Suppr超能文献

用于优化经颅磁刺激线圈放置的刺激效果映射

Stimulation Effects Mapping for Optimizing Coil Placement for Transcranial Magnetic Stimulation.

作者信息

Zhong Gangliang, Jin Fang, Ma Liang, Yang Yongfeng, Zhang Baogui, Cao Dan, Li Jin, Zuo Nianming, Fan Lingzhong, Yang Zhengyi, Jiang Tianzi

机构信息

Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.

Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, China.

出版信息

Neuroinformatics. 2025 Jan 7;23(1):1. doi: 10.1007/s12021-024-09714-1.

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.

摘要

经颅磁刺激(TMS)线圈的位置和方向,我们统称为线圈放置,会显著影响皮质兴奋性的评估和调节。TMS电场(E场)模拟可用于确定最佳线圈放置。然而,目前的E场模拟需要繁琐的分割和网格划分程序来确定最佳线圈放置。我们旨在创建一个框架,使我们能够在无需分割和网格划分程序的情况下提供最佳线圈放置。我们使用中科院自动化所(CASIA)数据集构建了刺激效果映射(SEM)框架以实现最佳线圈放置。我们采用留一法交叉验证来评估最佳线圈放置与由SEM确定的目标区域对于来自CASIA、HCP15和HCP100数据集的MRI数据中74个目标感兴趣区域(ROI)的一致性。此外,我们基于DP和CASIA II数据集对比了使用SEM和辅助偶极子方法(ADM)由最佳线圈放置确定的E范数。我们为目标区域提供了基于“头部解剖学”(HAC)极坐标和蒙特利尔神经学研究所(MNI)坐标的最佳线圈放置。结果还证明了SEM框架对于74个目标ROI的一致性。由SEM确定的正常E场比ADM得到的值更显著。我们使用CASIA数据库创建了SEM框架,以在无需分割或网格划分的情况下确定最佳线圈放置。我们为目标区域提供了HAC和MNI坐标下的最佳线圈放置。对来自各种数据集的几个目标ROI的验证证明了SEM方法的一致性。通过简化寻找最佳线圈放置的过程,我们旨在使TMS评估和治疗更加便捷。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验