Distinct neural representations during a brain-machine interface and manual reaching task in motor cortex, prefrontal cortex, and striatum.
Distinct neural representations during a brain-machine interface and manual reaching task in motor cortex, prefrontal cortex, and striatum.
复制标题
脑机接口和手动到达任务期间运动皮层、前额叶皮层和纹状体的独特神经表征。
DOI：
10.1101/2023.05.31.542532
复制
发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Carmena,JoseM
中科院分区：
文献类型：
--
作者：
Zippi,EllenL;Shvartsman,GabrielleF;Vendrell-Llopis,Nuria;Wallis,JoniD;Carmena,JoseM
尽管脑机接口（BMI）是通过调制选定的局部神经元群体来直接控制的，但由皮质和皮质下区域组成的分布式网络已参与学习和维持控制。先前在啮齿类动物中的工作已经证明纹状体参与BMI学习。然而，在研究运动BMI控制时，前额叶皮层在很大程度上被忽视了，尽管它在行动计划，行动选择和学习抽象任务中发挥着重要作用。在这里，我们比较了当地的场电位同时记录从初级运动皮层（M1），背外侧前额叶皮层（DLPFC），和纹状体的尾状核（镉），而非人灵长类动物进行二维的，自我发起的，中心出任务BMI控制和手动控制。我们的研究结果表明，存在不同的神经代表BMI和手动控制M1，DLPFC和镉。我们发现，从DLPFC和M1的神经活动最好区分控制类型在去线索和目标收购，分别，而M1最好预测目标方向在这两个任务事件。我们还发现，在两种控制类型中，DLPFC → M1和BMI控制期间Cd → M1的有效连接。这些结果表明，BMI控制期间M1、DLPFC和Cd之间的分布式网络活动与手动控制相似但不同。
Although brain–machine interfaces (BMIs) are directly controlled by the modulation of a select local population of neurons, distributed networks consisting of cortical and subcortical areas have been implicated in learning and maintaining control. Previous work in rodents has demonstrated the involvement of the striatum in BMI learning. However, the prefrontal cortex has been largely ignored when studying motor BMI control despite its role in action planning, action selection, and learning abstract tasks. Here, we compare local field potentials simultaneously recorded from primary motor cortex (M1), dorsolateral prefrontal cortex (DLPFC), and the caudate nucleus of the striatum (Cd) while nonhuman primates perform a two-dimensional, self-initiated, center-out task under BMI control and manual control. Our results demonstrate the presence of distinct neural representations for BMI and manual control in M1, DLPFC, and Cd. We find that neural activity from DLPFC and M1 best distinguishes control types at the go cue and target acquisition, respectively, while M1 best predicts target-direction at both task events. We also find effective connectivity from DLPFC → M1 throughout both control types and Cd → M1 during BMI control. These results suggest distributed network activity between M1, DLPFC, and Cd during BMI control that is similar yet distinct from manual control.