Chen Yuanyuan, Jiang Yihan, Zhang Zong, Li Zheng, Zhu Chaozhe
State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.
Center for the Cognitive Science of Language, Beijing Language and Culture University, Beijing, China.
Front Neurosci. 2023 Dec 7;17:1301075. doi: 10.3389/fnins.2023.1301075. eCollection 2023.
There 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.
Twelve 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.
The 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).
The 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.
目前有五种不同类型的经颅磁刺激（TMS）运动映射算法，从普通的基于点的算法到先进的基于场的算法。然而，进行的比较研究数量有限，且尚未对所有当前可用算法进行检验。这一缺陷阻碍了在临床和基础神经科学中明智地选择算法应用，也阻碍了潜在更优算法的推广。考虑到算法复杂性的影响，需要进一步研究来检验先前确定的功能磁共振成像（fMRI）峰值与TMS皮质热点之间的差异。
12名健康参与者在fMRI期间接受TMS运动映射和手指敲击任务。通过五种算法估计运动皮质TMS映射结果，并获得fMRI激活结果。对于每种算法，预测误差定义为测量的头皮热点与优化线圈位置之间的距离，该位置由估计运动皮质中的最大电场强度确定。此外，该研究确定了稳定映射所需的最小刺激次数。最后，分析了TMS映射皮质热点与fMRI激活峰值之间的位置差异。
在基于点的算法中，投影算法产生的预测误差最低（5.27±4.24毫米），在基于场的估计算法中，关联算法产生的预测误差最低（6.66±3.48毫米）。投影算法所需的刺激较少，这可能是由于其适用于基于网格的映射数据收集方法。所有算法的TMS皮质热点始终偏离fMRI激活峰值（五种算法为20.52±8.46毫米）。
关联算法可能是临床应用和基础神经科学研究的更佳选择，因为其预测误差较低，对深部皮质结构（尤其是脑沟）的估计灵敏度较高。它在包括语言区域映射等各种其他TMS领域也具有潜在适用性。否则，我们的结果进一步证明TMS运动映射与fMRI运动映射本质上不同。
