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Wang B, Peterchev AV, Gaugain G, Ilmoniemi RJ, Grill WM, Bikson M, Nikolayev D. Quasistatic approximation in neuromodulation. J Neural Eng 2024; 21:041002. [PMID: 38994790 DOI: 10.1088/1741-2552/ad625e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Accepted: 06/28/2024] [Indexed: 07/13/2024]
Abstract
We define and explain the quasistatic approximation (QSA) as applied to field modeling for electrical and magnetic stimulation. Neuromodulation analysis pipelines include discrete stages, and QSA is applied specifically when calculating the electric and magnetic fields generated in tissues by a given stimulation dose. QSA simplifies the modeling equations to support tractable analysis, enhanced understanding, and computational efficiency. The application of QSA in neuromodulation is based on four underlying assumptions: (A1) no wave propagation or self-induction in tissue, (A2) linear tissue properties, (A3) purely resistive tissue, and (A4) non-dispersive tissue. As a consequence of these assumptions, each tissue is assigned a fixed conductivity, and the simplified equations (e.g. Laplace's equation) are solved for the spatial distribution of the field, which is separated from the field's temporal waveform. Recognizing that electrical tissue properties may be more complex, we explain how QSA can be embedded in parallel or iterative pipelines to model frequency dependence or nonlinearity of conductivity. We survey the history and validity of QSA across specific applications, such as microstimulation, deep brain stimulation, spinal cord stimulation, transcranial electrical stimulation, and transcranial magnetic stimulation. The precise definition and explanation of QSA in neuromodulation are essential for rigor when using QSA models or testing their limits.
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Affiliation(s)
- Boshuo Wang
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC 27710, United States of America
| | - Angel V Peterchev
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC 27710, United States of America
- Department of Electrical and Computer Engineering, Duke University, Durham, NC 27708, United States of America
- Department of Biomedical Engineering, Duke University, Durham, NC 27708, United States of America
- Department of Neurosurgery, Duke University, Durham, NC 27710, United States of America
| | - Gabriel Gaugain
- Institut d'Électronique et des Technologies du numéRique (IETR UMR 6164), CNRS / University of Rennes, 35000 Rennes, France
| | - Risto J Ilmoniemi
- Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Espoo, Finland
| | - Warren M Grill
- Department of Electrical and Computer Engineering, Duke University, Durham, NC 27708, United States of America
- Department of Biomedical Engineering, Duke University, Durham, NC 27708, United States of America
- Department of Neurosurgery, Duke University, Durham, NC 27710, United States of America
- Department of Neurobiology, Duke University, Durham, NC 27710, United States of America
| | - Marom Bikson
- The City College of New York, New York, NY 11238, United States of America
| | - Denys Nikolayev
- Institut d'Électronique et des Technologies du numéRique (IETR UMR 6164), CNRS / University of Rennes, 35000 Rennes, France
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Wartman WA, Nuñez Ponasso G, Qi Z, Haueisen J, Maess B, Knösche TR, Weise K, Noetscher GM, Raij T, Makaroff SN. Fast and Accurate EEG/MEG BEM-Based Forward Problem Solution for High-Resolution Head Models. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.07.598024. [PMID: 38895215 PMCID: PMC11185788 DOI: 10.1101/2024.06.07.598024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/21/2024]
Abstract
A BEM (boundary element method) based approach is developed to accurately solve an EEG/MEG forward problem for a modern high-resolution head model in approximately 60 seconds using a common workstation. The method utilizes a charge-based BEM with fast multipole acceleration (BEM-FMM) and a "smart" mesh pre-refinement (called b-refinement) close to the singular source(s). No costly matrix-filling or direct solution steps typical for the standard BEM are required; the method generates on-skin voltages as well as MEG magnetic fields for high-resolution head models in approximately 60 seconds after initial model assembly. The method is verified both theoretically and experimentally.
