Localization of the epileptogenic network from scalp EEG using a patient-specific whole-brain model
Computational modeling is a key tool for elucidating the neuronal mechanisms underlying epileptic activity. Despite considerable progress, existing models often lack realistic accuracy in representing electrophysiological epileptic activity. In this study, we used a comprehensive human brain model b...
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Published in | Harvard data science review Vol. 9; no. 1; pp. 18 - 37 |
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Main Authors | , , , , , , , , |
Format | Journal Article |
Language | English |
Published |
255 Main Street, 9th Floor, Cambridge, Massachusetts 02142, USA
MIT Press
03.03.2025
MIT Press Journals, The The MIT Press |
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Online Access | Get full text |
ISSN | 2472-1751 2472-1751 2644-2353 |
DOI | 10.1162/netn_a_00418 |
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Abstract | Computational modeling is a key tool for elucidating the neuronal mechanisms underlying epileptic activity. Despite considerable progress, existing models often lack realistic accuracy in representing electrophysiological epileptic activity. In this study, we used a comprehensive human brain model based on a neural mass model, which is tailored to the layered structure of the neocortex and incorporates patient-specific imaging data. This approach allowed the simulation of scalp EEGs in an epileptic patient suffering from type 2 focal cortical dysplasia (FCD). The simulation specifically addressed epileptic activity induced by FCD, faithfully reproducing intracranial interictal epileptiform discharges (IEDs) recorded with electrocorticography. For constructing the patient-specific scalp EEG, we carefully defined a clear delineation of the epileptogenic zone by numerical simulations to ensure fidelity to the topography, polarity, and diffusion characteristics of IEDs. This nuanced approach improves the accuracy of the simulated EEG signal, provides a more accurate representation of epileptic activity, and enhances our understanding of the mechanism behind the epileptogenic networks. The accuracy of the model was confirmed by a postoperative reevaluation with a secondary EEG simulation that was consistent with the lesion’s removal. Ultimately, this personalized approach may prove instrumental in optimizing and tailoring epilepsy treatment strategies.
This study aimed to create a neurophysiologically grounded computer model of focal epilepsy. This is a feature frequently lacking to simulations in this domain, making the translation from in silico to in vivo results questionable and difficult to understand for clinical electrophysiologists. We adapted a whole-brain neuronal mass model for EEG generation in various conscious states to replicate the EEG patterns of a type 2 focal cortical dysplasia (FCD), a condition associated with epilepsy. Our model successfully simulated both intracranial and scalp EEGs of a complex patient with type 2 FCD, who was later cured through surgery. Importantly, the simulated lesion location matched the patient’s epileptogenic zone, and removing this area in the model eliminated epileptic activity in the EEG, demonstrating the model’s accuracy. |
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AbstractList | Computational modeling is a key tool for elucidating the neuronal mechanisms underlying epileptic activity. Despite considerable progress, existing models often lack realistic accuracy in representing electrophysiological epileptic activity. In this study, we used a comprehensive human brain model based on a neural mass model, which is tailored to the layered structure of the neocortex and incorporates patient-specific imaging data. This approach allowed the simulation of scalp EEGs in an epileptic patient suffering from type 2 focal cortical dysplasia (FCD). The simulation specifically addressed epileptic activity induced by FCD, faithfully reproducing intracranial interictal epileptiform discharges (IEDs) recorded with electrocorticography. For constructing the patient-specific scalp EEG, we carefully defined a clear delineation of the epileptogenic zone by numerical simulations to ensure fidelity to the topography, polarity, and diffusion characteristics of IEDs. This nuanced approach improves the accuracy of the simulated EEG signal, provides a more accurate representation of epileptic activity, and enhances our understanding of the mechanism behind the epileptogenic networks. The accuracy of the model was confirmed by a postoperative reevaluation with a secondary EEG simulation that was consistent with the lesion's removal. Ultimately, this personalized approach may prove instrumental in optimizing and tailoring epilepsy treatment strategies.Computational modeling is a key tool for elucidating the neuronal mechanisms underlying epileptic activity. Despite considerable progress, existing models often lack realistic accuracy in representing electrophysiological epileptic activity. In this study, we used a comprehensive human brain model based on a neural mass model, which is tailored to the layered structure of the neocortex and incorporates patient-specific imaging data. This approach