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Quantifying interictal intracranial EEG to predict focal epilepsy

INTRODUCTION: Intracranial EEG (IEEG) is used for 2 main purposes, to determine: (1) if epileptic networks are amenable to focal treatment and (2) where to intervene. Currently these questions are answered qualitatively and sometimes differently across centers. There is a need for objective, standar...

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Autores principales: Gallagher, Ryan S, Sinha, Nishant, Pattnaik, Akash R., Ojemann, William K.S., Lucas, Alfredo, LaRocque, Joshua J., Bernabei, John M, Greenblatt, Adam S, Sweeney, Elizabeth M, Chen, H Isaac, Davis, Kathryn A, Conrad, Erin C, Litt, Brian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cornell University 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10402195/
https://www.ncbi.nlm.nih.gov/pubmed/37547655
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author Gallagher, Ryan S
Sinha, Nishant
Pattnaik, Akash R.
Ojemann, William K.S.
Lucas, Alfredo
LaRocque, Joshua J.
Bernabei, John M
Greenblatt, Adam S
Sweeney, Elizabeth M
Chen, H Isaac
Davis, Kathryn A
Conrad, Erin C
Litt, Brian
author_facet Gallagher, Ryan S
Sinha, Nishant
Pattnaik, Akash R.
Ojemann, William K.S.
Lucas, Alfredo
LaRocque, Joshua J.
Bernabei, John M
Greenblatt, Adam S
Sweeney, Elizabeth M
Chen, H Isaac
Davis, Kathryn A
Conrad, Erin C
Litt, Brian
author_sort Gallagher, Ryan S
collection PubMed
description INTRODUCTION: Intracranial EEG (IEEG) is used for 2 main purposes, to determine: (1) if epileptic networks are amenable to focal treatment and (2) where to intervene. Currently these questions are answered qualitatively and sometimes differently across centers. There is a need for objective, standardized methods to guide surgical decision making and to enable large scale data analysis across centers and prospective clinical trials. METHODS: We analyzed interictal data from 101 patients with drug resistant epilepsy who underwent presurgical evaluation with IEEG. We chose interictal data because of its potential to reduce the morbidity and cost associated with ictal recording. 65 patients had unifocal seizure onset on IEEG, and 36 were non-focal or multi-focal. We quantified the spatial dispersion of implanted electrodes and interictal IEEG abnormalities for each patient. We compared these measures against the “5 Sense Score (5SS),” a pre-implant estimate of the likelihood of focal seizure onset, and assessed their ability to predict the clinicians’ choice of therapeutic intervention and the patient outcome. RESULTS: The spatial dispersion of IEEG electrodes predicted network focality with precision similar to the 5SS (AUC = 0.67), indicating that electrode placement accurately reflected pre-implant information. A cross-validated model combining the 5SS and the spatial dispersion of interictal IEEG abnormalities significantly improved this prediction (AUC = 0.79; p<0.05). The combined model predicted ultimate treatment strategy (surgery vs. device) with an AUC of 0.81 and post-surgical outcome at 2 years with an AUC of 0.70. The 5SS, interictal IEEG, and electrode placement were not correlated and provided complementary information. CONCLUSIONS: Quantitative, interictal IEEG significantly improved upon pre-implant estimates of network focality and predicted treatment with precision approaching that of clinical experts. We present this study as an important step in building standardized, quantitative tools to guide epilepsy surgery.
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spelling pubmed-104021952023-08-05 Quantifying interictal intracranial EEG to predict focal epilepsy Gallagher, Ryan S Sinha, Nishant Pattnaik, Akash R. Ojemann, William K.S. Lucas, Alfredo LaRocque, Joshua J. Bernabei, John M Greenblatt, Adam S Sweeney, Elizabeth M Chen, H Isaac Davis, Kathryn A Conrad, Erin C Litt, Brian ArXiv Article INTRODUCTION: Intracranial EEG (IEEG) is used for 2 main purposes, to determine: (1) if epileptic networks are amenable to focal treatment and (2) where to intervene. Currently these questions are answered qualitatively and sometimes differently across centers. There is a need for objective, standardized methods to guide surgical decision making and to enable large scale data analysis across centers and prospective clinical trials. METHODS: We analyzed interictal data from 101 patients with drug resistant epilepsy who underwent presurgical evaluation with IEEG. We chose interictal data because of its potential to reduce the morbidity and cost associated with ictal recording. 65 patients had unifocal seizure onset on IEEG, and 36 were non-focal or multi-focal. We quantified the spatial dispersion of implanted electrodes and interictal IEEG abnormalities for each patient. We compared these measures against the “5 Sense Score (5SS),” a pre-implant estimate of the likelihood of focal seizure onset, and assessed their ability to predict the clinicians’ choice of therapeutic intervention and the patient outcome. RESULTS: The spatial dispersion of IEEG electrodes predicted network focality with precision similar to the 5SS (AUC = 0.67), indicating that electrode placement accurately reflected pre-implant information. A cross-validated model combining the 5SS and the spatial dispersion of interictal IEEG abnormalities significantly improved this prediction (AUC = 0.79; p<0.05). The combined model predicted ultimate treatment strategy (surgery vs. device) with an AUC of 0.81 and post-surgical outcome at 2 years with an AUC of 0.70. The 5SS, interictal IEEG, and electrode placement were not correlated and provided complementary information. CONCLUSIONS: Quantitative, interictal IEEG significantly improved upon pre-implant estimates of network focality and predicted treatment with precision approaching that of clinical experts. We present this study as an important step in building standardized, quantitative tools to guide epilepsy surgery. Cornell University 2023-07-27 /pmc/articles/PMC10402195/ /pubmed/37547655 Text en https://creativecommons.org/licenses/by/4.0/This work is licensed under a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/) , which allows reusers to distribute, remix, adapt, and build upon the material in any medium or format, so long as attribution is given to the creator. The license allows for commercial use.
spellingShingle Article
Gallagher, Ryan S
Sinha, Nishant
Pattnaik, Akash R.
Ojemann, William K.S.
Lucas, Alfredo
LaRocque, Joshua J.
Bernabei, John M
Greenblatt, Adam S
Sweeney, Elizabeth M
Chen, H Isaac
Davis, Kathryn A
Conrad, Erin C
Litt, Brian
Quantifying interictal intracranial EEG to predict focal epilepsy
title Quantifying interictal intracranial EEG to predict focal epilepsy
title_full Quantifying interictal intracranial EEG to predict focal epilepsy
title_fullStr Quantifying interictal intracranial EEG to predict focal epilepsy
title_full_unstemmed Quantifying interictal intracranial EEG to predict focal epilepsy
title_short Quantifying interictal intracranial EEG to predict focal epilepsy
title_sort quantifying interictal intracranial eeg to predict focal epilepsy
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10402195/
https://www.ncbi.nlm.nih.gov/pubmed/37547655
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