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Affiliation(s)
- William A Wartman
- Dept. of Electrical and Computer Engineering, Worcester Polytechnic Institute, Worcester, MA, USA
| | - Guillermo Nuñez Ponasso
- Dept. of Electrical and Computer Engineering, Worcester Polytechnic Institute, Worcester, MA, USA
| | - Zhen Qi
- Dept. of Electrical and Computer Engineering, Worcester Polytechnic Institute, Worcester, MA, USA
| | | | - Burkhard Maess
- Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Thomas R Knösche
- Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Konstantin Weise
- Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Gregory M Noetscher
- Dept. of Electrical and Computer Engineering, Worcester Polytechnic Institute, Worcester, MA, USA
| | - Tommi Raij
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Sergey N Makaroff
- Dept. of Electrical and Computer Engineering, Worcester Polytechnic Institute, Worcester, MA, USA
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Nuñez Ponasso G, McSweeney RC, Wartman WA, Lai P, Haueisen J, Maess B, Knösche TR, Weise K, Noetscher GM, Raij T, Makaroff SN. Accuracy of dipole source reconstruction in the 3-layer BEM model against the 5-layer BEM-FMM model. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.17.594750. [PMID: 38826206 PMCID: PMC11142039 DOI: 10.1101/2024.05.17.594750] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2024]
Abstract
Objective To compare cortical dipole fitting spatial accuracy between the widely used yet highly simplified 3-layer and modern more realistic 5-layer BEM-FMM models with and without adaptive mesh refinement (AMR) methods. Methods We generate simulated noiseless 256-channel EEG data from 5-layer (7-compartment) meshes of 15 subjects from the Connectome Young Adult dataset. For each subject, we test four dipole positions, three sets of conductivity values, and two types of head segmentation. We use the boundary element method (BEM) with fast multipole method (FMM) acceleration, with or without (AMR), for forward modeling. Dipole fitting is carried out with the FieldTrip MATLAB toolbox. Results The average position error (across all tested dipoles, subjects, and models) is ~4 mm, with a standard deviation of ~2 mm. The orientation error is ~20° on average, with a standard deviation of ~15°. Without AMR, the numerical inaccuracies produce a larger disagreement between the 3- and 5-layer models, with an average position error of ~8 mm (6 mm standard deviation), and an orientation error of 28° (28° standard deviation). Conclusions The low-resolution 3-layer models provide excellent accuracy in dipole localization. On the other hand, dipole orientation is retrieved less accurately. Therefore, certain applications may require more realistic models for practical source reconstruction. AMR is a critical component for improving the accuracy of forward EEG computations using a high-resolution 5-layer volume conduction model. Significance Improving EEG source reconstruction accuracy is important for several clinical applications, including epilepsy and other seizure-inducing conditions.
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Affiliation(s)
- Guillermo Nuñez Ponasso
- Dept. of Electrical and Computer Engineering, Worcester Polytechnic Institute, Worcester, MA, USA
| | - Ryan C. McSweeney
- Dept. of Electrical and Computer Engineering, Worcester Polytechnic Institute, Worcester, MA, USA
| | - William A. Wartman
- Dept. of Electrical and Computer Engineering, Worcester Polytechnic Institute, Worcester, MA, USA
| | - Peiyao Lai
- Dept. of Electrical and Computer Engineering, Worcester Polytechnic Institute, Worcester, MA, USA
| | | | - Burkhard Maess
- Max Plank Insititute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Thomas R. Knösche
- Max Plank Insititute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Konstantin Weise
- Max Plank Insititute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Gregory M. Noetscher
- Dept. of Electrical and Computer Engineering, Worcester Polytechnic Institute, Worcester, MA, USA
| | - Tommi Raij
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Sergey N. Makaroff
- Dept. of Electrical and Computer Engineering, Worcester Polytechnic Institute, Worcester, MA, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
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Deng ZD, Argyelan M, Miller J, Jones TR, Upston J, McClintock SM, Abbott CC. On assumptions and key issues in electric field modeling for ECT. Mol Psychiatry 2024:10.1038/s41380-024-02567-9. [PMID: 38671213 DOI: 10.1038/s41380-024-02567-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Revised: 04/11/2024] [Accepted: 04/16/2024] [Indexed: 04/28/2024]
