewitt Foundation for Medical Research

Grantová Agentura České Republiky

Publisher

Wiley

Subject

Neurology (clinical),Neurology

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