Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, 90089, USA.
Center for Neural Science, New York University, New York City, NY, 10003, USA.
Nat Commun. 2021 Jan 27;12(1):607. doi: 10.1038/s41467-020-20197-x.
Motor function depends on neural dynamics spanning multiple spatiotemporal scales of population activity, from spiking of neurons to larger-scale local field potentials (LFP). How multiple scales of low-dimensional population dynamics are related in control of movements remains unknown. Multiscale neural dynamics are especially important to study in naturalistic reach-and-grasp movements, which are relatively under-explored. We learn novel multiscale dynamical models for spike-LFP network activity in monkeys performing naturalistic reach-and-grasps. We show low-dimensional dynamics of spiking and LFP activity exhibited several principal modes, each with a unique decay-frequency characteristic. One principal mode dominantly predicted movements. Despite distinct principal modes existing at the two scales, this predictive mode was multiscale and shared between scales, and was shared across sessions and monkeys, yet did not simply replicate behavioral modes. Further, this multiscale mode's decay-frequency explained behavior. We propose that multiscale, low-dimensional motor cortical state dynamics reflect the neural control of naturalistic reach-and-grasp behaviors.
运动功能依赖于跨越多个时空尺度的群体活动的神经动力学，从神经元的尖峰到更大尺度的局部场电位 (LFP)。在运动控制中，多个尺度的低维群体动力学是如何相关的仍然未知。多尺度神经动力学在研究自然的伸手抓握运动中尤为重要，而这些运动的研究相对较少。我们为猴子在执行自然伸手抓握时的尖峰-LFP 网络活动学习新的多尺度动力学模型。我们展示了尖峰和 LFP 活动的低维动力学表现出几种主要模式，每种模式都具有独特的衰减频率特征。一个主要模式主要预测运动。尽管在两个尺度上存在不同的主要模式，但这种预测模式是多尺度的，并且在尺度之间共享，并且在不同的会议和猴子之间共享，但它并没有简单地复制行为模式。此外，这种多尺度模式的衰减频率解释了行为。我们提出，多尺度、低维运动皮质状态动力学反映了自然伸手抓握行为的神经控制。
