Fast estimation of transcranial magnetic
stimulation motor threshold
Feng Qi,aAllan D. Wu,bNicolas Schweighofera,c
aNeuroscience, University of Southern California, Los Angeles, California
bDepartment of Neurology, University of California at Los Angeles, Los Angeles, California
cBiokinesiology and Physical Therapy, University of Southern California, Los Angeles, California
In Transcranial Magnetic Stimulation (TMS), the Motor Threshold (MT) is the minimum intensity
required to evoke a liminal response in the target muscle. Because the MT reflects cortical excitability,
the TMS intensity needs to be adjusted according to the subject’s MT at the beginning of every TMS
Shorten the MT estimation process compared to existing methods without compromising accuracy.
We propose a Bayesian adaptive method for MT determination that incorporates prior MT knowledge
and uses a stopping criterion based on estimation of MT precision. We compared the number of TMS
pulses required with this new method with existing MT determination methods.
The proposed method achieved the accuracy of existing methods with as few as seven TMS pulses on
average when using a common prior and three TMS pulses on average when using subject-specific
Our adaptive Bayesian method is effective in reducing the number of pulses to estimate the MT.
? 2011 Elsevier Inc. All rights reserved.
TMS; motor threshold; Bayesian method
neural stimulation technique that has broad applications.1
TMS generates an electromagnetic field that passes through
the scalp and induces an electrical current, which activates
neurons in the cortex.1In motor cortex studies, if TMS is
applied at an intensity above a threshold, the target muscle
contralateral to the stimulated cortical neurons responds
This work was in part supported by National Science Foundation grant
IIS 0535282 to NS.
Correspondence: Nicolas Schweighofer, Neuroscience, University of
Southern California, 1540 E. Alcazar, Los Angeles, CA 90089
E-mail address: email@example.com
Submitted October 17, 2009; revised June 7, 2010. Accepted for
publication June 7, 2010.
1935-861X/$ - see front matter ? 2011 Elsevier Inc. All rights reserved.
Brain Stimulation (2011) 4, 50–7
approach is required, ML regression can be used for the final
MT estimation based on all collected data. In such case,
Bayesian regression could then only be used for determina-
tion of the next sampling intensity. The effect of prior with
this ‘‘hybrid’’ method would thus only bias which intensities
are sampled, but would not affect the final MTestimation.
Third, because the Bayesian PESTuses all previous data,
erroneous data are a potential problem for both ‘‘bestPEST’’
and Bayesian PEST. During MT estimation process, MEP
identification may be inaccurate because of the volitional
muscle twitch or simply wrong data entry. Nonparametric
PEST9does not explicitly uses all data and is robust against
erroneous data. Besides careful experimental operation,
a systematic evaluation of the robustness of these techniques
or the development of robust Bayesian regression for PEST
may be necessary in future studies.
In our study, as in Awiszus,6we assumed that the hot-
spot, defined as the optimal position that has the lowest
MT on the scalp for placing the TMS coil, had already
been found before MT estimation. In actual TMS experi-
ments, however, locating the hotspot is not a trivial task.
Further, hotspot location and MT determination are often
carried out simultaneously. TMS studies would thus greatly
benefit from adaptive methods that extend our proposed
Bayesian adaptive MT determination method with the addi-
tion of priors in two-dimensional space to concurrently
optimize the sampling location and the sampling intensity.
We thank Dr. Beth Fisher director of the NAIL TMS
laboratory at USC for her help and support during this
work; Erica Pitsch (PT, MPT, Division of Biokinesiology,
University of Southern California) for sharing TMS
threshold data; and Dr. Zhong-Lin Lu for his comments
on a previous draft (Department of Psychology, University
of Southern California).
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in the online version, at doi:10.1016/j.brs.2010.06.002
Fast estimation of TMS MT57