Value and limitations of intracranial recordings for validating electric field modeling for transcranial brain stimulation
Comparing electric field simulations from individualized head models against in-vivo intra-cranial recordings is considered the gold standard for direct validation of computational field modeling for transcranial brain stimulation and brain mapping techniques such as electro- and magnetoencephalogra...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2020-03-01
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| Series: | NeuroImage |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1053811919310225 |
| _version_ | 1818402097010311168 |
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| author | Oula Puonti Guilherme B. Saturnino Kristoffer H. Madsen Axel Thielscher |
| author_facet | Oula Puonti Guilherme B. Saturnino Kristoffer H. Madsen Axel Thielscher |
| author_sort | Oula Puonti |
| collection | DOAJ |
| description | Comparing electric field simulations from individualized head models against in-vivo intra-cranial recordings is considered the gold standard for direct validation of computational field modeling for transcranial brain stimulation and brain mapping techniques such as electro- and magnetoencephalography. The measurements also help to improve simulation accuracy by pinning down the factors having the largest influence on the simulations. Here we compare field simulations from four different automated pipelines against intracranial voltage recordings in an existing dataset of 14 epilepsy patients. We show that modeling differences in the pipelines lead to notable differences in the simulated electric field distributions that are often large enough to change the conclusions regarding the dose distribution and strength in the brain. Specifically, differences in the automatic segmentations of the head anatomy from structural magnetic resonance images are a major factor contributing to the observed field differences. However, the differences in the simulated fields are not reflected in the comparison between the simulations and intra-cranial measurements. This apparent mismatch is partly explained by the noisiness of the intra-cranial measurements, which renders comparisons between the methods inconclusive. We further demonstrate that a standard regression analysis, which ignores uncertainties in the simulations, leads to a strong bias in the estimated linear relationship between simulated and measured fields. Ignoring this bias leads to the incorrect conclusion that the models systematically misestimate the field strength in the brain. We propose a new Bayesian regression analysis of the data that yields unbiased parameter estimates, along with their uncertainties, and gives further insights to the fit between simulations and measurements. Specifically, the unbiased results give only weak support for systematic misestimations of the fields by the models. |
| first_indexed | 2024-12-14T08:02:56Z |
| format | Article |
| id | doaj.art-df58f2056c5b4a60b726ab500c4cfc46 |
| institution | Directory Open Access Journal |
| issn | 1095-9572 |
| language | English |
| last_indexed | 2024-12-14T08:02:56Z |
| publishDate | 2020-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | NeuroImage |
| spelling | doaj.art-df58f2056c5b4a60b726ab500c4cfc462022-12-21T23:10:18ZengElsevierNeuroImage1095-95722020-03-01208116431Value and limitations of intracranial recordings for validating electric field modeling for transcranial brain stimulationOula Puonti0Guilherme B. Saturnino1Kristoffer H. Madsen2Axel Thielscher3Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, Hvidovre, Denmark; Department of Health Technology, Technical University of Denmark, Kgs. Lyngby, DenmarkDanish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, Hvidovre, Denmark; Department of Health Technology, Technical University of Denmark, Kgs. Lyngby, DenmarkDanish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, Hvidovre, Denmark; Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kgs. Lyngby, DenmarkDanish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, Hvidovre, Denmark; Department of Health Technology, Technical University of Denmark, Kgs. Lyngby, Denmark; Corresponding author. Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, Section 714, Kettegaard Allé 30, 2650, Hvidovre, Denmark.Comparing electric field simulations from individualized head models against in-vivo intra-cranial recordings is considered the gold standard for direct validation of computational field modeling for transcranial brain stimulation and brain mapping techniques such as electro- and magnetoencephalography. The measurements also help to improve simulation accuracy by pinning down the factors having the largest influence on the simulations. Here we compare field simulations from four different automated pipelines against intracranial voltage recordings in an existing dataset of 14 epilepsy patients. We show that modeling differences in the pipelines lead to notable differences in the simulated electric field distributions that are often large enough to change the conclusions regarding the dose distribution and strength in the brain. Specifically, differences in the automatic segmentations of the head anatomy from structural magnetic resonance images are a major factor contributing to the observed field differences. However, the differences in the simulated fields are not reflected in the comparison between the simulations and intra-cranial measurements. This apparent mismatch is partly explained by the noisiness of the intra-cranial measurements, which renders comparisons between the methods inconclusive. We further demonstrate that a standard regression analysis, which ignores uncertainties in the simulations, leads to a strong bias in the estimated linear relationship between simulated and measured fields. Ignoring this bias leads to the incorrect conclusion that the models systematically misestimate the field strength in the brain. We propose a new Bayesian regression analysis of the data that yields unbiased parameter estimates, along with their uncertainties, and gives further insights to the fit between simulations and measurements. Specifically, the unbiased results give only weak support for systematic misestimations of the fields by the models.http://www.sciencedirect.com/science/article/pii/S1053811919310225Transcranial brain stimulationTDCSTACSVolume conductor modelErrors-in-variables regressionBayesian regression |
| spellingShingle | Oula Puonti Guilherme B. Saturnino Kristoffer H. Madsen Axel Thielscher Value and limitations of intracranial recordings for validating electric field modeling for transcranial brain stimulation NeuroImage Transcranial brain stimulation TDCS TACS Volume conductor model Errors-in-variables regression Bayesian regression |
| title | Value and limitations of intracranial recordings for validating electric field modeling for transcranial brain stimulation |
| title_full | Value and limitations of intracranial recordings for validating electric field modeling for transcranial brain stimulation |
| title_fullStr | Value and limitations of intracranial recordings for validating electric field modeling for transcranial brain stimulation |
| title_full_unstemmed | Value and limitations of intracranial recordings for validating electric field modeling for transcranial brain stimulation |
| title_short | Value and limitations of intracranial recordings for validating electric field modeling for transcranial brain stimulation |
| title_sort | value and limitations of intracranial recordings for validating electric field modeling for transcranial brain stimulation |
| topic | Transcranial brain stimulation TDCS TACS Volume conductor model Errors-in-variables regression Bayesian regression |
| url | http://www.sciencedirect.com/science/article/pii/S1053811919310225 |
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