The Representation of Finger Movement and Force in Human Motor and Premotor Cortices
The Representation of Finger Movement and Force in Human Motor and Premotor Cortices
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DOI：
10.1523/eneuro.0063-20.2020
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发表时间：
2020-07-01
期刊：
影响因子：
3.4
通讯作者：
Slutzky, Marc W.
中科院分区：
文献类型：
--
作者：
Flint, Robert D.;Tate, Matthew C.;Slutzky, Marc W.
抓取和操纵物体的能力需要快速连续地控制手指的运动、运动学和等长力。之前的研究表明，这些行为模式是单独控制的，但尚不清楚大脑皮层是否以不同方式代表它们。在这里，我们问的问题是，当用同一根手指连续执行时，运动和力是如何表现出来的。我们记录了7名执行运动力运动任务的受试者的运动和运动前皮质的高密度皮层脑电图(ECoG)。我们高精度地解码了手指运动[0.7+/-0.3分数方差(FVAF)]和力量(0.7+/-60.2 FVAF)，但发现了不同的空间表征。此外，我们使用了最先进的深度学习方法，在运动力任务中通过ECoG状态空间发现了平滑、可重复的轨迹。我们还通过开发一种新的度量--神经向量角(NVA)来总结试验和参与者的ECoG。因此，状态空间技术有助于研究广泛的大脑皮层网络。最终，我们能够从神经信号中对行为模式进行高精度(90+/-6%)的分类。因此，手指运动和力似乎在运动/运动前皮质中有不同的表示。这些结果有助于我们理解运动的神经控制，以及GRAPH脑机接口(BMI)的设计。
The ability to grasp and manipulate objects requires controlling both finger movement kinematics and isometric force in rapid succession. Previous work suggests that these behavioral modes are controlled separately, but it is unknown whether the cerebral cortex represents them differently. Here, we asked the question of how movement and force were represented cortically, when executed sequentially with the same finger. We recorded high-density electrocorticography (ECoG) from the motor and premotor cortices of seven human subjects performing a movement-force motor task. We decoded finger movement [0.7 +/- 0.3 fractional variance accounted for (FVAF)] and force (0.7 +/- 60.2 FVAF) with high accuracy, yet found different spatial representations. In addition, we used a state-of-the-art deep learning method to uncover smooth, repeatable trajectories through ECoG state space during the movement-force task. We also summarized ECoG across trials and participants by developing a new metric, the neural vector angle (NVA). Thus, state-space techniques can help to investigate broad cortical networks. Finally, we were able to classify the behavioral mode from neural signals with high accuracy (90 +/- 6%). Thus, finger movement and force appear to have distinct representations in motor/premotor cortices. These results inform our understanding of the neural control of movement, as well as the design of grasp brain-machine interfaces (BMIs).