Estimating resting motor thresholds in transcranial magnetic stimulation research and practice: a computer simulation evaluation of best methods

J ECT. 2006 Sep;22(3):169-75. doi: 10.1097/01.yct.0000235923.52741.72.


Objectives: Resting motor threshold is the basic unit of dosing in transcranial magnetic stimulation (TMS) research and practice. There is little consensus on how best to estimate resting motor threshold with TMS, and only a few tools and resources are readily available to TMS researchers. The current study investigates the accuracy and efficiency of 5 different approaches to motor threshold assessment for TMS research and practice applications.

Methods: Computer simulation models are used to test the efficiency and accuracy of 5 different adaptive parameter estimation by sequential testing (PEST) procedures. For each approach, data are presented with respect to the mean number of TMS trials necessary to reach the motor threshold estimate as well as the mean accuracy of the estimates.

Results: A simple nonparametric PEST procedure appears to provide the most accurate motor threshold estimates, but takes slightly longer (on average, 3.48 trials) to complete than a popular parametric alternative (maximum likelihood PEST). Recommendations are made for the best starting values for each of the approaches to maximize both efficiency and accuracy.

Conclusions: In light of the computer simulation data provided in this article, the authors review and suggest which techniques might best fit different TMS research and clinical situations. Lastly, a free user-friendly software package is described and made available on the world wide web that allows users to run all of the motor threshold estimation procedures discussed in this article for clinical and research applications.

Publication types

  • Evaluation Study

MeSH terms

  • Computer Simulation*
  • Efferent Pathways / physiology*
  • Models, Neurological
  • Monte Carlo Method
  • Sensory Thresholds / physiology*
  • Transcranial Magnetic Stimulation / methods*