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Agboada D, Osnabruegge M, Rethwilm R, Kanig C, Schwitzgebel F, Mack W, Schecklmann M, Seiberl W, Schoisswohl S. Semi-automated motor hotspot search (SAMHS): a framework toward an optimised approach for motor hotspot identification. Front Hum Neurosci 2023; 17:1228859. [PMID: 38164193 PMCID: PMC10757939 DOI: 10.3389/fnhum.2023.1228859] [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: 05/25/2023] [Accepted: 11/23/2023] [Indexed: 01/03/2024] Open
Abstract
Background Motor hotspot identification represents the first step in the determination of the motor threshold and is the basis for the specification of stimulation intensity used for various Transcranial Magnetic Stimulation (TMS) applications. The level of experimenters' experience and the methodology of motor hotspot identification differ between laboratories. The need for an optimized and time-efficient technique for motor hotspot identification is therefore substantial. Objective With the current work, we present a framework for an optimized and time-efficient semi-automated motor hotspot search (SAMHS) technique utilizing a neuronavigated robot-assisted TMS system (TMS-cobot). Furthermore, we aim to test its practicality and accuracy by a comparison with a manual motor hotspot identification method. Method A total of 32 participants took part in this dual-center study. At both study centers, participants underwent manual hotspot search (MHS) with an experienced TMS researcher, and the novel SAMHS procedure with a TMS-cobot (hereafter, called cobot hotspot search, CHS) in a randomized order. Resting motor threshold (RMT), and stimulus intensity to produce 1 mV (SI1mV) peak-to-peak of motor-evoked potential (MEP), as well as MEPs with 120% RMT and SI1mV were recorded as outcome measures for comparison. Results Compared to the MHS method, the CHS produced lower RMT, lower SI1mV and a trend-wise higher peak-to-peak MEP amplitude in stimulations with SI1mV. The duration of the CHS procedure was longer than that of the MHS (15.60 vs. 2.43 min on average). However, accuracy of the hotspot was higher for the CHS compared to the MHS. Conclusions The SAMHS procedure introduces an optimized motor hotspot determination system that is easy to use, and strikes a fairly good balance between accuracy and speed. This new procedure can thus be deplored by experienced as well as beginner-level TMS researchers.
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Affiliation(s)
- Desmond Agboada
- Institute of Psychology, University of the Bundeswehr Munich, Neubiberg, Germany
| | - Mirja Osnabruegge
- Institute of Psychology, University of the Bundeswehr Munich, Neubiberg, Germany
- Department of Psychiatry and Psychotherapy, University of Regensburg, Regensburg, Germany
| | - Roman Rethwilm
- Institute of Sport Science, University of the Bundeswehr Munich, Neubiberg, Germany
| | - Carolina Kanig
- Institute of Psychology, University of the Bundeswehr Munich, Neubiberg, Germany
- Department of Psychiatry and Psychotherapy, University of Regensburg, Regensburg, Germany
| | - Florian Schwitzgebel
- Department of Electrical Engineering, University of the Bundeswehr Munich, Neubiberg, Germany
| | - Wolfgang Mack
- Institute of Psychology, University of the Bundeswehr Munich, Neubiberg, Germany
| | - Martin Schecklmann
- Department of Psychiatry and Psychotherapy, University of Regensburg, Regensburg, Germany
| | - Wolfgang Seiberl
- Institute of Sport Science, University of the Bundeswehr Munich, Neubiberg, Germany
| | - Stefan Schoisswohl
- Institute of Psychology, University of the Bundeswehr Munich, Neubiberg, Germany
- Department of Psychiatry and Psychotherapy, University of Regensburg, Regensburg, Germany
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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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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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