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Statistical Model of Motor Evoked Potentials for Simulation of Transcranial Magnetic and Electric Stimulation

Stefan M. Goetz, S. M. Madhi Alavi, View ORCID ProfileZhi-De Deng, Angel V. Peterchev
doi: https://doi.org/10.1101/406777
Stefan M. Goetz
1Department of Psychiatry & Behavioral Sciences, Duke University School of Medicine, Durham, NC 27708
2Department of Neurosurgery, Duke University School of Medicine, Durham, NC 27708
3Department of Electrical & Computer Engineering, Duke University, Durham, NC 27708
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  • For correspondence: stefan.goetz@duke.edu
S. M. Madhi Alavi
5Department of Electrical Engineering, Shahid Beheshti University, Tehran, Iran
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Zhi-De Deng
1Department of Psychiatry & Behavioral Sciences, Duke University School of Medicine, Durham, NC 27708
6Noninvasive Neuromodulation Unit, Experimental Therapeutics & Pathophysiology Branch, Intramural Research Program, National Institute of Mental Health, National Institutes of Health, Bethesda, MD 20892
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Angel V. Peterchev
1Department of Psychiatry & Behavioral Sciences, Duke University School of Medicine, Durham, NC 27708
2Department of Neurosurgery, Duke University School of Medicine, Durham, NC 27708
3Department of Electrical & Computer Engineering, Duke University, Durham, NC 27708
4Department of Biomedical Engineering, Duke University, Durham, NC 27708
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Abstract

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. However, models of MEPs that represent their characteristic features are lacking. This work 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 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 stimulusintensity-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.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted December 17, 2018.
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Statistical Model of Motor Evoked Potentials for Simulation of Transcranial Magnetic and Electric Stimulation
Stefan M. Goetz, S. M. Madhi Alavi, Zhi-De Deng, Angel V. Peterchev
bioRxiv 406777; doi: https://doi.org/10.1101/406777
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Statistical Model of Motor Evoked Potentials for Simulation of Transcranial Magnetic and Electric Stimulation
Stefan M. Goetz, S. M. Madhi Alavi, Zhi-De Deng, Angel V. Peterchev
bioRxiv 406777; doi: https://doi.org/10.1101/406777

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