allowed the simulation of scalp EEGs in an epileptic patient suffering from type 2 focal cortical dysplasia (FCD). The simulation specifically addressed epileptic activity induced by FCD, faithfully reproducing intracranial interictal epileptiform discharges (IEDs) recorded with electrocorticography. For constructing the patient-specific scalp EEG, we carefully defined a clear delineation of the epileptogenic zone by numerical simulations to ensure fidelity to the topography, polarity, and diffusion characteristics of IEDs. This nuanced approach improves the accuracy of the simulated EEG signal, provides a more accurate representation of epileptic activity, and enhances our understanding of the mechanism behind the epileptogenic networks. The accuracy of the model was confirmed by a postoperative reevaluation with a secondary EEG simulation that was consistent with the lesion's removal. Ultimately, this personalized approach may prove instrumental in optimizing and tailoring epilepsy treatment strategies. Computational modeling is a key tool for elucidating the neuronal mechanisms underlying epileptic activity. Despite considerable progress, existing models often lack realistic accuracy in representing electrophysiological epileptic activity. In this study, we used a comprehensive human brain model based on a neural mass model, which is tailored to the layered structure of the neocortex and incorporates patient-specific imaging data. This approach allowed the simulation of scalp EEGs in an epileptic patient suffering from type 2 focal cortical dysplasia (FCD). The simulation specifically addressed epileptic activity induced by FCD, faithfully reproducing intracranial interictal epileptiform discharges (IEDs) recorded with electrocorticography. For constructing the patient-specific scalp EEG, we carefully defined a clear delineation of the epileptogenic zone by numerical simulations to ensure fidelity to the topography, polarity, and diffusion characteristics of IEDs. This nuanced approach improves the accuracy of the simulated EEG signal, provides a more accurate representation of epileptic activity, and enhances our understanding of the mechanism behind the epileptogenic networks. The accuracy of the model was confirmed by a postoperative reevaluation with a secondary EEG simulation that was consistent with the lesion’s removal. Ultimately, this personalized approach may prove instrumental in optimizing and tailoring epilepsy treatment strategies. This study aimed to create a neurophysiologically grounded computer model of focal epilepsy. This is a feature frequently lacking to simulations in this domain, making the translation from in silico to in vivo results questionable and difficult to understand for clinical electrophysiologists. We adapted a whole-brain neuronal mass model for EEG generation in various conscious states to replicate the EEG patterns of a type 2 focal cortical dysplasia (FCD), a condition associated with epilepsy. Our model successfully simulated both intracranial and scalp EEGs of a complex patient with type 2 FCD, who was later cured through surgery. Importantly, the simulated lesion location matched the patient’s epileptogenic zone, and removing this area in the model eliminated epileptic activity in the EEG, demonstrating the model’s accuracy. Computational modeling is a key tool for elucidating the neuronal mechanisms underlying epileptic activity. Despite considerable progress, existing models often lack realistic accuracy in representing electrophysiological epileptic activity. In this study, we used a comprehensive human brain model based on a neural mass model, which is tailored to the layered structure of the neocortex and incorporates patient-specific imaging data. This approach allowed the simulation of scalp EEGs in an epileptic patient suffering from type 2 focal cortical dysplasia (FCD). The simulation specifically addressed epileptic activity induced by FCD, faithfully reproducing intracranial interictal epileptiform discharges (IEDs) recorded with electrocorticography. For constructing the patient-specific scalp EEG, we carefully defined a clear delineation of the epileptogenic zone by numerical simulations to ensure fidelity to the topography, polarity, and diffusion characteristics of IEDs. This nuanced approach improves the accuracy of the simulated EEG signal, provides a more accurate representation of epileptic activity, and enhances our understanding of the mechanism behind the epileptogenic networks. The accuracy of the model was confirmed by a postoperative reevaluation with a secondary EEG simulation that was consistent with the lesion’s removal. Ultimately, this personalized approach may prove instrumental in optimizing and tailoring epilepsy treatment strategies. This study aimed to create a neurophysiologically grounded computer model of focal epilepsy. This is a feature frequently lacking to simulations in this domain, making the translation from in silico to in vivo results questionable and difficult to understand for clinical electrophysiologists. We adapted a whole-brain neuronal mass model for EEG generation in various conscious states to replicate the EEG patterns of a type 2 focal cortical dysplasia (FCD), a condition associated with epilepsy. Our model successfully