Validating Patient-Specific Finite Element Models of Direct Electrocortical Stimulation
Direct electrocortical stimulation (DECS) with electrocorticography electrodes is an established therapy for epilepsy and an emerging application for stroke rehabilitation and brain-computer interfaces. However, the electrophysiological mechanisms that result in a therapeutic effect remain unclear....
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| Published in | Frontiers in neuroscience Vol. 15; p. 691701 |
|---|---|
| Main Authors | , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Lausanne
Frontiers Research Foundation
02.08.2021
Frontiers Media S.A |
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| Online Access | Get full text |
| ISSN | 1662-453X 1662-4548 1662-453X |
| DOI | 10.3389/fnins.2021.691701 |
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| Abstract | Direct electrocortical stimulation (DECS) with electrocorticography electrodes is an established therapy for epilepsy and an emerging application for stroke rehabilitation and brain-computer interfaces. However, the electrophysiological mechanisms that result in a therapeutic effect remain unclear. Patient-specific computational models are promising tools to predict the voltages in the brain and better understand the neural and clinical response to DECS, but the accuracy of such models has not been directly validated in humans. A key hurdle to modeling DECS is accurately locating the electrodes on the cortical surface due to brain shift after electrode implantation. Despite the inherent uncertainty introduced by brain shift, the effects of electrode localization parameters have not been investigated. The goal of this study was to validate patient-specific computational models of DECS against
in vivo
voltage recordings obtained during DECS and quantify the effects of electrode localization parameters on simulated voltages on the cortical surface. We measured intracranial voltages in six epilepsy patients during DECS and investigated the following electrode localization parameters: principal axis, Hermes, and Dykstra electrode projection methods combined with 0, 1, and 2 mm of cerebral spinal fluid (CSF) below the electrodes. Greater CSF depth between the electrode and cortical surface increased model errors and decreased predicted voltage accuracy. The electrode localization parameters that best estimated the recorded voltages across six patients with varying amounts of brain shift were the Hermes projection method and a CSF depth of 0 mm (
r
= 0.92 and linear regression slope = 1.21). These results are the first to quantify the effects of electrode localization parameters with
in vivo
intracranial recordings and may serve as the basis for future studies investigating the neuronal and clinical effects of DECS for epilepsy, stroke, and other emerging closed-loop applications. |
|---|---|
| AbstractList | Direct electrocortical stimulation (DECS) with electrocorticography electrodes is an established therapy for epilepsy and an emerging application for stroke rehabilitation and brain-computer interfaces. However, the electrophysiological mechanisms that result in a therapeutic effect remain unclear. Patient-specific computational models are promising tools to predict the voltages in the brain and better understand the neural and clinical response to DECS, but the accuracy of such models has not been directly validated in humans. A key hurdle to modeling DECS is accurately locating the electrodes on the cortical surface due to brain shift after electrode implantation. Despite the inherent uncertainty introduced by brain shift, the effects of electrode localization parameters have not been investigated. The goal of this study was to validate patient-specific computational models of DECS against in vivo voltage recordings obtained during DECS and quantify the effects of electrode localization parameters on simulated voltages on the cortical surface. We measured intracranial voltages in six epilepsy patients during DECS and investigated the following electrode localization parameters: principal axis, Hermes, and Dykstra electrode projection methods combined with 0, 1, and 2 mm of cerebral spinal fluid (CSF) below the electrodes. Greater CSF depth between the electrode and cortical surface increased model errors and decreased predicted voltage accuracy. The electrode localization parameters that best estimated the recorded voltages across six patients with varying amounts of brain shift were the Hermes projection method and a CSF depth of 0 mm (r = 0.92 and linear regression slope = 1.21). These results are the first to quantify the effects of electrode localization parameters with in vivo intracranial recordings and may serve as the basis for future studies investigating the neuronal and clinical effects of DECS for epilepsy, stroke, and other emerging closed-loop applications.Direct electrocortical stimulation (DECS) with electrocorticography electrodes is an established therapy for epilepsy and an emerging application for stroke rehabilitation and brain-computer interfaces. However, the electrophysiological mechanisms that result in a therapeutic effect remain unclear. Patient-specific computational models are promising tools to predict the voltages in the brain and better understand the neural and clinical response to DECS, but the accuracy of such models has not been directly validated in humans. A key hurdle to modeling DECS is accurately locating the electrodes on the cortical surface due to brain shift after electrode implantation. Despite the inherent uncertainty