Analysis and Comparison of Methods for Determining Motor Threshold with Transcranial Magnetic Stimulation

Abstract

AbstractObjectiveTranscranial magnetic stimulation (TMS) is a noninvasive neuromodulation method with both clinical and research applications. However, the stochastic aspect of neuronal activity and motor evoked potentials (MEPs) challenges TMS thresholding methods. We analyzed existing methods for obtaining TMS motor thresholds and their variations, introduced new methods from other fields, and compared their accuracy and speed.ApproachIn addition to existing relative-frequency-based methods, such as the five-out-of-ten method and related approximation of the probability definition of TMS threshold, we examined adaptive methods based on a probabilistic motor threshold model and maximum-likelihood (ML) or maximuma-posteriori(MAP) estimation. To improve the performance of the adaptive estimation methods, we explored variations in the estimation procedure and inclusion of population-level prior information. We adapted a Bayesian estimation method which iteratively incorporated information of the TMS responses into the probability density function. A family of non-parametric stochastic root-finding methods with different convergence criteria and stepping rules were explored as well. The performance of the thresholding methods was evaluated with an independent stochastic MEP model.Main ResultsThe conventional relative-frequency methods required a large number of stimuli and, despite good accuracy on the population level, had wide error distributions for individual subjects. The parametric estimation methods obtained the thresholds much faster, and their accuracy depended on the estimation method, with performance significantly improved when population-level prior information is included. Stochastic root-finding methods were comparable to adaptive estimation methods but were much simpler to implement and did not rely on an underlying estimation model.SignificanceTwo-parameter MAP estimation, Bayesian estimation, and stochastic root-finding methods require fewer TMS pulses for accurate estimation than conventional relative-frequency and single-parameter ML estimation. Stochastic root finding appears particularly attractive due to the low computational requirements, simplicity of the algorithmic implementation, and independence from potential model flaws in the parametric estimators.