simulated both intracranial and scalp EEGs of a complex patient with type 2 FCD, who was later cured through surgery. Importantly, the simulated lesion location matched the patient’s epileptogenic zone, and removing this area in the model eliminated epileptic activity in the EEG, demonstrating the model’s accuracy. Computational modeling is a key tool for elucidating the neuronal mechanisms underlying epileptic activity. Despite considerable progress, existing models often lack realistic accuracy in representing electrophysiological epileptic activity. In this study, we used a comprehensive human brain model based on a neural mass model, which is tailored to the layered structure of the neocortex and incorporates patient-specific imaging data. This approach allowed the simulation of scalp EEGs in an epileptic patient suffering from type 2 focal cortical dysplasia (FCD). The simulation specifically addressed epileptic activity induced by FCD, faithfully reproducing intracranial interictal epileptiform discharges (IEDs) recorded with electrocorticography. For constructing the patient-specific scalp EEG, we carefully defined a clear delineation of the epileptogenic zone by numerical simulations to ensure fidelity to the topography, polarity, and diffusion characteristics of IEDs. This nuanced approach improves the accuracy of the simulated EEG signal, provides a more accurate representation of epileptic activity, and enhances our understanding of the mechanism behind the epileptogenic networks. The accuracy of the model was confirmed by a postoperative reevaluation with a secondary EEG simulation that was consistent with the lesion’s removal. Ultimately, this personalized approach may prove instrumental in optimizing and tailoring epilepsy treatment strategies. Computational modeling is a key tool for elucidating the neuronal mechanisms underlying epileptic activity. Despite considerable progress, existing models often lack realistic accuracy in representing electrophysiological epileptic activity. In this study, we used a comprehensive human brain model based on a neural mass model, which is tailored to the layered structure of the neocortex and incorporates patient-specific imaging data. This approach allowed the simulation of scalp EEGs in an epileptic patient suffering from type 2 focal cortical dysplasia (FCD). The simulation specifically addressed epileptic activity induced by FCD, faithfully reproducing intracranial interictal epileptiform discharges (IEDs) recorded with electrocorticography. For constructing the patient-specific scalp EEG, we carefully defined a clear delineation of the epileptogenic zone by numerical simulations to ensure fidelity to the topography, polarity, and diffusion characteristics of IEDs. This nuanced approach improves the accuracy of the simulated EEG signal, provides a more accurate representation of epileptic activity, and enhances our understanding of the mechanism behind the epileptogenic networks. The accuracy of the model was confirmed by a postoperative reevaluation with a secondary EEG simulation that was consistent with the lesion’s removal. Ultimately, this personalized approach may prove instrumental in optimizing and tailoring epilepsy treatment strategies.Author Summary: This study aimed to create a neurophysiologically grounded computer model of focal epilepsy. This is a feature frequently lacking to simulations in this domain, making the translation from in silico to in vivo results questionable and difficult to understand for clinical electrophysiologists. We adapted a whole-brain neuronal mass model for EEG generation in various conscious states to replicate the EEG patterns of a type 2 focal cortical dysplasia (FCD), a condition associated with epilepsy. Our model successfully simulated both intracranial and scalp EEGs of a complex patient with type 2 FCD, who was later cured through surgery. Importantly, the simulated lesion location matched the patient’s epileptogenic zone, and removing this area in the model eliminated epileptic activity in the EEG, demonstrating the model’s accuracy. |
Author | Wendling, Fabrice Nica, Anca Köksal-Ersöz, Elif Benard, Adrien Calas, Tristan Maliia, Mihai Dragos Denoyer, Yves Benquet, Pascal Yochum, Maxime |
AuthorAffiliation | “Van Gogh” Epilepsy Surgery Unit, Neurology Department, CIC 1414, University Hospital, Rennes, France Neurology Department, Lorient Hospital, Lorient, France University of Rennes, INSERM, LTSI-U1099, Rennes, France |
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Keywords | Focal cortical dysplasia (FCD) Interictal epileptiform discharges (IED) Epilepsy surgery Digital brain ECoG EEG modeling |
Language | English |
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SubjectTerms | Accuracy Bioengineering Brain Cerebral cortex Consciousness Digital brain Dysplasia ECoG EEG EEG modeling Electroencephalography Epilepsy Epilepsy surgery Firing pattern Focal cortical dysplasia (FCD) Interictal epileptiform discharges (IED) Lesions Life Sciences Localization Neocortex Neuroimaging Patients Simulation |
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Title | Localization of the epileptogenic network from scalp EEG using a patient-specific whole-brain model |
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