Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/117526
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Type: Journal article
Title: Simulation of electromyographic recordings following transcranial magnetic stimulation
Author: Moezzi, B.
Schaworonkow, N.
Plogmacher, L.
Goldsworthy, M.R.
Hordacre, B.
McDonnell, M.
Iannella, N.
Ridding, M.C.
Triesch, J.
Citation: Journal of Neurophysiology, 2018; 120(5):2532-2541
Publisher: American Physiological Society
Issue Date: 2018
ISSN: 0022-3077
1522-1598
Statement of
Responsibility: 
Bahar Moezzi, Natalie Schaworonkow, Lukas Plogmacher, Mitchell R. oldsworthy, Brenton Hordacre, Mark D. McDonnell, Nicolangelo Iannella, Michael C. Ridding and Jochen Triesch
Abstract: Transcranial magnetic stimulation (TMS) is a technique that enables noninvasive manipulation of neural activity and holds promise in both clinical and basic research settings. The effect of TMS on the motor cortex is often measured by electromyography (EMG) recordings from a small hand muscle. However, the details of how TMS generates responses measured with EMG are not completely understood. We aim to develop a biophysically detailed computational model to study the potential mechanisms underlying the generation of EMG signals following TMS. Our model comprises a feed-forward network of cortical layer 2/3 cells, which drive morphologically detailed layer 5 corticomotoneuronal cells, which in turn project to a pool of motoneurons. EMG signals are modeled as the sum of motor unit action potentials. EMG recordings from the first dorsal interosseous (FDI) muscle were performed in four subjects and compared to simulated EMG signals. Our model successfully reproduces several characteristics of the experimental data. The simulated EMG signals match experimental EMG recordings in shape and size, and change with stimulus intensity and contraction level as in experimental recordings. They exhibit cortical silent periods that are close to the biological values, and reveal an interesting dependence on inhibitory synaptic transmission properties. Our model predicts several characteristics of the firing patterns of neurons along the entire pathway from cortical layer 2/3 cells down to spinal motoneurons and should be considered as a viable tool for explaining and analyzing EMG signals following TMS.
Keywords: Computational model
electromyography
motor cortex
rst dorsal interosseous muscle
transcranial magnetic stimulation
Rights: © 2018 the American Physiological Society
DOI: 10.1152/jn.00626.2017
Grant ID: http://purl.org/au-research/grants/nhmrc/1102272
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