Including prior knowledge for accurate and fast motor threshold estimation

Abstract

Accurately estimating motor threshold (MT) is important in transcranial magnetic stimulation (TMS), because the MT is used as a reference to adjust individual subject’s machine output intensity. 1 Hallett M. Transcranial magnetic stimulation: a primer. Neuron. 2007; 55: 187-199 Abstract Full Text Full Text PDF PubMed Scopus (1072) Google Scholar MT is defined as the minimum TMS output intensity that can induce reliable motor evoked potentionals (MEPs) (usually > 100 μV), with a probability of 50%. The Parameter Estimation in Sequential Test (PEST) method, which is based on maximum likelihood regression, has previously been proposed to speed up MT estimation. 2 Awiszus F. TMS and threshold hunting. Suppl Clin Neurophysiol. 2003; 56: 13-23 Crossref PubMed Scopus (263) Google Scholar We recently proposed a new Bayesian PEST method that modifies the previous PEST method in two ways. 3 Qi F, Wu AD, Schweighofer N. Fast estimation of transcranial magnetic stimulation motor threshold. Brain Stimulation. [Epub ahead of print]. Google Scholar First, we used prior knowledge, in the form of a probabilistic model, to facilitate MT estimation. Second, we used the probability interval of the estimated MT distribution to control the estimation error. Specifically, the width of 95% of the area under the curve of MTs probability density function was taken as a measure of the estimation precision. We experimentally tested the accuracy, and hence safety, of our Bayesian method in 10 healthy subjects. 3 Qi F, Wu AD, Schweighofer N. Fast estimation of transcranial magnetic stimulation motor threshold. Brain Stimulation. [Epub ahead of print]. Google Scholar