Brainstorm-DUNEuro: An integrated and user-friendly Finite Element Method for modeling electromagnetic brain activity
•Full FEM pipeline for electrophysiology (e-phys) forward modeling is proposed and integrated into brainstorm.•Realistic FEM mesh head models can be generated from the MR data (T1w /and T2w).•Realistic conductivity tensors can be generated from DWI and mapped to the FEM mesh.•E-phys FEM forward mode...
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| Published in | NeuroImage (Orlando, Fla.) Vol. 267; p. 119851 |
|---|---|
| Main Authors | , , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
Elsevier Inc
15.02.2023
Elsevier Limited Elsevier |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1053-8119 1095-9572 1095-9572 |
| DOI | 10.1016/j.neuroimage.2022.119851 |
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| Abstract | •Full FEM pipeline for electrophysiology (e-phys) forward modeling is proposed and integrated into brainstorm.•Realistic FEM mesh head models can be generated from the MR data (T1w /and T2w).•Realistic conductivity tensors can be generated from DWI and mapped to the FEM mesh.•E-phys FEM forward modeling can be performed either from an easy-to-use GUI or scripting using the DUNEuro solver.•Full anatomical data (MRI/DWI) and functional data (EEG/MEG) are distributed for easy analysis replication.
Human brain activity generates scalp potentials (electroencephalography – EEG), intracranial potentials (iEEG), and external magnetic fields (magnetoencephalography – MEG). These electrophysiology (e-phys) signals can often be measured simultaneously for research and clinical applications. The forward problem involves modeling these signals at their sensors for a given equivalent current dipole configuration within the brain. While earlier researchers modeled the head as a simple set of isotropic spheres, today's magnetic resonance imaging (MRI) data allow for a detailed anatomic description of brain structures and anisotropic characterization of tissue conductivities. We present a complete pipeline, integrated into the Brainstorm software, that allows users to automatically generate an individual and accurate head model based on the subject's MRI and calculate the electromagnetic forward solution using the finite element method (FEM). The head model generation is performed by integrating the latest tools for MRI segmentation and FEM mesh generation. The final head model comprises the five main compartments: white-matter, gray-matter, CSF, skull, and scalp. The anisotropic brain conductivity model is based on the effective medium approach (EMA), which estimates anisotropic conductivity tensors from diffusion-weighted imaging (DWI) data. The FEM electromagnetic forward solution is obtained through the DUNEuro library, integrated into Brainstorm, and accessible with either a user-friendly graphical interface or scripting. With tutorials and example data sets available in an open-source format on the Brainstorm website, this integrated pipeline provides access to advanced FEM tools for electromagnetic modeling to a broader neuroscience community.
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| AbstractList | Human brain activity generates scalp potentials (electroencephalography - EEG), intracranial potentials (iEEG), and external magnetic fields (magnetoencephalography - MEG). These electrophysiology (e-phys) signals can often be measured simultaneously for research and clinical applications. The forward problem involves modeling these signals at their sensors for a given equivalent current dipole configuration within the brain. While earlier researchers modeled the head as a simple set of isotropic spheres, today's magnetic resonance imaging (MRI) data allow for a detailed anatomic description of brain structures and anisotropic characterization of tissue conductivities. We present a complete pipeline, integrated into the Brainstorm software, that allows users to automatically generate an individual and accurate head model based on the subject's MRI and calculate the electromagnetic forward solution using the finite element method (FEM). The head model generation is performed by integrating the latest tools for MRI segmentation and FEM mesh generation. The final head model comprises the five main compartments: white-matter, gray-matter, CSF, skull, and scalp. The anisotropic brain conductivity model is based on the effective medium approach (EMA), which estimates anisotropic conductivity tensors from diffusion-weighted imaging (DWI) data. The FEM electromagnetic forward solution is obtained through the DUNEuro library, integrated into Brainstorm, and accessible with either a user-friendly graphical interface or scripting. With tutorials and example data sets available in an open-source format on the Brainstorm website, this integrated pipeline provides access to advanced FEM tools for electromagnetic modeling to a broader neuroscience community.Human brain activity generates scalp potentials (electroencephalography - EEG), intracranial potentials (iEEG), and external magnetic fields (magnetoencephalography - MEG). These electrophysiology (e-phys) signals can often be measured simultaneously for research and clinical applications. The forward problem involves modeling these signals at their sensors for a given equivalent current dipole configuration within the brain. While earlier researchers modeled the head as a simple set of isotropic spheres, today's magnetic resonance imaging (MRI) data allow for a detailed