ArticleIEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society2019
Statistical Model of Motor-Evoked Potentials.
Article in IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- A method of compensating for the excitability of spinal motoneurones when estimating the magnitude of potentials evoked in skeletal muscles.The Journal of physiology · 2026Article
- Stochastic Approximator of Motor Threshold (SAMT) for Transcranial Magnetic Stimulation: Online Software and Its Performance in Clinical Studies.Transcranial magnetic stimulation (2024) · 2026Article
- Influence of Remimazolam and Propofol on Intraoperative Motor Evoked Potentials During Spinal Surgery: A Randomized Crossover Trial.Journal of clinical medicine · 2025Article
- Transcranial magnetic stimulation input-output curve slope differences suggest variation in recruitment across muscle representations in primary motor cortex.Frontiers in human neuroscience · 2024Article
- Three novel methods for determining motor threshold with transcranial magnetic stimulation outperform conventional procedures.Journal of neural engineering · 2023Article
- Closed-loop optimal and automatic tuning of pulse amplitude and width in EMG-guided controllable transcranial magnetic stimulation.Biomedical engineering letters · 2023Article
- Assessment of Motor Evoked Potentials in Multiple Sclerosis.Sensors (Basel, Switzerland) · 2023Review
- Detection of motor-evoked potentials below the noise floor: rethinking the motor stimulation threshold.Journal of neural engineering · 2022Article
- Isolating two sources of variability of subcortical stimulation to quantify fluctuations of corticospinal tract excitability.Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology · 2022Article
- Prediction of Force Recruitment of Neuromuscular Magnetic Stimulation From 3D Field Model of the Thigh.IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society · 2022Article
- Input-output slope curve estimation in neural stimulation based on optimal sampling principlesJournal of neural engineering · 2021Article
- Prior-information Robbins-Monro sequence for rapid motor thresholding of transcranial magnetic stimulation.Brain stimulationArticle
- Preventing misestimation of transcranial magnetic stimulation motor threshold with MTAT 2.0.Brain stimulationArticle
- Hierarchical Bayesian estimation of motor-evoked potential recruitment curves yields accurate and robust estimates.Brain stimulationArticle
Corrections and comments
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Authors and funding
4 authors.
Funding
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. 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.