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Protocol for multicentre comparison of interictal high-frequency oscillations as a predictor of seizure freedom

In drug-resistant focal epilepsy, interictal high-frequency oscillations (HFOs) recorded from intracranial EEG (iEEG) may provide clinical information for delineating epileptogenic brain tissue. The iEEG electrode contacts that contain HFO are hypothesized to delineate the epileptogenic zone; their...

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Autores principales: Dimakopoulos, Vasileios, Gotman, Jean, Stacey, William, von Ellenrieder, Nicolás, Jacobs, Julia, Papadelis, Christos, Cimbalnik, Jan, Worrell, Gregory, Sperling, Michael R, Zijlmans, Maike, Imbach, Lucas, Frauscher, Birgit, Sarnthein, Johannes
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9234061/
https://www.ncbi.nlm.nih.gov/pubmed/35770134
http://dx.doi.org/10.1093/braincomms/fcac151
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author Dimakopoulos, Vasileios
Gotman, Jean
Stacey, William
von Ellenrieder, Nicolás
Jacobs, Julia
Papadelis, Christos
Cimbalnik, Jan
Worrell, Gregory
Sperling, Michael R
Zijlmans, Maike
Imbach, Lucas
Frauscher, Birgit
Sarnthein, Johannes
author_facet Dimakopoulos, Vasileios
Gotman, Jean
Stacey, William
von Ellenrieder, Nicolás
Jacobs, Julia
Papadelis, Christos
Cimbalnik, Jan
Worrell, Gregory
Sperling, Michael R
Zijlmans, Maike
Imbach, Lucas
Frauscher, Birgit
Sarnthein, Johannes
author_sort Dimakopoulos, Vasileios
collection PubMed
description In drug-resistant focal epilepsy, interictal high-frequency oscillations (HFOs) recorded from intracranial EEG (iEEG) may provide clinical information for delineating epileptogenic brain tissue. The iEEG electrode contacts that contain HFO are hypothesized to delineate the epileptogenic zone; their resection should then lead to postsurgical seizure freedom. We test whether our prospective definition of clinically relevant HFO is in agreement with postsurgical seizure outcome. The algorithm is fully automated and is equally applied to all data sets. The aim is to assess the reliability of the proposed detector and analysis approach. We use an automated data-independent prospective definition of clinically relevant HFO that has been validated in data from two independent epilepsy centres. In this study, we combine retrospectively collected data sets from nine independent epilepsy centres. The analysis is blinded to clinical outcome. We use iEEG recordings during NREM sleep with a minimum of 12 epochs of 5 min of NREM sleep. We automatically detect HFO in the ripple (80–250 Hz) and in the fast ripple (250–500 Hz) band. There is no manual rejection of events in this fully automated algorithm. The type of HFO that we consider clinically relevant is defined as the simultaneous occurrence of a fast ripple and a ripple. We calculate the temporal consistency of each patient’s HFO rates over several data epochs within and between nights. Patients with temporal consistency <50% are excluded from further analysis. We determine whether all electrode contacts with high HFO rate are included in the resection volume and whether seizure freedom (ILAE 1) was achieved at ≥2 years follow-up. Applying a previously validated algorithm to a large cohort from several independent epilepsy centres may advance the clinical relevance and the generalizability of HFO analysis as essential next step for use of HFO in clinical practice.
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spelling pubmed-92340612022-06-28 Protocol for multicentre comparison of interictal high-frequency oscillations as a predictor of seizure freedom Dimakopoulos, Vasileios Gotman, Jean Stacey, William von Ellenrieder, Nicolás Jacobs, Julia Papadelis, Christos Cimbalnik, Jan Worrell, Gregory Sperling, Michael R Zijlmans, Maike Imbach, Lucas Frauscher, Birgit Sarnthein, Johannes Brain Commun Original Article In drug-resistant focal epilepsy, interictal high-frequency oscillations (HFOs) recorded from intracranial EEG (iEEG) may provide clinical information for delineating epileptogenic brain tissue. The iEEG electrode contacts that contain HFO are hypothesized to delineate the epileptogenic zone; their resection should then lead to postsurgical seizure freedom. We test whether our prospective definition of clinically relevant HFO is in agreement with postsurgical seizure outcome. The algorithm is fully automated and is equally applied to all data sets. The aim is to assess the reliability of the proposed detector and analysis approach. We use an automated data-independent prospective definition of clinically relevant HFO that has been validated in data from two independent epilepsy centres. In this study, we combine retrospectively collected data sets from nine independent epilepsy centres. The analysis is blinded to clinical outcome. We use iEEG recordings during NREM sleep with a minimum of 12 epochs of 5 min of NREM sleep. We automatically detect HFO in the ripple (80–250 Hz) and in the fast ripple (250–500 Hz) band. There is no manual rejection of events in this fully automated algorithm. The type of HFO that we consider clinically relevant is defined as the simultaneous occurrence of a fast ripple and a ripple. We calculate the temporal consistency of each patient’s HFO rates over several data epochs within and between nights. Patients with temporal consistency <50% are excluded from further analysis. We determine whether all electrode contacts with high HFO rate are included in the resection volume and whether seizure freedom (ILAE 1) was achieved at ≥2 years follow-up. Applying a previously validated algorithm to a large cohort from several independent epilepsy centres may advance the clinical relevance and the generalizability of HFO analysis as essential next step for use of HFO in clinical practice. Oxford University Press 2022-06-09 /pmc/articles/PMC9234061/ /pubmed/35770134 http://dx.doi.org/10.1093/braincomms/fcac151 Text en © The Author(s) 2022. Published by Oxford University Press on behalf of the Guarantors of Brain. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Original Article
Dimakopoulos, Vasileios
Gotman, Jean
Stacey, William
von Ellenrieder, Nicolás
Jacobs, Julia
Papadelis, Christos
Cimbalnik, Jan
Worrell, Gregory
Sperling, Michael R
Zijlmans, Maike
Imbach, Lucas
Frauscher, Birgit
Sarnthein, Johannes
Protocol for multicentre comparison of interictal high-frequency oscillations as a predictor of seizure freedom
title Protocol for multicentre comparison of interictal high-frequency oscillations as a predictor of seizure freedom
title_full Protocol for multicentre comparison of interictal high-frequency oscillations as a predictor of seizure freedom
title_fullStr Protocol for multicentre comparison of interictal high-frequency oscillations as a predictor of seizure freedom
title_full_unstemmed Protocol for multicentre comparison of interictal high-frequency oscillations as a predictor of seizure freedom
title_short Protocol for multicentre comparison of interictal high-frequency oscillations as a predictor of seizure freedom
title_sort protocol for multicentre comparison of interictal high-frequency oscillations as a predictor of seizure freedom
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9234061/
https://www.ncbi.nlm.nih.gov/pubmed/35770134
http://dx.doi.org/10.1093/braincomms/fcac151
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