Moezzi Bahar, Schaworonkow Natalie, Plogmacher Lukas, Goldsworthy Mitchell R, Hordacre Brenton, McDonnell Mark D, Iannella Nicolangelo, Ridding Michael C, Triesch Jochen
Computational and Theoretical Neuroscience Laboratory, School of Information Technology and Mathematical Sciences, University of South Australia , Adelaide , Australia.
Robinson Research Institute, School of Medicine, University of Adelaide , Adelaide , Australia.
J Neurophysiol. 2018 Nov 1;120(5):2532-2541. doi: 10.1152/jn.00626.2017. Epub 2018 Jul 5.
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 muscle were performed in four subjects and compared with 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. NEW & NOTEWORTHY A biophysically detailed model of EMG signal generation following transcranial magnetic stimulation (TMS) is proposed. Simulated EMG signals match experimental EMG recordings in shape and amplitude. Motor-evoked potential and cortical silent period properties match experimental data. The model is a viable tool to analyze, explain, and predict EMG signals following TMS.
经颅磁刺激（TMS）是一种能够对神经活动进行非侵入性操控的技术，在临床和基础研究领域都具有应用前景。TMS对运动皮层的影响通常通过记录手部一小块肌肉的肌电图（EMG）来测量。然而，TMS如何产生可通过EMG测量的反应的具体细节尚未完全明确。我们旨在建立一个具有生物物理细节的计算模型，以研究TMS后EMG信号产生的潜在机制。我们的模型包括一个由皮层第2/3层细胞组成的前馈网络，该网络驱动形态学上详细的第5层皮质脊髓运动神经元，这些神经元进而投射到一群运动神经元。EMG信号被建模为运动单位动作电位的总和。在四名受试者中进行了第一背侧骨间肌的EMG记录，并与模拟的EMG信号进行了比较。我们的模型成功地再现了实验数据的几个特征。模拟的EMG信号在形状和大小上与实验EMG记录相匹配，并且像实验记录一样随刺激强度和收缩水平而变化。它们表现出接近生物学值的皮层静息期，并揭示了对抑制性突触传递特性的有趣依赖性。我们的模型预测了从皮层第2/3层细胞到脊髓运动神经元的整个通路中神经元放电模式的几个特征，应被视为解释和分析TMS后EMG信号的可行工具。新内容及值得注意之处：提出了一个经颅磁刺激（TMS）后EMG信号产生的具有生物物理细节的模型。模拟的EMG信号在形状和幅度上与实验EMG记录相匹配。运动诱发电位和皮层静息期特性与实验数据相匹配。该模型是分析、解释和预测TMS后EMG信号的可行工具。