anatomic description of brain structures and anisotropic characterization of tissue conductivities. We present a complete pipeline, integrated into the Brainstorm software, that allows users to automatically generate an individual and accurate head model based on the subject's MRI and calculate the electromagnetic forward solution using the finite element method (FEM). The head model generation is performed by integrating the latest tools for MRI segmentation and FEM mesh generation. The final head model comprises the five main compartments: white-matter, gray-matter, CSF, skull, and scalp. The anisotropic brain conductivity model is based on the effective medium approach (EMA), which estimates anisotropic conductivity tensors from diffusion-weighted imaging (DWI) data. The FEM electromagnetic forward solution is obtained through the DUNEuro library, integrated into Brainstorm, and accessible with either a user-friendly graphical interface or scripting. With tutorials and example data sets available in an open-source format on the Brainstorm website, this integrated pipeline provides access to advanced FEM tools for electromagnetic modeling to a broader neuroscience community. Human brain activity generates scalp potentials (electroencephalography – EEG), intracranial potentials (iEEG), and external magnetic fields (magnetoencephalography – MEG). These electrophysiology (e-phys) signals can often be measured simultaneously for research and clinical applications. The forward problem involves modeling these signals at their sensors for a given equivalent current dipole configuration within the brain. While earlier researchers modeled the head as a simple set of isotropic spheres, today’s magnetic resonance imaging (MRI) data allow for a detailed anatomic description of brain structures and anisotropic characterization of tissue conductivities. We present a complete pipeline, integrated into the Brainstorm software, that allows users to automatically generate an individual and accurate head model based on the subject’s MRI and calculate the electromagnetic forward solution using the finite element method (FEM). The head model generation is performed by integrating the latest tools for MRI segmentation and FEM mesh generation. The final head model comprises the five main compartments: white-matter, gray-matter, CSF, skull, and scalp. The anisotropic brain conductivity model is based on the effective medium approach (EMA), which estimates anisotropic conductivity tensors from diffusion-weighted imaging (DWI) data. The FEM electromagnetic forward solution is obtained through the DUNEuro library, integrated into Brainstorm, and accessible with either a user-friendly graphical interface or scripting. With tutorials and example data sets available in an open-source format on the Brainstorm website, this integrated pipeline provides access to advanced FEM tools for electromagnetic modeling to a broader neuroscience community. •Full FEM pipeline for electrophysiology (e-phys) forward modeling is proposed and integrated into brainstorm.•Realistic FEM mesh head models can be generated from the MR data (T1w /and T2w).•Realistic conductivity tensors can be generated from DWI and mapped to the FEM mesh.•E-phys FEM forward modeling can be performed either from an easy-to-use GUI or scripting using the DUNEuro solver.•Full anatomical data (MRI/DWI) and functional data (EEG/MEG) are distributed for easy analysis replication. Human brain activity generates scalp potentials (electroencephalography – EEG), intracranial potentials (iEEG), and external magnetic fields (magnetoencephalography – MEG). These electrophysiology (e-phys) signals can often be measured simultaneously for research and clinical applications. The forward problem involves modeling these signals at their sensors for a given equivalent current dipole configuration within the brain. While earlier researchers modeled the head as a simple set of isotropic spheres, today's magnetic resonance imaging (MRI) data allow for a detailed anatomic description of brain structures and anisotropic characterization of tissue conductivities. We present a complete pipeline, integrated into the Brainstorm software, that allows users to automatically generate an individual and accurate head model based on the subject's MRI and calculate the electromagnetic forward solution using the finite element method (FEM). The head model generation is performed by integrating the latest tools for MRI segmentation and FEM mesh generation. The final head model comprises the five main compartments: white-matter, gray-matter, CSF, skull, and scalp. The anisotropic brain conductivity model is based on the effective medium approach (EMA), which estimates anisotropic conductivity tensors from diffusion-weighted imaging (DWI) data. The FEM electromagnetic forward solution is obtained through the DUNEuro library, integrated into Brainstorm, and accessible with either a user-friendly graphical interface or scripting. With tutorials and example data sets available in an open-source format on the Brainstorm website, this integrated pipeline provides access to advanced FEM tools for electromagnetic modeling to a broader neuroscience community. [Display omitted] |
| ArticleNumber | 119851 |
| Author | Mosher, John C. Leahy, Richard M. Wolters, Carsten H. Antonakakis, Marios Tadel, Francois Schrader, Sophie Höltershinken, Malte Erdbrügger, Tim Garcia-Prieto, Juan Joshi, Anand Mead, Wayne Engwer, Christian Medani, Takfarinas |
