Research Center of Neurology, Moscow, Russia, 125367.
Valiev Institute of Physics and Technology of Russian Academy of Sciences, Moscow, Russia, 117218.
Brain Topogr. 2019 Sep;32(5):859-872. doi: 10.1007/s10548-019-00714-y. Epub 2019 May 9.
Motor evoked potentials (MEPs) caused by transcranial magnetic stimulation (TMS) provide a possibility of noninvasively mapping cortical muscle representations for clinical and research purposes. The interpretation of such results is complicated by the high variability in MEPs and the lack of a standard optimal mapping protocol. Comparing protocols requires the determination of the accuracy of estimated representation parameters (such as the area), which is problematic without ground truth data. We addressed this problem and obtained two main results: (1) the development of a bootstrapping-based approach for estimating the within-session variability and bias of representation parameters and (2) estimations of the area and amplitude-weighted area accuracies for motor representations using this approach. The method consists in the simulation of TMS mapping results by subsampling MEPs from a single map with a large number of stimuli. We studied the extensor digitorum communis (EDC) and flexor digitorum superficialis (FDS) muscle maps of 15 healthy subjects processed using Voronoi diagrams. We calculated the (decreasing) dependency of the errors in the area and weighted area on the number of stimuli. This result can be used to choose a number of stimuli sufficient for studying the effects of a given size (e.g., the protocol with 150 stimuli leads to relative errors of 7% for the area and 11% for the weighted area in 90% of the maps). The approach is applicable to other parameters (e.g., the center of gravity) and other map processing methods, such as spline interpolation.
经颅磁刺激(TMS)引发的运动诱发电位(MEPs)为临床和研究目的提供了一种非侵入性的皮质肌肉代表区映射的可能性。由于 MEP 的高度可变性和缺乏标准的最佳映射协议,这种结果的解释变得复杂。要比较协议,需要确定代表参数(如面积)的估计准确性,而没有真实数据,这是有问题的。我们解决了这个问题,并取得了两个主要结果:(1)开发了一种基于自举的方法来估计代表参数的会话内变异性和偏差;(2)使用该方法估计运动代表的面积和加权面积准确性。该方法包括通过从具有大量刺激的单个映射中对 MEPs 进行子采样来模拟 TMS 映射结果。我们研究了 15 名健康受试者的伸肌(EDC)和指浅屈肌(FDS)肌肉图,这些图使用 Voronoi 图处理。我们计算了面积和加权面积误差对刺激数量的(递减)依赖性。该结果可用于选择足够数量的刺激来研究给定大小的影响(例如,在 90%的映射中,150 个刺激的协议会导致面积的相对误差为 7%,加权面积的相对误差为 11%)。该方法适用于其他参数(例如,重心)和其他映射处理方法,例如样条插值。