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....
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2021-08-01
|
| Series: | Frontiers in Neuroscience |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fnins.2021.691701/full |
| _version_ | 1819132443012104192 |
|---|---|
| author | Chantel M. Charlebois Chantel M. Charlebois David J. Caldwell David J. Caldwell David J. Caldwell Sumientra M. Rampersad Andrew P. Janson Andrew P. Janson Jeffrey G. Ojemann Dana H. Brooks Rob S. MacLeod Rob S. MacLeod Christopher R. Butson Christopher R. Butson Christopher R. Butson Alan D. Dorval |
| author_facet | Chantel M. Charlebois Chantel M. Charlebois David J. Caldwell David J. Caldwell David J. Caldwell Sumientra M. Rampersad Andrew P. Janson Andrew P. Janson Jeffrey G. Ojemann Dana H. Brooks Rob S. MacLeod Rob S. MacLeod Christopher R. Butson Christopher R. Butson Christopher R. Butson Alan D. Dorval |
| author_sort | Chantel M. Charlebois |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-22T09:31:29Z |
| format | Article |
| id | doaj.art-55193184fe644afdb01776cb3c147e67 |
| institution | Directory Open Access Journal |
| issn | 1662-453X |
| language | English |
| last_indexed | 2024-12-22T09:31:29Z |
| publishDate | 2021-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroscience |
| spelling | doaj.art-55193184fe644afdb01776cb3c147e672022-12-21T18:30:56ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2021-08-011510.3389/fnins.2021.691701691701Validating Patient-Specific Finite Element Models of Direct Electrocortical StimulationChantel M. Charlebois0Chantel M. Charlebois1David J. Caldwell2David J. Caldwell3David J. Caldwell4Sumientra M. Rampersad5Andrew P. Janson6Andrew P. Janson7Jeffrey G. Ojemann8Dana H. Brooks9Rob S. MacLeod10Rob S. MacLeod11Christopher R. Butson12Christopher R. Butson13Christopher R. Butson14Alan D. Dorval15Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, United StatesScientific Computing and Imaging (SCI) Institute, University of Utah, Salt Lake City, UT, United StatesDepartment of Bioengineering, University of Washington, Seattle, WA, United StatesCenter for Neurotechnology, University of Washington, Seattle, WA, United StatesMedical Scientist Training Program, University of Washington, Seattle, WA, United StatesDepartment of Electrical and Computer Engineering, Northeastern University, Boston, MA, United StatesDepartment of Biomedical Engineering, University of Utah, Salt Lake City, UT, United StatesScientific Computing and Imaging (SCI) Institute, University of Utah, Salt Lake City, UT, United StatesDepartment of Neurological Surgery, University of Washington, Seattle, WA, United StatesDepartment of Electrical and Computer Engineering, Northeastern University, Boston, MA, United StatesDepartment of Biomedical Engineering, University of Utah, Salt Lake City, UT, United StatesScientific Computing and Imaging (SCI) Institute, University of Utah, Salt Lake City, UT, United StatesDepartment of Biomedical Engineering, University of Utah, Salt Lake City, UT, United StatesScientific Computing and Imaging (SCI) Institute, University of Utah, Salt Lake City, UT, United StatesDepartment of Neurology, Neurosurgery and Psychiatry, University of Utah, Salt Lake City, UT, United StatesDepartment of Biomedical Engineering, University of Utah, Salt Lake City, UT, United StatesDirect 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.https://www.frontiersin.org/articles/10.3389/fnins.2021.691701/fulldirect electrocortical stimulationelectrocorticographyfinite element modelingbioelectricity simulationpatient-specific modeling |
| spellingShingle | Chantel M. Charlebois Chantel M. Charlebois David J. Caldwell David J. Caldwell David J. Caldwell Sumientra M. Rampersad Andrew P. Janson Andrew P. Janson Jeffrey G. Ojemann Dana H. Brooks Rob S. MacLeod Rob S. MacLeod Christopher R. Butson Christopher R. Butson Christopher R. Butson Alan D. Dorval Validating Patient-Specific Finite Element Models of Direct Electrocortical Stimulation Frontiers in Neuroscience direct electrocortical stimulation electrocorticography finite element modeling bioelectricity simulation patient-specific modeling |
| title | Validating Patient-Specific Finite Element Models of Direct Electrocortical Stimulation |
| title_full | Validating Patient-Specific Finite Element Models of Direct Electrocortical Stimulation |
| title_fullStr | Validating Patient-Specific Finite Element Models of Direct Electrocortical Stimulation |
| title_full_unstemmed | Validating Patient-Specific Finite Element Models of Direct Electrocortical Stimulation |
| title_short | Validating Patient-Specific Finite Element Models of Direct Electrocortical Stimulation |
| title_sort | validating patient specific finite element models of direct electrocortical stimulation |
| topic | direct electrocortical stimulation electrocorticography finite element modeling bioelectricity simulation patient-specific modeling |
| url | https://www.frontiersin.org/articles/10.3389/fnins.2021.691701/full |
| work_keys_str_mv | AT chantelmcharlebois validatingpatientspecificfiniteelementmodelsofdirectelectrocorticalstimulation AT chantelmcharlebois validatingpatientspecificfiniteelementmodelsofdirectelectrocorticalstimulation AT davidjcaldwell validatingpatientspecificfiniteelementmodelsofdirectelectrocorticalstimulation AT davidjcaldwell validatingpatientspecificfiniteelementmodelsofdirectelectrocorticalstimulation AT davidjcaldwell validatingpatientspecificfiniteelementmodelsofdirectelectrocorticalstimulation AT sumientramrampersad validatingpatientspecificfiniteelementmodelsofdirectelectrocorticalstimulation AT andrewpjanson validatingpatientspecificfiniteelementmodelsofdirectelectrocorticalstimulation AT andrewpjanson validatingpatientspecificfiniteelementmodelsofdirectelectrocorticalstimulation AT jeffreygojemann validatingpatientspecificfiniteelementmodelsofdirectelectrocorticalstimulation AT danahbrooks validatingpatientspecificfiniteelementmodelsofdirectelectrocorticalstimulation AT robsmacleod validatingpatientspecificfiniteelementmodelsofdirectelectrocorticalstimulation AT robsmacleod validatingpatientspecificfiniteelementmodelsofdirectelectrocorticalstimulation AT christopherrbutson validatingpatientspecificfiniteelementmodelsofdirectelectrocorticalstimulation AT christopherrbutson validatingpatientspecificfiniteelementmodelsofdirectelectrocorticalstimulation AT christopherrbutson validatingpatientspecificfiniteelementmodelsofdirectelectrocorticalstimulation AT alanddorval validatingpatientspecificfiniteelementmodelsofdirectelectrocorticalstimulation |