Faculty of Mechanical Engineering, Technion, Haifa, Israel.
PLoS One. 2007 Jul 18;2(7):e619. doi: 10.1371/journal.pone.0000619.
During planning and execution of reaching movements, the activity of cortical motor neurons is modulated by a diversity of motor, sensory, and cognitive signals. Brain-machine interfaces (BMIs) extract part of these modulations to directly control artificial actuators. However, cortical modulations that emerge in the novel context of operating the BMI are poorly understood.
METHODOLOGY/PRINCIPAL FINDINGS: Here we analyzed the changes in neuronal modulations that occurred in different cortical motor areas as monkeys learned to use a BMI to control reaching movements. Using spike-train analysis methods we demonstrate that the modulations of the firing-rates of cortical neurons increased abruptly after the monkeys started operating the BMI. Regression analysis revealed that these enhanced modulations were not correlated with the kinematics of the movement. The initial enhancement in firing rate modulations declined gradually with subsequent training in parallel with the improvement in behavioral performance.
CONCLUSIONS/SIGNIFICANCE: We conclude that the enhanced modulations are related to computational tasks that are significant especially in novel motor contexts. Although the function and neuronal mechanism of the enhanced cortical modulations are open for further inquiries, we discuss their potential role in processing execution errors and representing corrective or explorative activity. These representations are expected to contribute to the formation of internal models of the external actuator and their decoding may facilitate BMI improvement.
在进行到达运动的规划和执行时，皮质运动神经元的活动会受到多种运动、感觉和认知信号的调节。脑机接口（BMI）提取这些调节的一部分，以直接控制人工执行器。然而，在操作 BMI 的新环境中出现的皮质调节机制还了解甚少。
方法/主要发现：在这里，我们分析了猴子在学习使用 BMI 控制到达运动时，不同皮质运动区中出现的神经元调节变化。使用尖峰时间分析方法，我们证明了猴子开始操作 BMI 后，皮质神经元的放电率调节突然增加。回归分析表明，这些增强的调节与运动的运动学无关。随着后续训练的进行，初始的放电率调节增强逐渐下降，而行为表现的改善则与之平行。
结论/意义：我们的结论是，增强的调节与计算任务有关，这些任务在新的运动环境中尤为重要。尽管增强的皮质调节的功能和神经元机制还需要进一步研究，但我们讨论了它们在处理执行错误和表示纠正或探索性活动中的潜在作用。这些表示有望有助于形成外部执行器的内部模型，并且它们的解码可能有助于 BMI 的改进。