Affiliation(s)
- Zhi-De Deng
- Noninvasive Neuromodulation Unit, Experimental Therapeutics and Pathophysiology Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
- Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC, USA
| | - Miklos Argyelan
- Department of Psychiatry, The Zucker Hillside Hospital, Glen Oaks, NY, USA
- Center for Neuroscience, Feinstein Institute for Medical Research, Manhasset, NY, USA
- Zucker School of Medicine at Hofstra/Northwell, Department of Psychiatry, Hempstead, NY, USA
| | - Jeremy Miller
- Department of Psychiatry, University of New Mexico, Albuquerque, NM, USA
| | - Thomas R Jones
- Department of Psychiatry, University of New Mexico, Albuquerque, NM, USA
| | - Joel Upston
- Department of Psychiatry, University of New Mexico, Albuquerque, NM, USA
| | - Shawn M McClintock
- Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC, USA
- Division of Psychology, Department of Psychiatry, UT Southwestern Medical Center, Dallas, TX, USA
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Dannhauer M, Gomez LJ, Robins PL, Wang D, Hasan NI, Thielscher A, Siebner HR, Fan Y, Deng ZD. Electric Field Modeling in Personalizing Transcranial Magnetic Stimulation Interventions. Biol Psychiatry 2024; 95:494-501. [PMID: 38061463 PMCID: PMC10922371 DOI: 10.1016/j.biopsych.2023.11.022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Revised: 11/21/2023] [Accepted: 11/25/2023] [Indexed: 01/21/2024]
Abstract
The modeling of transcranial magnetic stimulation (TMS)-induced electric fields (E-fields) is a versatile technique for evaluating and refining brain targeting and dosing strategies, while also providing insights into dose-response relationships in the brain. This review outlines the methodologies employed to derive E-field estimations, covering TMS physics, modeling assumptions, and aspects of subject-specific head tissue and coil modeling. We also summarize various numerical methods for solving the E-field and their suitability for various applications. Modeling methodologies have been optimized to efficiently execute numerous TMS simulations across diverse scalp coil configurations, facilitating the identification of optimal setups or rapid cortical E-field visualization for specific brain targets. These brain targets are extrapolated from neurophysiological measurements and neuroimaging, enabling precise and individualized E-field dosing in experimental and clinical applications. This necessitates the quantification of E-field estimates using metrics that enable the comparison of brain target engagement, functional localization, and TMS intensity adjustments across subjects. The integration of E-field modeling with empirical data has the potential to uncover pivotal insights into the aspects of E-fields responsible for stimulating and modulating brain function and states, enhancing behavioral task performance, and impacting the clinical outcomes of personalized TMS interventions.
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Affiliation(s)
- Moritz Dannhauer
- Computational Neurostimulation Research Program, Noninvasive Neuromodulation Unit, Experimental Therapeutics and Pathophysiology Branch, National Institute of Mental Health, Bethesda, Maryland
| | - Luis J Gomez
- School of Electrical and Computer Engineering, Purdue University, West Lafayette, Indiana
| | - Pei L Robins
- Computational Neurostimulation Research Program, Noninvasive Neuromodulation Unit, Experimental Therapeutics and Pathophysiology Branch, National Institute of Mental Health, Bethesda, Maryland
| | - Dezhi Wang
- School of Electrical and Computer Engineering, Purdue University, West Lafayette, Indiana
| | - Nahian I Hasan
- School of Electrical and Computer Engineering, Purdue University, West Lafayette, Indiana
| | - Axel Thielscher
- Danish 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, Kongens Lyngby, Denmark
| | - Hartwig R Siebner
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, Hvidovre, Denmark; Department of Neurology, Copenhagen University Hospital Bispebjerg, Copenhagen, Denmark; Institute for Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Yong Fan
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Zhi-De Deng
- Computational Neurostimulation Research Program, Noninvasive Neuromodulation Unit, Experimental Therapeutics and Pathophysiology Branch, National Institute of Mental Health, Bethesda, Maryland.
