Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Hong Kong Special Administrative Region of China.
College of Computer Science, Zhejiang University, Hangzhou, People's Republic of China.
J Neural Eng. 2022 Jul 29;19(4). doi: 10.1088/1741-2552/ac8180.
. Brain-machine interfaces (BMIs) aim to help people with motor disabilities by interpreting brain signals into motor intentions using advanced signal processing methods. Currently, BMI users require intensive training to perform a pre-defined task, not to mention learning a new task. Thus, it is essential to understand neural information pathways among the cortical areas in task learning to provide principles for designing BMIs with learning abilities. We propose to investigate the relationship between the medial prefrontal cortex (mPFC) and primary motor cortex (M1), which are actively involved in motor control and task learning, and show how information is conveyed in spikes between the two regions on a single-trial basis by computational models.. We are interested in modeling the functional relationship between mPFC and M1 activities during task learning. Six Sprague Dawley rats were trained to learn a new behavioral task. Neural spike data was recorded from mPFC and M1 during learning. We then implement the generalized linear model, the second-order generalized Laguerre-Volterra model, and the staged point-process model to predict M1 spikes from mPFC spikes across multiple days during task learning. The prediction performance is compared across different models or learning stages to reveal the relationship between mPFC and M1 spike activities.. We find that M1 neural spikes can be well predicted from mPFC spikes on the single-trial level, which indicates a highly correlated relationship between mPFC and M1 activities during task learning. By comparing the performance across models, we find that models with higher nonlinear capacity perform significantly better than linear models. This indicates that predicting M1 activity from mPFC activity requires the model to consider higher-order nonlinear interactions beyond pairwise interactions. We also find that the correlation coefficient between the mPFC and M1 spikes increases during task learning. The spike prediction models perform the best when the subjects become well trained on the new task compared with the early and middle stages. The results suggest that the co-activation between mPFC and M1 activities evolves during task learning, and becomes stronger as subjects become well trained.. This study demonstrates that the dynamic patterns of M1 spikes can be predicted from mPFC spikes during task learning, and this will further help in the design of adaptive BMI decoders for task learning.
脑机接口 (BMI) 旨在通过使用先进的信号处理方法将大脑信号解释为运动意图，帮助运动障碍患者。目前，BMI 用户需要进行密集的训练才能执行预定义的任务，更不用说学习新任务了。因此，了解任务学习中皮质区域之间的神经信息通路对于设计具有学习能力的 BMI 至关重要。我们建议研究积极参与运动控制和任务学习的内侧前额叶皮层 (mPFC) 和初级运动皮层 (M1) 之间的关系，并展示如何通过计算模型在单次试验的基础上在两个区域之间传递信息。我们有兴趣对任务学习过程中 mPFC 和 M1 活动之间的功能关系进行建模。六只斯普拉格-道利大鼠接受训练以学习新的行为任务。在学习过程中记录 mPFC 和 M1 的神经尖峰数据。然后，我们在多个学习日中，使用广义线性模型、二阶广义拉盖尔-沃尔泰拉模型和分阶段点过程模型来预测任务学习过程中 mPFC 尖峰对 M1 尖峰的预测。通过比较不同模型或学习阶段的预测性能来揭示 mPFC 和 M1 尖峰活动之间的关系。我们发现，在单次试验水平上，M1 神经尖峰可以很好地从 mPFC 尖峰预测，这表明在任务学习过程中 mPFC 和 M1 活动之间存在高度相关关系。通过比较模型之间的性能，我们发现具有更高非线性能力的模型明显优于线性模型。这表明，从 mPFC 活动预测 M1 活动需要模型考虑高于成对相互作用的高阶非线性相互作用。我们还发现，在任务学习过程中，mPFC 和 M1 尖峰之间的相关系数增加。与早期和中期相比，当受试者对新任务进行良好训练时，尖峰预测模型的表现最佳。结果表明，在任务学习过程中，mPFC 和 M1 活动的共同激活会演变，并随着受试者的训练而变得更强。这项研究表明，在任务学习过程中可以从 mPFC 尖峰预测 M1 尖峰的动态模式，这将有助于设计用于任务学习的自适应 BMI 解码器。