introduced by brain shift, the effects of electrode localization parameters have not been investigated. The goal of this study was to validate patient-specific computational models of DECS against in vivo voltage recordings obtained during DECS and quantify the effects of electrode localization parameters on simulated voltages on the cortical surface. We measured intracranial voltages in six epilepsy patients during DECS and investigated the following electrode localization parameters: principal axis, Hermes, and Dykstra electrode projection methods combined with 0, 1, and 2 mm of cerebral spinal fluid (CSF) below the electrodes. Greater CSF depth between the electrode and cortical surface increased model errors and decreased predicted voltage accuracy. The electrode localization parameters that best estimated the recorded voltages across six patients with varying amounts of brain shift were the Hermes projection method and a CSF depth of 0 mm (r = 0.92 and linear regression slope = 1.21). These results are the first to quantify the effects of electrode localization parameters with in vivo intracranial recordings and may serve as the basis for future studies investigating the neuronal and clinical effects of DECS for epilepsy, stroke, and other emerging closed-loop applications. Direct electrocortical stimulation (DECS) with electrocorticography electrodes is an established therapy for epilepsy and an emerging application for stroke rehabilitation and brain-computer interfaces. However, the electrophysiological mechanisms that result in a therapeutic effect remain unclear. Patient-specific computational models are promising tools to predict the voltages in the brain and better understand the neural and clinical response to DECS, but the accuracy of such models has not been directly validated in humans. A key hurdle to modeling DECS is accurately locating the electrodes on the cortical surface due to brain shift after electrode implantation. Despite the inherent uncertainty introduced by brain shift, the effects of electrode localization parameters have not been investigated. The goal of this study was to validate patient-specific computational models of DECS against in vivo voltage recordings obtained during DECS and quantify the effects of electrode localization parameters on simulated voltages on the cortical surface. We measured intracranial voltages in six epilepsy patients during DECS and investigated the following electrode localization parameters: principal axis, Hermes, and Dykstra electrode projection methods combined with 0, 1, and 2 mm of cerebral spinal fluid (CSF) below the electrodes. Greater CSF depth between the electrode and cortical surface increased model errors and decreased predicted voltage accuracy. The electrode localization parameters that best estimated the recorded voltages across six patients with varying amounts of brain shift were the Hermes projection method and a CSF depth of 0 mm (r = 0.92 and linear regression slope = 1.21). These results are the first to quantify the effects of electrode localization parameters with in vivo intracranial recordings and may serve as the basis for future studies investigating the neuronal and clinical effects of DECS for epilepsy, stroke, and other emerging closed-loop applications. Direct electrocortical stimulation (DECS) with electrocorticography electrodes is an established therapy for epilepsy and an emerging application for stroke rehabilitation and brain-computer interfaces. However, the electrophysiological mechanisms that result in a therapeutic effect remain unclear. Patient-specific computational models are promising tools to predict the voltages in the brain and better understand the neural and clinical response to DECS, but the accuracy of such models has not been directly validated in humans. A key hurdle to modeling DECS is accurately locating the electrodes on the cortical surface due to brain shift after electrode implantation. Despite the inherent uncertainty introduced by brain shift, the effects of electrode localization parameters have not been investigated. The goal of this study was to validate patient-specific computational models of DECS against in vivo voltage recordings obtained during DECS and quantify the effects of electrode localization parameters on simulated voltages on the cortical surface. We measured intracranial voltages in six epilepsy patients during DECS and investigated the following electrode localization parameters: principal axis, Hermes, and Dykstra electrode projection methods combined with 0, 1, and 2 mm of cerebral spinal fluid (CSF) below the electrodes. Greater CSF depth between the electrode and cortical surface increased model errors and decreased predicted voltage accuracy. The electrode localization parameters that best estimated the recorded voltages across six patients with varying amounts of brain shift were the Hermes projection method and a CSF depth of 0 mm ( r = 0.92 and linear regression slope = 1.21). These results are the first to quantify the effects of electrode localization parameters with in vivo intracranial recordings and may serve as the basis for future studies investigating the neuronal and clinical effects of DECS for epilepsy, stroke, and other emerging closed-loop applications. Direct electrocortical stimulation (DECS) with electrocorticography electrodes is an established therapy for epilepsy and an emerging application for stroke rehabilitation and brain-computer interfaces. However, the electrophysiological mechanisms that result in a therapeutic effect remain unclear. Patient-specific