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

Area of Science:

• Neuroscience
• Motor System Physiology

Background:

• Interrogating motor system function involves stimulating the nervous system and measuring muscle responses, commonly via motor cortex stimulation and electromyography to record motor evoked potentials (MEPs).
• Analyzing the relationship between stimulation intensity and MEP amplitude, often resulting in sigmoidal response curves, is crucial for understanding system dynamics but can be complex and time-consuming.

Purpose of the Study:

• To develop a user-friendly software tool, Motometrics, that facilitates the analysis of motor evoked potentials (MEPs) and their response curves.
• To combine robust signal processing with an intuitive interface for efficient MEP data analysis.

Main Methods:

• Motometrics software was created to annotate data files, extract and label responses, and provide visual representations of datasets for quality control.
• The software offers various response curve analyses and real-time MEP quantification for immediate experimental feedback.
• It is designed as a modular, open-source tool compatible with multiple electrophysiological systems.

Main Results:

• Motometrics enables users to annotate and visualize MEP data, ensuring signal quality.
• The software provides rapid quantification of MEPs and analysis of response curves.
• Initial use demonstrates that Motometrics allows neuroscientists without programming expertise to perform efficient MEP analysis.

Conclusions:

• Motometrics significantly streamlines the complex analysis of MEP response curves.
• The software provides a rapid, robust, and intuitive solution for studying motor system function.
• Motometrics empowers neuroscientists to analyze MEP data effectively, accelerating research in motor control and neurophysiology.