Statistical Model of Motor-Evoked Potentials.

Journal Article (Journal Article)

Motor-evoked potentials (MEPs) are widely used for biomarkers and dose individualization in transcranial stimulation. The large variability of MEPs requires sophisticated methods of analysis to extract information fast and correctly. Development and testing of such methods relies on the availability for realistic models of MEP generation, which are presently lacking. This paper presents a statistical model that can simulate long sequences of individualized MEP amplitude data with properties matching experimental observations. The MEP model includes three sources of trial-to-trial variability: excitability fluctuations, variability in the neural and muscular pathways, and physiological and measurement noise. It also generates virtual human subject data from statistics of population variability. All parameters are extracted as statistical distributions from experimental data from the literature. The model exhibits previously described features, such as stimulus-intensity-dependent MEP amplitude distributions, including bimodal ones. The model can generate long sequences of test data for individual subjects with specified parameters or for subjects from a virtual population. The presented MEP model is the most detailed to date and can be used for the development and implementation of dosing and biomarker estimation algorithms for transcranial stimulation.

Full Text

Duke Authors

Cited Authors

  • Goetz, SM; Alavi, SMM; Deng, Z-D; Peterchev, AV

Published Date

  • August 2019

Published In

Volume / Issue

  • 27 / 8

Start / End Page

  • 1539 - 1545

PubMed ID

  • 31283508

Pubmed Central ID

  • PMC6719775

Electronic International Standard Serial Number (EISSN)

  • 1558-0210

Digital Object Identifier (DOI)

  • 10.1109/TNSRE.2019.2926543


  • eng

Conference Location

  • United States