Transcranial magnetic stimulation mapping of the motor cortex: comparison of five estimation algorithms
BackgroundThere are currently five different kinds of transcranial magnetic stimulation (TMS) motor mapping algorithms available, from ordinary point-based algorithms to advanced field-based algorithms. However, there have been only a limited number of comparison studies conducted, and they have not...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-12-01
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| Series: | Frontiers in Neuroscience |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fnins.2023.1301075/full |
| _version_ | 1827593362431016960 |
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| author | Yuanyuan Chen Yihan Jiang Yihan Jiang Zong Zhang Zheng Li Zheng Li Chaozhe Zhu Chaozhe Zhu Chaozhe Zhu |
| author_facet | Yuanyuan Chen Yihan Jiang Yihan Jiang Zong Zhang Zheng Li Zheng Li Chaozhe Zhu Chaozhe Zhu Chaozhe Zhu |
| author_sort | Yuanyuan Chen |
| collection | DOAJ |
| description | BackgroundThere are currently five different kinds of transcranial magnetic stimulation (TMS) motor mapping algorithms available, from ordinary point-based algorithms to advanced field-based algorithms. However, there have been only a limited number of comparison studies conducted, and they have not yet examined all of the currently available algorithms. This deficiency impedes the judicious selection of algorithms for application in both clinical and basic neuroscience, and hinders the potential promotion of a potential superior algorithm. Considering the influence of algorithm complexity, further investigation is needed to examine the differences between fMRI peaks and TMS cortical hotspots that were identified previously.MethodsTwelve healthy participants underwent TMS motor mapping and a finger-tapping task during fMRI. The motor cortex TMS mapping results were estimated by five algorithms, and fMRI activation results were obtained. For each algorithm, the prediction error was defined as the distance between the measured scalp hotspot and optimized coil position, which was determined by the maximum electric field strength in the estimated motor cortex. Additionally, the study identified the minimum number of stimuli required for stable mapping. Finally, the location difference between the TMS mapping cortical hotspot and the fMRI activation peak was analyzed.ResultsThe projection yielded the lowest prediction error (5.27 ± 4.24 mm) among the point-based algorithms and the association algorithm yielded the lowest (6.66 ± 3.48 mm) among field-based estimation algorithms. The projection algorithm required fewer stimuli, possibly resulting from its suitability for the grid-based mapping data collection method. The TMS cortical hotspots from all algorithms consistently deviated from the fMRI activation peak (20.52 ± 8.46 mm for five algorithms).ConclusionThe association algorithm might be a superior choice for clinical applications and basic neuroscience research, due to its lower prediction error and higher estimation sensitivity in the deep cortical structure, especially for the sulcus. It also has potential applicability in various other TMS domains, including language area mapping and more. Otherwise, our results provide further evidence that TMS motor mapping intrinsically differs from fMRI motor mapping. |
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| format | Article |
| id | doaj.art-1bfcb6b488bb4f548ed8e54e5c387582 |
| institution | Directory Open Access Journal |
| issn | 1662-453X |
| language | English |
| last_indexed | 2024-03-09T02:10:26Z |
| publishDate | 2023-12-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Neuroscience |
| spelling | doaj.art-1bfcb6b488bb4f548ed8e54e5c3875822023-12-07T12:32:04ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2023-12-011710.3389/fnins.2023.13010751301075Transcranial magnetic stimulation mapping of the motor cortex: comparison of five estimation algorithmsYuanyuan Chen0Yihan Jiang1Yihan Jiang2Zong Zhang3Zheng Li4Zheng Li5Chaozhe Zhu6Chaozhe Zhu7Chaozhe Zhu8State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, ChinaState Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, ChinaCenter for the Cognitive Science of Language, Beijing Language and Culture University, Beijing, ChinaState Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, ChinaState Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, ChinaCenter for Cognition and Neuroergonomics, State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University Zhuhai, Zhuhai, ChinaState Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, ChinaIDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, ChinaCenter for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing, ChinaBackgroundThere are currently five different kinds of transcranial magnetic stimulation (TMS) motor mapping algorithms available, from ordinary point-based algorithms to advanced field-based algorithms. However, there have been only a limited number of comparison studies conducted, and they have not yet examined all of the currently available algorithms. This deficiency impedes the judicious selection of algorithms for application in both clinical and basic neuroscience, and hinders the potential promotion of a potential superior algorithm. Considering the influence of algorithm complexity, further investigation is needed to examine the differences between fMRI peaks and TMS cortical hotspots that were identified previously.MethodsTwelve healthy participants underwent TMS motor mapping and a finger-tapping task during fMRI. The motor cortex TMS mapping results were estimated by five algorithms, and fMRI activation results were obtained. For each algorithm, the prediction error was defined as the distance between the measured scalp hotspot and optimized coil position, which was determined by the maximum electric field strength in the estimated motor cortex. Additionally, the study identified the minimum number of stimuli required for stable mapping. Finally, the location difference between the TMS mapping cortical hotspot and the fMRI activation peak was analyzed.ResultsThe projection yielded the lowest prediction error (5.27 ± 4.24 mm) among the point-based algorithms and the association algorithm yielded the lowest (6.66 ± 3.48 mm) among field-based estimation algorithms. The projection algorithm required fewer stimuli, possibly resulting from its suitability for the grid-based mapping data collection method. The TMS cortical hotspots from all algorithms consistently deviated from the fMRI activation peak (20.52 ± 8.46 mm for five algorithms).ConclusionThe association algorithm might be a superior choice for clinical applications and basic neuroscience research, due to its lower prediction error and higher estimation sensitivity in the deep cortical structure, especially for the sulcus. It also has potential applicability in various other TMS domains, including language area mapping and more. Otherwise, our results provide further evidence that TMS motor mapping intrinsically differs from fMRI motor mapping.https://www.frontiersin.org/articles/10.3389/fnins.2023.1301075/fulltranscranial magnetic stimulationTMS motor mappingestimation algorithmelectric field modelingfunctional magnetic resonance imaging |
| spellingShingle | Yuanyuan Chen Yihan Jiang Yihan Jiang Zong Zhang Zheng Li Zheng Li Chaozhe Zhu Chaozhe Zhu Chaozhe Zhu Transcranial magnetic stimulation mapping of the motor cortex: comparison of five estimation algorithms Frontiers in Neuroscience transcranial magnetic stimulation TMS motor mapping estimation algorithm electric field modeling functional magnetic resonance imaging |
| title | Transcranial magnetic stimulation mapping of the motor cortex: comparison of five estimation algorithms |
| title_full | Transcranial magnetic stimulation mapping of the motor cortex: comparison of five estimation algorithms |
| title_fullStr | Transcranial magnetic stimulation mapping of the motor cortex: comparison of five estimation algorithms |
| title_full_unstemmed | Transcranial magnetic stimulation mapping of the motor cortex: comparison of five estimation algorithms |
| title_short | Transcranial magnetic stimulation mapping of the motor cortex: comparison of five estimation algorithms |
| title_sort | transcranial magnetic stimulation mapping of the motor cortex comparison of five estimation algorithms |
| topic | transcranial magnetic stimulation TMS motor mapping estimation algorithm electric field modeling functional magnetic resonance imaging |
| url | https://www.frontiersin.org/articles/10.3389/fnins.2023.1301075/full |
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