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Updated: Jun 28, 2026

Utilizing Transcranial Magnetic Stimulation to Study the Human Neuromuscular System
Published on: January 20, 2012
Inducing ipsilateral motor-evoked potentials in the biceps brachii muscle in healthy humans
Nijia Hu1, Meghan Tanel1, Stuart N Baker2
1NeuroMuscular Research Center, Faculty of Sport and Health Sciences, University of Jyväskylä, Jyväskylä, Finland.
Abstract:
To assess reticulospinal tract excitability, high-intensity transcranial magnetic stimulation (TMS) has been used to elicit ipsilateral motor-evoked potentials (iMEPs). However, there is no consensus on robust and valid methods for use in human studies. The present study proposes a standardized method for eliciting and analysing iMEPs in the biceps brachii. Twenty-four healthy young adults participated in this study. Electromyography (EMG) electrodes recorded contralateral MEPs (cMEPs) from the right and iMEPs from the left biceps brachii. A dynamic preacher curl task was used with ~15% of the subject's one-repetition maximum load. The protocol included maximal compound action potential (M-max) determination of the right biceps brachii muscle, TMS hotspot determination, and four sets of five repetitions where 100% stimulator output was delivered at an elbow angle of 110° of flexion. We normalized cMEP amplitude by M-max (% M-max) and iMEP by cMEP amplitude ratio (ICAR). Clear iMEPs above background EMG were observed in 21 subjects (88%, ICAR = .31 ± .19). Good-to-excellent agreement (intraclass correlation coefficient [ICC] = .795-1.000) and low bias (.01-.08 mV and .60-1.11 ms) were demonstrated when comparing two different analysis methods (i.e. fixed time-window vs. manual onset detection) to determine the cMEP and iMEP amplitude and latency, respectively. Most subjects demonstrated clear iMEPs above background EMG triggered at a pre-determined joint angle during a light-load dynamic preacher curl exercise. Similar results were obtained when comparing a single-trial manual identification of iMEP and a semi-automated time-window data analysis approach.
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