computational models are promising tools to predict the voltages in the brain and better understand the neural and clinical response to DECS, but the accuracy of such models has not been directly validated in humans. A key hurdle to modeling DECS is accurately locating the electrodes on the cortical surface due to brain shift after electrode implantation. Despite the inherent uncertainty introduced by brain shift, the effects of electrode localization parameters have not been investigated. The goal of this study was to validate patient-specific computational models of DECS against in vivo voltage recordings obtained during DECS and quantify the effects of electrode localization parameters on simulated voltages on the cortical surface. We measured intracranial voltages in six epilepsy patients during DECS and investigated the following electrode localization parameters: principal axis, Hermes, and Dykstra electrode projection methods combined with 0, 1, and 2 mm of cerebral spinal fluid (CSF) below the electrodes. Greater CSF depth between the electrode and cortical surface increased model errors and decreased predicted voltage accuracy. The electrode localization parameters that best predicted the recorded voltages across six patients with varying amounts of brain shift were the Hermes projection method and a CSF depth of 0 mm (r=0.92 and linear regression slope = 1.21). These results are the first to quantify the effects of electrode localization parameters with in vivo intracranial recordings and may serve as the basis for future studies investigating the neuronal and clinical effects of DECS for epilepsy, stroke, and other emerging closed-loop applications. |
| Author | Rampersad, Sumientra M. Caldwell, David J. MacLeod, Rob S. Dorval, Alan D. Ojemann, Jeffrey G. Charlebois, Chantel M. Janson, Andrew P. Brooks, Dana H. Butson, Christopher R. |
| AuthorAffiliation | 6 Department of Electrical and Computer Engineering, Northeastern University , Boston, MA , United States 3 Department of Bioengineering, University of Washington , Seattle, WA , United States 4 Center for Neurotechnology, University of Washington , Seattle, WA , United States 1 Department of Biomedical Engineering, University of Utah , Salt Lake City, UT , United States 8 Department of Neurology, Neurosurgery and Psychiatry, University of Utah , Salt Lake City, UT , United States 7 Department of Neurological Surgery, University of Washington , Seattle, WA , United States 5 Medical Scientist Training Program, University of Washington , Seattle, WA , United States 2 Scientific Computing and Imaging (SCI) Institute, University of Utah , Salt Lake City, UT , United States |
| AuthorAffiliation_xml | – name: 8 Department of Neurology, Neurosurgery and Psychiatry, University of Utah , Salt Lake City, UT , United States – name: 3 Department of Bioengineering, University of Washington , Seattle, WA , United States – name: 4 Center for Neurotechnology, University of Washington , Seattle, WA , United States – name: 7 Department of Neurological Surgery, University of Washington , Seattle, WA , United States – name: 5 Medical Scientist Training Program, University of Washington , Seattle, WA , United States – name: 2 Scientific Computing and Imaging (SCI) Institute, University of Utah , Salt Lake City, UT , United States – name: 6 Department of Electrical and Computer Engineering, Northeastern University , Boston, MA , United States – name: 1 Department of Biomedical Engineering, University of Utah , Salt Lake City, UT , United States |
| Author_xml | – sequence: 1 givenname: Chantel M. surname: Charlebois fullname: Charlebois, Chantel M. – sequence: 2 givenname: David J. surname: Caldwell fullname: Caldwell, David J. – sequence: 3 givenname: Sumientra M. surname: Rampersad fullname: Rampersad, Sumientra M. – sequence: 4 givenname: Andrew P. surname: Janson fullname: Janson, Andrew P. – sequence: 5 givenname: Jeffrey G. surname: Ojemann fullname: Ojemann, Jeffrey G. – sequence: 6 givenname: Dana H. surname: Brooks fullname: Brooks, Dana H. – sequence: 7 givenname: Rob S. surname: MacLeod fullname: MacLeod, Rob S. – sequence: 8 givenname: Christopher R. surname: Butson fullname: Butson, Christopher R. – sequence: 9 givenname: Alan D. surname: Dorval fullname: Dorval, Alan D. |
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| CitedBy_id | crossref_primary_10_1038_s41598_023_28769_9 crossref_primary_10_1016_j_cmpb_2023_107889 crossref_primary_10_1038_s41598_021_03414_5 crossref_primary_10_2139_ssrn_4049696 crossref_primary_10_1016_j_compbiomed_2022_106407 |
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| Copyright | 2021. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. Copyright © 2021 Charlebois, Caldwell, Rampersad, Janson, Ojemann, Brooks, MacLeod, Butson and Dorval. Copyright © 2021 Charlebois, Caldwell, Rampersad, Janson, Ojemann, Brooks, MacLeod, Butson and Dorval. 2021 Charlebois, Caldwell, Rampersad, Janson, Ojemann, Brooks, MacLeod, Butson and Dorval |
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| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 This article was submitted to Brain Imaging Methods, a section of the journal Frontiers in Neuroscience Reviewed by: Georgios Naros, University of Tübingen, Germany; Clayton Scott Bingham, Case Western Reserve University, United States Edited by: Dong Song, University of Southern California, United States |
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| Title | Validating Patient-Specific Finite Element Models of Direct Electrocortical Stimulation |
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