Admittance Method for Estimating Local Field Potentials Generated in a Multi-Scale Neuron Model of the Hippocampus
Significant progress has been made toward model-based prediction of neral tissue activation in response to extracellular electrical stimulation, but challenges remain in the accurate and efficient estimation of distributed local field potentials (LFP). Analytical methods of estimating electric field...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2020-08-01
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| Series: | Frontiers in Computational Neuroscience |
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| Online Access: | https://www.frontiersin.org/article/10.3389/fncom.2020.00072/full |
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| author | Clayton S. Bingham Javad Paknahad Christopher B. C. Girard Kyle Loizos Jean-Marie C. Bouteiller Dong Song Gianluca Lazzi Theodore W. Berger |
| author_facet | Clayton S. Bingham Javad Paknahad Christopher B. C. Girard Kyle Loizos Jean-Marie C. Bouteiller Dong Song Gianluca Lazzi Theodore W. Berger |
| author_sort | Clayton S. Bingham |
| collection | DOAJ |
| description | Significant progress has been made toward model-based prediction of neral tissue activation in response to extracellular electrical stimulation, but challenges remain in the accurate and efficient estimation of distributed local field potentials (LFP). Analytical methods of estimating electric fields are a first-order approximation that may be suitable for model validation, but they are computationally expensive and cannot accurately capture boundary conditions in heterogeneous tissue. While there are many appropriate numerical methods of solving electric fields in neural tissue models, there isn't an established standard for mesh geometry nor a well-known rule for handling any mismatch in spatial resolution. Moreover, the challenge of misalignment between current sources and mesh nodes in a finite-element or resistor-network method volume conduction model needs to be further investigated. Therefore, using a previously published and validated multi-scale model of the hippocampus, the authors have formulated an algorithm for LFP estimation, and by extension, bidirectional communication between discretized and numerically solved volume conduction models and biologically detailed neural circuit models constructed in NEURON. Development of this algorithm required that we assess meshes of (i) unstructured tetrahedral and grid-based hexahedral geometries as well as (ii) differing approaches for managing the spatial misalignment of current sources and mesh nodes. The resulting algorithm is validated through the comparison of Admittance Method predicted evoked potentials with analytically estimated LFPs. Establishing this method is a critical step toward closed-loop integration of volume conductor and NEURON models that could lead to substantial improvement of the predictive power of multi-scale stimulation models of cortical tissue. These models may be used to deepen our understanding of hippocampal pathologies and the identification of efficacious electroceutical treatments. |
| first_indexed | 2024-12-21T04:51:32Z |
| format | Article |
| id | doaj.art-125ecd74efbd4025970610766ff55ceb |
| institution | Directory Open Access Journal |
| issn | 1662-5188 |
| language | English |
| last_indexed | 2024-12-21T04:51:32Z |
| publishDate | 2020-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Computational Neuroscience |
| spelling | doaj.art-125ecd74efbd4025970610766ff55ceb2022-12-21T19:15:26ZengFrontiers Media S.A.Frontiers in Computational Neuroscience1662-51882020-08-011410.3389/fncom.2020.00072532604Admittance Method for Estimating Local Field Potentials Generated in a Multi-Scale Neuron Model of the HippocampusClayton S. Bingham0Javad Paknahad1Christopher B. C. Girard2Kyle Loizos3Jean-Marie C. Bouteiller4Dong Song5Gianluca Lazzi6Theodore W. Berger7Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United StatesDepartment of Electrical Engineering, University of Southern California, Los Angeles, CA, United StatesDepartment of Biomedical Engineering, University of Southern California, Los Angeles, CA, United StatesDepartment of Electrical Engineering, University of Southern California, Los Angeles, CA, United StatesDepartment of Biomedical Engineering, University of Southern California, Los Angeles, CA, United StatesDepartment of Biomedical Engineering, University of Southern California, Los Angeles, CA, United StatesDepartment of Electrical Engineering, University of Southern California, Los Angeles, CA, United StatesDepartment of Biomedical Engineering, University of Southern California, Los Angeles, CA, United StatesSignificant progress has been made toward model-based prediction of neral tissue activation in response to extracellular electrical stimulation, but challenges remain in the accurate and efficient estimation of distributed local field potentials (LFP). Analytical methods of estimating electric fields are a first-order approximation that may be suitable for model validation, but they are computationally expensive and cannot accurately capture boundary conditions in heterogeneous tissue. While there are many appropriate numerical methods of solving electric fields in neural tissue models, there isn't an established standard for mesh geometry nor a well-known rule for handling any mismatch in spatial resolution. Moreover, the challenge of misalignment between current sources and mesh nodes in a finite-element or resistor-network method volume conduction model needs to be further investigated. Therefore, using a previously published and validated multi-scale model of the hippocampus, the authors have formulated an algorithm for LFP estimation, and by extension, bidirectional communication between discretized and numerically solved volume conduction models and biologically detailed neural circuit models constructed in NEURON. Development of this algorithm required that we assess meshes of (i) unstructured tetrahedral and grid-based hexahedral geometries as well as (ii) differing approaches for managing the spatial misalignment of current sources and mesh nodes. The resulting algorithm is validated through the comparison of Admittance Method predicted evoked potentials with analytically estimated LFPs. Establishing this method is a critical step toward closed-loop integration of volume conductor and NEURON models that could lead to substantial improvement of the predictive power of multi-scale stimulation models of cortical tissue. These models may be used to deepen our understanding of hippocampal pathologies and the identification of efficacious electroceutical treatments.https://www.frontiersin.org/article/10.3389/fncom.2020.00072/fullneural networkfinite-element (FE)local field potential (LFP)multi-scalenumerical algorithmvolume conduction |
| spellingShingle | Clayton S. Bingham Javad Paknahad Christopher B. C. Girard Kyle Loizos Jean-Marie C. Bouteiller Dong Song Gianluca Lazzi Theodore W. Berger Admittance Method for Estimating Local Field Potentials Generated in a Multi-Scale Neuron Model of the Hippocampus Frontiers in Computational Neuroscience neural network finite-element (FE) local field potential (LFP) multi-scale numerical algorithm volume conduction |
| title | Admittance Method for Estimating Local Field Potentials Generated in a Multi-Scale Neuron Model of the Hippocampus |
| title_full | Admittance Method for Estimating Local Field Potentials Generated in a Multi-Scale Neuron Model of the Hippocampus |
| title_fullStr | Admittance Method for Estimating Local Field Potentials Generated in a Multi-Scale Neuron Model of the Hippocampus |
| title_full_unstemmed | Admittance Method for Estimating Local Field Potentials Generated in a Multi-Scale Neuron Model of the Hippocampus |
| title_short | Admittance Method for Estimating Local Field Potentials Generated in a Multi-Scale Neuron Model of the Hippocampus |
| title_sort | admittance method for estimating local field potentials generated in a multi scale neuron model of the hippocampus |
| topic | neural network finite-element (FE) local field potential (LFP) multi-scale numerical algorithm volume conduction |
| url | https://www.frontiersin.org/article/10.3389/fncom.2020.00072/full |
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