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Wartman WA, Weise K, Rachh M, Morales L, Deng ZD, Nummenmaa A, Makaroff SN. An adaptive h-refinement method for the boundary element fast multipole method for quasi-static electromagnetic modeling. Phys Med Biol 2024; 69:055030. [PMID: 38316038 PMCID: PMC10902857 DOI: 10.1088/1361-6560/ad2638] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2023] [Accepted: 02/05/2024] [Indexed: 02/07/2024]
Abstract
Objective.In our recent work pertinent to modeling of brain stimulation and neurophysiological recordings, substantial modeling errors in the computed electric field and potential have sometimes been observed for standard multi-compartment head models. The goal of this study is to quantify those errors and, further, eliminate them through an adaptive mesh refinement (AMR) algorithm. The study concentrates on transcranial magnetic stimulation (TMS), transcranial electrical stimulation (TES), and electroencephalography (EEG) forward problems.Approach.We propose, describe, and systematically investigate an AMR method using the boundary element method with fast multipole acceleration (BEM-FMM) as the base numerical solver. The goal is to efficiently allocate additional unknowns to critical areas of the model, where they will best improve solution accuracy. The implemented AMR method's accuracy improvement is measured on head models constructed from 16 Human Connectome Project subjects under problem classes of TES, TMS, and EEG. Errors are computed between three solutions: an initial non-adaptive solution, a solution found after applying AMR with a conservative refinement rate, and a 'silver-standard' solution found by subsequent 4:1 global refinement of the adaptively-refined model.Main results.Excellent agreement is shown between the adaptively-refined and silver-standard solutions for standard head models. AMR is found to be vital for accurate modeling of TES and EEG forward problems for standard models: an increase of less than 25% (on average) in number of mesh elements for these problems, efficiently allocated by AMR, exposes electric field/potential errors exceeding 60% (on average) in the solution for the unrefined models.Significance.This error has especially important implications for TES dosing prediction-where the stimulation strength plays a central role-and for EEG lead fields. Though the specific form of the AMR method described here is implemented for the BEM-FMM, we expect that AMR is applicable and even required for accurate electromagnetic simulations by other numerical modeling packages as well.
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Affiliation(s)
- William A Wartman
- Electrical and Computer Engineering Department, Worcester Polytechnic Inst., Worcester, MA 01609 United States of America
| | - Konstantin Weise
- Max Planck Institute for Human Cognitive and Brain Sciences, Stephanstr. 1a, D-04103 Leipzig, Germany
- Department of Clinical Medicine, Aarhus University, DNK-8200, Aarhus, Denmark
| | - Manas Rachh
- Center for Computational Mathematics, Flatiron Institute, New York, NY 10012, United States of America
| | - Leah Morales
- Electrical and Computer Engineering Department, Worcester Polytechnic Inst., Worcester, MA 01609 United States of America
| | - Zhi-De Deng
- Computational Neurostimulation Research Program, Noninvasive Neuromodulation Unit, Experimental Therapeutics & Pathophysiology Branch, National Institute of Mental Health Intramural Research Program, National Institutes of Health, Bethesda, MD, United States of America
| | - Aapo Nummenmaa
- Athinoula A. Martinos Ctr. for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA 02129 United States of America
| | - Sergey N Makaroff
- Electrical and Computer Engineering Department, Worcester Polytechnic Inst., Worcester, MA 01609 United States of America
- Athinoula A. Martinos Ctr. for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA 02129 United States of America
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Schroën JAM, Gunter TC, Numssen O, Kroczek LOH, Hartwigsen G, Friederici AD. Causal evidence for a coordinated temporal interplay within the language network. Proc Natl Acad Sci U S A 2023; 120:e2306279120. [PMID: 37963247 PMCID: PMC10666120 DOI: 10.1073/pnas.2306279120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Accepted: 10/06/2023] [Indexed: 11/16/2023] Open
Abstract
Recent neurobiological models on language suggest that auditory sentence comprehension is supported by a coordinated temporal interplay within a left-dominant brain network, including the posterior inferior frontal gyrus (pIFG), posterior superior temporal gyrus and sulcus (pSTG/STS), and angular gyrus (AG). Here, we probed the timing and causal relevance of the interplay between these regions by means of concurrent transcranial magnetic stimulation and electroencephalography (TMS-EEG). Our TMS-EEG experiments reveal region- and time-specific causal evidence for a bidirectional information flow from left pSTG/STS to left pIFG and back during auditory sentence processing. Adapting a condition-and-perturb approach, our findings further suggest that the left pSTG/STS can be supported by the left AG in a state-dependent manner.
