Evidence map›Paper›PMID 30971908›Full record

ArticleFrontiers in neuroinformatics2019

Motometrics: A Toolbox for Annotation and Efficient Analysis of Motor Evoked Potentials.

Shivakeshavan Ratnadurai Giridharan, Disha Gupta, Ajay Pal, Asht M Mishra, N Jeremy Hill, Jason B Carmel

Open access · goldAbstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
0.9field-weighted citation impact, top 29% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

The trial behind it

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed, 14 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors at 3 institutions in 1 country.

Shivakeshavan Ratnadurai GiridharanMotor Recovery Lab, Burke Neurological Institute, White Plains, NY, United States.
Disha GuptaDepartment of Neurology, New York University School of Medicine, New York, NY, United States.
Ajay PalMotor Recovery Lab, Burke Neurological Institute, White Plains, NY, United States.
Asht M MishraMotor Recovery Lab, Burke Neurological Institute, White Plains, NY, United States.
N Jeremy HillMotor Recovery Lab, Burke Neurological Institute, White Plains, NY, United States.
Jason B CarmelMotor Recovery Lab, Burke Neurological Institute, White Plains, NY, United States.
Burke Medical Research Institute · USNew York University · USWeill Cornell Medicine · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

analysisEMGevokedMEPmotorrecruitmentsoftware

Identifiers

PMID30971908
PMCPMC6444173
OpenAlexW2914715429

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.