Abstract
The position and orientation of transcranial magnetic stimulation (TMS) coil, which we collectively refer to as coil placement, significantly affect both the assessment and modulation of cortical excitability. TMS electric field (E-field) simulation can be used to identify optimal coil placement. However, the present E-field simulation required a laborious segmentation and meshing procedure to determine optimal coil placement. We intended to create a framework that would enable us to offer optimal coil placement without requiring the segmentation and meshing procedure. We constructed the stimulation effects map (SEM) framework using the CASIA dataset for optimal coil placement. We used leave-one-subject-out cross-validation to evaluate the consistency of the optimal coil placement and the target regions determined by SEM for the 74 target ROIs in MRI data from the CASIA, HCP15 and HCP100 datasets. Additionally, we contrasted the E-norms determined by optimal coil placements using SEM and auxiliary dipole method (ADM) based on the DP and CASIA II datasets. We provided optimal coil placement in ‘head-anatomy-based’ (HAC) polar coordinates and MNI coordinates for the target region. The results also demonstrated the consistency of the SEM framework for the 74 target ROIs. The normal E-field determined by SEM was more significant than the value received by ADM. We created the SEM framework using the CASIA database to determine optimal coil placement without segmentation or meshing. We provided optimal coil placement in HAC and MNI coordinates for the target region. The validation of several target ROIs from various datasets demonstrated the consistency of the SEM approach. By streamlining the process of finding optimal coil placement, we intended to make TMS assessment and therapy more convenient.
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Data and Code Availability
We have made the simulation dataset, constructed using neuroimages from the CASIA I and CASIA II datasets, publicly available as a resource. This dataset is ready for E-fields modeling and can be accessed through Science Data Bank upon acceptance. The HCP dataset is also publicly available for reference. For clinical data, interested parties can request access from the corresponding author, T. J., by submitting a reasonable request. The code used for mapping stimulation effects and constructing the optimal coil placement atlas, along with the data supporting the results of this study, has been uploaded to GitHub. You can find the code and data at the following GitHub repository: https://github.com/ZhongGangliang/TMS_OPA.
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Acknowledgements
We are very grateful to all the participants who contributed to this article. R. E. Perozzi and E. F. Perozzi assisted with English language and editing.
Funding
This work was partially supported by grants from the Science and Technology Innovation 2030 - Brain Science and Brain-Inspired Intelligence Project (Grant No. 2021ZD0200200), Natural Science Foundation of China (Grant Nos. 82151307 and 31620103905), Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No. XDB32030207), and Science Frontier Program of the Chinese Academy of Sciences (Grant No. QYZDJ-SSW-SMC019).
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T.J. proposed the concept and designed the protocol. G.Z. performed the concept and analyzed data. F.J., L.M. and Y.Y. performed the experiments, and B.Z. performed the MRI scanning. D.C., J.L., and N.Z. contributed to the data analysis. T.J., Z.Y., and L.F. led the project and supervised the experiments. All authors contributed to the writing of the manuscript. Data access and verification: G.Z., L.M., and Y.Y.
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Zhong, G., Jin, F., Ma, L. et al. Stimulation Effects Mapping for Optimizing Coil Placement for Transcranial Magnetic Stimulation. Neuroinform 23, 1 (2025). https://doi.org/10.1007/s12021-024-09714-1
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DOI: https://doi.org/10.1007/s12021-024-09714-1