Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States of America.
J Neural Eng. 2018 Jun;15(3):036006. doi: 10.1088/1741-2552/aaac93. Epub 2018 Feb 2.
Dexterous movement involves the activation and coordination of networks of neuronal populations across multiple cortical regions. Attempts to model firing of individual neurons commonly treat the firing rate as directly modulating with motor behavior. However, motor behavior may additionally be associated with modulations in the activity and functional connectivity of neurons in a broader ensemble. Accounting for variations in neural ensemble connectivity may provide additional information about the behavior being performed.
In this study, we examined neural ensemble activity in primary motor cortex (M1) and premotor cortex (PM) of two male rhesus monkeys during performance of a center-out reach, grasp and manipulate task. We constructed point process encoding models of neuronal firing that incorporated task-specific variations in the baseline firing rate as well as variations in functional connectivity with the neural ensemble. Models were evaluated both in terms of their encoding capabilities and their ability to properly classify the grasp being performed.
Task-specific ensemble models correctly predicted the performed grasp with over 95% accuracy and were shown to outperform models of neuronal activity that assume only a variable baseline firing rate. Task-specific ensemble models exhibited superior decoding performance in 82% of units in both monkeys (p < 0.01). Inclusion of ensemble activity also broadly improved the ability of models to describe observed spiking. Encoding performance of task-specific ensemble models, measured by spike timing predictability, improved upon baseline models in 62% of units.
These results suggest that additional discriminative information about motor behavior found in the variations in functional connectivity of neuronal ensembles located in motor-related cortical regions is relevant to decode complex tasks such as grasping objects, and may serve the basis for more reliable and accurate neural prosthesis.
灵巧运动涉及到多个皮质区域的神经元群体的激活和协调。将个体神经元的放电率直接与运动行为进行调制，这是尝试建立模型的常用方法。然而，运动行为可能还与更广泛的神经元集合的活动和功能连接的调制有关。考虑到神经元集合连接的变化可能会提供有关正在执行的行为的额外信息。
在这项研究中，我们在两只雄性恒河猴执行中心外伸手、抓握和操作任务期间，检查了初级运动皮层（M1）和前运动皮层（PM）中的神经集合活动。我们构建了神经元放电的点过程编码模型，该模型包含了与基线放电率相关的特定任务变化以及与神经集合的功能连接变化。模型是根据其编码能力和正确分类正在执行的抓握能力进行评估的。
特定于任务的集合模型以超过 95%的准确率正确预测了执行的抓握，并且被证明优于仅假设可变基线放电率的神经元活动模型。在两只猴子中，82%的单元中，特定于任务的集合模型表现出更好的解码性能（p < 0.01）。集合活动的包含还广泛提高了模型描述观察到的尖峰的能力。通过尖峰时间可预测性衡量的特定于任务的集合模型的编码性能，在 62%的单元中优于基线模型。
这些结果表明，在与运动相关的皮质区域中发现的神经元集合的功能连接变化中，有关运动行为的额外可区分信息与解码复杂任务（例如抓握物体）相关，并且可能为更可靠和准确的神经假体提供基础。