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Affiliation(s)
- Joëlle A. M. Schroën
- Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig04103, Germany
| | - Thomas C. Gunter
- Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig04103, Germany
| | - Ole Numssen
- Methods and Development Group Brain Networks, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig04103, Germany
- Lise Meitner Research Group Cognition and Plasticity, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig04103, Germany
| | - Leon O. H. Kroczek
- Department of Psychology, Clinical Psychology and Psychotherapy, Universität Regensburg, Regensburg93053, Germany
| | - Gesa Hartwigsen
- Lise Meitner Research Group Cognition and Plasticity, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig04103, Germany
- Cognitive and Biological Psychology, Wilhelm Wundt Institute for Psychology, Leipzig04109, Germany
| | - Angela D. Friederici
- Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig04103, Germany
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Van Hoornweder S, Nuyts M, Frieske J, Verstraelen S, Meesen RLJ, Caulfield KA. Outcome measures for electric field modeling in tES and TMS: A systematic review and large-scale modeling study. Neuroimage 2023; 281:120379. [PMID: 37716590 PMCID: PMC11008458 DOI: 10.1016/j.neuroimage.2023.120379] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 08/18/2023] [Accepted: 09/13/2023] [Indexed: 09/18/2023] Open
Abstract
BACKGROUND Electric field (E-field) modeling is a potent tool to estimate the amount of transcranial magnetic and electrical stimulation (TMS and tES, respectively) that reaches the cortex and to address the variable behavioral effects observed in the field. However, outcome measures used to quantify E-fields vary considerably and a thorough comparison is missing. OBJECTIVES This two-part study aimed to examine the different outcome measures used to report on tES and TMS induced E-fields, including volume- and surface-level gray matter, region of interest (ROI), whole brain, geometrical, structural, and percentile-based approaches. The study aimed to guide future research in informed selection of appropriate outcome measures. METHODS Three electronic databases were searched for tES and/or TMS studies quantifying E-fields. The identified outcome measures were compared across volume- and surface-level E-field data in ten tES and TMS modalities targeting two common targets in 100 healthy individuals. RESULTS In the systematic review, we extracted 308 outcome measures from 202 studies that adopted either a gray matter volume-level (n = 197) or surface-level (n = 111) approach. Volume-level results focused on E-field magnitude, while surface-level data encompassed E-field magnitude (n = 64) and normal/tangential E-field components (n = 47). E-fields were extracted in ROIs, such as brain structures and shapes (spheres, hexahedra and cylinders), or the whole brain. Percentiles or mean values were mostly used to quantify E-fields. Our modeling study, which involved 1,000 E-field models and > 1,000,000 extracted E-field values, revealed that different outcome measures yielded distinct E-field values, analyzed different brain regions, and did not always exhibit strong correlations in the same within-subject E-field model. CONCLUSIONS Outcome measure selection significantly impacts the locations and intensities of extracted E-field data in both tES and TMS E-field models. The suitability of different outcome measures depends on the target region, TMS/tES modality, individual anatomy, the analyzed E-field component and the research question. To enhance the quality, rigor, and reproducibility in the E-field modeling domain, we suggest standard reporting practices across studies and provide four recommendations.
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Affiliation(s)
- Sybren Van Hoornweder
- REVAL - Rehabilitation Research Center, Faculty of Rehabilitation Sciences, University of Hasselt, Diepenbeek, Belgium.
| | - Marten Nuyts
- REVAL - Rehabilitation Research Center, Faculty of Rehabilitation Sciences, University of Hasselt, Diepenbeek, Belgium
| | - Joana Frieske
- REVAL - Rehabilitation Research Center, Faculty of Rehabilitation Sciences, University of Hasselt, Diepenbeek, Belgium; Movement Control and Neuroplasticity Research Group, Department of Movement Sciences, Group Biomedical Sciences, KU Leuven, Leuven, Belgium
| | - Stefanie Verstraelen
- REVAL - Rehabilitation Research Center, Faculty of Rehabilitation Sciences, University of Hasselt, Diepenbeek, Belgium
| | - Raf L J Meesen
- REVAL - Rehabilitation Research Center, Faculty of Rehabilitation Sciences, University of Hasselt, Diepenbeek, Belgium; Movement Control and Neuroplasticity Research Group, Department of Movement Sciences, Group Biomedical Sciences, KU Leuven, Leuven, Belgium
| | - Kevin A Caulfield
- Brain Stimulation Laboratory, Department of Psychiatry, Medical University of South Carolina, Charleston, SC, United States.
