Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, United States of America.
J Neural Eng. 2017 Aug;14(4):046016. doi: 10.1088/1741-2552/aa7329.
Challenges in improving the performance of dexterous upper-limb brain-machine interfaces (BMIs) have prompted renewed interest in quantifying the amount and type of sensory information naturally encoded in the primary motor cortex (M1). Previous single unit studies in monkeys showed M1 is responsive to tactile stimulation, as well as passive and active movement of the limbs. However, recent work in this area has focused primarily on proprioception. Here we examined instead how tactile somatosensation of the hand and fingers is represented in M1.
We recorded multi- and single units and thresholded neural activity from macaque M1 while gently brushing individual finger pads at 2 Hz. We also recorded broadband neural activity from electrocorticogram (ECoG) grids placed on human motor cortex, while applying the same tactile stimulus.
Units displaying significant differences in firing rates between individual fingers (p < 0.05) represented up to 76.7% of sorted multiunits across four monkeys. After normalizing by the number of channels with significant motor finger responses, the percentage of electrodes with significant tactile responses was 74.9% ± 24.7%. No somatotopic organization of finger preference was obvious across cortex, but many units exhibited cosine-like tuning across multiple digits. Sufficient sensory information was present in M1 to correctly decode stimulus position from multiunit activity above chance levels in all monkeys, and also from ECoG gamma power in two human subjects.
These results provide some explanation for difficulties experienced by motor decoders in clinical trials of cortically controlled prosthetic hands, as well as the general problem of disentangling motor and sensory signals in primate motor cortex during dextrous tasks. Additionally, examination of unit tuning during tactile and proprioceptive inputs indicates cells are often tuned differently in different contexts, reinforcing the need for continued refinement of BMI training and decoding approaches to closed-loop BMI systems for dexterous grasping.
提高灵巧上肢脑机接口 (BMI) 性能所面临的挑战，促使人们重新关注量化初级运动皮层 (M1) 中自然编码的感觉信息量和类型。之前在猴子身上进行的单单元研究表明，M1 对手部触觉刺激、肢体被动和主动运动都有反应。然而，该领域最近的研究主要集中在本体感觉上。在这里，我们研究了手部和手指的触觉感觉是如何在 M1 中被表示的。
我们记录了猕猴 M1 中的多单元和单单元活动，并在以 2 Hz 的频率轻轻刷单个指垫时对其进行阈值处理。我们还记录了放置在人类运动皮层上的脑电描记图 (ECoG) 网格的宽带神经活动，同时施加相同的触觉刺激。
在四个猴子中，有高达 76.7%的分类多单元的放电率在单个手指之间存在显著差异（p < 0.05）。在通过对具有显著运动手指反应的通道数量进行归一化后，具有显著触觉反应的电极百分比为 74.9% ± 24.7%。在整个皮层中，手指偏好的躯体感觉组织并不明显，但许多单元在多个手指上表现出余弦样调谐。在所有猴子中，从多单元活动中解码刺激位置的信息，以及从两个人类对象的 ECoG 伽马功率中解码刺激位置的信息，都存在足够的感觉信息，超过了随机水平。
这些结果为临床皮层控制假肢手试验中运动解码器所遇到的困难，以及灵巧任务中灵长类运动皮层中运动和感觉信号分离的一般问题提供了一些解释。此外，在触觉和本体感觉输入过程中检查单元调谐表明，在不同的情况下，细胞通常会以不同的方式调谐，这加强了对 BMI 训练和解码方法的持续改进的需求，以实现灵巧抓握的闭环 BMI 系统。
