Abstract
Motor evoked potentials (MEPs) are used for biomarkers or dose individualization in transcranial stimulation. We aimed to develop a statistical model that can generate long sequences of individualized MEP amplitude data with the experimentally observed properties. The MEP model includes three sources of trial-to-trial variability to mimic 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.
S. M. Goetz is with the Departments of Psychiatry & Behavioral Sciences, Neurosurgery, and Electrical & Computer Engineering, Duke University, Durham, NC 27708, USA; email: stefan.goetz{at}duke.edu.
S. M. Mahdi Alavi is with the Department of Electrical Engineering, Shahid Beheshti University, Tehran, Iran
Z.-D. Deng is with the Noninvasive Neuromodulation Unit, Experimental Therapeutics & Pathophysiology Branch, Intramural Research Program, National Institute of Mental Health, National Institutes of Health, Bethesda, MD 20892, and Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC 27708, USA
A. V. Peterchev is with the Departments of Psychiatry & Behavioral Sciences, Neurosurgery, Electrical & Computer Engineering, and Biomedical Engineering, Duke University, Durham, NC 27708, USA