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Makaroff SN, Qi Z, Rachh M, Wartman WA, Weise K, Noetscher GM, Daneshzand M, Deng ZD, Greengard L, Nummenmaa AR. A fast direct solver for surface-based whole-head modeling of transcranial magnetic stimulation. Sci Rep 2023; 13:18657. [PMID: 37907689 PMCID: PMC10618282 DOI: 10.1038/s41598-023-45602-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2023] [Accepted: 10/21/2023] [Indexed: 11/02/2023] Open
Abstract
When modeling transcranial magnetic stimulation (TMS) in the brain, a fast and accurate electric field solver can support interactive neuronavigation tasks as well as comprehensive biophysical modeling. We formulate, test, and disseminate a direct (i.e., non-iterative) TMS solver that can accurately determine global TMS fields for any coil type everywhere in a high-resolution MRI-based surface model with ~ 200,000 or more arbitrarily selected observation points within approximately 5 s, with the solution time itself of 3 s. The solver is based on the boundary element fast multipole method (BEM-FMM), which incorporates the latest mathematical advancement in the theory of fast multipole methods-an FMM-based LU decomposition. This decomposition is specific to the head model and needs to be computed only once per subject. Moreover, the solver offers unlimited spatial numerical resolution. Despite the fast execution times, the present direct solution is numerically accurate for the default model resolution. In contrast, the widely used brain modeling software SimNIBS employs a first-order finite element method that necessitates additional mesh refinement, resulting in increased computational cost. However, excellent agreement between the two methods is observed for various practical test cases following mesh refinement, including a biophysical modeling task. The method can be readily applied to a wide range of TMS analyses involving multiple coil positions and orientations, including image-guided neuronavigation. It can even accommodate continuous variations in coil geometry, such as flexible H-type TMS coils. The FMM-LU direct solver is freely available to academic users.
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Affiliation(s)
- S N Makaroff
- Electrical and Computer Engineering Department, Worcester Polytechnic Institute, Worcester, MA, 01609, USA
- Athinoula A. Martinos Ctr. for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, 02129, USA
| | - Z Qi
- Electrical and Computer Engineering Department, Worcester Polytechnic Institute, Worcester, MA, 01609, USA.
| | - M Rachh
- Center for Computational Mathematics, Flatiron Institute, New York, NY, 10010, USA
| | - W A Wartman
- Electrical and Computer Engineering Department, Worcester Polytechnic Institute, Worcester, MA, 01609, USA
| | - K Weise
- Max Planck Institute for Human Cognitive and Brain Sciences, Stephanstr. 1a, 04103, Leipzig, Germany
- Advanced Electromagnetics Group, Technische Universität Ilmenau, Helmholtzplatz 2, 98693, Ilmenau, Germany
| | - G M Noetscher
- Electrical and Computer Engineering Department, Worcester Polytechnic Institute, Worcester, MA, 01609, USA
| | - M Daneshzand
- Athinoula A. Martinos Ctr. for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, 02129, USA
| | - Zhi-De Deng
- Computational Neurostimulation Research Program, Noninvasive Neuromodulation Unit, Experimental Therapeutics and Pathophysiology Branch, National Institute of Mental Health, NIH 10 Center Drive, Bethesda, MD, 20892, USA
| | - L Greengard
- Center for Computational Mathematics, Flatiron Institute, New York, NY, 10010, USA
- Courant Institute of Mathematical Sciences, 251 Mercer Street, New York, NY, 10012, USA
| | - A R Nummenmaa
- Athinoula A. Martinos Ctr. for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, 02129, USA
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10
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Makaroff SN, Qi Z, Rachh M, Wartman WA, Weise K, Noetscher GM, Daneshzand M, Deng ZD, Greengard L, Nummenmaa AR. A fast direct solver for surface-based whole-head modeling of transcranial magnetic stimulation. RESEARCH SQUARE 2023:rs.3.rs-3079433. [PMID: 37503106 PMCID: PMC10371170 DOI: 10.21203/rs.3.rs-3079433/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
Background When modeling transcranial magnetic stimulation (TMS) in the brain, a fast and accurate electric field solver can support interactive neuronavigation tasks as well as comprehensive biophysical modeling. Objective We formulate, test, and disseminate a direct (i.e., non-iterative) TMS solver that can accurately determine global TMS fields for any coil type everywhere in a high-resolution MRI-based surface model with ~200,000 or more arbitrarily selected observation points within approximately 5 sec, with the solution time itself of 3 sec. Method The solver is based on the boundary element fast multipole method (BEM-FMM), which incorporates the latest mathematical advancement in the theory of fast multipole methods - an FMM-based LU decomposition. This decomposition is specific to the head model and needs to be computed only once per subject. Moreover, the solver offers unlimited spatial numerical resolution. Results Despite the fast execution times, the present direct solution is numerically accurate for the default model resolution. In contrast, the widely used brain modeling software SimNIBS employs a first-order finite element method that necessitates additional mesh refinement, resulting in increased computational cost. However, excellent agreement between the two methods is observed for various practical test cases following mesh refinement, including a biophysical modeling task. Conclusion The method can be readily applied to a wide range of TMS analyses involving multiple coil positions and orientations, including image-guided neuronavigation. It can even accommodate continuous variations in coil geometry, such as flexible H-type TMS coils. The FMM-LU direct solver is freely available to academic users.
