Objective. Transcranial magnetic stimulation (TMS) can be used to safely and noninvasively activate brain tissue. However, the characteristic parameters of the neuronal activation have been largely unclear. In this work, we propose a novel neuronal activation model and develop a method to infer its parameters from measured motor evoked potential signals. Approach. The connection between neuronal activation due to an induced electric field and a measured motor threshold is modeled. The posterior distribution of the model parameters are inferred from measurement data using Bayes’ formula. The measurements are the active motor thresholds obtained with multiple stimulating coil locations, and the parameters of the model are the location, preferred direction of activation, and threshold electric field value of the activation site. The posterior distribution is sampled using a Markov chain Monte Carlo method. We quantify the plausibility of the model by calculating the marginal likelihood of the measured thresholds. The method is validated with synthetic data and applied to motor threshold measurements from the first dorsal interosseus muscle in five healthy participants. Main results. The method produces a probability distribution for the activation location, from which a minimal volume where the activation occurs with 95% probability can be derived. For eight or nine stimulating coil locations, the smallest such a volume obtained was approximately 100 mm³. The 95% probability volume intersected the pre-central gyral crown and the anterior wall of the central sulcus, and the preferred direction was perpendicular to the central sulcus, both findings being consistent with the literature. Furthermore, it was not possible to rule out if the activation occurred either in the white or grey matter. In one participant, two distinct activations sites were found while others exhibited a unique site. Significance. The method is both generic and robust, and it lays a foundation for a framework that enables accurate analysis and characterization of TMS activation mechanisms.

客观的。经颅磁刺激 (TMS) 可用于安全且无创地激活脑组织。然而，神经元激活的特征参数在很大程度上还不清楚。在这项工作中，我们提出了一种新的神经元激活模型，并开发了一种从测量的运动诱发电位信号推断其参数的方法。方法。对感应电场引起的神经元激活与测量的运动阈值之间的联系进行建模。使用贝叶斯公式从测量数据推断模型参数的后验分布。测量值是通过多个刺激线圈位置获得的活动运动阈值，模型的参数是激活部位的位置、首选激活方向和阈值电场值。使用马尔可夫链蒙特卡罗方法对后验分布进行采样。我们通过计算测量阈值的边际似然来量化模型的合理性。该方法用合成数据进行了验证，并应用于五名健康参与者第一背骨间肌的运动阈值测量。主要结果。该方法生成激活位置的概率分布，从中可以导出以 95% 概率发生激活的最小体积。对于八个或九个刺激线圈位置，获得的最小体积约为 100 毫米³。95%概率体积与前中央回旋冠和中央沟前壁相交，首选方向垂直于中央沟，与文献一致。此外，无法排除激活是否发生在白质或灰质中。在一名参与者中，发现了两个不同的激活站点，而其他参与者则展示了一个独特的站点。意义。 该方法既通用又稳健，它为能够准确分析和表征 TMS 激活机制的框架奠定了基础。