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首页> 外文期刊>Journal of Neurophysiology >A muscle-activity-dependent gain between motor cortex and EMG
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A muscle-activity-dependent gain between motor cortex and EMG

机译：电机皮层和EMG之间的肌肉活动依赖性增益

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Whether one is delicately placing a contact lens on the surface of the eye or lifting a heavy weight from the floor, the motor system must produce a wide range of forces under different dynamical loads. How does the motor cortex, with neurons that have a limited activity range, function effectively under these widely varying conditions? In this study, we explored the interaction of activity in primary motor cortex (M1) and muscles (electromyograms, EMGs) of two male rhesus monkeys for wrist movements made during three tasks requiring different dynamical loads and forces. Despite traditionally providing adequate predictions in single tasks, in our experiments, a single linear model failed to account for the relation between M1 activity and EMG across conditions. However, a model with a gain parameter that increased with the target force remained accurate across forces and dynamical loads. Surprisingly, this model showed that a greater proportion of EMG changes were explained by the nonlinear gain than the linear mapping from M1. In addition to its theoretical implications, the strength of this nonlinearity has important implications for brain-computer interfaces (BCIs). If BCI decoders are to be used to control movement dynamics (including interaction forces) directly. they will need to be nonlinear and include training data from broad data sets to function effectively across tasks. Our study reinforces the need to investigate neural control of movement across a wide range of conditions to understand its basic characteristics as well as translational implications.
机译：无论是在眼睛的表面上熟酌还是从地板上抬起重的重量，电机系统必须在不同的动力量负载下产生宽范围的力。电动机皮质如何具有有限的活动范围的神经元，在这些广泛变化的条件下有效地发挥作用？在这项研究中，我们探讨了在需要不同动力负荷和力的三个任务中制作的手腕运动的主要运动皮质（M1）和肌肉（M1）和肌肉（电灰度，emgs）中的活动相互作用。尽管传统上，但在我们的实验中，在单一任务中提供了足够的预测，但是单个线性模型未能考虑M1活动与EMG之间的关系。然而，具有增益参数的模型随着目标力的增加，跨越力和动态负载仍然准确。令人惊讶的是，该模型表明，通过来自M1的线性映射，通过非线性增益解释了更大比例的EMG变化。除了理论上的影响外，这种非线性的强度对脑电器界面（BCI）对脑电脑界面具有重要意义。如果要使用BCI解码器来直接控制移动动态（包括交互力）。它们需要是非线性的，包括从广泛的数据集中培训数据，以有效地跨任务功能。我们的研究强化了在广泛的条件下调查神经控制，以了解其基本特征以及平移意义。

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