Statistical Model of Motor-Evoked Potentials
| Autor: | Angel V. Peterchev, S. M. Mahdi Alavi, Stefan M. Goetz, Zhi-De Deng |
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| Rok vydání: | 2019 |
| Předmět: |
Matching (statistics)
Computer science Models Neurological Population Biomedical Engineering Article 050105 experimental psychology 03 medical and health sciences 0302 clinical medicine Internal Medicine Humans 0501 psychology and cognitive sciences education education.field_of_study Models Statistical Electromyography business.industry General Neuroscience 05 social sciences Rehabilitation Motor Cortex Reproducibility of Results Experimental data Pattern recognition Statistical model Evoked Potentials Motor Transcranial Magnetic Stimulation Noise Brain stimulation Probability distribution Artificial intelligence business Algorithms 030217 neurology & neurosurgery Test data |
| Zdroj: | IEEE Trans Neural Syst Rehabil Eng |
| ISSN: | 1558-0210 1534-4320 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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