Mice
Neural mechanisms of motor control and learning
Tracking of mouse tongue and limb kinematics with high spatiotemporal resolution
Forelimb. We developed a suites of technologies that combine high resolution sensing of mouse forelimb, automated high-throughput training in the homecage, and closed-loop optogenetic manipulation of neural activity in freely moving mice. We trained mice in a complex center-out reach task, inspired by past work in primates, that required mice to carve complex reach sequences, or trajectories through space. We found that motor cortex inactivation did not affect sequence timing, direction, or trajectory shape, but uniformly reduced the peak speed of kinematic primitives - the units of movement in a sequence. Thus, trajectories during motor cortical inactivation were ‘shrunk,’ as if the letter C was drawn as a miniature c.
Tongue. To test if this result would generalize to a different motor effector, we combined kilohertz frame-rate imaging and novel deep learning based artificial neural network, to track the tongue in 3D at decamicron-millisecond spatiotemporal precision. Cue-evoked licks exhibited previously unobserved fine-scale movements which, like a primate hand searching for an unseen object, were produced after tongue protrusions and were directionally biased towards remembered spout locations. Anterolateral motor cortex inactivation abolished these fine-scale adjustments, resulting in well-aimed but hypometric licks that missed the spout. Together, our results showed cortical inactivations caused hypometria of both limb and tongue. By watching the tongue in action for the first time, we additionally discovered that licks cannot be explained by open loop central pattern generators that drive simple binary ballistic events. Instead, individual licks exhibited complex, variable trajectories with limb-like dynamics, including the production of motor cortex-dependent online adjustments that facilitate target contact.
Tongue kinematics
Forelimb kinematics
Precise control of the tongue is necessary for drinking, eating, and vocalizing. Yet because tongue movements are fast and difficult to resolve, neural control of lingual kinematics remains poorly understood. We combine kilohertz frame-rate imaging and a deep-learning based artificial neural network to resolve 3D tongue kinematics in mice performing a cued lick task. Cue-evoked licks exhibit previously unobserved fine-scale movements which, like a hand searching for an unseen object, were produced after misses and were directionally biased towards remembered locations. Photoinhibition of anterolateral motor cortex (ALM) abolished these fine-scale adjustments, resulting in well-aimed but hypometric licks that missed the spout. Our results show that cortical activity is required for online corrections during licking and reveal novel, limb-like dynamics of the mouse tongue as it reaches for, and misses, targets.
Motor sequences are constructed from primitives, hypothesized building blocks of movement, but mechanisms of primitive generation remain unclear. Using automated homecage training and a novel forelimb sensor, we trained freely-moving mice to initiate forelimb sequences with clearly resolved submillimeter-scale micromovements followed by millimeter-scale reaches to learned spatial targets. Hundreds of thousands of trajectories were decomposed into millions of kinematic primitives, while closed-loop photoinhibition was used to test roles of motor cortical areas. Inactivation of contralateral motor cortex reduced primitive peak speed but, surprisingly, did not substantially affect primitive direction, initiation, termination, or complexity, resulting in isomorphic, spatially contracted trajectories that undershot targets. Our findings demonstrate separable loss of a single kinematic parameter, speed, and identify conditions where loss of cortical drive reduces the gain of motor primitives but does not affect their generation, timing or direction. The combination of high precision forelimb sensing with automated training and neural manipulation provides a system for studying how motor sequences are constructed from elemental building blocks.