Stimulation Effects Mapping for Optimizing Coil Placement for Transcranial Magnetic Stimulation

• Published in: Neuroinformatics (Totowa, N.J.) Vol. 23; no. 1; p. 1
• Main Authors: Zhong, Gangliang, Jin, Fang, Ma, Liang, Yang, Yongfeng, Zhang, Baogui, Cao, Dan, Li, Jin, Zuo, Nianming, Fan, Lingzhong, Yang, Zhengyi, Jiang, Tianzi
• Format: Journal Article
• Language: English
• Published: New York Springer US 07.01.2025; Springer Nature B.V
• Subjects: Adult; Bioinformatics; Biomedical and Life Sciences; Biomedicine; Brain - diagnostic imaging; Brain - physiology; Brain Mapping - methods; Computational Biology/Bioinformatics; Computer Appl. in Life Sciences; Datasets; Excitability; Female; Humans; Magnetic fields; Magnetic Resonance Imaging - methods; Male; Neurology; Neurosciences; Segmentation; Transcranial magnetic stimulation; Transcranial Magnetic Stimulation - methods; Stimulation effects mapping; Transcranial magnetic stimulation; E-field; Coil placement
• ISSN: 1559-0089; 1539-2791; 1559-0089
• DOI: 10.1007/s12021-024-09714-1

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.