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Affiliation(s)
- S N Makaroff
- Electrical and Computer Engineering Department, Worcester Polytechnic Institute, Worcester, MA 01609 USA
- Athinoula A. Martinos Ctr. for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA 02129 USA
| | - Z Qi
- Electrical and Computer Engineering Department, Worcester Polytechnic Institute, Worcester, MA 01609 USA
| | - M Rachh
- Center for Computational Mathematics, Flatiron Institute, New York, NY 10010 USA
| | - W A Wartman
- Electrical and Computer Engineering Department, Worcester Polytechnic Institute, Worcester, MA 01609 USA
| | - K Weise
- Max Planck Institute for Human Cognitive and Brain Sciences, Stephanstr. 1a, 04103, Leipzig Germany
- Technische Universität Ilmenau, Advanced Electromagnetics Group, Helmholtzplatz 2, 98693 Ilmenau Germany
| | - G M Noetscher
- Electrical and Computer Engineering Department, Worcester Polytechnic Institute, Worcester, MA 01609 USA
| | - M Daneshzand
- Athinoula A. Martinos Ctr. for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA 02129 USA
| | - Zhi-De Deng
- Computational Neurostimulation Research Program, Noninvasive Neuromodulation Unit, Experimental Therapeutics & Pathophysiology Branch, National Institute of Mental Health, NIH 10 Center Drive, Bethesda, MD 20892 USA
| | - L Greengard
- Center for Computational Mathematics, Flatiron Institute, New York, NY 10010 USA
- Courant Institute of Mathematical Sciences, 251 Mercer Street, New York, NY 10012 USA
| | - A R Nummenmaa
- Athinoula A. Martinos Ctr. for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA 02129 USA
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Van Hoornweder S, Nuyts M, Frieske J, Verstraelen S, Meesen RLJ, Caulfield KA. A Systematic Review and Large-Scale tES and TMS Electric Field Modeling Study Reveals How Outcome Measure Selection Alters Results in a Person- and Montage-Specific Manner. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.22.529540. [PMID: 36865243 PMCID: PMC9980068 DOI: 10.1101/2023.02.22.529540] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/24/2023]
Abstract
Background Electric field (E-field) modeling is a potent tool to examine the cortical effects of transcranial magnetic and electrical stimulation (TMS and tES, respectively) and to address the high variability in efficacy observed in the literature. However, outcome measures used to report E-field magnitude vary considerably and have not yet been compared in detail. Objectives The goal of this two-part study, encompassing a systematic review and modeling experiment, was to provide an overview of the different outcome measures used to report the magnitude of tES and TMS E-fields, and to conduct a direct comparison of these measures across different stimulation montages. Methods Three electronic databases were searched for tES and/or TMS studies reporting E-field magnitude. We extracted and discussed outcome measures in studies meeting the inclusion criteria. Additionally, outcome measures were compared via models of four common tES and two TMS modalities in 100 healthy younger adults. Results In the systematic review, we included 118 studies using 151 outcome measures related to E-field magnitude. Structural and spherical regions of interest (ROI) analyses and percentile-based whole-brain analyses were used most often. In the modeling analyses, we found that there was an average of only 6% overlap between ROI and percentile-based whole-brain analyses in the investigated volumes within the same person. The overlap between ROI and whole-brain percentiles was montage- and person-specific, with more focal montages such as 4Ã-1 and APPS-tES, and figure-of-eight TMS showing up to 73%, 60%, and 52% overlap between ROI and percentile approaches respectively. However, even in these cases, 27% or more of the analyzed volume still differed between outcome measures in every analyses. Conclusions The choice of outcome measures meaningfully alters the interpretation of tES and TMS E-field models. Well-considered outcome measure selection is imperative for accurate interpretation of results, valid between-study comparisons, and depends on stimulation focality and study goals. We formulated four recommendations to increase the quality and rigor of E-field modeling outcome measures. With these data and recommendations, we hope to guide future studies towards informed outcome measure selection, and improve the comparability of studies.
