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Accuracy of high-frequency oscillations recorded intraoperatively for classification of epileptogenic regions

To see whether acute intraoperative recordings using stereo EEG (SEEG) electrodes can replace prolonged interictal intracranial EEG (iEEG) recording, making the process more efficient and safer, 10 min of iEEG were recorded following electrode implantation in 16 anesthetized patients, and 1–2 days l...

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Autores principales: Weiss, Shennan A., Staba, Richard J., Sharan, Ashwini, Wu, Chengyuan, Rubinstein, Daniel, Das, Sandhitsu, Waldman, Zachary, Orosz, Iren, Worrell, Gregory, Engel, Jerome, Sperling, Michael R.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8560764/
https://www.ncbi.nlm.nih.gov/pubmed/34725412
http://dx.doi.org/10.1038/s41598-021-00894-3
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author Weiss, Shennan A.
Staba, Richard J.
Sharan, Ashwini
Wu, Chengyuan
Rubinstein, Daniel
Das, Sandhitsu
Waldman, Zachary
Orosz, Iren
Worrell, Gregory
Engel, Jerome
Sperling, Michael R.
author_facet Weiss, Shennan A.
Staba, Richard J.
Sharan, Ashwini
Wu, Chengyuan
Rubinstein, Daniel
Das, Sandhitsu
Waldman, Zachary
Orosz, Iren
Worrell, Gregory
Engel, Jerome
Sperling, Michael R.
author_sort Weiss, Shennan A.
collection PubMed
description To see whether acute intraoperative recordings using stereo EEG (SEEG) electrodes can replace prolonged interictal intracranial EEG (iEEG) recording, making the process more efficient and safer, 10 min of iEEG were recorded following electrode implantation in 16 anesthetized patients, and 1–2 days later during non-rapid eye movement (REM) sleep. Ripples on oscillations (RonO, 80–250 Hz), ripples on spikes (RonS), sharp-spikes, fast RonO (fRonO, 250–600 Hz), and fast RonS (fRonS) were semi-automatically detected. HFO power and frequency were compared between the conditions using a generalized linear mixed-effects model. HFO rates were compared using a two-way repeated measures ANOVA with anesthesia type and SOZ as factors. A receiver-operating characteristic (ROC) curve analysis quantified seizure onset zone (SOZ) classification accuracy, and the scalar product was used to assess spatial reliability. Resection of contacts with the highest rate of events was compared with outcome. During sleep, all HFOs, except fRonO, were larger in amplitude compared to intraoperatively (p < 0.01). HFO frequency was also affected (p < 0.01). Anesthesia selection affected HFO and sharp-spike rates. In both conditions combined, sharp-spikes and all HFO subtypes were increased in the SOZ (p < 0.01). However, the increases were larger during the sleep recordings (p < 0.05). The area under the ROC curves for SOZ classification were significantly smaller for intraoperative sharp-spikes, fRonO, and fRonS rates (p < 0.05). HFOs and spikes were only significantly spatially reliable for a subset of the patients (p < 0.05). A failure to resect fRonO areas in the sleep recordings trended the most sensitive and accurate for predicting failure. In summary, HFO morphology is altered by anesthesia. Intraoperative SEEG recordings exhibit increased rates of HFOs in the SOZ, but their spatial distribution can differ from sleep recordings. Recording these biomarkers during non-REM sleep offers a more accurate delineation of the SOZ and possibly the epileptogenic zone.
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spelling pubmed-85607642021-11-03 Accuracy of high-frequency oscillations recorded intraoperatively for classification of epileptogenic regions Weiss, Shennan A. Staba, Richard J. Sharan, Ashwini Wu, Chengyuan Rubinstein, Daniel Das, Sandhitsu Waldman, Zachary Orosz, Iren Worrell, Gregory Engel, Jerome Sperling, Michael R. Sci Rep Article To see whether acute intraoperative recordings using stereo EEG (SEEG) electrodes can replace prolonged interictal intracranial EEG (iEEG) recording, making the process more efficient and safer, 10 min of iEEG were recorded following electrode implantation in 16 anesthetized patients, and 1–2 days later during non-rapid eye movement (REM) sleep. Ripples on oscillations (RonO, 80–250 Hz), ripples on spikes (RonS), sharp-spikes, fast RonO (fRonO, 250–600 Hz), and fast RonS (fRonS) were semi-automatically detected. HFO power and frequency were compared between the conditions using a generalized linear mixed-effects model. HFO rates were compared using a two-way repeated measures ANOVA with anesthesia type and SOZ as factors. A receiver-operating characteristic (ROC) curve analysis quantified seizure onset zone (SOZ) classification accuracy, and the scalar product was used to assess spatial reliability. Resection of contacts with the highest rate of events was compared with outcome. During sleep, all HFOs, except fRonO, were larger in amplitude compared to intraoperatively (p < 0.01). HFO frequency was also affected (p < 0.01). Anesthesia selection affected HFO and sharp-spike rates. In both conditions combined, sharp-spikes and all HFO subtypes were increased in the SOZ (p < 0.01). However, the increases were larger during the sleep recordings (p < 0.05). The area under the ROC curves for SOZ classification were significantly smaller for intraoperative sharp-spikes, fRonO, and fRonS rates (p < 0.05). HFOs and spikes were only significantly spatially reliable for a subset of the patients (p < 0.05). A failure to resect fRonO areas in the sleep recordings trended the most sensitive and accurate for predicting failure. In summary, HFO morphology is altered by anesthesia. Intraoperative SEEG recordings exhibit increased rates of HFOs in the SOZ, but their spatial distribution can differ from sleep recordings. Recording these biomarkers during non-REM sleep offers a more accurate delineation of the SOZ and possibly the epileptogenic zone. Nature Publishing Group UK 2021-11-01 /pmc/articles/PMC8560764/ /pubmed/34725412 http://dx.doi.org/10.1038/s41598-021-00894-3 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Weiss, Shennan A.
Staba, Richard J.
Sharan, Ashwini
Wu, Chengyuan
Rubinstein, Daniel
Das, Sandhitsu
Waldman, Zachary
Orosz, Iren
Worrell, Gregory
Engel, Jerome
Sperling, Michael R.
Accuracy of high-frequency oscillations recorded intraoperatively for classification of epileptogenic regions
title Accuracy of high-frequency oscillations recorded intraoperatively for classification of epileptogenic regions
title_full Accuracy of high-frequency oscillations recorded intraoperatively for classification of epileptogenic regions
title_fullStr Accuracy of high-frequency oscillations recorded intraoperatively for classification of epileptogenic regions
title_full_unstemmed Accuracy of high-frequency oscillations recorded intraoperatively for classification of epileptogenic regions
title_short Accuracy of high-frequency oscillations recorded intraoperatively for classification of epileptogenic regions
title_sort accuracy of high-frequency oscillations recorded intraoperatively for classification of epileptogenic regions
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8560764/
https://www.ncbi.nlm.nih.gov/pubmed/34725412
http://dx.doi.org/10.1038/s41598-021-00894-3
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