Ah Sen Christelle B, Fassett Hunter J, El-Sayes Jenin, Turco Claudia V, Hameer Mahdiya M, Nelson Aimee J
Department of Kinesiology, McMaster University, Hamilton, Ontario, Canada.
PLoS One. 2017 Oct 5;12(10):e0186007. doi: 10.1371/journal.pone.0186007. eCollection 2017.
Transcranial magnetic studies typically rely on measures of active and resting motor threshold (i.e. AMT, RMT). Previous work has demonstrated that adaptive threshold hunting approaches are efficient for estimating RMT. To date, no study has compared motor threshold estimation approaches for measures of AMT, yet this measure is fundamental in transcranial magnetic stimulation (TMS) studies that probe intracortical circuits. The present study compared two methods for acquiring AMT and RMT: the Rossini-Rothwell (R-R) relative-frequency estimation method and an adaptive threshold-hunting method based on maximum-likelihood parameter estimation by sequential testing (ML-PEST). AMT and RMT were quantified via the R-R and ML-PEST methods in 15 healthy right-handed participants in an experimenter-blinded within-subject study design. AMT and RMT estimations obtained with both the R-R and ML-PEST approaches were not different, with strong intraclass correlation and good limits of agreement. However, ML-PEST required 17 and 15 fewer stimuli than the R-R method for the AMT and RMT estimation, respectively. ML-PEST is effective in reducing the number of TMS pulses required to estimate AMT and RMT without compromising the accuracy of these estimates. Using ML-PEST to estimate AMT and RMT increases the efficiency of the TMS experiment as it reduces the number of pulses to acquire these measures without compromising accuracy. The benefits of using the ML-PEST approach are amplified when multiple target muscles are tested within a session.
经颅磁研究通常依赖于主动和静息运动阈值的测量（即主动运动阈值、静息运动阈值）。先前的研究表明，自适应阈值搜寻方法在估计静息运动阈值方面是有效的。迄今为止，尚无研究比较用于主动运动阈值测量的运动阈值估计方法，然而，这一测量在探究皮质内回路的经颅磁刺激（TMS）研究中至关重要。本研究比较了两种获取主动运动阈值和静息运动阈值的方法：罗西尼 - 罗斯韦尔（R - R）相对频率估计方法和基于序贯检验最大似然参数估计的自适应阈值搜寻方法（ML - PEST）。在一项实验者盲法的受试者内研究设计中，通过R - R和ML - PEST方法对15名健康右利手参与者的主动运动阈值和静息运动阈值进行了量化。R - R和ML - PEST方法获得的主动运动阈值和静息运动阈值估计并无差异，具有很强的组内相关性和良好的一致性界限。然而，在估计主动运动阈值和静息运动阈值时，ML - PEST分别比R - R方法少需要17次和15次刺激。ML - PEST在不影响这些估计准确性的情况下，能有效减少估计主动运动阈值和静息运动阈值所需的TMS脉冲数量。使用ML - PEST估计主动运动阈值和静息运动阈值可提高TMS实验的效率，因为它在不影响准确性的情况下减少了获取这些测量所需的脉冲数量。当在一个疗程内测试多个目标肌肉时，使用ML - PEST方法的优势会更加明显。