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Carlson HL, Giuffre A, Ciechanski P, Kirton A. Electric field simulations of transcranial direct current stimulation in children with perinatal stroke. Front Hum Neurosci 2023; 17:1075741. [PMID: 36816507 PMCID: PMC9932338 DOI: 10.3389/fnhum.2023.1075741] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Accepted: 01/16/2023] [Indexed: 02/05/2023] Open
Abstract
Introduction Perinatal stroke (PS) is a focal vascular brain injury and the leading cause of hemiparetic cerebral palsy. Motor impairments last a lifetime but treatments are limited. Transcranial direct-current stimulation (tDCS) may enhance motor learning in adults but tDCS effects on motor learning are less studied in children. Imaging-based simulations of tDCS-induced electric fields (EF) suggest differences in the developing brain compared to adults but have not been applied to common pediatric disease states. We created estimates of tDCS-induced EF strength using five tDCS montages targeting the motor system in children with PS [arterial ischemic stroke (AIS) or periventricular infarction (PVI)] and typically developing controls (TDC) aged 6-19 years to explore associates between simulation values and underlying anatomy. Methods Simulations were performed using SimNIBS https://simnibs.github.io/simnibs/build/html/index.html using T1, T2, and diffusion-weighted images. After tissue segmentation and tetrahedral mesh generation, tDCS-induced EF was estimated based on the finite element model (FEM). Five 1mA tDCS montages targeting motor function in the paretic (non-dominant) hand were simulated. Estimates of peak EF strength, EF angle, field focality, and mean EF in motor cortex (M1) were extracted for each montage and compared between groups. Results Simulations for eighty-three children were successfully completed (21 AIS, 30 PVI, 32 TDC). Conventional tDCS montages utilizing anodes over lesioned cortex had higher peak EF strength values for the AIS group compared to TDC. These montages showed lower mean EF strength within target M1 regions suggesting that peaks were not necessarily localized to motor network-related targets. EF angle was lower for TDC compared to PS groups for a subset of montages. Montages using anodes over lesioned cortex were more sensitive to variations in underlying anatomy (lesion and tissue volumes) than those using cathodes over non-lesioned cortex. Discussion Individualized patient-centered tDCS EF simulations are prudent for clinical trial planning and may provide insight into the efficacy of tDCS interventions in children with PS.
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Affiliation(s)
- Helen L. Carlson
- Calgary Pediatric Stroke Program, Alberta Children’s Hospital, Calgary, AB, Canada,Alberta Children’s Hospital Research Institute (ACHRI), Calgary, AB, Canada,Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada,Department of Pediatrics, University of Calgary, Calgary, AB, Canada,*Correspondence: Helen L. Carlson,
| | - Adrianna Giuffre
- Calgary Pediatric Stroke Program, Alberta Children’s Hospital, Calgary, AB, Canada,Alberta Children’s Hospital Research Institute (ACHRI), Calgary, AB, Canada,Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada,Department of Pediatrics, University of Calgary, Calgary, AB, Canada
| | - Patrick Ciechanski
- Calgary Pediatric Stroke Program, Alberta Children’s Hospital, Calgary, AB, Canada,Alberta Children’s Hospital Research Institute (ACHRI), Calgary, AB, Canada,Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada,Department of Pediatrics, University of Calgary, Calgary, AB, Canada
| | - Adam Kirton
- Calgary Pediatric Stroke Program, Alberta Children’s Hospital, Calgary, AB, Canada,Alberta Children’s Hospital Research Institute (ACHRI), Calgary, AB, Canada,Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada,Department of Pediatrics, University of Calgary, Calgary, AB, Canada,Department of Clinical Neuroscience and Radiology, University of Calgary, Calgary, AB, Canada
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