| AuthorAffiliation | f Department of Neurology, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, United States c Harvard Medical School, Boston, Massachusetts, United States b Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, United States g Department of Applied Mathematics, University of Munster, Germany e School of Electrical and Computer Engineering, Technical University of Crete, Greece h Otto Creutzfeldt Center for Cognitive and Behavioral Neuroscience, University of Munster, Munster, Germany d Institute for Biomagnetism and Biosignalanalysis, University of Munster, Munster, Germany a Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA 90089, United States |
| AuthorAffiliation_xml | – name: f Department of Neurology, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, United States – name: b Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, United States – name: h Otto Creutzfeldt Center for Cognitive and Behavioral Neuroscience, University of Munster, Munster, Germany – name: c Harvard Medical School, Boston, Massachusetts, United States – name: d Institute for Biomagnetism and Biosignalanalysis, University of Munster, Munster, Germany – name: e School of Electrical and Computer Engineering, Technical University of Crete, Greece – name: g Department of Applied Mathematics, University of Munster, Germany – name: a Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA 90089, United States |
| Author_xml | – sequence: 1 givenname: Takfarinas orcidid: 0000-0003-0774-067X surname: Medani fullname: Medani, Takfarinas email: medani@usc.edu organization: Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA 90089, United States – sequence: 2 givenname: Juan surname: Garcia-Prieto fullname: Garcia-Prieto, Juan email: juangpc@gmail.com organization: Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, United States – sequence: 3 givenname: Francois surname: Tadel fullname: Tadel, Francois organization: Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA 90089, United States – sequence: 4 givenname: Marios surname: Antonakakis fullname: Antonakakis, Marios organization: Institute for Biomagnetism and Biosignalanalysis, University of Münster, Münster, Germany – sequence: 5 givenname: Tim surname: Erdbrügger fullname: Erdbrügger, Tim organization: Institute for Biomagnetism and Biosignalanalysis, University of Münster, Münster, Germany – sequence: 6 givenname: Malte orcidid: 0000-0002-8291-816X surname: Höltershinken fullname: Höltershinken, Malte organization: Institute for Biomagnetism and Biosignalanalysis, University of Münster, Münster, Germany – sequence: 7 givenname: Wayne surname: Mead fullname: Mead, Wayne organization: Department of Neurology, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, United States – sequence: 8 givenname: Sophie surname: Schrader fullname: Schrader, Sophie organization: Institute for Biomagnetism and Biosignalanalysis, University of Münster, Münster, Germany – sequence: 9 givenname: Anand surname: Joshi fullname: Joshi, Anand organization: Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA 90089, United States – sequence: 10 givenname: Christian surname: Engwer fullname: Engwer, Christian organization: Department of Applied Mathematics, University of Münster, Germany – sequence: 11 givenname: Carsten H. surname: Wolters fullname: Wolters, Carsten H. organization: Institute for Biomagnetism and Biosignalanalysis, University of Münster, Münster, Germany – sequence: 12 givenname: John C. surname: Mosher fullname: Mosher, John C. organization: Department of Neurology, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, United States – sequence: 13 givenname: Richard M. surname: Leahy fullname: Leahy, Richard M. organization: Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA 90089, United States |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/36599389$$D View this record in MEDLINE/PubMed |
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| Keywords | Finite element method DUNEuro Electrophysiology Head modeling Forward model Brainstorm EEG/MEG/SEEG |
| Language | English |
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| Snippet | •Full FEM pipeline for electrophysiology (e-phys) forward modeling is proposed and integrated into brainstorm.•Realistic FEM mesh head models can be generated... Human brain activity generates scalp potentials (electroencephalography - EEG), intracranial potentials (iEEG), and external magnetic fields... Human brain activity generates scalp potentials (electroencephalography – EEG), intracranial potentials (iEEG), and external magnetic fields... |
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| SubjectTerms | Accuracy Alzheimer's disease Anisotropy Automation Brain - diagnostic imaging Brain - pathology Brain Mapping - methods Brainstorm Cerebrospinal fluid DUNEuro EEG EEG/MEG/SEEG Electric Conductivity Electroencephalography Electroencephalography - methods Electrophysiology Finite Element Analysis Finite element method Finite volume method Forward model Geometry Head Head modeling Humans Image processing Interfaces Inverse problems Libraries Localization Magnetic resonance imaging Magnetoencephalography Magnetoencephalography - methods Models, Neurological Nervous system Neuroimaging Open source software Scalp Segmentation Substantia alba |
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| Title | Brainstorm-DUNEuro: An integrated and user-friendly Finite Element Method for modeling electromagnetic brain activity |
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