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Electrocorticography and stereo EEG provide distinct measures of brain connectivity: implications for network models
Brain network models derived from graph theory have the potential to guide functional neurosurgery, and to improve rates of post-operative seizure freedom for patients with epilepsy. A barrier to applying these models clinically is that intracranial EEG electrode implantation strategies vary by cent...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8361393/ https://www.ncbi.nlm.nih.gov/pubmed/34396112 http://dx.doi.org/10.1093/braincomms/fcab156 |
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author | Bernabei, John M Arnold, T Campbell Shah, Preya Revell, Andrew Ong, Ian Z Kini, Lohith G Stein, Joel M Shinohara, Russell T Lucas, Timothy H Davis, Kathryn A Bassett, Danielle S Litt, Brian |
author_facet | Bernabei, John M Arnold, T Campbell Shah, Preya Revell, Andrew Ong, Ian Z Kini, Lohith G Stein, Joel M Shinohara, Russell T Lucas, Timothy H Davis, Kathryn A Bassett, Danielle S Litt, Brian |
author_sort | Bernabei, John M |
collection | PubMed |
description | Brain network models derived from graph theory have the potential to guide functional neurosurgery, and to improve rates of post-operative seizure freedom for patients with epilepsy. A barrier to applying these models clinically is that intracranial EEG electrode implantation strategies vary by centre, region and country, from cortical grid & strip electrodes (Electrocorticography), to purely stereotactic depth electrodes (Stereo EEG), to a mixture of both. To determine whether models derived from one type of study are broadly applicable to others, we investigate the differences in brain networks mapped by electrocorticography and stereo EEG in a cohort of patients who underwent surgery for temporal lobe epilepsy and achieved a favourable outcome. We show that networks derived from electrocorticography and stereo EEG define distinct relationships between resected and spared tissue, which may be driven by sampling bias of temporal depth electrodes in patients with predominantly cortical grids. We propose a method of correcting for the effect of internodal distance that is specific to electrode type and explore how additional methods for spatially correcting for sampling bias affect network models. Ultimately, we find that smaller surgical targets tend to have lower connectivity with respect to the surrounding network, challenging notions that abnormal connectivity in the epileptogenic zone is typically high. Our findings suggest that effectively applying computational models to localize epileptic networks requires accounting for the effects of spatial sampling, particularly when analysing both electrocorticography and stereo EEG recordings in the same cohort, and that future network studies of epilepsy surgery should also account for differences in focality between resection and ablation. We propose that these findings are broadly relevant to intracranial EEG network modelling in epilepsy and an important step in translating them clinically into patient care. |
format | Online Article Text |
id | pubmed-8361393 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-83613932021-08-13 Electrocorticography and stereo EEG provide distinct measures of brain connectivity: implications for network models Bernabei, John M Arnold, T Campbell Shah, Preya Revell, Andrew Ong, Ian Z Kini, Lohith G Stein, Joel M Shinohara, Russell T Lucas, Timothy H Davis, Kathryn A Bassett, Danielle S Litt, Brian Brain Commun Original Article Brain network models derived from graph theory have the potential to guide functional neurosurgery, and to improve rates of post-operative seizure freedom for patients with epilepsy. A barrier to applying these models clinically is that intracranial EEG electrode implantation strategies vary by centre, region and country, from cortical grid & strip electrodes (Electrocorticography), to purely stereotactic depth electrodes (Stereo EEG), to a mixture of both. To determine whether models derived from one type of study are broadly applicable to others, we investigate the differences in brain networks mapped by electrocorticography and stereo EEG in a cohort of patients who underwent surgery for temporal lobe epilepsy and achieved a favourable outcome. We show that networks derived from electrocorticography and stereo EEG define distinct relationships between resected and spared tissue, which may be driven by sampling bias of temporal depth electrodes in patients with predominantly cortical grids. We propose a method of correcting for the effect of internodal distance that is specific to electrode type and explore how additional methods for spatially correcting for sampling bias affect network models. Ultimately, we find that smaller surgical targets tend to have lower connectivity with respect to the surrounding network, challenging notions that abnormal connectivity in the epileptogenic zone is typically high. Our findings suggest that effectively applying computational models to localize epileptic networks requires accounting for the effects of spatial sampling, particularly when analysing both electrocorticography and stereo EEG recordings in the same cohort, and that future network studies of epilepsy surgery should also account for differences in focality between resection and ablation. We propose that these findings are broadly relevant to intracranial EEG network modelling in epilepsy and an important step in translating them clinically into patient care. Oxford University Press 2021-07-11 /pmc/articles/PMC8361393/ /pubmed/34396112 http://dx.doi.org/10.1093/braincomms/fcab156 Text en © The Author(s) (2021). 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 (http://creativecommons.org/licenses/by/4.0/ (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 Bernabei, John M Arnold, T Campbell Shah, Preya Revell, Andrew Ong, Ian Z Kini, Lohith G Stein, Joel M Shinohara, Russell T Lucas, Timothy H Davis, Kathryn A Bassett, Danielle S Litt, Brian Electrocorticography and stereo EEG provide distinct measures of brain connectivity: implications for network models |
title | Electrocorticography and stereo EEG provide distinct measures of brain connectivity: implications for network models |
title_full | Electrocorticography and stereo EEG provide distinct measures of brain connectivity: implications for network models |
title_fullStr | Electrocorticography and stereo EEG provide distinct measures of brain connectivity: implications for network models |
title_full_unstemmed | Electrocorticography and stereo EEG provide distinct measures of brain connectivity: implications for network models |
title_short | Electrocorticography and stereo EEG provide distinct measures of brain connectivity: implications for network models |
title_sort | electrocorticography and stereo eeg provide distinct measures of brain connectivity: implications for network models |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8361393/ https://www.ncbi.nlm.nih.gov/pubmed/34396112 http://dx.doi.org/10.1093/braincomms/fcab156 |
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