Stochastic Approximator of Motor Threshold (SAMT) for Transcranial Magnetic Stimulation: Online Software and Its Performance in Clinical Studies
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Background
The motor threshold (MT) plays a central role in probing brain excitability and individualizing transcranial magnetic stimulation (TMS). Previously, we proposed stochastic approximation (SA) as a new method for determining TMS MT and demonstrated its excellent speed and accuracy via simulations. SA also has low computational requirements and is robust to potential model flaws.
Objective
This project aimed to develop a practical SA thresholding method and assess its performance in clinical studies.
Methods
The SA thresholding method was implemented as an online software application—SAMT (Stochastic Approximator of MT)—that incorporated features for detection of inaccurate MT estimation. Two ongoing clinical studies use SAMT and have collected 281 finger muscle MTs from 124 participants to date. SAMT’s misestimation detection method marked MTs of 7 thresholding trials as inaccurate, and SAMT’s performance in the remaining 274 trials was assessed by comparing the MT at each step to the threshold estimated by fitting a sigmoidal probability distribution to the complete muscle response data from the session.
Results
By the 25 th TMS pulse, 99% of the SAMT MTs deviated by less than 3.0% (relative) and 1.3% of maximum stimulator output (absolute) from the corresponding fitted sigmoid thresholds and were within their 95% confidence intervals.
Conclusions
We provide the TMS community with a new motor thresholding tool, SAMT. Combined with the prior simulation results, the experimental assessment presented here supports the practicality and accuracy of the SA thresholding method and the SAMT software.
