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Identification of Negative BOLD Responses in Epilepsy Using Windkessel Models
Alongside positive blood oxygenation level–dependent (BOLD) responses associated with interictal epileptic discharges, a variety of negative BOLD responses (NBRs) are typically found in epileptic patients. Previous studies suggest that, in general, up to four mechanisms might underlie the genesis of...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8531269/ https://www.ncbi.nlm.nih.gov/pubmed/34690906 http://dx.doi.org/10.3389/fneur.2021.659081 |
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author | Suarez, Alejandro Valdés-Hernández, Pedro A. Bernal, Byron Dunoyer, Catalina Khoo, Hui Ming Bosch-Bayard, Jorge Riera, Jorge J. |
author_facet | Suarez, Alejandro Valdés-Hernández, Pedro A. Bernal, Byron Dunoyer, Catalina Khoo, Hui Ming Bosch-Bayard, Jorge Riera, Jorge J. |
author_sort | Suarez, Alejandro |
collection | PubMed |
description | Alongside positive blood oxygenation level–dependent (BOLD) responses associated with interictal epileptic discharges, a variety of negative BOLD responses (NBRs) are typically found in epileptic patients. Previous studies suggest that, in general, up to four mechanisms might underlie the genesis of NBRs in the brain: (i) neuronal disruption of network activity, (ii) altered balance of neurometabolic/vascular couplings, (iii) arterial blood stealing, and (iv) enhanced cortical inhibition. Detecting and classifying these mechanisms from BOLD signals are pivotal for the improvement of the specificity of the electroencephalography–functional magnetic resonance imaging (EEG-fMRI) image modality to identify the seizure-onset zones in refractory local epilepsy. This requires models with physiological interpretation that furnish the understanding of how these mechanisms are fingerprinted by their BOLD responses. Here, we used a Windkessel model with viscoelastic compliance/inductance in combination with dynamic models of both neuronal population activity and tissue/blood O(2) to classify the hemodynamic response functions (HRFs) linked to the above mechanisms in the irritative zones of epileptic patients. First, we evaluated the most relevant imprints on the BOLD response caused by variations of key model parameters. Second, we demonstrated that a general linear model is enough to accurately represent the four different types of NBRs. Third, we tested the ability of a machine learning classifier, built from a simulated ensemble of HRFs, to predict the mechanism underlying the BOLD signal from irritative zones. Cross-validation indicates that these four mechanisms can be classified from realistic fMRI BOLD signals. To demonstrate proof of concept, we applied our methodology to EEG-fMRI data from five epileptic patients undergoing neurosurgery, suggesting the presence of some of these mechanisms. We concluded that a proper identification and interpretation of NBR mechanisms in epilepsy can be performed by combining general linear models and biophysically inspired models. |
format | Online Article Text |
id | pubmed-8531269 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-85312692021-10-23 Identification of Negative BOLD Responses in Epilepsy Using Windkessel Models Suarez, Alejandro Valdés-Hernández, Pedro A. Bernal, Byron Dunoyer, Catalina Khoo, Hui Ming Bosch-Bayard, Jorge Riera, Jorge J. Front Neurol Neurology Alongside positive blood oxygenation level–dependent (BOLD) responses associated with interictal epileptic discharges, a variety of negative BOLD responses (NBRs) are typically found in epileptic patients. Previous studies suggest that, in general, up to four mechanisms might underlie the genesis of NBRs in the brain: (i) neuronal disruption of network activity, (ii) altered balance of neurometabolic/vascular couplings, (iii) arterial blood stealing, and (iv) enhanced cortical inhibition. Detecting and classifying these mechanisms from BOLD signals are pivotal for the improvement of the specificity of the electroencephalography–functional magnetic resonance imaging (EEG-fMRI) image modality to identify the seizure-onset zones in refractory local epilepsy. This requires models with physiological interpretation that furnish the understanding of how these mechanisms are fingerprinted by their BOLD responses. Here, we used a Windkessel model with viscoelastic compliance/inductance in combination with dynamic models of both neuronal population activity and tissue/blood O(2) to classify the hemodynamic response functions (HRFs) linked to the above mechanisms in the irritative zones of epileptic patients. First, we evaluated the most relevant imprints on the BOLD response caused by variations of key model parameters. Second, we demonstrated that a general linear model is enough to accurately represent the four different types of NBRs. Third, we tested the ability of a machine learning classifier, built from a simulated ensemble of HRFs, to predict the mechanism underlying the BOLD signal from irritative zones. Cross-validation indicates that these four mechanisms can be classified from realistic fMRI BOLD signals. To demonstrate proof of concept, we applied our methodology to EEG-fMRI data from five epileptic patients undergoing neurosurgery, suggesting the presence of some of these mechanisms. We concluded that a proper identification and interpretation of NBR mechanisms in epilepsy can be performed by combining general linear models and biophysically inspired models. Frontiers Media S.A. 2021-10-08 /pmc/articles/PMC8531269/ /pubmed/34690906 http://dx.doi.org/10.3389/fneur.2021.659081 Text en Copyright © 2021 Suarez, Valdés-Hernández, Bernal, Dunoyer, Khoo, Bosch-Bayard and Riera. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neurology Suarez, Alejandro Valdés-Hernández, Pedro A. Bernal, Byron Dunoyer, Catalina Khoo, Hui Ming Bosch-Bayard, Jorge Riera, Jorge J. Identification of Negative BOLD Responses in Epilepsy Using Windkessel Models |
title | Identification of Negative BOLD Responses in Epilepsy Using Windkessel Models |
title_full | Identification of Negative BOLD Responses in Epilepsy Using Windkessel Models |
title_fullStr | Identification of Negative BOLD Responses in Epilepsy Using Windkessel Models |
title_full_unstemmed | Identification of Negative BOLD Responses in Epilepsy Using Windkessel Models |
title_short | Identification of Negative BOLD Responses in Epilepsy Using Windkessel Models |
title_sort | identification of negative bold responses in epilepsy using windkessel models |
topic | Neurology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8531269/ https://www.ncbi.nlm.nih.gov/pubmed/34690906 http://dx.doi.org/10.3389/fneur.2021.659081 |
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