University of Colorado Neuroscience Graduate Program, Aurora, Colorado.
University of Colorado Medical Scientist Training Program, Aurora, Colorado.
J Neurophysiol. 2020 Dec 1;124(6):1637-1655. doi: 10.1152/jn.00324.2020. Epub 2020 Sep 30.
Reaching movements, as a basic yet complex motor behavior, are a foundational model system in neuroscience. In particular, there has been a significant recent expansion of investigation into the neural circuit mechanisms of reach behavior in mice. Nevertheless, quantification of mouse reach kinematics remains lacking, limiting comparison to the primate literature. In this study, we quantitatively demonstrate the homology of mouse reach kinematics to primate reach and also discover novel late-phase correlational structure that implies online control. Overall, our results highlight the decelerative phase of reach as important in driving successful outcome. Specifically, we develop and implement a novel statistical machine-learning algorithm to identify kinematic features associated with successful reaches and find that late-phase kinematics are most predictive of outcome, signifying online reach control as opposed to preplanning. Moreover, we identify and characterize late-phase kinematic adjustments that are yoked to midflight position and velocity of the limb, allowing for dynamic correction of initial variability, with head-fixed reaches being less dependent on position in comparison to freely behaving reaches. Furthermore, consecutive reaches exhibit positional error correction but not hot-handedness, implying opponent regulation of motor variability. Overall, our results establish foundational mouse reach kinematics in the context of neuroscientific investigation, characterizing mouse reach production as an active process that relies on dynamic online control mechanisms. Mice use reaching movements to grasp and manipulate objects in their environment, similar to primates. To better establish mouse reach as a model for motor control, we implement several analytical frameworks, from basic kinematic relationships to statistical machine learning, to quantify mouse reach, finding many canonical features of primate reaches are conserved in mice, as well as evidence for midflight course corrections, expanding the utility of mouse reach paradigms for motor control studies.
运动是一种基本而复杂的运动行为,是神经科学的基础模型系统。特别是,最近对老鼠的伸展行为的神经回路机制的研究有了显著的扩展。然而,对老鼠伸展运动学的定量分析仍然缺乏,这限制了与灵长类文献的比较。在这项研究中,我们定量地证明了老鼠伸展运动学与灵长类动物伸展运动学的同源性,并且还发现了新的晚期相关结构,暗示了在线控制。总的来说,我们的研究结果突出了伸展的减速阶段对成功结果的重要性。具体来说,我们开发并实施了一种新的统计机器学习算法,以识别与成功伸展相关的运动学特征,发现晚期运动学对结果的预测性最强,表明是在线伸展控制而不是预先规划。此外,我们确定并描述了与肢体的中途位置和速度相关的晚期运动学调整,从而允许对初始可变性进行动态校正,与头部固定的伸展相比,自由行为的伸展对位置的依赖性较小。此外,连续的伸展表现出位置误差校正,但没有热手效应,这意味着运动可变性的对手调节。总的来说,我们的研究结果在神经科学研究的背景下建立了基础的老鼠伸展运动学,将老鼠的伸展运动描述为一种依赖于动态在线控制机制的主动过程。老鼠用伸展运动来抓取和操纵环境中的物体,这与灵长类动物相似。为了更好地将老鼠伸展作为运动控制的模型,我们实施了几个分析框架,从基本运动关系到统计机器学习,以定量老鼠伸展,发现许多灵长类动物伸展的典型特征在老鼠中得到了保留,也有证据表明在中途进行了路线修正,扩大了老鼠伸展模型在运动控制研究中的应用。