标题： 用于开发闭环程序的经颅磁刺激诱发运动电位的统计-空间模型 显示英文标题

标题： Statistical-Spatial Model for Motor Potentials Evoked Through Transcranial Magnetic Stimulation for the Development of Closed-Loop Procedures

摘要： 初级运动皮层似乎位于经颅磁刺激（TMS）的中心。它是少数几个能直接观察到反应的部位之一，其生理学特性作为几乎所有其他TMS目标的模型或参考，例如通过运动阈值和相对于其位置的空间定位。它还设定了整个大脑的安全限制。其易于检测的反应导致了针对多个方面的闭环方法，例如自动阈值设定、振幅跟踪和定位。脑刺激方法的高变异性将从快速无偏的闭环方法中获益良多。然而，在设计阶段早期，开发更有效的方法需要适当的模型，这些模型能够在不需要大量实验的情况下进行调优和测试，而这些实验耗时且昂贵，甚至在所需规模上不可能进行。一方面，没有实验访问权限的理论研究人员缺乏现实的空间反应模型来开发更好的方法。另一方面，受试者在没有充分前期测试的情况下不应暴露于早期的闭环方法，因为尚未充分调优的反馈已知会导致不稳定行为。为了弥合这一差距，我们开发了一个数字孪生风格的群体模型，该模型对虚拟刺激产生运动诱发电位，并包含关于空间（线圈位置和方向）以及群体中的募集统计信息，以表示个体间和个体内的变异性。该模型允许用户模拟不同的受试者和数百万次运行，用于软件在环测试。该模型包含所有代码以促进进一步开发。 显示英文摘要

摘要： The primary motor cortex appears to be in the center of transcranial magnetic stimulation (TMS). It is one of few locations that provide directly observable responses, and its physiology serves as model or reference for almost all other TMS targets, e.g., through the motor threshold and spatial targeting relative to its position. It furthermore sets the safety limits for the entire brain. Its easily detectable responses have led to closed-loop methods for a range of aspects, e.g., for automated thresholding, amplitude tracking, and targeting. The high variability of brain stimulation methods would substantially benefit from fast unbiased closed-loop methods. However, the development of more potent methods would early on in the design phase require proper models that allowed tuning and testing with sufficient without a high number of experiments, which are time-consuming and expensive or even impossible at the needed scale. On the one hand, theoretical researchers without access to experiments miss realistic spatial response models of brain stimulation to develop better methods. On the other hand, subjects should potentially not be exposed to early closed-loop-methods without sufficient prior testing as not yet well tuned feed-back as needed for closed-loop operation is known to erratic behavior. To bridge this gap, we developed a digital-twin-style population model that generates motor evoked potentials in response to virtual stimuli and includes statistical information on spatial (coil position and orientation) as well as recruitment in the population to represent inter- and intra-individual variability. The model allows users to simulate different subjects and millions of runs for software-in-the loop testing. The model includes all code to stimulate further development.