ArticleFrontiers in neuroinformatics2019
Motometrics: A Toolbox for Annotation and Efficient Analysis of Motor Evoked Potentials.
Article in Frontiers in neuroinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed, 14 citations in OpenAlex.
- Heterogeneous Recruitment Curve Responses to Anodal Transcranial Direct Current Stimulation in Children With Unilateral Spastic Cerebral Palsy.Neurorehabilitation and neural repair · 2026Article
- MEPFeatX-automated feature extraction of motor-evoked potentials in transcranial magnetic stimulation.Frontiers in neuroscience · 2024Article
- DELMEP: a deep learning algorithm for automated annotation of motor evoked potential latencies.Scientific reports · 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
- Input-output slope curve estimation in neural stimulation based on optimal sampling principlesJournal of neural engineering · 2021Article
- 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
6 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Stimulating the nervous system and measuring muscle response offers a unique opportunity to interrogate motor system function. Often, this is performed by stimulating motor cortex and recording muscle activity with electromyography; the evoked response is called the motor evoked potential (MEP). To understand system dynamics, MEPs are typically recorded through a range of motor cortex stimulation intensities. The MEPs increase with increasing stimulation intensities, and these typically produce a sigmoidal response curve. Analysis of MEPs is often complex and analysis of response curves is time-consuming. We created an MEP analysis software, called Motometrics, to facilitate analysis of MEPs and response curves. The goal is to combine robust signal processing algorithms with a simple user interface. Motometrics first enables the user to annotate data files acquired from the recording system so that the responses can be extracted and labeled with the correct subject and experimental condition. The software enables quick visual representations of entire datasets, to ensure uniform quality of the signal. It then enables the user to choose a variety of response curve analyses and to perform near real time quantification of the MEPs for quick feedback during experimental procedures. This is a modular open source tool that is compatible with several popular electrophysiological systems. Initial use indicates that Motometrics enables rapid, robust, and intuitive analysis of MEP response curves by neuroscientists without programming or signal processing expertise.
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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.